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reference/nn_soft_margin_loss.html create mode 100644 reference/nn_triplet_margin_loss.html create mode 100644 reference/nn_triplet_margin_with_distance_loss.html create mode 100644 reference/nnf_triplet_margin_with_distance_loss.html diff --git a/404.html b/404.html index b7d16e7ab..fa1a4c119 100644 --- a/404.html +++ b/404.html @@ -38,6 +38,8 @@ + + @@ -54,6 +56,16 @@ + + + + @@ -71,7 +83,7 @@ torch - 0.0.3 + 0.1.0 @@ -144,6 +156,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -157,7 +172,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • diff --git a/CONTRIBUTING.html b/CONTRIBUTING.html index 32e2bbed7..1fbbeaa6c 100644 --- a/CONTRIBUTING.html +++ b/CONTRIBUTING.html @@ -38,6 +38,8 @@ + + @@ -54,6 +56,16 @@ + + + + @@ -71,7 +83,7 @@ torch - 0.0.3 + 0.1.0 @@ -144,6 +156,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -157,7 +172,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • diff --git a/LICENSE-text.html b/LICENSE-text.html index df7874b73..6df809a96 100644 --- a/LICENSE-text.html +++ b/LICENSE-text.html @@ -38,6 +38,8 @@ + + @@ -54,6 +56,16 @@ + + + + @@ -71,7 +83,7 @@ torch - 0.0.3 + 0.1.0 @@ -144,6 +156,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -157,7 +172,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • diff --git a/LICENSE.html b/LICENSE.html index 4ab810144..596f226f4 100644 --- a/LICENSE.html +++ b/LICENSE.html @@ -38,6 +38,8 @@ + + @@ -54,6 +56,16 @@ + + + + @@ -71,7 +83,7 @@ torch - 0.0.3 + 0.1.0 @@ -144,6 +156,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -157,7 +172,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • diff --git a/articles/examples/basic-autograd.html b/articles/examples/basic-autograd.html new file mode 100644 index 000000000..a5b35f777 --- /dev/null +++ b/articles/examples/basic-autograd.html @@ -0,0 +1,230 @@ + + + + + + + +basic-autograd • torch + + + + + + + + + + + +
    +
    + + + + +
    +
    +
    +

    basic-autograd

    + + + Source: vignettes/examples/basic-autograd.Rmd + + +
    + + + +
    +library(torch)
+
+# creates example tensors. x requires_grad = TRUE tells that 
+# we are going to take derivatives over it.
+x <- torch_tensor(3, requires_grad = TRUE)
+y <- torch_tensor(2)
+
+# executes the forward operation x^2
+o <- x^2
+
+# computes the backward operation for each tensor that is marked with
+# requires_grad = TRUE
+o$backward()
+
+# get do/dx = 2 * x (at x = 3)
+x$grad
    +
    ## torch_tensor 
+##  6
+## [ CPUFloatType{1} ]
    +
    + + + +
    + + + +
    +

    Developed by Daniel Falbel, Javier Luraschi.

    +
    + +
    +

    Site built with pkgdown 1.6.1.

    +
    + +
    +
    + + + + + + diff --git a/articles/examples/basic-autograd_files/accessible-code-block-0.0.1/empty-anchor.js b/articles/examples/basic-autograd_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 000000000..ca349fd6a --- /dev/null +++ b/articles/examples/basic-autograd_files/accessible-code-block-0.0.1/empty-anchor.js @@ -0,0 +1,15 @@ +// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> +// v0.0.1 +// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. + +document.addEventListener('DOMContentLoaded', function() { + const codeList = document.getElementsByClassName("sourceCode"); + for (var i = 0; i < codeList.length; i++) { + var linkList = codeList[i].getElementsByTagName('a'); + for (var j = 0; j < linkList.length; j++) { + if (linkList[j].innerHTML === "") { + linkList[j].setAttribute('aria-hidden', 'true'); + } + } + } +}); diff --git a/articles/examples/basic-nn-module.html b/articles/examples/basic-nn-module.html new file mode 100644 index 000000000..afebc49ae --- /dev/null +++ b/articles/examples/basic-nn-module.html @@ -0,0 +1,286 @@ + + + + + + + +basic-nn-module • torch + + + + + + + + + + + +
    +
    + + + + +
    +
    +
    +

    basic-nn-module

    + + + Source: vignettes/examples/basic-nn-module.Rmd + + +
    + + + +
    +library(torch)
+
+# creates example tensors. x requires_grad = TRUE tells that 
+# we are going to take derivatives over it.
+dense <- nn_module(
+  clasname = "dense",
+  # the initialize function tuns whenever we instantiate the model
+  initialize = function(in_features, out_features) {
+    
+    # just for you to see when this function is called
+    cat("Calling initialize!") 
+    
+    # we use nn_parameter to indicate that those tensors are special
+    # and should be treated as parameters by `nn_module`.
+    self$w <- nn_parameter(torch_randn(in_features, out_features))
+    self$b <- nn_parameter(torch_zeros(out_features))
+    
+  },
+  # this function is called whenever we call our model on input.
+  forward = function(x) {
+    cat("Calling forward!")
+    torch_mm(x, self$w) + self$b
+  }
+)
+
+model <- dense(3, 1)
    +
    ## Calling initialize!
    +
    +# you can get all parameters 
+model$parameters
    +
    ## $w
+## torch_tensor 
+## 0.01 *
+##  4.5107
+##  -45.9509
+##  -246.0987
+## [ CPUFloatType{3,1} ]
+## 
+## $b
+## torch_tensor 
+##  0
+## [ CPUFloatType{1} ]
    +
    +# or individually
+model$w
    +
    ## torch_tensor 
+## 0.01 *
+##  4.5107
+##  -45.9509
+##  -246.0987
+## [ CPUFloatType{3,1} ]
    +
    +model$b
    +
    ## torch_tensor 
+##  0
+## [ CPUFloatType{1} ]
    +
    +# create an input tensor
+x <- torch_randn(10, 3)
+y_pred <- model(x)
    +
    ## Calling forward!
    +
    +y_pred
    +
    ## torch_tensor 
+## -0.5823
+##  1.1249
+##  0.7531
+## -0.5148
+##  0.1950
+## -4.0449
+##  2.2684
+##  0.5924
+##  3.6237
+## -1.8567
+## [ CPUFloatType{10,1} ]
    +
    + + + +
    + + + +
    +

    Developed by Daniel Falbel, Javier Luraschi.

    +
    + +
    +

    Site built with pkgdown 1.6.1.

    +
    + +
    +
    + + + + + + diff --git a/articles/examples/basic-nn-module_files/accessible-code-block-0.0.1/empty-anchor.js b/articles/examples/basic-nn-module_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 000000000..ca349fd6a --- /dev/null +++ b/articles/examples/basic-nn-module_files/accessible-code-block-0.0.1/empty-anchor.js @@ -0,0 +1,15 @@ +// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> +// v0.0.1 +// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. + +document.addEventListener('DOMContentLoaded', function() { + const codeList = document.getElementsByClassName("sourceCode"); + for (var i = 0; i < codeList.length; i++) { + var linkList = codeList[i].getElementsByTagName('a'); + for (var j = 0; j < linkList.length; j++) { + if (linkList[j].innerHTML === "") { + linkList[j].setAttribute('aria-hidden', 'true'); + } + } + } +}); diff --git a/articles/examples/dataset.html b/articles/examples/dataset.html new file mode 100644 index 000000000..022e151b0 --- /dev/null +++ b/articles/examples/dataset.html @@ -0,0 +1,305 @@ + + + + + + + +dataset • torch + + + + + + + + + + + +
    +
    + + + + +
    +
    +
    +

    dataset

    + + + Source: vignettes/examples/dataset.Rmd + + +
    + + + +
    +library(torch)
+
+# In deep learning models you don't usually have all your data in RAM
+# because you are usually training using mini-batch gradient descent
+# thus only needing a mini-batch on RAM each time.
+
+# In torch we use the `datasets` abstraction to define the process of
+# loading data. Once you have defined your dataset you can use torch
+# dataloaders that allows you to iterate over this dataset in batches.
+
+# Note that datasets are optional in torch. They are jut there as a
+# recommended way to load data.
+
+# Below you will see an example of how to create a simple torch dataset
+# that pre-process a data.frame into tensors so you can feed them to
+# a model.
+
+df_dataset <- dataset(
+  "mydataset",
+  
+  # the input data to your dataset goes in the initialize function.
+  # our dataset will take a dataframe and the name of the response
+  # variable.
+  initialize = function(df, response_variable) {
+    self$df <- df[,-which(names(df) == response_variable)]
+    self$response_variable <- df[[response_variable]]
+  },
+  
+  # the .getitem method takes an index as input and returns the
+  # corresponding item from the dataset.
+  # the index could be anything. the dataframe could have many
+  # rows for each index and the .getitem method would do some
+  # kind of aggregation before returning the element.
+  # in our case the index will be a row of the data.frame,
+  .getitem = function(index) {
+    response <- torch_tensor(self$response_variable[index])
+    x <- torch_tensor(as.numeric(self$df[index,]))
+    
+    # note that the dataloaders will automatically stack tensors
+    # creating a new dimension
+    list(x = x, y = response)
+  },
+  
+  # It's optional, but helpful to define the .length method returning 
+  # the number of elements in the dataset. This is needed if you want 
+  # to shuffle your dataset.
+  .length = function() {
+    length(self$response_variable)
+  }
+  
+)
+
+
+# we can now initialize an instance of our dataset.
+# for example
+mtcars_dataset <- df_dataset(mtcars, "mpg")
+
+# now we can get an item with
+mtcars_dataset$.getitem(1)
    +
    ## $x
+## torch_tensor 
+##    6.0000
+##  160.0000
+##  110.0000
+##    3.9000
+##    2.6200
+##   16.4600
+##    0.0000
+##    1.0000
+##    4.0000
+##    4.0000
+## [ CPUFloatType{10} ]
+## 
+## $y
+## torch_tensor 
+##  21
+## [ CPUFloatType{1} ]
    +
    +# Given a dataset you can create a dataloader with
+dl <- dataloader(mtcars_dataset, batch_size = 15, shuffle = TRUE)
+
+# we can then loop trough the elements of the dataloader with
+for(batch in enumerate(dl)) {
+  cat("X size:  ")
+  print(batch[[1]]$size())
+  cat("Y size:  ")
+  print(batch[[2]]$size())
+}
    +
    ## X size:  [1] 15 10
+## Y size:  [1] 15  1
+## X size:  [1] 15 10
+## Y size:  [1] 15  1
+## X size:  [1]  2 10
+## Y size:  [1] 2 1
    +
    + + + +
    + + + +
    +

    Developed by Daniel Falbel, Javier Luraschi.

    +
    + +
    +

    Site built with pkgdown 1.6.1.

    +
    + +
    +
    + + + + + + diff --git a/articles/examples/dataset_files/accessible-code-block-0.0.1/empty-anchor.js b/articles/examples/dataset_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 000000000..ca349fd6a --- /dev/null +++ b/articles/examples/dataset_files/accessible-code-block-0.0.1/empty-anchor.js @@ -0,0 +1,15 @@ +// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> +// v0.0.1 +// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. + +document.addEventListener('DOMContentLoaded', function() { + const codeList = document.getElementsByClassName("sourceCode"); + for (var i = 0; i < codeList.length; i++) { + var linkList = codeList[i].getElementsByTagName('a'); + for (var j = 0; j < linkList.length; j++) { + if (linkList[j].innerHTML === "") { + linkList[j].setAttribute('aria-hidden', 'true'); + } + } + } +}); diff --git a/articles/examples/index.html b/articles/examples/index.html new file mode 100644 index 000000000..45a26b127 --- /dev/null +++ b/articles/examples/index.html @@ -0,0 +1,227 @@ + + + + + + + +Examples • torch + + + + + + + + + + + +
    +
    + + + + +
    +
    +
    +

    Examples

    + + + Source: vignettes/examples/index.Rmd + + +
    + + + +

    Gallery of scripts demonstrating torch functionality.

    + + + + + + + + + + + + + + + +
    Examples
    basic-autograd
    basic-nn-module
    dataset
    +
    + + + +
    + + + +
    +

    Developed by Daniel Falbel, Javier Luraschi.

    +
    + +
    +

    Site built with pkgdown 1.6.1.

    +
    + +
    +
    + + + + + + diff --git a/articles/examples/index_files/accessible-code-block-0.0.1/empty-anchor.js b/articles/examples/index_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 000000000..ca349fd6a --- /dev/null +++ b/articles/examples/index_files/accessible-code-block-0.0.1/empty-anchor.js @@ -0,0 +1,15 @@ +// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> +// v0.0.1 +// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. + +document.addEventListener('DOMContentLoaded', function() { + const codeList = document.getElementsByClassName("sourceCode"); + for (var i = 0; i < codeList.length; i++) { + var linkList = codeList[i].getElementsByTagName('a'); + for (var j = 0; j < linkList.length; j++) { + if (linkList[j].innerHTML === "") { + linkList[j].setAttribute('aria-hidden', 'true'); + } + } + } +}); diff --git a/articles/extending-autograd.html b/articles/extending-autograd.html index 05802b50d..eef1bb338 100644 --- a/articles/extending-autograd.html +++ b/articles/extending-autograd.html @@ -11,12 +11,19 @@ - + + +
    @@ -31,7 +38,7 @@ torch - 0.0.3 + 0.1.0
    @@ -104,6 +111,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -117,7 +127,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -156,8 +183,7 @@ 
    -library(torch)
-
    +library(torch)

    Adding operations to autograd requires implementing a new autograd_function for each operation. Recall that autograd_functionss are what autograd uses to compute the results and gradients, and encode the operation history. Every new function requires you to implement 2 methods:

    -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -159,8 +186,7 @@

    Note: This is an R port of the official tutorial available here. All credits goes to Soumith Chintala.

    -library(torch)
-
    +library(torch)

    Central to all neural networks in torch is the autograd functionality. Let’s first briefly visit this, and we will then go to training our first neural network.

    Autograd provides automatic differentiation for all operations on Tensors. It is a define-by-run framework, which means that your backprop is defined by how your code is run, and that every single iteration can be different.

    Let us see this in more simple terms with some examples.

    @@ -175,74 +201,67 @@

    If you want to compute the derivatives, you can call $backward() on a Tensor. If Tensor is a scalar (i.e. it holds a one element data), you don’t need to specify any arguments to backward(), however if it has more elements, you need to specify a gradient argument that is a tensor of matching shape.

    Create a tensor and set requires_grad=TRUE to track computation with it:

    -x <- torch_ones(2, 2, requires_grad = TRUE)
-x
+x <- torch_ones(2, 2, requires_grad = TRUE)
+x
 #> torch_tensor 
 #>  1  1
 #>  1  1
-#> [ CPUFloatType{2,2} ]
-
    +#> [ CPUFloatType{2,2} ]

    Do a tensor operation:

    -y <- x + 2
-y
+y <- x + 2
+y
 #> torch_tensor 
 #>  3  3
 #>  3  3
-#> [ CPUFloatType{2,2} ]
-
    +#> [ CPUFloatType{2,2} ]

    y was created as a result of an operation, so it has a grad_fn.

    -y$grad_fn
-#> AddBackward1
-
    +y$grad_fn +#> AddBackward1

    Do more operations on y

    -z <- y * y * 3
-z
+z <- y * y * 3
+z
 #> torch_tensor 
 #>  27  27
 #>  27  27
 #> [ CPUFloatType{2,2} ]
-out <- z$mean()
-out
+out <- z$mean()
+out
 #> torch_tensor 
 #> 27
-#> [ CPUFloatType{} ]
-
    +#> [ CPUFloatType{} ]

    $requires_grad_( ... ) changes an existing Tensor’s requires_grad flag in-place. The input flag defaults to FALSE if not given.

    -a <- torch_randn(2, 2)
-a <- (a * 3) / (a - 1)
-a$requires_grad
+a <- torch_randn(2, 2)
+a <- (a * 3) / (a - 1)
+a$requires_grad
 #> [1] FALSE
-a$requires_grad_(TRUE)
+a$requires_grad_(TRUE)
 #> torch_tensor 
-#> -0.4350  1.4882
-#> -0.5849  9.3457
+#>  1.8070  0.0621
+#> -3.7943  1.4618
 #> [ CPUFloatType{2,2} ]
-a$requires_grad
+a$requires_grad
 #> [1] TRUE
-b <- (a * a)$sum()
-b$grad_fn
-#> SumBackward0
-
    +b <- (a * a)$sum() +b$grad_fn +#> SumBackward0

    Gradients

    Let’s backprop now. Because out contains a single scalar, out$backward() is equivalent to out$backward(torch.tensor(1.)).

    -out$backward()
-
    +out$backward()

    Print gradients d(out)/dx

    -x$grad
+x$grad
 #> torch_tensor 
 #>  4.5000  4.5000
 #>  4.5000  4.5000
-#> [ CPUFloatType{2,2} ]
-
    +#> [ CPUFloatType{2,2} ]

    You should have got a matrix of 4.5. Let’s call the out Tensor \(o\).

    We have that \(o = \frac{1}{4}\sum_i z_i\), \(z_i = 3(x_i+2)^2\) and \(z_i\bigr\rvert_{x_i=1} = 27\). Therefore, \(\frac{\partial o}{\partial x_i} = \frac{3}{2}(x_i+2)\), hence \(\frac{\partial o}{\partial x_i}\bigr\rvert_{x_i=1} = \frac{9}{2} = 4.5\).

    Mathematically, if you have a vector valued function \(\vec{y}=f(\vec{x})\), then the gradient of \(\vec{y}\) with respect to \(\vec{x}\) is a Jacobian matrix:

    @@ -273,50 +292,46 @@

    This characteristic of vector-Jacobian product makes it very convenient to feed external gradients into a model that has non-scalar output.

    Now let’s take a look at an example of vector-Jacobian product:

    -x <- torch_randn(3, requires_grad=TRUE)
-y <- 100 * x
-y
+x <- torch_randn(3, requires_grad=TRUE)
+y <- 100 * x
+y
 #> torch_tensor 
-#>  -50.4960
-#>  -28.4113
-#>  101.7135
-#> [ CPUFloatType{3} ]
-
    +#> -25.7096 +#> -34.4775 +#> 124.1432 +#> [ CPUFloatType{3} ]

    Now in this case y is no longer a scalar. autograd could not compute the full Jacobian directly, but if we just want the vector-Jacobian product, simply pass the vector to backward as argument:

    -v <- torch_tensor(c(0.1, 1.0, 0.0001))
-y$backward(v)
+v <- torch_tensor(c(0.1, 1.0, 0.0001))
+y$backward(v)
 
-x$grad
+x$grad
 #> torch_tensor 
 #>  1.0000e+01
 #>  1.0000e+02
 #>  1.0000e-02
-#> [ CPUFloatType{3} ]
-
    +#> [ CPUFloatType{3} ]

    You can also stop autograd from tracking history on Tensors with $requires_grad=TRUE either by wrapping the code block in with with_no_grad():

    -x$requires_grad
+x$requires_grad
 #> [1] TRUE
-(x ** 2)$requires_grad
+(x ** 2)$requires_grad
 #> [1] TRUE
 
-with_no_grad({
-  print((x ** 2)$requires_grad)
-})
-#> [1] FALSE
-
    +with_no_grad({ + print((x ** 2)$requires_grad) +}) +#> [1] FALSE 
    -x$requires_grad
+x$requires_grad
 #> [1] TRUE
-y <- x$detach()
-y$requires_grad
+y <- x$detach()
+y$requires_grad
 #> [1] FALSE
-x$eq(y)$all()
+x$eq(y)$all()
 #> torch_tensor 
 #> 1
-#> [ CPUBoolType{} ]
-
    +#> [ CPUBoolType{} ]

    Read Later:

    Document about help(autograd_function), vignette("using-autograd"), vignette("extending-autograd").

    diff --git a/articles/getting-started/control-flow-and-weight-sharing.html b/articles/getting-started/control-flow-and-weight-sharing.html index 89a344091..ed3f69d96 100644 --- a/articles/getting-started/control-flow-and-weight-sharing.html +++ b/articles/getting-started/control-flow-and-weight-sharing.html @@ -11,12 +11,19 @@ - + + +
    @@ -31,7 +38,7 @@ torch - 0.0.3 + 0.1.0
    @@ -104,6 +111,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -117,7 +127,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -159,21 +186,20 @@

    Note: This is an R port of the official tutorial available here. All credits goes to Justin Johnson.

    -library(torch)
-
    +library(torch)

    As an example of dynamic graphs and weight sharing, we implement a very strange model: a fully-connected ReLU network that on each forward pass chooses a random number between 1 and 4 and uses that many hidden layers, reusing the same weights multiple times to compute the innermost hidden layers.

    For this model we can use normal R flow control to implement the loop, and we can implement weight sharing among the innermost layers by simply reusing the same Module multiple times when defining the forward pass.

    We can easily implement this model using nn_module:

    -dynamic_net <- nn_module(
+dynamic_net <- nn_module(
    "dynamic_net",
    # In the constructor we construct three nn_linear instances that we will use
    # in the forward pass.
-   initialize = function(D_in, H, D_out) {
-      self$input_linear <- nn_linear(D_in, H)
-      self$middle_linear <- nn_linear(H, H)
-      self$output_linear <- nn_linear(H, D_out)
-   },
+   initialize = function(D_in, H, D_out) {
+      self$input_linear <- nn_linear(D_in, H)
+      self$middle_linear <- nn_linear(H, H)
+      self$output_linear <- nn_linear(H, D_out)
+   },
    # For the forward pass of the model, we randomly choose either 0, 1, 2, or 3
    # and reuse the middle_linear Module that many times to compute hidden layer
    # representations.
@@ -185,85 +211,84 @@
    # Here we also see that it is perfectly safe to reuse the same Module many
    # times when defining a computational graph. This is a big improvement from Lua
    # Torch, where each Module could be used only once.
-   forward = function(x) {
-      h_relu <- self$input_linear(x)$clamp(min = 0)
-      for (i in seq_len(sample.int(4, size = 1))) {
-         h_relu <- self$middle_linear(h_relu)$clamp(min=0)
-      }
-      y_pred <- self$output_linear(h_relu)
-      y_pred
-   }
-)
+   forward = function(x) {
+      h_relu <- self$input_linear(x)$clamp(min = 0)
+      for (i in seq_len(sample.int(4, size = 1))) {
+         h_relu <- self$middle_linear(h_relu)$clamp(min=0)
+      }
+      y_pred <- self$output_linear(h_relu)
+      y_pred
+   }
+)
 
 
-if (cuda_is_available()) {
-   device <- torch_device("cuda")
-} else {
-   device <- torch_device("cpu")
-}
+if (cuda_is_available()) {
+   device <- torch_device("cuda")
+} else {
+   device <- torch_device("cpu")
+}
    
 # N is batch size; D_in is input dimension;
 # H is hidden dimension; D_out is output dimension.
-N <- 64
-D_in <- 1000
-H <- 100
-D_out <- 10
+N <- 64
+D_in <- 1000
+H <- 100
+D_out <- 10
 
 # Create random input and output data
 # Setting requires_grad=FALSE (the default) indicates that we do not need to 
 # compute gradients with respect to these Tensors during the backward pass.
-x <- torch_randn(N, D_in, device=device)
-y <- torch_randn(N, D_out, device=device)
+x <- torch_randn(N, D_in, device=device)
+y <- torch_randn(N, D_out, device=device)
 
 # Construct our model by instantiating the class defined above
-model <- dynamic_net(D_in, H, D_out)
+model <- dynamic_net(D_in, H, D_out)
 
 # The nn package also contains definitions of popular loss functions; in this
 # case we will use Mean Squared Error (MSE) as our loss function.
-loss_fn <- nnf_mse_loss
+loss_fn <- nnf_mse_loss
 
 # Use the optim package to define an Optimizer that will update the weights of
 # the model for us. Here we will use Adam; the optim package contains many other
 # optimization algorithms. The first argument to the Adam constructor tells the
 # optimizer which Tensors it should update.
-learning_rate <- 1e-4
-optimizer <- optim_sgd(model$parameters, lr=learning_rate, momentum = 0.9)
+learning_rate <- 1e-4
+optimizer <- optim_sgd(model$parameters, lr=learning_rate, momentum = 0.9)
 
-for (t in seq_len(500)) {
+for (t in seq_len(500)) {
    # Forward pass: compute predicted y by passing x to the model. Module objects
    # can be called like functions. When doing so you pass a Tensor of input
    # data to the Module and it produces a Tensor of output data.
-   y_pred <- model(x)
+   y_pred <- model(x)
    
    # Compute and print loss. We pass Tensors containing the predicted and true
    # values of y, and the loss function returns a Tensor containing the
    # loss.
-   loss <- loss_fn(y_pred, y)
-   if (t %% 100 == 0 || t == 1)
-      cat("Step:", t, ":", as.numeric(loss), "\n")
+   loss <- loss_fn(y_pred, y)
+   if (t %% 100 == 0 || t == 1)
+      cat("Step:", t, ":", as.numeric(loss), "\n")
    
    # Before the backward pass, use the optimizer object to zero all of the
    # gradients for the variables it will update (which are the learnable
    # weights of the model). This is because by default, gradients are
    # accumulated in buffers( i.e, not overwritten) whenever $backward()
    # is called. Checkout docs of `autograd_backward` for more details.
-   optimizer$zero_grad()
+   optimizer$zero_grad()
 
    # Backward pass: compute gradient of the loss with respect to model
    # parameters
-   loss$backward()
+   loss$backward()
 
    # Calling the step function on an Optimizer makes an update to its
    # parameters
-   optimizer$step()
-}
-#> Step: 1 : 1.054659 
-#> Step: 100 : 1.05705 
-#> Step: 200 : 1.048708 
-#> Step: 300 : 1.052647 
-#> Step: 400 : 1.042869 
-#> Step: 500 : 1.039991
-
    + optimizer$step() +} +#> Step: 1 : 1.016029 +#> Step: 100 : 1.016147 +#> Step: 200 : 1.014156 +#> Step: 300 : 1.012017 +#> Step: 400 : 1.006162 +#> Step: 500 : 1.010858

    Sometimes you will want to specify models that are more complex than a sequence of existing Modules; for these cases you can define your own Modules by using nn_module function and defining a forward which receives input Tensors and produces output Tensors using other modules or other autograd operations on Tensors.

    In this example we implement our two-layer network as a custom Module subclass:

    -two_layer_net <- nn_module(
+two_layer_net <- nn_module(
    "two_layer_net",
-   initialize = function(D_in, H, D_out) {
-      self$linear1 <- nn_linear(D_in, H)
-      self$linear2 <- nn_linear(H, D_out)
-   },
-   forward = function(x) {
-      x %>% 
-         self$linear1() %>% 
-         nnf_relu() %>% 
-         self$linear2()
-   }
-)
+   initialize = function(D_in, H, D_out) {
+      self$linear1 <- nn_linear(D_in, H)
+      self$linear2 <- nn_linear(H, D_out)
+   },
+   forward = function(x) {
+      x %>% 
+         self$linear1() %>% 
+         nnf_relu() %>% 
+         self$linear2()
+   }
+)
 
 
-if (cuda_is_available()) {
-   device <- torch_device("cuda")
-} else {
-   device <- torch_device("cpu")
-}
+if (cuda_is_available()) {
+   device <- torch_device("cuda")
+} else {
+   device <- torch_device("cpu")
+}
    
 # N is batch size; D_in is input dimension;
 # H is hidden dimension; D_out is output dimension.
-N <- 64
-D_in <- 1000
-H <- 100
-D_out <- 10
+N <- 64
+D_in <- 1000
+H <- 100
+D_out <- 10
 
 # Create random input and output data
 # Setting requires_grad=FALSE (the default) indicates that we do not need to 
 # compute gradients with respect to these Tensors during the backward pass.
-x <- torch_randn(N, D_in, device=device)
-y <- torch_randn(N, D_out, device=device)
+x <- torch_randn(N, D_in, device=device)
+y <- torch_randn(N, D_out, device=device)
 
 # Construct our model by instantiating the class defined above
-model <- two_layer_net(D_in, H, D_out)
+model <- two_layer_net(D_in, H, D_out)
 
 # The nn package also contains definitions of popular loss functions; in this
 # case we will use Mean Squared Error (MSE) as our loss function.
-loss_fn <- nnf_mse_loss
+loss_fn <- nnf_mse_loss
 
 # Use the optim package to define an Optimizer that will update the weights of
 # the model for us. Here we will use Adam; the optim package contains many other
 # optimization algorithms. The first argument to the Adam constructor tells the
 # optimizer which Tensors it should update.
-learning_rate <- 1e-4
-optimizer <- optim_sgd(model$parameters, lr=learning_rate)
+learning_rate <- 1e-4
+optimizer <- optim_sgd(model$parameters, lr=learning_rate)
 
-for (t in seq_len(500)) {
+for (t in seq_len(500)) {
    # Forward pass: compute predicted y by passing x to the model. Module objects
    # can be called like functions. When doing so you pass a Tensor of input
    # data to the Module and it produces a Tensor of output data.
-   y_pred <- model(x)
+   y_pred <- model(x)
    
    # Compute and print loss. We pass Tensors containing the predicted and true
    # values of y, and the loss function returns a Tensor containing the
    # loss.
-   loss <- loss_fn(y_pred, y)
-   if (t %% 100 == 0 || t == 1)
-      cat("Step:", t, ":", as.numeric(loss), "\n")
+   loss <- loss_fn(y_pred, y)
+   if (t %% 100 == 0 || t == 1)
+      cat("Step:", t, ":", as.numeric(loss), "\n")
    
    # Before the backward pass, use the optimizer object to zero all of the
    # gradients for the variables it will update (which are the learnable
    # weights of the model). This is because by default, gradients are
    # accumulated in buffers( i.e, not overwritten) whenever $backward()
    # is called. Checkout docs of `autograd_backward` for more details.
-   optimizer$zero_grad()
+   optimizer$zero_grad()
 
    # Backward pass: compute gradient of the loss with respect to model
    # parameters
-   loss$backward()
+   loss$backward()
 
    # Calling the step function on an Optimizer makes an update to its
    # parameters
-   optimizer$step()
-}
-#> Step: 1 : 1.04065 
-#> Step: 100 : 1.026708 
-#> Step: 200 : 1.013019 
-#> Step: 300 : 0.9996911 
-#> Step: 400 : 0.986709 
-#> Step: 500 : 0.9740159
-
    + optimizer$step() +} +#> Step: 1 : 1.096671 +#> Step: 100 : 1.082999 +#> Step: 200 : 1.06948 +#> Step: 300 : 1.056253 +#> Step: 400 : 1.043316 +#> Step: 500 : 1.030622

    In the next example we will about dynamic graphs in torch.

    diff --git a/articles/getting-started/neural-networks.html b/articles/getting-started/neural-networks.html index 9c40455a7..ca6fdb796 100644 --- a/articles/getting-started/neural-networks.html +++ b/articles/getting-started/neural-networks.html @@ -11,12 +11,19 @@ - + + +
    @@ -31,7 +38,7 @@ torch - 0.0.3 + 0.1.0
    @@ -104,6 +111,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -117,7 +127,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -159,8 +186,7 @@

    Note: This is an R port of the official tutorial available here. All credits goes to Soumith Chintala.

    -library(torch)
-
    +library(torch)

    Neural networks can be constructed using the nn functionality.

    Now that you had a glimpse of autograd, nn depends on autograd to define models and differentiate them. An nn.Module contains layers, and a method forward(input) that returns the output.

    For example, look at this network that classifies digit images:

    @@ -182,44 +208,43 @@ Define the network

    Let’s define this network:

    -Net <- nn_module(
-  initialize = function() {
-    self$conv1 = nn_conv2d(1, 6, 3)
-    self$conv2 = nn_conv2d(6, 16, 3)
+Net <- nn_module(
+  initialize = function() {
+    self$conv1 = nn_conv2d(1, 6, 3)
+    self$conv2 = nn_conv2d(6, 16, 3)
     # an affine operation: y = Wx + b
-    self$fc1 = nn_linear(16 * 6 * 6, 120)  # 6*6 from image dimension
-    self$fc2 = nn_linear(120, 84)
-    self$fc3 = nn_linear(84, 10)
-  },
-  forward = function(x) {
-    x %>% 
+    self$fc1 = nn_linear(16 * 6 * 6, 120)  # 6*6 from image dimension
+    self$fc2 = nn_linear(120, 84)
+    self$fc3 = nn_linear(84, 10)
+  },
+  forward = function(x) {
+    x %>% 
       
-      self$conv1() %>% 
-      nnf_relu() %>% 
-      nnf_max_pool2d(c(2,2)) %>% 
+      self$conv1() %>% 
+      nnf_relu() %>% 
+      nnf_max_pool2d(c(2,2)) %>% 
       
-      self$conv2() %>% 
-      nnf_relu() %>% 
-      nnf_max_pool2d(c(2,2)) %>% 
+      self$conv2() %>% 
+      nnf_relu() %>% 
+      nnf_max_pool2d(c(2,2)) %>% 
       
-      torch_flatten(start_dim = 2) %>% 
+      torch_flatten(start_dim = 2) %>% 
       
-      self$fc1() %>% 
-      nnf_relu() %>% 
+      self$fc1() %>% 
+      nnf_relu() %>% 
       
-      self$fc2() %>% 
-      nnf_relu() %>% 
+      self$fc2() %>% 
+      nnf_relu() %>% 
       
-      self$fc3()
-  }
-)
+      self$fc3()
+  }
+)
 
-net <- Net()
-
    +net <- Net()

    You just have to define the forward function, and the backward function (where gradients are computed) is automatically defined for you using autograd. You can use any of the Tensor operations in the forward function.

    The learnable parameters of a model are returned by net$parameters.

    -str(net$parameters)
+str(net$parameters)
 #> List of 10
 #>  $ conv1.weight:Float [1:6, 1:1, 1:3, 1:3]
 #>  $ conv1.bias  :Float [1:6]
@@ -230,22 +255,19 @@
 #>  $ fc2.weight  :Float [1:84, 1:120]
 #>  $ fc2.bias    :Float [1:84]
 #>  $ fc3.weight  :Float [1:10, 1:84]
-#>  $ fc3.bias    :Float [1:10]
-
    +#> $ fc3.bias :Float [1:10]

    Let’s try a random 32x32 input. Note: expected input size of this net (LeNet) is 32x32. To use this net on the MNIST dataset, please resize the images from the dataset to 32x32.

    -input <- torch_randn(1, 1, 32, 32)
-out <- net(input)
-out
+input <- torch_randn(1, 1, 32, 32)
+out <- net(input)
+out
 #> torch_tensor 
-#> -0.0560  0.0916  0.0401 -0.1081 -0.0183 -0.0508  0.1250 -0.0574  0.0058  0.0025
-#> [ CPUFloatType{1,10} ]
-
    +#> -0.0417 0.0845 0.0490 -0.0918 -0.0495 -0.0496 0.1334 -0.0540 0.0141 0.0121 +#> [ CPUFloatType{1,10} ]

    Zero the gradient buffers of all parameters and backprops with random gradients:

    -net$zero_grad()
-out$backward(torch_randn(1, 10))
-
    +net$zero_grad() +out$backward(torch_randn(1, 10))

    Note: nn only supports mini-batches. The entire torch.nn package only supports inputs that are a mini-batch of samples, and not a single sample. For example, nn_conv2d will take in a 4D Tensor of nSamples x nChannels x Height x Width. If you have a single sample, just use input$unsqueeze(1) to add a fake batch dimension.

    @@ -284,16 +306,15 @@

    There are several different loss functions under the nn package . A simple loss is: nnf_mse_loss which computes the mean-squared error between the input and the target.

    For example:

    -output <- net(input)
-target <- torch_randn(10)  # a dummy target, for example
-target <- target$view(c(1, -1))  # make it the same shape as output
+output <- net(input)
+target <- torch_randn(10)  # a dummy target, for example
+target <- target$view(c(1, -1))  # make it the same shape as output
 
-loss <- nnf_mse_loss(output, target)
-loss
+loss <- nnf_mse_loss(output, target)
+loss
 #> torch_tensor 
-#> 0.388282
-#> [ CPUFloatType{} ]
-
    +#> 1.83784 +#> [ CPUFloatType{} ]

    Now, if you follow loss in the backward direction, using its $grad_fn attribute, you will see a graph of computations that looks like this:

    input -> conv2d -> relu -> maxpool2d -> conv2d -> relu -> maxpool2d
       -> view -> linear -> relu -> linear -> relu -> linear
@@ -302,13 +323,12 @@
 
    So, when we call loss$backward(), the whole graph is differentiated w.r.t. the loss, and all Tensors in the graph that has requires_grad=True will have their #grad Tensor accumulated with the gradient.
    
 
    For illustration, let us follow a few steps backward:
    
 
    -loss$grad_fn
+loss$grad_fn
 #> MseLossBackward
-loss$grad_fn$next_functions[[1]]
+loss$grad_fn$next_functions[[1]]
 #> AddmmBackward
-loss$grad_fn$next_functions[[1]]$next_functions[[1]]
-#> torch::autograd::AccumulateGrad
-
    
+loss$grad_fn$next_functions[[1]]$next_functions[[1]]
+#> torch::autograd::AccumulateGrad

    @@ -316,10 +336,10 @@

    To backpropagate the error all we have to do is to loss$backward(). You need to clear the existing gradients though, else gradients will be accumulated to existing gradients.

    Now we shall call loss$backward(), and have a look at conv1’s bias gradients before and after the backward.

    -net$zero_grad()     # zeroes the gradient buffers of all parameters
+net$zero_grad()     # zeroes the gradient buffers of all parameters
 
 # conv1.bias.grad before backward
-net$conv1$bias$grad
+net$conv1$bias$grad
 #> torch_tensor 
 #>  0
 #>  0
@@ -329,20 +349,19 @@
 #>  0
 #> [ CPUFloatType{6} ]
 
-loss$backward()
+loss$backward()
 
 # conv1.bias.grad after backward
-net$conv1$bias$grad
+net$conv1$bias$grad
 #> torch_tensor 
-#> 0.001 *
-#>  2.3567
-#> -1.3589
-#> -0.6749
-#>  5.5939
-#> -4.2062
-#>  0.6161
-#> [ CPUFloatType{6} ]
-
    +#> 0.01 * +#> -2.0062 +#> 0.9085 +#> -0.2479 +#> -1.8640 +#> 0.5936 +#> 0.5216 +#> [ CPUFloatType{6} ]

    Now, we have seen how to use loss functions.

    @@ -352,29 +371,27 @@

    \[weight = weight - learning_rate * gradient\]

    We can implement this using simple R code:

    -learning_rate <- 0.01
-for (f in net$parameters) {
-  with_no_grad({
-    f$sub_(f$grad * learning_rate)
-  })
-}
-
    +learning_rate <- 0.01 +for (f in net$parameters) { + with_no_grad({ + f$sub_(f$grad * learning_rate) + }) +}

    Note: Weight updates here is wraped around with_no_grad as we don’t the updates to be tracked by the autograd engine.

    However, as you use neural networks, you want to use various different update rules such as SGD, Nesterov-SGD, Adam, RMSProp, etc.

     # create your optimizer
-optimizer <- optim_sgd(net$parameters, lr = 0.01)
+optimizer <- optim_sgd(net$parameters, lr = 0.01)
 
 # in your training loop:
-optimizer$zero_grad()   # zero the gradient buffers
-output <- net(input)
-loss <- nnf_mse_loss(output, target)
-loss$backward()
-optimizer$step()    # Does the update
-#> NULL
-
    +optimizer$zero_grad() # zero the gradient buffers +output <- net(input) +loss <- nnf_mse_loss(output, target) +loss$backward() +optimizer$step() # Does the update +#> NULL

    Note: Observe how gradient buffers had to be manually set to zero using optimizer$zero_grad(). This is because gradients are accumulated as explained in the Backprop section.

    diff --git a/articles/getting-started/new-autograd-functions.html b/articles/getting-started/new-autograd-functions.html index 7b118773a..cf978bc00 100644 --- a/articles/getting-started/new-autograd-functions.html +++ b/articles/getting-started/new-autograd-functions.html @@ -11,12 +11,19 @@ - + + +
    @@ -31,7 +38,7 @@ torch - 0.0.3 + 0.1.0
    @@ -104,6 +111,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -117,7 +127,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -159,8 +186,7 @@

    Note: This is an R port of the official tutorial available here. All credits goes to Justin Johnson.

    +library(torch)

    Under the hood, each primitive autograd operator is really two functions that operate on Tensors. The forward function computes output Tensors from input Tensors. The backward function receives the gradient of the output Tensors with respect to some scalar value, and computes the gradient of the input Tensors with respect to that same scalar value.

    In torch we can easily define our own autograd operator by defining a subclass of autograd_function and implementing the forward and backward functions. We can then use our new autograd operator by constructing an instance and calling it like a function, passing Tensors containing input data.

    In this example we define our own custom autograd function for performing the ReLU nonlinearity, and use it to implement our two-layer network:

    @@ -168,93 +194,92 @@ # We can implement our own custom autograd Functions by subclassing # autograd_functioon and implementing the forward and backward passes # which operate on Tensors. -my_relu <- autograd_function( +my_relu <- autograd_function( # In the forward pass we receive a Tensor containing the input and return # a Tensor containing the output. ctx is a context object that can be used # to stash information for backward computation. You can cache arbitrary # objects for use in the backward pass using the ctx$save_for_backward method. - forward = function(ctx, input) { - ctx$save_for_backward(input = input) - input$clamp(min = 0) - }, + forward = function(ctx, input) { + ctx$save_for_backward(input = input) + input$clamp(min = 0) + }, # In the backward pass we receive a Tensor containing the gradient of the loss # with respect to the output, and we need to compute the gradient of the loss # with respect to the input. - backward = function(ctx, grad_output) { - v <- ctx$saved_variables - grad_input <- grad_output$clone() - grad_input[v$input < 0] <- 0 - list(input = grad_input) - } -) + backward = function(ctx, grad_output) { + v <- ctx$saved_variables + grad_input <- grad_output$clone() + grad_input[v$input < 0] <- 0 + list(input = grad_input) + } +) -if (cuda_is_available()) { - device <- torch_device("cuda") -} else { - device <- torch_device("cpu") -} +if (cuda_is_available()) { + device <- torch_device("cuda") +} else { + device <- torch_device("cpu") +} # N is batch size; D_in is input dimension; # H is hidden dimension; D_out is output dimension. -N <- 64 -D_in <- 1000 -H <- 100 -D_out <- 10 +N <- 64 +D_in <- 1000 +H <- 100 +D_out <- 10 # Create random input and output data # Setting requires_grad=FALSE (the default) indicates that we do not need to # compute gradients with respect to these Tensors during the backward pass. -x <- torch_randn(N, D_in, device=device) -y <- torch_randn(N, D_out, device=device) +x <- torch_randn(N, D_in, device=device) +y <- torch_randn(N, D_out, device=device) # Randomly initialize weights # Setting requires_grad=TRUE indicates that we want to compute gradients with # respect to these Tensors during the backward pass. -w1 <- torch_randn(D_in, H, device=device, requires_grad = TRUE) -w2 <- torch_randn(H, D_out, device=device, requires_grad = TRUE) +w1 <- torch_randn(D_in, H, device=device, requires_grad = TRUE) +w2 <- torch_randn(H, D_out, device=device, requires_grad = TRUE) -learning_rate <- 1e-6 -for (t in seq_len(500)) { +learning_rate <- 1e-6 +for (t in seq_len(500)) { # Forward pass: compute predicted y using operations on Tensors; these # are exactly the same operations we used to compute the forward pass using # Tensors, but we do not need to keep references to intermediate values since # we are not implementing the backward pass by hand. - y_pred <- my_relu(x$mm(w1))$mm(w2) + y_pred <- my_relu(x$mm(w1))$mm(w2) # Compute and print loss using operations on Tensors. # Now loss is a Tensor of shape (1,) - loss <- (y_pred - y)$pow(2)$sum() - if (t %% 100 == 0 || t == 1) - cat("Step:", t, ":", as.numeric(loss), "\n") + loss <- (y_pred - y)$pow(2)$sum() + if (t %% 100 == 0 || t == 1) + cat("Step:", t, ":", as.numeric(loss), "\n") # Use autograd to compute the backward pass. This call will compute the # gradient of loss with respect to all Tensors with requires_grad=True. # After this call w1$grad and w2$grad will be Tensors holding the gradient # of the loss with respect to w1 and w2 respectively. - loss$backward() + loss$backward() # Manually update weights using gradient descent. Wrap in `with_no_grad` # because weights have requires_grad=TRUE, but we don't need to track this # in autograd. # You can also use optim_sgd to achieve this. - with_no_grad({ + with_no_grad({ # operations suffixed with an `_` operates on in-place on the tensor. - w1$sub_(learning_rate * w1$grad) - w2$sub_(learning_rate * w2$grad) + w1$sub_(learning_rate * w1$grad) + w2$sub_(learning_rate * w2$grad) # Manually zero the gradients after updating weights - w1$grad$zero_() - w2$grad$zero_() - }) -} -#> Step: 1 : 25332368 -#> Step: 100 : 473.5124 -#> Step: 200 : 2.001738 -#> Step: 300 : 0.01279241 -#> Step: 400 : 0.0002553065 -#> Step: 500 : 4.130635e-05 - + w1$grad$zero_() + w2$grad$zero_() + }) +} +#> Step: 1 : 29097476 +#> Step: 100 : 387.3773 +#> Step: 200 : 1.046869 +#> Step: 300 : 0.005221077 +#> Step: 400 : 0.0001420454 +#> Step: 500 : 2.901151e-05

    In the next example we will learn how to use the neural networks abstractions in torch.

    diff --git a/articles/getting-started/nn.html b/articles/getting-started/nn.html index f2d0e7bc1..95b935805 100644 --- a/articles/getting-started/nn.html +++ b/articles/getting-started/nn.html @@ -11,12 +11,19 @@ - + + +
    @@ -31,7 +38,7 @@ torch - 0.0.3 + 0.1.0
    @@ -104,6 +111,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -117,7 +127,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -159,85 +186,83 @@

    Note: This is an R port of the official tutorial available here. All credits goes to Justin Johnson.

    +library(torch)

    Computational graphs and autograd are a very powerful paradigm for defining complex operators and automatically taking derivatives; however for large neural networks raw autograd can be a bit too low-level.

    When building neural networks we frequently think of arranging the computation into layers, some of which have learnable parameters which will be optimized during learning.

    In TensorFlow, packages like Keras, TensorFlow-Slim, and TFLearn provide higher-level abstractions over raw computational graphs that are useful for building neural networks.

    In torch, the nn functionality serves this same purpose. The nn feature defines a set of Modules, which are roughly equivalent to neural network layers. A Module receives input Tensors and computes output Tensors, but may also hold internal state such as Tensors containing learnable parameters. The nn collection also defines a set of useful loss functions that are commonly used when training neural networks.

    In this example we use nn to implement our two-layer network:

    -if (cuda_is_available()) {
-   device <- torch_device("cuda")
-} else {
-   device <- torch_device("cpu")
-}
+if (cuda_is_available()) {
+   device <- torch_device("cuda")
+} else {
+   device <- torch_device("cpu")
+}
    
 # N is batch size; D_in is input dimension;
 # H is hidden dimension; D_out is output dimension.
-N <- 64
-D_in <- 1000
-H <- 100
-D_out <- 10
+N <- 64
+D_in <- 1000
+H <- 100
+D_out <- 10
 
 # Create random input and output data
 # Setting requires_grad=FALSE (the default) indicates that we do not need to 
 # compute gradients with respect to these Tensors during the backward pass.
-x <- torch_randn(N, D_in, device=device)
-y <- torch_randn(N, D_out, device=device)
+x <- torch_randn(N, D_in, device=device)
+y <- torch_randn(N, D_out, device=device)
 
 # Use the nn package to define our model as a sequence of layers. nn_sequential
 # is a Module which contains other Modules, and applies them in sequence to
 # produce its output. Each Linear Module computes output from input using a
 # linear function, and holds internal Tensors for its weight and bias.
-model <- nn_sequential(
-    nn_linear(D_in, H),
-    nn_relu(),
-    nn_linear(H, D_out)
-)
+model <- nn_sequential(
+    nn_linear(D_in, H),
+    nn_relu(),
+    nn_linear(H, D_out)
+)
 
 # The nn package also contains definitions of popular loss functions; in this
 # case we will use Mean Squared Error (MSE) as our loss function.
-loss_fn <- nnf_mse_loss
+loss_fn <- nnf_mse_loss
 
-learning_rate <- 1e-6
-for (t in seq_len(500)) {
+learning_rate <- 1e-6
+for (t in seq_len(500)) {
    # Forward pass: compute predicted y by passing x to the model. Module objects
    # can be called like functions. When doing so you pass a Tensor of input
    # data to the Module and it produces a Tensor of output data.
-   y_pred <- model(x)
+   y_pred <- model(x)
    
    # Compute and print loss. We pass Tensors containing the predicted and true
    # values of y, and the loss function returns a Tensor containing the
    # loss.
-   loss <- loss_fn(y_pred, y)
-   if (t %% 100 == 0 || t == 1)
-      cat("Step:", t, ":", as.numeric(loss), "\n")
+   loss <- loss_fn(y_pred, y)
+   if (t %% 100 == 0 || t == 1)
+      cat("Step:", t, ":", as.numeric(loss), "\n")
    
    # Zero the gradients before running the backward pass.
-   model$zero_grad()
+   model$zero_grad()
 
    # Backward pass: compute gradient of the loss with respect to all the learnable
    # parameters of the model. Internally, the parameters of each Module are stored
    # in Tensors with requires_grad=TRUE, so this call will compute gradients for
    # all learnable parameters in the model.
-   loss$backward()
+   loss$backward()
    
    # Update the weights using gradient descent. Each parameter is a Tensor, so
    # we can access its gradients like we did before.
-   with_no_grad({
-      for (param in model$parameters) {
-         param$sub_(learning_rate * param$grad)
-      }
-   })
-}
-#> Step: 1 : 1.04115 
-#> Step: 100 : 1.041026 
-#> Step: 200 : 1.040901 
-#> Step: 300 : 1.040776 
-#> Step: 400 : 1.04065 
-#> Step: 500 : 1.040525
-
    + with_no_grad({ + for (param in model$parameters) { + param$sub_(learning_rate * param$grad) + } + }) +} +#> Step: 1 : 1.038781 +#> Step: 100 : 1.038654 +#> Step: 200 : 1.038526 +#> Step: 300 : 1.038399 +#> Step: 400 : 1.038271 +#> Step: 500 : 1.038143

    In the next example we will learn how to use optimizers implemented in torch.

    diff --git a/articles/getting-started/optim.html b/articles/getting-started/optim.html index d5bcc6308..4c93b57aa 100644 --- a/articles/getting-started/optim.html +++ b/articles/getting-started/optim.html @@ -11,12 +11,19 @@ - + + +
    @@ -31,7 +38,7 @@ torch - 0.0.3 + 0.1.0
    @@ -104,6 +111,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -117,7 +127,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -159,87 +186,85 @@

    Note: This is an R port of the official tutorial available here. All credits goes to Justin Johnson.

    +library(torch)

    Up to this point we have updated the weights of our models by manually mutating the Tensors holding learnable parameters (with with_no_grad to avoid tracking history in autograd). This is not a huge burden for simple optimization algorithms like stochastic gradient descent, but in practice we often train neural networks using more sophisticated optimizers like AdaGrad, RMSProp, Adam, etc.

    The optim package in torch abstracts the idea of an optimization algorithm and provides implementations of commonly used optimization algorithms.

    In this example we will use the nn package to define our model as before, but we will optimize the model using the Adam algorithm provided by optim:

    -if (cuda_is_available()) {
-   device <- torch_device("cuda")
-} else {
-   device <- torch_device("cpu")
-}
+if (cuda_is_available()) {
+   device <- torch_device("cuda")
+} else {
+   device <- torch_device("cpu")
+}
    
 # N is batch size; D_in is input dimension;
 # H is hidden dimension; D_out is output dimension.
-N <- 64
-D_in <- 1000
-H <- 100
-D_out <- 10
+N <- 64
+D_in <- 1000
+H <- 100
+D_out <- 10
 
 # Create random input and output data
 # Setting requires_grad=FALSE (the default) indicates that we do not need to 
 # compute gradients with respect to these Tensors during the backward pass.
-x <- torch_randn(N, D_in, device=device)
-y <- torch_randn(N, D_out, device=device)
+x <- torch_randn(N, D_in, device=device)
+y <- torch_randn(N, D_out, device=device)
 
 # Use the nn package to define our model as a sequence of layers. nn_sequential
 # is a Module which contains other Modules, and applies them in sequence to
 # produce its output. Each Linear Module computes output from input using a
 # linear function, and holds internal Tensors for its weight and bias.
-model <- nn_sequential(
-    nn_linear(D_in, H),
-    nn_relu(),
-    nn_linear(H, D_out)
-)
+model <- nn_sequential(
+    nn_linear(D_in, H),
+    nn_relu(),
+    nn_linear(H, D_out)
+)
 
 # The nn package also contains definitions of popular loss functions; in this
 # case we will use Mean Squared Error (MSE) as our loss function.
-loss_fn <- nnf_mse_loss
+loss_fn <- nnf_mse_loss
 
 # Use the optim package to define an Optimizer that will update the weights of
 # the model for us. Here we will use Adam; the optim package contains many other
 # optimization algorithms. The first argument to the Adam constructor tells the
 # optimizer which Tensors it should update.
-learning_rate <- 1e-4
-optimizer <- optim_adam(model$parameters, lr=learning_rate)
+learning_rate <- 1e-4
+optimizer <- optim_adam(model$parameters, lr=learning_rate)
 
-for (t in seq_len(500)) {
+for (t in seq_len(500)) {
    # Forward pass: compute predicted y by passing x to the model. Module objects
    # can be called like functions. When doing so you pass a Tensor of input
    # data to the Module and it produces a Tensor of output data.
-   y_pred <- model(x)
+   y_pred <- model(x)
    
    # Compute and print loss. We pass Tensors containing the predicted and true
    # values of y, and the loss function returns a Tensor containing the
    # loss.
-   loss <- loss_fn(y_pred, y)
-   if (t %% 100 == 0 || t == 1)
-      cat("Step:", t, ":", as.numeric(loss), "\n")
+   loss <- loss_fn(y_pred, y)
+   if (t %% 100 == 0 || t == 1)
+      cat("Step:", t, ":", as.numeric(loss), "\n")
    
    # Before the backward pass, use the optimizer object to zero all of the
    # gradients for the variables it will update (which are the learnable
    # weights of the model). This is because by default, gradients are
    # accumulated in buffers( i.e, not overwritten) whenever $backward()
    # is called. Checkout docs of `autograd_backward` for more details.
-   optimizer$zero_grad()
+   optimizer$zero_grad()
 
    # Backward pass: compute gradient of the loss with respect to model
    # parameters
-   loss$backward()
+   loss$backward()
 
    # Calling the step function on an Optimizer makes an update to its
    # parameters
-   optimizer$step()
-}
-#> Step: 1 : 1.03194 
-#> Step: 100 : 0.08338322 
-#> Step: 200 : 0.001254716 
-#> Step: 300 : 3.605265e-06 
-#> Step: 400 : 2.155708e-09 
-#> Step: 500 : 5.439427e-13
-
    + optimizer$step() +} +#> Step: 1 : 0.9450495 +#> Step: 100 : 0.06193794 +#> Step: 200 : 0.0007609552 +#> Step: 300 : 2.95975e-06 +#> Step: 400 : 4.247736e-09 +#> Step: 500 : 1.415559e-12

    In the next example we will learn how to create custom nn_modules.

    diff --git a/articles/getting-started/tensors-and-autograd.html b/articles/getting-started/tensors-and-autograd.html index b26b93941..565fb9bab 100644 --- a/articles/getting-started/tensors-and-autograd.html +++ b/articles/getting-started/tensors-and-autograd.html @@ -11,12 +11,19 @@ - + + +
    @@ -31,7 +38,7 @@ torch - 0.0.3 + 0.1.0
    @@ -104,6 +111,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -117,7 +127,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -159,80 +186,78 @@

    Note: This is an R port of the official tutorial available here. All credits goes to Justin Johnson.

    +library(torch)

    In the previous examples, we had to manually implement both the forward and backward passes of our neural network. Manually implementing the backward pass is not a big deal for a small two-layer network, but can quickly get very hairy for large complex networks.

    Thankfully, we can use automatic differentiation to automate the computation of backward passes in neural networks. The autograd feature in torch provides exactly this functionality. When using autograd, the forward pass of your network will define a computational graph; nodes in the graph will be Tensors, and edges will be functions that produce output Tensors from input Tensors. Backpropagating through this graph then allows you to easily compute gradients.

    This sounds complicated, it’s pretty simple to use in practice. Each Tensor represents a node in a computational graph. If x is a Tensor that has x$requires_grad=TRUE then x$grad is another Tensor holding the gradient of x with respect to some scalar value.

    Here we use torch Tensors and autograd to implement our two-layer network; now we no longer need to manually implement the backward pass through the network:

    -if (cuda_is_available()) {
-   device <- torch_device("cuda")
-} else {
-   device <- torch_device("cpu")
-}
+if (cuda_is_available()) {
+   device <- torch_device("cuda")
+} else {
+   device <- torch_device("cpu")
+}
    
 # N is batch size; D_in is input dimension;
 # H is hidden dimension; D_out is output dimension.
-N <- 64
-D_in <- 1000
-H <- 100
-D_out <- 10
+N <- 64
+D_in <- 1000
+H <- 100
+D_out <- 10
 
 # Create random input and output data
 # Setting requires_grad=FALSE (the default) indicates that we do not need to 
 # compute gradients with respect to these Tensors during the backward pass.
-x <- torch_randn(N, D_in, device=device)
-y <- torch_randn(N, D_out, device=device)
+x <- torch_randn(N, D_in, device=device)
+y <- torch_randn(N, D_out, device=device)
 
 # Randomly initialize weights
 # Setting requires_grad=TRUE indicates that we want to compute gradients with
 # respect to these Tensors during the backward pass.
-w1 <- torch_randn(D_in, H, device=device, requires_grad = TRUE)
-w2 <- torch_randn(H, D_out, device=device, requires_grad = TRUE)
+w1 <- torch_randn(D_in, H, device=device, requires_grad = TRUE)
+w2 <- torch_randn(H, D_out, device=device, requires_grad = TRUE)
 
-learning_rate <- 1e-6
-for (t in seq_len(500)) {
+learning_rate <- 1e-6
+for (t in seq_len(500)) {
    # Forward pass: compute predicted y using operations on Tensors; these
    # are exactly the same operations we used to compute the forward pass using
    # Tensors, but we do not need to keep references to intermediate values since
    # we are not implementing the backward pass by hand.
-   y_pred <- x$mm(w1)$clamp(min=0)$mm(w2)
+   y_pred <- x$mm(w1)$clamp(min=0)$mm(w2)
    
    # Compute and print loss using operations on Tensors.
    # Now loss is a Tensor of shape (1,)
-   loss <- (y_pred - y)$pow(2)$sum()
-   if (t %% 100 == 0 || t == 1)
-      cat("Step:", t, ":", as.numeric(loss), "\n")
+   loss <- (y_pred - y)$pow(2)$sum()
+   if (t %% 100 == 0 || t == 1)
+      cat("Step:", t, ":", as.numeric(loss), "\n")
    
    # Use autograd to compute the backward pass. This call will compute the
    # gradient of loss with respect to all Tensors with requires_grad=True.
    # After this call w1$grad and w2$grad will be Tensors holding the gradient
    # of the loss with respect to w1 and w2 respectively.
-   loss$backward()
+   loss$backward()
    
    # Manually update weights using gradient descent. Wrap in `with_no_grad`
    # because weights have requires_grad=TRUE, but we don't need to track this
    # in autograd.
    # You can also use optim_sgd to achieve this.
-   with_no_grad({
+   with_no_grad({
       
       # operations suffixed with an `_` operates on in-place on the tensor.
-      w1$sub_(learning_rate * w1$grad)
-      w2$sub_(learning_rate * w2$grad)
+      w1$sub_(learning_rate * w1$grad)
+      w2$sub_(learning_rate * w2$grad)
       
       # Manually zero the gradients after updating weights
-      w1$grad$zero_()
-      w2$grad$zero_()
-   })
-}
-#> Step: 1 : 27399256 
-#> Step: 100 : 756.7294 
-#> Step: 200 : 9.271971 
-#> Step: 300 : 0.205474 
-#> Step: 400 : 0.00579866 
-#> Step: 500 : 0.0003981641
-
    + w1$grad$zero_() + w2$grad$zero_() + }) +} +#> Step: 1 : 32265452 +#> Step: 100 : 617.4926 +#> Step: 200 : 3.489705 +#> Step: 300 : 0.03414487 +#> Step: 400 : 0.0006231664 +#> Step: 500 : 6.901405e-05

    In the next example we will learn how to create new autograd functions.

    diff --git a/articles/getting-started/tensors.html b/articles/getting-started/tensors.html index f79bad2fb..9eb3f10a7 100644 --- a/articles/getting-started/tensors.html +++ b/articles/getting-started/tensors.html @@ -11,12 +11,19 @@ - + + +
    @@ -31,7 +38,7 @@ torch - 0.0.3 + 0.1.0
    @@ -104,6 +111,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -117,7 +127,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -159,65 +186,63 @@

    Note: This is an R port of the official tutorial available here. All credits goes to Justin Johnson.

    +library(torch)

    R arrays are great, but they cannot utilize GPUs to accelerate its numerical computations. For modern deep neural networks, GPUs often provide speedups of 50x or greater, so unfortunately pure R won’t be enough for modern deep learning.

    Here we introduce the most fundamental torch concept: the Tensor. A torch Tensor is conceptually similar to an R array: a Tensor is an n-dimensional array, and torch provides many functions for operating on these Tensors. Behind the scenes, Tensors can keep track of a computational graph and gradients, but they’re also useful as a generic tool for scientific computing.

    Also unlike R, torch Tensors can utilize GPUs to accelerate their numeric computations. To run a torch Tensor on GPU, you simply need to cast it to a new datatype.

    Here we use torch Tensors to fit a two-layer network to random data. Like the R before we need to manually implement the forward and backward passes through the network:

    -if (cuda_is_available()) {
-   device <- torch_device("cuda")
-} else {
-   device <- torch_device("cpu")
-}
+if (cuda_is_available()) {
+   device <- torch_device("cuda")
+} else {
+   device <- torch_device("cpu")
+}
    
 # N is batch size; D_in is input dimension;
 # H is hidden dimension; D_out is output dimension.
-N <- 64
-D_in <- 1000
-H <- 100
-D_out <- 10
+N <- 64
+D_in <- 1000
+H <- 100
+D_out <- 10
 
 # Create random input and output data
-x <- torch_randn(N, D_in, device=device)
-y <- torch_randn(N, D_out, device=device)
+x <- torch_randn(N, D_in, device=device)
+y <- torch_randn(N, D_out, device=device)
 
 # Randomly initialize weights
-w1 <- torch_randn(D_in, H, device=device)
-w2 <- torch_randn(H, D_out, device=device)
+w1 <- torch_randn(D_in, H, device=device)
+w2 <- torch_randn(H, D_out, device=device)
 
-learning_rate <- 1e-6
-for (t in seq_len(500)) {
+learning_rate <- 1e-6
+for (t in seq_len(500)) {
    # Forward pass: compute predicted y
-   h <- x$mm(w1)
-   h_relu <- h$clamp(min=0)
-   y_pred <- h_relu$mm(w2)
+   h <- x$mm(w1)
+   h_relu <- h$clamp(min=0)
+   y_pred <- h_relu$mm(w2)
    
    # Compute and print loss
-   loss <- as.numeric((y_pred - y)$pow(2)$sum())
-   if (t %% 100 == 0 || t == 1)
-      cat("Step:", t, ":", loss, "\n")
+   loss <- as.numeric((y_pred - y)$pow(2)$sum())
+   if (t %% 100 == 0 || t == 1)
+      cat("Step:", t, ":", loss, "\n")
    
    # Backprop to compute gradients of w1 and w2 with respect to loss
-   grad_y_pred <- 2.0 * (y_pred - y)
-   grad_w2 <- h_relu$t()$mm(grad_y_pred)
-   grad_h_relu <- grad_y_pred$mm(w2$t())
-   grad_h <- grad_h_relu$clone()
-   grad_h[h < 0] <- 0
-   grad_w1 <- x$t()$mm(grad_h)
+   grad_y_pred <- 2.0 * (y_pred - y)
+   grad_w2 <- h_relu$t()$mm(grad_y_pred)
+   grad_h_relu <- grad_y_pred$mm(w2$t())
+   grad_h <- grad_h_relu$clone()
+   grad_h[h < 0] <- 0
+   grad_w1 <- x$t()$mm(grad_h)
    
    # Update weights using gradient descent
-   w1 <- w1 - learning_rate * grad_w1
-   w2 <- w2 - learning_rate * grad_w2
-}
-#> Step: 1 : 31418640 
-#> Step: 100 : 959.8534 
-#> Step: 200 : 34.41588 
-#> Step: 300 : 1.953276 
-#> Step: 400 : 0.1213879 
-#> Step: 500 : 0.008213471
-
    + w1 <- w1 - learning_rate * grad_w1 + w2 <- w2 - learning_rate * grad_w2 +} +#> Step: 1 : 35593956 +#> Step: 100 : 1013.579 +#> Step: 200 : 12.88333 +#> Step: 300 : 0.2700103 +#> Step: 400 : 0.007026888 +#> Step: 500 : 0.0004394832

    In the next example we will use autograd instead of computing the gradients manually.

    diff --git a/articles/getting-started/warmup.html b/articles/getting-started/warmup.html index f85e2bf06..ed4195e82 100644 --- a/articles/getting-started/warmup.html +++ b/articles/getting-started/warmup.html @@ -11,12 +11,19 @@ - + + +
    @@ -31,7 +38,7 @@ torch - 0.0.3 + 0.1.0
    @@ -104,6 +111,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -117,7 +127,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -159,58 +186,56 @@

    Note: This is an R port of the official tutorial available here. All credits goes to Justin Johnson.

    +library(torch)

    A fully-connected ReLU network with one hidden layer and no biases, trained to predict y from x using Euclidean error.

    This implementation uses pure R to manually compute the forward pass, loss, and backward pass.

    An R array is a generic n-dimensional array; it does not know anything about deep learning or gradients or computational graphs, and is just a way to perform generic numeric computations.

     # N is batch size; D_in is input dimension;
 # H is hidden dimension; D_out is output dimension.
-N <- 64
-D_in <- 1000
-H <- 100
-D_out <- 10
+N <- 64
+D_in <- 1000
+H <- 100
+D_out <- 10
 
 # Create random input and output data
-x <- array(rnorm(N*D_in), dim = c(N, D_in))
-y <- array(rnorm(N*D_out), dim = c(N, D_out))
+x <- array(rnorm(N*D_in), dim = c(N, D_in))
+y <- array(rnorm(N*D_out), dim = c(N, D_out))
 
 # Randomly initialize weights
-w1 <- array(rnorm(D_in*H), dim = c(D_in, H))
-w2 <- array(rnorm(H*D_out), dim = c(H, D_out))
+w1 <- array(rnorm(D_in*H), dim = c(D_in, H))
+w2 <- array(rnorm(H*D_out), dim = c(H, D_out))
 
-learning_rate <- 1e-6
-for (t in seq_len(500)) {
+learning_rate <- 1e-6
+for (t in seq_len(500)) {
    # Forward pass: compute predicted y
-   h <- x %*% w1
-   h_relu <- ifelse(h < 0, 0, h)
-   y_pred <- h_relu %*% w2
+   h <- x %*% w1
+   h_relu <- ifelse(h < 0, 0, h)
+   y_pred <- h_relu %*% w2
    
    # Compute and print loss
-   loss <- sum((y_pred - y)^2)
-   if (t %% 100 == 0 || t == 1)
-      cat("Step:", t, ":", loss, "\n")
+   loss <- sum((y_pred - y)^2)
+   if (t %% 100 == 0 || t == 1)
+      cat("Step:", t, ":", loss, "\n")
    
    # Backprop to compute gradients of w1 and w2 with respect to loss
-   grad_y_pred <- 2 * (y_pred - y)
-   grad_w2 <- t(h_relu) %*% grad_y_pred
-   grad_h_relu <- grad_y_pred %*% t(w2)
-   grad_h <- grad_h_relu
-   grad_h[h < 0] <- 0
-   grad_w1 <- t(x) %*% grad_h
+   grad_y_pred <- 2 * (y_pred - y)
+   grad_w2 <- t(h_relu) %*% grad_y_pred
+   grad_h_relu <- grad_y_pred %*% t(w2)
+   grad_h <- grad_h_relu
+   grad_h[h < 0] <- 0
+   grad_w1 <- t(x) %*% grad_h
    
    # Update weights
-   w1 <- w1 - learning_rate * grad_w1
-   w2 <- w2 - learning_rate * grad_w2
-}
-#> Step: 1 : 28115720 
-#> Step: 100 : 536.8496 
-#> Step: 200 : 2.748443 
-#> Step: 300 : 0.01913319 
-#> Step: 400 : 0.0001405911 
-#> Step: 500 : 1.056453e-06
-
    + w1 <- w1 - learning_rate * grad_w1 + w2 <- w2 - learning_rate * grad_w2 +} +#> Step: 1 : 41617929 +#> Step: 100 : 853.0884 +#> Step: 200 : 6.804854 +#> Step: 300 : 0.1015232 +#> Step: 400 : 0.001972299 +#> Step: 500 : 4.278207e-05

    In the next example we will replace the R array for a torch Tensor.

    diff --git a/articles/getting-started/what-is-torch.html b/articles/getting-started/what-is-torch.html index cf83c6ada..3f7253688 100644 --- a/articles/getting-started/what-is-torch.html +++ b/articles/getting-started/what-is-torch.html @@ -11,12 +11,19 @@ - + + +
    @@ -31,7 +38,7 @@ torch - 0.0.3 + 0.1.0
    @@ -104,6 +111,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -117,7 +127,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -159,8 +186,7 @@

    Note: This is an R port of the official tutorial available here. All credits goes to Soumith Chintala.

    +library(torch)

    It’s a scientific computing package targeted at two sets of audiences:

    -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -193,6 +225,14 @@

    +
    basic-autograd
    +
    +
    basic-nn-module
    +
    +
    dataset
    +
    +
    Examples
    +
    Extending Autograd
    Autograd: automatic differentiation
    @@ -221,6 +261,8 @@
    Loading data
    +
    Serialization
    +
    Tensor objects
    Creating tensors
    diff --git a/articles/indexing.html b/articles/indexing.html index cf9232fa0..5c494bdfa 100644 --- a/articles/indexing.html +++ b/articles/indexing.html @@ -11,12 +11,19 @@ - + + +
    @@ -31,7 +38,7 @@ torch - 0.0.3 + 0.1.0
    @@ -104,6 +111,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -117,7 +127,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -156,8 +183,7 @@ +library(torch)

    In this article we describe the indexing operator for torch tensors and how it compares to the R indexing operator for arrays.

    Torch’s indexing semantics are closer to numpy’s semantics than R’s. You will find a lot of similarities between this article and the numpy indexing article available here.

    @@ -165,52 +191,49 @@ Single element indexing

    Single element indexing for a 1-D tensors works mostly as expected. Like R, it is 1-based. Unlike R though, it accepts negative indices for indexing from the end of the array. (In R, negative indices are used to remove elements.)

    -x <- torch_tensor(1:10)
-x[1]
+x <- torch_tensor(1:10)
+x[1]
 #> torch_tensor 
 #> 1
 #> [ CPULongType{} ]
-x[-1]
+x[-1]
 #> torch_tensor 
 #> 10
-#> [ CPULongType{} ]
-
    +#> [ CPULongType{} ]

    You can also subset matrices and higher dimensions arrays using the same syntax:

    -x <- x$reshape(shape = c(2,5))
-x
+x <- x$reshape(shape = c(2,5))
+x
 #> torch_tensor 
 #>   1   2   3   4   5
 #>   6   7   8   9  10
 #> [ CPULongType{2,5} ]
-x[1,3]
+x[1,3]
 #> torch_tensor 
 #> 3
 #> [ CPULongType{} ]
-x[1,-1]
+x[1,-1]
 #> torch_tensor 
 #> 5
-#> [ CPULongType{} ]
-
    +#> [ CPULongType{} ]

    Note that if one indexes a multidimensional tensor with fewer indices than dimensions, one gets an error, unlike in R that would flatten the array. For example:

    -x[1]
+x[1]
 #> torch_tensor 
 #>  1
 #>  2
 #>  3
 #>  4
 #>  5
-#> [ CPULongType{5} ]
-
    +#> [ CPULongType{5} ]

    Slicing and striding

    It is possible to slice and stride arrays to extract sub-arrays of the same number of dimensions, but of different sizes than the original. This is best illustrated by a few examples:

    -x <- torch_tensor(1:10)
-x
+x <- torch_tensor(1:10)
+x
 #> torch_tensor 
 #>   1
 #>   2
@@ -223,33 +246,31 @@
 #>   9
 #>  10
 #> [ CPULongType{10} ]
-x[2:5]
+x[2:5]
 #> torch_tensor 
 #>  2
 #>  3
 #>  4
 #>  5
 #> [ CPULongType{4} ]
-x[1:(-7)]
+x[1:(-7)]
 #> torch_tensor 
 #>  1
 #>  2
 #>  3
 #>  4
-#> [ CPULongType{4} ]
-
    +#> [ CPULongType{4} ]

    You can also use the 1:10:2 syntax which means: In the range from 1 to 10, take every second item. For example:

    -x[1:5:2]
+x[1:5:2]
 #> torch_tensor 
 #>  1
 #>  3
 #>  5
-#> [ CPULongType{3} ]
-
    +#> [ CPULongType{3} ]

    Another special syntax is the N, meaning the size of the specified dimension.

    -x[5:N]
+x[5:N]
 #> torch_tensor 
 #>   5
 #>   6
@@ -257,8 +278,7 @@
 #>   8
 #>   9
 #>  10
-#> [ CPULongType{6} ]
-
    +#> [ CPULongType{6} ]

    @@ -266,72 +286,65 @@

    Like in R, you can take all elements in a dimension by leaving an index empty.

    Consider a matrix:

    -x <- torch_randn(2, 3)
-x
+x <- torch_randn(2, 3)
+x
 #> torch_tensor 
-#>  1.4158  0.9219 -0.1461
-#>  0.9801  0.7556  0.2140
-#> [ CPUFloatType{2,3} ]
-
    +#> -0.3778 -0.2716 0.9928 +#> 0.5261 1.5789 -0.5842 +#> [ CPUFloatType{2,3} ]

    The following syntax will give you the first row:

    -x[1,]
+x[1,]
 #> torch_tensor 
-#>  1.4158
-#>  0.9219
-#> -0.1461
-#> [ CPUFloatType{3} ]
-
    +#> -0.3778 +#> -0.2716 +#> 0.9928 +#> [ CPUFloatType{3} ]

    And this would give you the first 2 columns:

    -x[,1:2]
+x[,1:2]
 #> torch_tensor 
-#>  1.4158  0.9219
-#>  0.9801  0.7556
-#> [ CPUFloatType{2,2} ]
-
    +#> -0.3778 -0.2716 +#> 0.5261 1.5789 +#> [ CPUFloatType{2,2} ]

    Dropping dimensions

    By default, when indexing by a single integer, this dimension will be dropped to avoid the singleton dimension:

    -x <- torch_randn(2, 3)
-x[1,]$shape
-#> [1] 3
-
    +x <- torch_randn(2, 3) +x[1,]$shape +#> [1] 3

    You can optionally use the drop = FALSE argument to avoid dropping the dimension.

    -x[1,,drop = FALSE]$shape
-#> [1] 1 3
-
    +x[1,,drop = FALSE]$shape +#> [1] 1 3

    Adding a new dimension

    It’s possible to add a new dimension to a tensor using index-like syntax:

    -x <- torch_tensor(c(10))
-x$shape
+x <- torch_tensor(c(10))
+x$shape
 #> [1] 1
-x[, newaxis]$shape
+x[, newaxis]$shape
 #> [1] 1 1
-x[, newaxis, newaxis]$shape
-#> [1] 1 1 1
-
    +x[, newaxis, newaxis]$shape +#> [1] 1 1 1

    You can also use NULL instead of newaxis:

    -x[,NULL]$shape
-#> [1] 1 1
-
    +x[,NULL]$shape +#> [1] 1 1

    Dealing with variable number of indices

    Sometimes we don’t know how many dimensions a tensor has, but we do know what to do with the last available dimension, or the first one. To subsume all others, we can use ..:

    -z <- torch_tensor(1:125)$reshape(c(5,5,5))
-z[1,..]
+z <- torch_tensor(1:125)$reshape(c(5,5,5))
+z[1,..]
 #> torch_tensor 
 #>   1   2   3   4   5
 #>   6   7   8   9  10
@@ -339,15 +352,14 @@
 #>  16  17  18  19  20
 #>  21  22  23  24  25
 #> [ CPULongType{5,5} ]
-z[..,1]
+z[..,1]
 #> torch_tensor 
 #>    1    6   11   16   21
 #>   26   31   36   41   46
 #>   51   56   61   66   71
 #>   76   81   86   91   96
 #>  101  106  111  116  121
-#> [ CPULongType{5,5} ]
-
    +#> [ CPULongType{5,5} ]
    diff --git a/articles/loading-data.html b/articles/loading-data.html index a00e41ea3..29c4b7b68 100644 --- a/articles/loading-data.html +++ b/articles/loading-data.html @@ -11,12 +11,19 @@ - + + +
    @@ -31,7 +38,7 @@ torch - 0.0.3 + 0.1.0
    @@ -104,6 +111,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -117,7 +127,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -156,8 +183,7 @@ +library(torch)

    Datasets and data loaders

    @@ -186,10 +212,10 @@

    A custom dataset

    -library(palmerpenguins)
-library(magrittr)
+library(palmerpenguins)
+library(magrittr)
 
-penguins
+penguins
 #> # A tibble: 344 x 8
 #>    species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g
 #>    <fct>   <fct>           <dbl>         <dbl>            <int>       <int>
@@ -203,52 +229,50 @@
 #>  8 Adelie  Torge…           39.2          19.6              195        4675
 #>  9 Adelie  Torge…           34.1          18.1              193        3475
 #> 10 Adelie  Torge…           42            20.2              190        4250
-#> # … with 334 more rows, and 2 more variables: sex <fct>, year <int>
-
    +#> # … with 334 more rows, and 2 more variables: sex <fct>, year <int>

    Datasets are R6 classes created using the dataset() constructor. You can pass a name and various member functions. Among those should be initialize(), to create instance variables, .getitem(), to indicate how the data should be returned, and .length(), to say how many items we have.

    In addition, any number of helper functions can be defined.

    Here, we assume the penguins have already been loaded, and all preprocessing consists in removing lines with NA values, transforming factors to numbers starting from 0, and converting from R data types to torch tensors.

    In .getitem, we essentially decide how this data is going to be used: All variables besides species go into x, the predictor, and species will constitute y, the target. Predictor and target are returned in a list, to be accessed as batch[[1]] and batch[[2]] during training.

    -penguins_dataset <- dataset(
+penguins_dataset <- dataset(
   
-  name = "penguins_dataset",
+  name = "penguins_dataset",
   
-  initialize = function() {
-    self$data <- self$prepare_penguin_data()
-  },
+  initialize = function() {
+    self$data <- self$prepare_penguin_data()
+  },
   
-  .getitem = function(index) {
+  .getitem = function(index) {
     
-    x <- self$data[index, 2:-1]
-    y <- self$data[index, 1]$to(torch_long())
+    x <- self$data[index, 2:-1]
+    y <- self$data[index, 1]$to(torch_long())
     
-    list(x, y)
-  },
+    list(x, y)
+  },
   
-  .length = function() {
-    self$data$size()[[1]]
-  },
+  .length = function() {
+    self$data$size()[[1]]
+  },
   
-  prepare_penguin_data = function() {
+  prepare_penguin_data = function() {
     
-    input <- na.omit(penguins) 
+    input <- na.omit(penguins) 
     # conveniently, the categorical data are already factors
-    input$species <- as.numeric(input$species)
-    input$island <- as.numeric(input$island)
-    input$sex <- as.numeric(input$sex)
+    input$species <- as.numeric(input$species)
+    input$island <- as.numeric(input$island)
+    input$sex <- as.numeric(input$sex)
     
-    input <- as.matrix(input)
-    torch_tensor(input)
-  }
-)
-
    + input <- as.matrix(input) + torch_tensor(input) + } +)

    Let’s create the dataset , query for it’s length, and look at its first item:

    -tuxes <- penguins_dataset()
-tuxes$.length()
+tuxes <- penguins_dataset()
+tuxes$.length()
 #> [1] 333
-tuxes$.getitem(1)
+tuxes$.getitem(1)
 #> [[1]]
 #> torch_tensor 
 #>     3.0000
@@ -263,22 +287,19 @@
 #> [[2]]
 #> torch_tensor 
 #> 1
-#> [ CPULongType{} ]
-
    +#> [ CPULongType{} ]

    To be able to iterate over tuxes, we need a data loader (we override the default batch size of 1):

    -dl <-tuxes %>% dataloader(batch_size = 8)
-
    +dl <-tuxes %>% dataloader(batch_size = 8)

    Calling .length() on a data loader (as opposed to a dataset) will return the number of batches we have:

    -dl$.length()
-#> [1] 42
-
    +dl$.length() +#> [1] 42

    And we can create an iterator to inspect the first batch:

    -iter <- dl$.iter()
-b <- iter$.next()
-b
+iter <- dl$.iter()
+b <- iter$.next()
+b
 #> [[1]]
 #> torch_tensor 
 #>     3.0000    39.1000    18.7000   181.0000  3750.0000     2.0000  2007.0000
@@ -301,8 +322,7 @@
 #>  1
 #>  1
 #>  1
-#> [ CPULongType{8} ]
-
    +#> [ CPULongType{8} ]

    To train a network, we can use enumerate to iterate over batches.

    @@ -310,44 +330,42 @@ Training with data loaders

    Our example network is very simple. (In reality, we would want to treat island as the categorical variable it is, and either one-hot-encode or embed it.)

    -net <- nn_module(
+net <- nn_module(
   "PenguinNet",
-  initialize = function() {
-    self$fc1 <- nn_linear(7, 32)
-    self$fc2 <- nn_linear(32, 3)
-  },
-  forward = function(x) {
-    x %>% 
-      self$fc1() %>% 
-      nnf_relu() %>% 
-      self$fc2() %>% 
-      nnf_log_softmax(dim = 1)
-  }
-)
+  initialize = function() {
+    self$fc1 <- nn_linear(7, 32)
+    self$fc2 <- nn_linear(32, 3)
+  },
+  forward = function(x) {
+    x %>% 
+      self$fc1() %>% 
+      nnf_relu() %>% 
+      self$fc2() %>% 
+      nnf_log_softmax(dim = 1)
+  }
+)
 
-model <- net()
-
    +model <- net()

    We still need an optimizer:

    -optimizer <- optim_sgd(model$parameters, lr = 0.01)
-
    +optimizer <- optim_sgd(model$parameters, lr = 0.01)

    And we’re ready to train:

    -for (epoch in 1:10) {
+for (epoch in 1:10) {
   
-  l <- c()
+  l <- c()
   
-  for (b in enumerate(dl)) {
-    optimizer$zero_grad()
-    output <- model(b[[1]])
-    loss <- nnf_nll_loss(output, b[[2]])
-    loss$backward()
-    optimizer$step()
-    l <- c(l, loss$item())
-  }
+  for (b in enumerate(dl)) {
+    optimizer$zero_grad()
+    output <- model(b[[1]])
+    loss <- nnf_nll_loss(output, b[[2]])
+    loss$backward()
+    optimizer$step()
+    l <- c(l, loss$item())
+  }
   
-  cat(sprintf("Loss at epoch %d: %3f\n", epoch, mean(l)))
-}
+  cat(sprintf("Loss at epoch %d: %3f\n", epoch, mean(l)))
+}
 #> Loss at epoch 1: 51.747068
 #> Loss at epoch 2: 2.068251
 #> Loss at epoch 3: 2.068251
@@ -357,8 +375,7 @@
 #> Loss at epoch 7: 2.068251
 #> Loss at epoch 8: 2.068251
 #> Loss at epoch 9: 2.068251
-#> Loss at epoch 10: 2.068251
-
    +#> Loss at epoch 10: 2.068251 diff --git a/articles/serialization.html b/articles/serialization.html new file mode 100644 index 000000000..e0d195aee --- /dev/null +++ b/articles/serialization.html @@ -0,0 +1,276 @@ + + + + + + + +Serialization • torch + + + + + + + + + + + +
    +
    + + + + +
    +
    +
    +

    Serialization

    + + + Source: vignettes/serialization.Rmd + + +
    + + + + +

    Torch tensors in R are pointers to Tensors allocated by LibTorch. This has one major consequence for serialization. One cannot simply use saveRDS for serializing tensors, as you would save the pointer but not the data itself. When reloading a tensor saved with saveRDS the pointer might have been deleted in LibTorch and you would get wrong results.

    +

    To solve this problem, torch implements specialized functions for serializing tensors to the disk:

    +
      +
    • +torch_save(): to save tensors and models to the disk.
      • +
    • +torch_load(): to load the models or tensors back to the session.
      • +
    +

    Please note that this format is still experimental and you shouldn’t use it for long term storage.

    +
    +

    +Saving tensors

    +

    You can save any object of type torch_tensor to the disk using:

    +
    +x <- torch_randn(10, 10)
+torch_save(x, "tensor.pt")
+x_ <- torch_load("tensor.pt")
+
+torch_allclose(x, x_)
+#> [1] TRUE
    +
    +
    +

    +Saving modules

    +

    The torch_save and torch_load functions also work for nn_modules objects.

    +

    When saving an nn_module, all the object is serialized including the model structure and it’s state.

    +
    +module <- nn_module(
+  "my_module",
+  initialize = function() {
+    self$fc1 <- nn_linear(10, 10)
+    self$fc2 <- nn_linear(10, 1)
+  },
+  forward = function(x) {
+    x %>% 
+      self$fc1() %>% 
+      self$fc2()
+  }
+)
+
+model <- module()
+torch_save(model, "model.pt")
+model_ <- torch_load("model.pt")
+
+# input tensor
+x <- torch_randn(50, 10)
+torch_allclose(model(x), model_(x))
+#> [1] TRUE
    +
    +
    +

    +Loading models saved in python

    +

    Currently the only way to load models from python is to rewrite the model architecture in R. All the parameter names must be identical.

    +

    You can then save the PyTorch model state_dict using:

    +
    torch.save(model, fpath, _use_new_zipfile_serialization=True)
    +

    You can then reload the state dict in R and reload it into the model with:

    +
    +state_dict <- load_state_dict(fpath)
+model <- Model()
+model$load_state_dict(state_dict)
    +

    You can find working examples in torchvision. For example this is what we do for the AlexNet model.

    +
    +
    + + + +
    + + + +
    +

    Developed by Daniel Falbel, Javier Luraschi.

    +
    + +
    +

    Site built with pkgdown 1.6.1.

    +
    + +
    +
    + + + + + + diff --git a/articles/serialization_files/accessible-code-block-0.0.1/empty-anchor.js b/articles/serialization_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 000000000..ca349fd6a --- /dev/null +++ b/articles/serialization_files/accessible-code-block-0.0.1/empty-anchor.js @@ -0,0 +1,15 @@ +// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> +// v0.0.1 +// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. + +document.addEventListener('DOMContentLoaded', function() { + const codeList = document.getElementsByClassName("sourceCode"); + for (var i = 0; i < codeList.length; i++) { + var linkList = codeList[i].getElementsByTagName('a'); + for (var j = 0; j < linkList.length; j++) { + if (linkList[j].innerHTML === "") { + linkList[j].setAttribute('aria-hidden', 'true'); + } + } + } +}); diff --git a/articles/tensor-creation.html b/articles/tensor-creation.html index a60aa0f72..5f7499f82 100644 --- a/articles/tensor-creation.html +++ b/articles/tensor-creation.html @@ -11,12 +11,19 @@ - + + +
    @@ -31,7 +38,7 @@ torch - 0.0.3 + 0.1.0
    @@ -104,6 +111,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -117,7 +127,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -156,8 +183,7 @@ +library(torch)

    In this article we describe various ways of creating torch tensors in R.

    @@ -165,7 +191,7 @@

    You can create tensors from R objects using the torch_tensor function. The torch_tensor function takes an R vector, matrix or array and creates an equivalent torch_tensor.

    You can see a few examples below:

    -torch_tensor(c(1,2,3))
+torch_tensor(c(1,2,3))
 #> torch_tensor 
 #>  1
 #>  2
@@ -173,37 +199,35 @@
 #> [ CPUFloatType{3} ]
 
 # conform to row-major indexing used in torch
-torch_tensor(matrix(1:10, ncol = 5, nrow = 2, byrow = TRUE))
+torch_tensor(matrix(1:10, ncol = 5, nrow = 2, byrow = TRUE))
 #> torch_tensor 
 #>   1   2   3   4   5
 #>   6   7   8   9  10
 #> [ CPULongType{2,5} ]
-torch_tensor(array(runif(12), dim = c(2, 2, 3)))
+torch_tensor(array(runif(12), dim = c(2, 2, 3)))
 #> torch_tensor 
 #> (1,.,.) = 
-#>   0.5612  0.4325  0.6571
-#>   0.4899  0.4636  0.9910
+#>   0.8530  0.3081  0.4772
+#>   0.3160  0.0449  0.0551
 #> 
 #> (2,.,.) = 
-#>   0.7073  0.8791  0.3117
-#>   0.9112  0.6383  0.1045
-#> [ CPUFloatType{2,2,3} ]
-
    +#> 0.8822 0.8065 0.0366 +#> 0.5736 0.1065 0.2902 +#> [ CPUFloatType{2,2,3} ]

    By default, we will create tensors in the cpu device, converting their R datatype to the corresponding torch dtype.

    Note currently, only numeric and boolean types are supported.

    You can always modify dtype and device when converting an R object to a torch tensor. For example:

    -torch_tensor(1, dtype = torch_long())
+torch_tensor(1, dtype = torch_long())
 #> torch_tensor 
 #>  1
 #> [ CPULongType{1} ]
-torch_tensor(1, device = "cpu", dtype = torch_float64())
+torch_tensor(1, device = "cpu", dtype = torch_float64())
 #> torch_tensor 
 #>  1
-#> [ CPUDoubleType{1} ]
-
    +#> [ CPUDoubleType{1} ]

    Other options available when creating a tensor are:

     -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -156,8 +183,7 @@ +library(torch)

    So far, all we’ve been using from torch is tensors, but we’ve been performing all calculations ourselves – the computing the predictions, the loss, the gradients (and thus, the necessary updates to the weights), and the new weight values. In this chapter, we’ll make a significant change: Namely, we spare ourselves the cumbersome calculation of gradients, and have torch do it for us.

    Before we see that in action, let’s get some more background.

    @@ -166,92 +192,84 @@

    Torch uses a module called autograd to record operations performed on tensors, and store what has to be done to obtain the respective gradients. These actions are stored as functions, and those functions are applied in order when the gradient of the output (normally, the loss) with respect to those tensors is calculated: starting from the output node and propagating gradients back through the network. This is a form of reverse mode automatic differentiation.

    As users, we can see a bit of this implementation. As a prerequisite for this “recording” to happen, tensors have to be created with requires_grad = TRUE. E.g.

    -x <- torch_ones(2,2, requires_grad = TRUE)
-
    +x <- torch_ones(2,2, requires_grad = TRUE)

    To be clear, this is a tensor with respect to which gradients have to be calculated – normally, a tensor representing a weight or a bias, not the input data 1. If we now perform some operation on that tensor, assigning the result to y

    -y <- x$mean()
-
    +y <- x$mean()

    we find that y now has a non-empty grad_fn that tells torch how to compute the gradient of y with respect to x:

    -y$grad_fn
-#> MeanBackward0
-
    +y$grad_fn +#> MeanBackward0

    Actual computation of gradients is triggered by calling backward() on the output tensor.

    -y$backward()
-
    +y$backward()

    That executed, x now has a non-empty field grad that stores the gradient of y with respect to x:

    -x$grad
+x$grad
 #> torch_tensor 
 #>  0.2500  0.2500
 #>  0.2500  0.2500
-#> [ CPUFloatType{2,2} ]
-
    +#> [ CPUFloatType{2,2} ]

    With a longer chain of computations, we can peek at how torch builds up a graph of backward operations.

    Here is a slightly more complex example. We call retain_grad() on y and z just for demonstration purposes; by default, intermediate gradients – while of course they have to be computed – aren’t stored, in order to save memory.

    -x1 <- torch_ones(2,2, requires_grad = TRUE)
-x2 <- torch_tensor(1.1, requires_grad = TRUE)
-y <- x1 * (x2 + 2)
-y$retain_grad()
-z <- y$pow(2) * 3
-z$retain_grad()
-out <- z$mean()
-
    +x1 <- torch_ones(2,2, requires_grad = TRUE) +x2 <- torch_tensor(1.1, requires_grad = TRUE) +y <- x1 * (x2 + 2) +y$retain_grad() +z <- y$pow(2) * 3 +z$retain_grad() +out <- z$mean()

    Starting from out$grad_fn, we can follow the graph all back to the leaf nodes:

     # how to compute the gradient for mean, the last operation executed
-out$grad_fn
+out$grad_fn
 #> MeanBackward0
 # how to compute the gradient for the multiplication by 3 in z = y$pow(2) * 3
-out$grad_fn$next_functions
+out$grad_fn$next_functions
 #> [[1]]
 #> MulBackward1
 # how to compute the gradient for pow in z = y.pow(2) * 3
-out$grad_fn$next_functions[[1]]$next_functions
+out$grad_fn$next_functions[[1]]$next_functions
 #> [[1]]
 #> PowBackward0
 # how to compute the gradient for the multiplication in y = x * (x + 2)
-out$grad_fn$next_functions[[1]]$next_functions[[1]]$next_functions
+out$grad_fn$next_functions[[1]]$next_functions[[1]]$next_functions
 #> [[1]]
 #> MulBackward0
 # how to compute the gradient for the two branches of y = x * (x + 2),
 # where the left branch is a leaf node (AccumulateGrad for x1)
-out$grad_fn$next_functions[[1]]$next_functions[[1]]$next_functions[[1]]$next_functions
+out$grad_fn$next_functions[[1]]$next_functions[[1]]$next_functions[[1]]$next_functions
 #> [[1]]
 #> torch::autograd::AccumulateGrad
 #> [[2]]
 #> AddBackward1
 # here we arrive at the other leaf node (AccumulateGrad for x2)
-out$grad_fn$next_functions[[1]]$next_functions[[1]]$next_functions[[1]]$next_functions[[2]]$next_functions
+out$grad_fn$next_functions[[1]]$next_functions[[1]]$next_functions[[1]]$next_functions[[2]]$next_functions
 #> [[1]]
-#> torch::autograd::AccumulateGrad
-
    +#> torch::autograd::AccumulateGrad

    After calling out$backward(), all tensors in the graph will have their respective gradients created. Without our calls to retain_grad above, z$grad and y$grad would be empty:

    -out$backward()
-z$grad
+out$backward()
+z$grad
 #> torch_tensor 
 #>  0.2500  0.2500
 #>  0.2500  0.2500
 #> [ CPUFloatType{2,2} ]
-y$grad
+y$grad
 #> torch_tensor 
 #>  4.6500  4.6500
 #>  4.6500  4.6500
 #> [ CPUFloatType{2,2} ]
-x2$grad
+x2$grad
 #> torch_tensor 
 #>  18.6000
 #> [ CPUFloatType{1} ]
-x1$grad
+x1$grad
 #> torch_tensor 
 #>  14.4150  14.4150
 #>  14.4150  14.4150
-#> [ CPUFloatType{2,2} ]
-
    +#> [ CPUFloatType{2,2} ]

    Thus acquainted with autograd, we’re ready to modify our example.

    @@ -261,80 +279,79 @@ 
     ### generate training data -----------------------------------------------------
 # input dimensionality (number of input features)
-d_in <- 3
+d_in <- 3
 # output dimensionality (number of predicted features)
-d_out <- 1
+d_out <- 1
 # number of observations in training set
-n <- 100
+n <- 100
 # create random data
-x <- torch_randn(n, d_in)
-y <- x[,1]*0.2 - x[..,2]*1.3 - x[..,3]*0.5 + torch_randn(n)
-y <- y$unsqueeze(dim = 1)
+x <- torch_randn(n, d_in)
+y <- x[,1]*0.2 - x[..,2]*1.3 - x[..,3]*0.5 + torch_randn(n)
+y <- y$unsqueeze(dim = 1)
 ### initialize weights ---------------------------------------------------------
 # dimensionality of hidden layer
-d_hidden <- 32
+d_hidden <- 32
 # weights connecting input to hidden layer
-w1 <- torch_randn(d_in, d_hidden, requires_grad = TRUE)
+w1 <- torch_randn(d_in, d_hidden, requires_grad = TRUE)
 # weights connecting hidden to output layer
-w2 <- torch_randn(d_hidden, d_out, requires_grad = TRUE)
+w2 <- torch_randn(d_hidden, d_out, requires_grad = TRUE)
 # hidden layer bias
-b1 <- torch_zeros(1, d_hidden, requires_grad = TRUE)
+b1 <- torch_zeros(1, d_hidden, requires_grad = TRUE)
 # output layer bias
-b2 <- torch_zeros(1, d_out,requires_grad = TRUE)
+b2 <- torch_zeros(1, d_out,requires_grad = TRUE)
 ### network parameters ---------------------------------------------------------
-learning_rate <- 1e-4
+learning_rate <- 1e-4
 ### training loop --------------------------------------------------------------
-for (t in 1:200) {
+for (t in 1:200) {
 
     ### -------- Forward pass -------- 
-    y_pred <- x$mm(w1)$add(b1)$clamp(min = 0)$mm(w2)$add(b2)
+    y_pred <- x$mm(w1)$add(b1)$clamp(min = 0)$mm(w2)$add(b2)
     ### -------- compute loss -------- 
-    loss <- (y_pred - y)$pow(2)$mean()
-    if (t %% 10 == 0) cat(t, as_array(loss), "\n")
+    loss <- (y_pred - y)$pow(2)$mean()
+    if (t %% 10 == 0) cat(t, as_array(loss), "\n")
     ### -------- Backpropagation -------- 
     # compute the gradient of loss with respect to all tensors with requires_grad = True.
-    loss$backward()
+    loss$backward()
  
     ### -------- Update weights -------- 
     
     # Wrap in torch.no_grad() because this is a part we DON'T want to record for automatic gradient computation
-    with_no_grad({
+    with_no_grad({
       
-      w1$sub_(learning_rate * w1$grad)
-      w2$sub_(learning_rate * w2$grad)
-      b1$sub_(learning_rate * b1$grad)
-      b2$sub_(learning_rate * b2$grad)
+      w1$sub_(learning_rate * w1$grad)
+      w2$sub_(learning_rate * w2$grad)
+      b1$sub_(learning_rate * b1$grad)
+      b2$sub_(learning_rate * b2$grad)
       
       # Zero the gradients after every pass, because they'd accumulate otherwise
-      w1$grad$zero_()
-      w2$grad$zero_()
-      b1$grad$zero_()
-      b2$grad$zero_()
+      w1$grad$zero_()
+      w2$grad$zero_()
+      b1$grad$zero_()
+      b2$grad$zero_()
     
-    })
+    })
     
-}
-#> 10 27.60956 
-#> 20 25.39985 
-#> 30 23.42485 
-#> 40 21.65899 
-#> 50 20.07844 
-#> 60 18.66107 
-#> 70 17.38713 
-#> 80 16.24375 
-#> 90 15.21763 
-#> 100 14.29351 
-#> 110 13.45975 
-#> 120 12.70921 
-#> 130 12.03104 
-#> 140 11.41835 
-#> 150 10.86677 
-#> 160 10.36613 
-#> 170 9.911062 
-#> 180 9.496947 
-#> 190 9.121381 
-#> 200 8.778724
-
    +} +#> 10 16.32032 +#> 20 15.48668 +#> 30 14.73107 +#> 40 14.046 +#> 50 13.42319 +#> 60 12.85455 +#> 70 12.33454 +#> 80 11.85764 +#> 90 11.41966 +#> 100 11.01673 +#> 110 10.64522 +#> 120 10.30196 +#> 130 9.9842 +#> 140 9.689827 +#> 150 9.416393 +#> 160 9.162653 +#> 170 8.927996 +#> 180 8.709177 +#> 190 8.504665 +#> 200 8.31341

    We still manually compute the forward pass, and we still manually update the weights. In the last two chapters of this section, we’ll see how these parts of the logic can be made more modular and reusable, as well.

    diff --git a/authors.html b/authors.html index 95f59cb12..21537c16e 100644 --- a/authors.html +++ b/authors.html @@ -38,6 +38,8 @@ + + @@ -54,6 +56,16 @@ + + + + @@ -71,7 +83,7 @@ torch - 0.0.3 + 0.1.0
    @@ -144,6 +156,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -157,7 +172,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • diff --git a/extra.css b/extra.css new file mode 100644 index 000000000..d8c4f4705 --- /dev/null +++ b/extra.css @@ -0,0 +1,44 @@ +.navbar-default { + background-color: #7e1f77; + border-color: #7e1f77; +} + +.navbar-default .navbar-toggle:hover, .navbar-default .navbar-toggle:focus { + background-color: #a953a2; +} + +.navbar-default .navbar-collapse, .navbar-default .navbar-form { + border-color: #ffffff; +} + +.navbar-default .navbar-nav .open .dropdown-menu>.dropdown-header { + border-color: #ffffff !important; +} + +.navbar-default .navbar-nav>li>a:hover, .navbar-default .navbar-nav>li>a:focus { + color: #ffffff; + background-color: #a953a2; +} + +.navbar-default .navbar-nav>.active>a, .navbar-default .navbar-nav>.active>a:hover, .navbar-default .navbar-nav>.active>a:focus { + background-color: #a953a2; +} + +.navbar-default .navbar-nav>.open>a, .navbar-default .navbar-nav>.open>a:hover, .navbar-default .navbar-nav>.open>a:focus { + background-color: #a953a2; +} + +.nav-pills>li.active>a, .nav-pills>li.active>a:hover, .nav-pills>li.active>a:focus { + background-color: #a953a2; +} + +.dropdown-menu>.active>a, +.dropdown-menu>.active>a:hover, +.dropdown-menu>.active>a:focus { + background-color: #7e1f77; +} + +.dropdown-menu>a:hover, +.dropdown-menu>a:focus { + background-color: #7e1f77; +} \ No newline at end of file diff --git a/index.html b/index.html index 881a7c951..8fb268bbf 100644 --- a/index.html +++ b/index.html @@ -11,7 +11,8 @@ - + + +
    @@ -34,7 +41,7 @@ torch - 0.0.3 + 0.1.0
    @@ -107,6 +114,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -120,7 +130,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • diff --git a/news/index.html b/news/index.html index 5cedf5d54..97275fc1d 100644 --- a/news/index.html +++ b/news/index.html @@ -38,6 +38,8 @@ + + @@ -54,6 +56,16 @@ + + + + @@ -71,7 +83,7 @@ torch - 0.0.3 + 0.1.0 @@ -144,6 +156,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -157,7 +172,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -189,10 +221,17 @@ Source: NEWS.md -
    -

    -torch (development version) Unreleased +
    +

    +torch 0.1.0 Unreleased

    +
      +
    • Added many missing losses (#252)
      • +
    • Implemented the $<- and [[<- operators for the nn_module class. (#253)
      • +
    • Export nn_parameter, nn_buffer, and is_* auxiliary functions.
      • +
    • Added a new serialization vignette.
      • +
    • Added a few learning rate schedulers (#258)
      • +

    diff --git a/pkgdown.yml b/pkgdown.yml index 2d96c50e4..2c0e72a50 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -2,6 +2,10 @@ pandoc: 2.7.3 pkgdown: 1.6.1 pkgdown_sha: ~ articles: + examples/basic-autograd: basic-autograd.html + examples/basic-nn-module: basic-nn-module.html + examples/dataset: dataset.html + examples/index: index.html extending-autograd: extending-autograd.html getting-started/autograd: autograd.html getting-started/control-flow-and-weight-sharing: control-flow-and-weight-sharing.html @@ -16,8 +20,9 @@ articles: getting-started/what-is-torch: what-is-torch.html indexing: indexing.html loading-data: loading-data.html + serialization: serialization.html tensor/index: index.html tensor-creation: tensor-creation.html using-autograd: using-autograd.html -last_built: 2020-09-17T22:12Z +last_built: 2020-09-28T13:32Z diff --git a/reference/AutogradContext.html b/reference/AutogradContext.html index 08ef97f1a..985f14ab8 100644 --- a/reference/AutogradContext.html +++ b/reference/AutogradContext.html @@ -38,6 +38,8 @@ + + + + + + @@ -73,7 +85,7 @@ Class representing the context." /> torch - 0.0.3 + 0.1.0

    @@ -146,6 +158,9 @@ Class representing the context." />
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -159,7 +174,24 @@ Class representing the context." />
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -228,12 +260,12 @@ Class representing the context." />

    Method new()

    (Dev related) Initializes the context. Not user related.

    Usage

    -

    AutogradContext$new(
-  ptr,
-  env,
-  argument_names = NULL,
-  argument_needs_grad = NULL
-)

    +

    AutogradContext$new(
+  ptr,
+  env,
+  argument_names = NULL,
+  argument_needs_grad = NULL
+)

    Arguments

    @@ -255,7 +287,7 @@ method.

    Before returning them to the user, a check is made to ensure they weren’t used in any in-place operation that modified their content.

    Arguments can also be any kind of R object.

    Usage

    -

    AutogradContext$save_for_backward(...)

    +

    AutogradContext$save_for_backward(...)

    Arguments

    @@ -273,7 +305,7 @@ of backward computation. You still need to accept a gradient for each output in backward(), but it’s always going to be a zero tensor with the same shape as the shape of a corresponding output.

    This is used e.g. for indices returned from a max Function.

    Usage

    -

    AutogradContext$mark_non_differentiable(...)

    +

    AutogradContext$mark_non_differentiable(...)

    Arguments

    @@ -288,7 +320,7 @@ and all arguments should be inputs.

    Every tensor that’s been modified in-place in a call to forward() should be given to this function, to ensure correctness of our checks. It doesn’t matter whether the function is called before or after modification.

    Usage

    -

    AutogradContext$mark_dirty(...)

    +

    AutogradContext$mark_dirty(...)

    Arguments

    @@ -298,7 +330,7 @@ matter whether the function is called before or after modification.

    Method clone()

    The objects of this class are cloneable with this method.

    Usage

    -

    AutogradContext$clone(deep = FALSE)

    +

    AutogradContext$clone(deep = FALSE)

    Arguments

    diff --git a/reference/as_array.html b/reference/as_array.html index a903d715c..04a0e43f7 100644 --- a/reference/as_array.html +++ b/reference/as_array.html @@ -38,6 +38,8 @@ + + @@ -55,6 +57,16 @@ + + + + @@ -72,7 +84,7 @@ torch - 0.0.3 + 0.1.0
    @@ -145,6 +157,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -158,7 +173,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -195,7 +227,7 @@

    Converts to array

    - 
    as_array(x)
    + 
    as_array(x)

    Arguments

    diff --git a/reference/autograd_backward.html b/reference/autograd_backward.html index 95eae9d7b..3b0f369af 100644 --- a/reference/autograd_backward.html +++ b/reference/autograd_backward.html @@ -38,6 +38,8 @@ + + + + + + @@ -79,7 +91,7 @@ tensors)." /> torch - 0.0.3 + 0.1.0 @@ -152,6 +164,9 @@ tensors)." />
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -165,7 +180,24 @@ tensors)." />
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -209,12 +241,12 @@ tensors (None is an acceptable value for all tensors that don’t need gradient tensors).

    - 
    autograd_backward(
-  tensors,
-  grad_tensors = NULL,
-  retain_graph = create_graph,
-  create_graph = FALSE
-)
    + 
    autograd_backward(
+  tensors,
+  grad_tensors = NULL,
+  retain_graph = create_graph,
+  create_graph = FALSE
+)

    Arguments

    @@ -250,16 +282,16 @@ Defaults to FALSE.

    them before calling it.

    Examples

    - 
    if (torch_is_installed()) {
-x <- torch_tensor(1, requires_grad = TRUE)
-y <- 2 * x
+    
    if (torch_is_installed()) {
+x <- torch_tensor(1, requires_grad = TRUE)
+y <- 2 * x
 
-a <- torch_tensor(1, requires_grad = TRUE)
-b <- 3 * a
+a <- torch_tensor(1, requires_grad = TRUE)
+b <- 3 * a
 
-autograd_backward(list(y, b))
+autograd_backward(list(y, b))
 
-}
+}
 
    
   
    
   
 
@@ -150,6 +162,9 @@ Function object, and passing returned gradients on to next Function's." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -163,7 +178,24 @@ Function object, and passing returned gradients on to next Function's." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -205,7 +237,7 @@ processed in the topological ordering, by calling backward() method
 Function object, and passing returned gradients on to next Function's.
    
     
 
-    
    autograd_function(forward, backward)
    
+    
    autograd_function(forward, backward)
    
 
     
    Arguments
    @@ -234,20 +266,20 @@ See AutogradContext for more information abou

    Examples

    - 
    if (torch_is_installed()) {
-
-exp2 <- autograd_function(
-  forward = function(ctx, i) {
-    result <- i$exp()
-    ctx$save_for_backward(result = result)
-    result
-  },
-  backward = function(ctx, grad_output) {
-    list(i = grad_output * ctx$saved_variable$result)
-  }
-)
-
-}
+    
    if (torch_is_installed()) {
+
+exp2 <- autograd_function(
+  forward = function(ctx, i) {
+    result <- i$exp()
+    ctx$save_for_backward(result = result)
+    result
+  },
+  backward = function(ctx, grad_output) {
+    list(i = grad_output * ctx$saved_variable$result)
+  }
+)
+
+}
 
    
   
    
   
 
@@ -147,6 +159,9 @@ the outputs. If an output doesn’t require_grad, then the gradient can be None)
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -160,7 +175,24 @@ the outputs. If an output doesn’t require_grad, then the gradient can be None)
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -199,14 +231,14 @@ in Jacobian-vector product, usually the pre-computed gradients w.r.t. each of
 the outputs. If an output doesn’t require_grad, then the gradient can be None).
    
     
 
-    
    autograd_grad(
-  outputs,
-  inputs,
-  grad_outputs = NULL,
-  retain_graph = create_graph,
-  create_graph = FALSE,
-  allow_unused = FALSE
-)
    
+    
    autograd_grad(
+  outputs,
+  inputs,
+  grad_outputs = NULL,
+  retain_graph = create_graph,
+  create_graph = FALSE,
+  allow_unused = FALSE
+)
    
 
     
    Arguments
    @@ -253,18 +285,18 @@ the specified inputs. If it’s FALSE, then gradient w.r.t. all rem will still be computed, and will be accumulated into their .grad attribute.

    Examples

    - 
    if (torch_is_installed()) {
-w <- torch_tensor(0.5, requires_grad = TRUE)
-b <- torch_tensor(0.9, requires_grad = TRUE)
-x <- torch_tensor(runif(100))
-y <- 2 * x + 1
-loss <- (y - (w*x + b))^2
-loss <- loss$mean()
-
-o <- autograd_grad(loss, list(w, b))
-o
+    
    if (torch_is_installed()) {
+w <- torch_tensor(0.5, requires_grad = TRUE)
+b <- torch_tensor(0.9, requires_grad = TRUE)
+x <- torch_tensor(runif(100))
+y <- 2 * x + 1
+loss <- (y - (w*x + b))^2
+loss <- loss$mean()
+
+o <- autograd_grad(loss, list(w, b))
+o
  
-}
+}
 
    #> [[1]]
 #> torch_tensor 
 #> -0.9935
diff --git a/reference/autograd_set_grad_mode.html b/reference/autograd_set_grad_mode.html
index 485d2028f..bbd4ff301 100644
--- a/reference/autograd_set_grad_mode.html
+++ b/reference/autograd_set_grad_mode.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Sets or disables gradient history.
    
     
    
 
-    
    autograd_set_grad_mode(enabled)
    
+    
    autograd_set_grad_mode(enabled)
    
 
     
    Arguments
    diff --git a/reference/cuda_current_device.html b/reference/cuda_current_device.html index 528914706..aaa729020 100644 --- a/reference/cuda_current_device.html +++ b/reference/cuda_current_device.html @@ -38,6 +38,8 @@ + + @@ -55,6 +57,16 @@ + + + + @@ -72,7 +84,7 @@ torch - 0.0.3 + 0.1.0 @@ -145,6 +157,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -158,7 +173,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -195,7 +227,7 @@

    Returns the index of a currently selected device.

    - 
    cuda_current_device()
    + 
    cuda_current_device()
    diff --git a/reference/cuda_device_count.html b/reference/cuda_device_count.html index 8e58bf3ed..727a49b1d 100644 --- a/reference/cuda_device_count.html +++ b/reference/cuda_device_count.html @@ -38,6 +38,8 @@ + + @@ -55,6 +57,16 @@ + + + + @@ -72,7 +84,7 @@ torch - 0.0.3 + 0.1.0 @@ -145,6 +157,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -158,7 +173,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -195,7 +227,7 @@

    Returns the number of GPUs available.

    - 
    cuda_device_count()
    + 
    cuda_device_count()
    diff --git a/reference/cuda_is_available.html b/reference/cuda_is_available.html index 2282ae137..2529b5e9a 100644 --- a/reference/cuda_is_available.html +++ b/reference/cuda_is_available.html @@ -38,6 +38,8 @@ + + @@ -55,6 +57,16 @@ + + + + @@ -72,7 +84,7 @@ torch - 0.0.3 + 0.1.0 @@ -145,6 +157,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -158,7 +173,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -195,7 +227,7 @@

    Returns a bool indicating if CUDA is currently available.

    - 
    cuda_is_available()
    + 
    cuda_is_available()
    diff --git a/reference/dataloader.html b/reference/dataloader.html index 5f20021f4..0d965b6a3 100644 --- a/reference/dataloader.html +++ b/reference/dataloader.html @@ -39,6 +39,8 @@ single- or multi-process iterators over the dataset. — dataloader • torch + @@ -58,6 +60,16 @@ single- or multi-process iterators over the dataset." /> + + + + @@ -75,7 +87,7 @@ single- or multi-process iterators over the dataset." /> torch - 0.0.3 + 0.1.0 @@ -148,6 +160,9 @@ single- or multi-process iterators over the dataset." />
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -161,7 +176,24 @@ single- or multi-process iterators over the dataset." />
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -200,19 +232,19 @@ single- or multi-process iterators over the dataset. single- or multi-process iterators over the dataset.

    - 
    dataloader(
-  dataset,
-  batch_size = 1,
-  shuffle = FALSE,
-  sampler = NULL,
-  batch_sampler = NULL,
-  num_workers = 0,
-  collate_fn = NULL,
-  pin_memory = FALSE,
-  drop_last = FALSE,
-  timeout = 0,
-  worker_init_fn = NULL
-)
    + 
    dataloader(
+  dataset,
+  batch_size = 1,
+  shuffle = FALSE,
+  sampler = NULL,
+  batch_sampler = NULL,
+  num_workers = 0,
+  collate_fn = NULL,
+  pin_memory = FALSE,
+  drop_last = FALSE,
+  timeout = 0,
+  worker_init_fn = NULL
+)

    Arguments

    diff --git a/reference/dataloader_make_iter.html b/reference/dataloader_make_iter.html index ed0b16f26..979a5f926 100644 --- a/reference/dataloader_make_iter.html +++ b/reference/dataloader_make_iter.html @@ -38,6 +38,8 @@ + + @@ -55,6 +57,16 @@ + + + + @@ -72,7 +84,7 @@ torch - 0.0.3 + 0.1.0 @@ -145,6 +157,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -158,7 +173,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -195,7 +227,7 @@

    Creates an iterator from a DataLoader

    - 
    dataloader_make_iter(dataloader)
    + 
    dataloader_make_iter(dataloader)

    Arguments

    diff --git a/reference/dataloader_next.html b/reference/dataloader_next.html index 966e40f3b..ff091df01 100644 --- a/reference/dataloader_next.html +++ b/reference/dataloader_next.html @@ -38,6 +38,8 @@ + + @@ -55,6 +57,16 @@ + + + + @@ -72,7 +84,7 @@ torch - 0.0.3 + 0.1.0 @@ -145,6 +157,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -158,7 +173,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -195,7 +227,7 @@

    Get the next element of a dataloader iterator

    - 
    dataloader_next(iter)
    + 
    dataloader_next(iter)

    Arguments

    diff --git a/reference/dataset.html b/reference/dataset.html index 558e19b17..0a6257e3f 100644 --- a/reference/dataset.html +++ b/reference/dataset.html @@ -38,6 +38,8 @@ + + + + + + @@ -77,7 +89,7 @@ of ~torch.utils.data.DataLoader." /> torch - 0.0.3 + 0.1.0 @@ -150,6 +162,9 @@ of ~torch.utils.data.DataLoader." />
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -163,7 +178,24 @@ of ~torch.utils.data.DataLoader." />
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -205,7 +237,7 @@ data sample for a given key. Subclasses could also optionally overwrite of ~torch.utils.data.DataLoader.

    - 
    dataset(name = NULL, inherit = Dataset, ..., parent_env = parent.frame())
    + 
    dataset(name = NULL, inherit = Dataset, ..., parent_env = parent.frame())

    Arguments

    diff --git a/reference/default_dtype.html b/reference/default_dtype.html index a517ff8bf..df0f5e7bf 100644 --- a/reference/default_dtype.html +++ b/reference/default_dtype.html @@ -38,6 +38,8 @@ + + @@ -55,6 +57,16 @@ + + + + @@ -72,7 +84,7 @@ torch - 0.0.3 + 0.1.0 @@ -145,6 +157,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -158,7 +173,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -195,9 +227,9 @@

    Gets and sets the default floating point dtype.

    - 
    torch_set_default_dtype(d)
+    
    torch_set_default_dtype(d)
 
-torch_get_default_dtype()
    
+torch_get_default_dtype()

    Arguments

    diff --git a/reference/enumerate.dataloader.html b/reference/enumerate.dataloader.html index cdace84a5..4bbd7d24c 100644 --- a/reference/enumerate.dataloader.html +++ b/reference/enumerate.dataloader.html @@ -38,6 +38,8 @@ + + @@ -55,6 +57,16 @@ + + + + @@ -72,7 +84,7 @@ torch - 0.0.3 + 0.1.0 @@ -145,6 +157,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -158,7 +173,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -196,7 +228,7 @@ 
    # S3 method for dataloader
-enumerate(x, max_len = 1e+06, ...)
    +enumerate(x, max_len =1e+06, ...)

    Arguments

    diff --git a/reference/enumerate.html b/reference/enumerate.html index 5726147af..978f0850f 100644 --- a/reference/enumerate.html +++ b/reference/enumerate.html @@ -38,6 +38,8 @@ + + @@ -55,6 +57,16 @@ + + + + @@ -72,7 +84,7 @@ torch - 0.0.3 + 0.1.0 @@ -145,6 +157,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -158,7 +173,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -195,7 +227,7 @@

    Enumerate an iterator

    - 
    enumerate(x, ...)
    + 
    enumerate(x, ...)

    Arguments

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     + + + + + + + + @@ -1945,12 +1989,24 @@ planes.

     + + + + + + + + @@ -2029,6 +2085,12 @@ planes.

     + + + + @@ -2125,6 +2187,18 @@ planes.

     + + + + + + + + @@ -2163,6 +2237,12 @@ planes.

    planes.

     + + + + @@ -2212,12 +2292,60 @@ planes.

     + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -2266,6 +2394,18 @@ planes.

     + + + + + + + + @@ -2320,6 +2460,18 @@ planes.

     + + + + + + + + @@ -2342,6 +2494,24 @@ planes.

    nn_utils_rnn_pad_sequence()

    + + + + + + + + + + + + @@ -2921,6 +3091,12 @@ planes.

     + + + + @@ -2956,6 +3132,57 @@ planes.

    optim_sgd()

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/reference/install_torch.html b/reference/install_torch.html index c4e14bd5f..ef89e1dea 100644 --- a/reference/install_torch.html +++ b/reference/install_torch.html @@ -38,6 +38,8 @@ + + @@ -55,6 +57,16 @@ + + + + @@ -72,7 +84,7 @@ torch - 0.0.3 + 0.1.0 @@ -145,6 +157,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -158,7 +173,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -195,13 +227,13 @@

    Installs Torch and its dependencies.

    - 
    install_torch(
-  version = "1.5.0",
-  type = install_type(version = version),
-  reinstall = FALSE,
-  path = install_path(),
-  ...
-)
    + 
    install_torch(
+  version = "1.5.0",
+  type = install_type(version = version),
+  reinstall = FALSE,
+  path = install_path(),
+  ...
+)

    Arguments

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  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -157,7 +172,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -1897,12 +1929,24 @@ planes.

    Binary cross entropy loss
    +

    nn_bce_with_logits_loss()

    +

    BCE with logits loss

    nn_bilinear()

    Bilinear module
    +

    nn_buffer()

    +

    Creates a nn_buffer

    nn_celu()

    ConvTranpose3D module
    +

    nn_cosine_embedding_loss()

    +

    Cosine embedding loss

    nn_cross_entropy_loss()

    CrossEntropyLoss module
    +

    nn_ctc_loss()

    +

    The Connectionist Temporal Classification loss.

    nn_dropout()

    Hardtanh module
    +

    nn_hinge_embedding_loss()

    +

    Hinge embedding loss

    nn_identity()

    Zeros initialization
    +

    nn_kl_div_loss()

    +

    Kullback-Leibler divergence loss

    +

    nn_l1_loss()

    +

    L1 loss

    nn_leaky_relu()

    +

    nn_margin_ranking_loss()

    +

    Margin ranking loss

    nn_max_pool1d()

    Holds submodules in a list.
    +

    nn_mse_loss()

    +

    MSE loss

    +

    nn_multi_margin_loss()

    +

    Multi margin loss

    nn_multihead_attention()

    MultiHead attention
    +

    nn_multilabel_margin_loss()

    +

    Multilabel margin loss

    +

    nn_multilabel_soft_margin_loss()

    +

    Multi label soft margin loss

    +

    nn_nll_loss()

    +

    Nll loss

    +

    nn_pairwise_distance()

    +

    Pairwise distance

    +

    nn_parameter()

    +

    Creates an nn_parameter

    +

    nn_poisson_nll_loss()

    +

    Poisson NLL loss

    nn_prelu()

    Sigmoid module
    +

    nn_smooth_l1_loss()

    +

    Smooth L1 loss

    +

    nn_soft_margin_loss()

    +

    Soft margin loss

    nn_softmax()

    Threshoold module
    +

    nn_triplet_margin_loss()

    +

    Triplet margin loss

    +

    nn_triplet_margin_with_distance_loss()

    +

    Triplet margin with distance loss

    nn_utils_rnn_pack_padded_sequence()

    Pad a list of variable length Tensors with padding_value

    +

    is_nn_module()

    +

    Checks if the object is an nn_module

    +

    is_nn_parameter()

    +

    Checks if an object is a nn_parameter

    +

    is_nn_buffer()

    +

    Checks if the object is a nn_buffer

    Triplet_margin_loss
    +

    nnf_triplet_margin_with_distance_loss()

    +

    Triplet margin with distance loss

    nnf_unfold()

    SGD optimizer

    +

    is_optimizer()

    +

    Checks if the object is a torch optimizer

    +

    Learning rate schedulers

    +

    +
    +

    lr_lambda()

    +

    Sets the learning rate of each parameter group to the initial lr +times a given function. When last_epoch=-1, sets initial lr as lr.

    +

    lr_multiplicative()

    +

    Multiply the learning rate of each parameter group by the factor given +in the specified function. When last_epoch=-1, sets initial lr as lr.

    +

    lr_one_cycle()

    +

    Once cycle learning rate

    +

    lr_scheduler()

    +

    Creates learning rate schedulers

    +

    lr_step()

    +

    Step learning rate decay
    diff --git a/reference/is_dataloader.html b/reference/is_dataloader.html index 5688453ef..9a2501287 100644 --- a/reference/is_dataloader.html +++ b/reference/is_dataloader.html @@ -38,6 +38,8 @@ + + @@ -55,6 +57,16 @@ + + + + @@ -72,7 +84,7 @@ torch - 0.0.3 + 0.1.0 @@ -145,6 +157,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -158,7 +173,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -195,7 +227,7 @@

    Checks if the object is a dataloader

    - 
    is_dataloader(x)
    + 
    is_dataloader(x)

    Arguments

    diff --git a/reference/is_nn_buffer.html b/reference/is_nn_buffer.html new file mode 100644 index 000000000..5a6ecbf60 --- /dev/null +++ b/reference/is_nn_buffer.html @@ -0,0 +1,269 @@ + + + + + + + + +Checks if the object is a nn_buffer — is_nn_buffer • torch + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    +
    +

    Checks if the object is a nn_buffer

    + Source: R/nn.R + +
    + +
    +

    Checks if the object is a nn_buffer

    +
    + + 
    is_nn_buffer(x)
    + +

    Arguments

    +
    + + + + + +
    x

    object to check

    + + +
    + +
    + + +
    +
    +

    Developed by Daniel Falbel, Javier Luraschi.

    +
    + +
    +

    Site built with pkgdown 1.6.1.

    +
    + +
    +
    + + + + + + + + diff --git a/reference/is_nn_module.html b/reference/is_nn_module.html new file mode 100644 index 000000000..494a2d97a --- /dev/null +++ b/reference/is_nn_module.html @@ -0,0 +1,269 @@ + + + + + + + + +Checks if the object is an nn_module — is_nn_module • torch + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    +
    +

    Checks if the object is an nn_module

    + Source: R/nn.R + +
    + +
    +

    Checks if the object is an nn_module

    +
    + + 
    is_nn_module(x)
    + +

    Arguments

    + + + + + + +
    x

    object to check

    + + +
    + +
    + + +
    +
    +

    Developed by Daniel Falbel, Javier Luraschi.

    +
    + +
    +

    Site built with pkgdown 1.6.1.

    +
    + +
    +
    + + + + + + + + diff --git a/reference/is_nn_parameter.html b/reference/is_nn_parameter.html new file mode 100644 index 000000000..5163543b1 --- /dev/null +++ b/reference/is_nn_parameter.html @@ -0,0 +1,269 @@ + + + + + + + + +Checks if an object is a nn_parameter — is_nn_parameter • torch + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    +
    +

    Checks if an object is a nn_parameter

    + Source: R/nn.R + +
    + +
    +

    Checks if an object is a nn_parameter

    +
    + + 
    is_nn_parameter(x)
    + +

    Arguments

    + + + + + + +
    x

    the object to check

    + + +
    + +
    + + +
    +
    +

    Developed by Daniel Falbel, Javier Luraschi.

    +
    + +
    +

    Site built with pkgdown 1.6.1.

    +
    + +
    +
    + + + + + + + + diff --git a/reference/is_optimizer.html b/reference/is_optimizer.html new file mode 100644 index 000000000..1b831b798 --- /dev/null +++ b/reference/is_optimizer.html @@ -0,0 +1,269 @@ + + + + + + + + +Checks if the object is a torch optimizer — is_optimizer • torch + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    +
    +

    Checks if the object is a torch optimizer

    + Source: R/optim.R + +
    + +
    +

    Checks if the object is a torch optimizer

    +
    + + 
    is_optimizer(x)
    + +

    Arguments

    + + + + + + +
    x

    object to check

    + + +
    + +
    + + +
    +
    +

    Developed by Daniel Falbel, Javier Luraschi.

    +
    + +
    +

    Site built with pkgdown 1.6.1.

    +
    + +
    +
    + + + + + + + + diff --git a/reference/is_torch_device.html b/reference/is_torch_device.html index 760d1dd2b..36892e09c 100644 --- a/reference/is_torch_device.html +++ b/reference/is_torch_device.html @@ -38,6 +38,8 @@ + + @@ -55,6 +57,16 @@ + + + + @@ -72,7 +84,7 @@ torch - 0.0.3 + 0.1.0
    @@ -145,6 +157,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -158,7 +173,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -195,7 +227,7 @@

    Checks if object is a device

    - 
    is_torch_device(x)
    + 
    is_torch_device(x)

    Arguments

    diff --git a/reference/is_torch_dtype.html b/reference/is_torch_dtype.html index 8315ff6b9..7977b802b 100644 --- a/reference/is_torch_dtype.html +++ b/reference/is_torch_dtype.html @@ -38,6 +38,8 @@ + + @@ -55,6 +57,16 @@ + + + + @@ -72,7 +84,7 @@ torch - 0.0.3 + 0.1.0 @@ -145,6 +157,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -158,7 +173,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -195,7 +227,7 @@

    Check if object is a torch data type

    - 
    is_torch_dtype(x)
    + 
    is_torch_dtype(x)

    Arguments

    diff --git a/reference/is_torch_layout.html b/reference/is_torch_layout.html index 393fd39df..03d83e7aa 100644 --- a/reference/is_torch_layout.html +++ b/reference/is_torch_layout.html @@ -38,6 +38,8 @@ + + @@ -55,6 +57,16 @@ + + + + @@ -72,7 +84,7 @@ torch - 0.0.3 + 0.1.0 @@ -145,6 +157,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -158,7 +173,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -195,7 +227,7 @@

    Check if an object is a torch layout.

    - 
    is_torch_layout(x)
    + 
    is_torch_layout(x)

    Arguments

    diff --git a/reference/is_torch_memory_format.html b/reference/is_torch_memory_format.html index a0e1b0f8d..1fc2b8399 100644 --- a/reference/is_torch_memory_format.html +++ b/reference/is_torch_memory_format.html @@ -38,6 +38,8 @@ + + @@ -55,6 +57,16 @@ + + + + @@ -72,7 +84,7 @@ torch - 0.0.3 + 0.1.0 @@ -145,6 +157,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -158,7 +173,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -195,7 +227,7 @@

    Check if an object is a memory format

    - 
    is_torch_memory_format(x)
    + 
    is_torch_memory_format(x)

    Arguments

    diff --git a/reference/is_torch_qscheme.html b/reference/is_torch_qscheme.html index aee7821a6..3c40a69f0 100644 --- a/reference/is_torch_qscheme.html +++ b/reference/is_torch_qscheme.html @@ -38,6 +38,8 @@ + + @@ -55,6 +57,16 @@ + + + + @@ -72,7 +84,7 @@ torch - 0.0.3 + 0.1.0 @@ -145,6 +157,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -158,7 +173,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -195,7 +227,7 @@

    Checks if an object is a QScheme

    - 
    is_torch_qscheme(x)
    + 
    is_torch_qscheme(x)

    Arguments

    diff --git a/reference/is_undefined_tensor.html b/reference/is_undefined_tensor.html index f2a893716..a86303e26 100644 --- a/reference/is_undefined_tensor.html +++ b/reference/is_undefined_tensor.html @@ -38,6 +38,8 @@ + + @@ -55,6 +57,16 @@ + + + + @@ -72,7 +84,7 @@ torch - 0.0.3 + 0.1.0 @@ -145,6 +157,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -158,7 +173,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -195,7 +227,7 @@

    Checks if a tensor is undefined

    - 
    is_undefined_tensor(x)
    + 
    is_undefined_tensor(x)

    Arguments

    diff --git a/reference/load_state_dict.html b/reference/load_state_dict.html index 3cf56efae..7d3078505 100644 --- a/reference/load_state_dict.html +++ b/reference/load_state_dict.html @@ -38,6 +38,8 @@ + + + + + + @@ -75,7 +87,7 @@ classes from the tensors in the dict." /> torch - 0.0.3 + 0.1.0 @@ -148,6 +160,9 @@ classes from the tensors in the dict." />
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -161,7 +176,24 @@ classes from the tensors in the dict." />
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -201,7 +233,7 @@ For it to work correctly you need to use torch.save with the flag: classes from the tensors in the dict.

    - 
    load_state_dict(path)
    + 
    load_state_dict(path)

    Arguments

    diff --git a/reference/lr_lambda.html b/reference/lr_lambda.html new file mode 100644 index 000000000..8483f0d52 --- /dev/null +++ b/reference/lr_lambda.html @@ -0,0 +1,305 @@ + + + + + + + + +Sets the learning rate of each parameter group to the initial lr +times a given function. When last_epoch=-1, sets initial lr as lr. — lr_lambda • torch + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    +
    +

    Sets the learning rate of each parameter group to the initial lr +times a given function. When last_epoch=-1, sets initial lr as lr.

    + Source: R/optim-lr_scheduler.R + +
    + +
    +

    Sets the learning rate of each parameter group to the initial lr +times a given function. When last_epoch=-1, sets initial lr as lr.

    +
    + + 
    lr_lambda(optimizer, lr_lambda, last_epoch = -1, verbose = FALSE)
    + +

    Arguments

    +
    + + + + + + + + + + + + + + + + + +
    optimizer

    (Optimizer): Wrapped optimizer.

    lr_lambda

    (function or list): A function which computes a multiplicative +factor given an integer parameter epoch, or a list of such +functions, one for each group in optimizer.param_groups.

    last_epoch

    (int): The index of last epoch. Default: -1.

    verbose

    (bool): If TRUE, prints a message to stdout for +each update. Default: FALSE.

    + + +

    Examples

    + 
    if (torch_is_installed()) {
+# Assuming optimizer has two groups.
+lambda1 <- function(epoch) epoch %/% 30
+lambda2 <- function(epoch) 0.95^epoch
+if (FALSE) {
+scheduler <- lr_lambda(optimizer, lr_lambda = list(lambda1, lambda2))
+for (epoch in 1:100) {
+  train(...)
+  validate(...)
+  scheduler$step()
+}
+}
+
+}
+
    + + + + + +
    +
    +

    Developed by Daniel Falbel, Javier Luraschi.

    +
    + +
    +

    Site built with pkgdown 1.6.1.

    +
    + +
    + + + + + + + + + diff --git a/reference/lr_multiplicative.html b/reference/lr_multiplicative.html new file mode 100644 index 000000000..7ffb61b6a --- /dev/null +++ b/reference/lr_multiplicative.html @@ -0,0 +1,303 @@ + + + + + + + + +Multiply the learning rate of each parameter group by the factor given +in the specified function. When last_epoch=-1, sets initial lr as lr. — lr_multiplicative • torch + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    +
    +

    Multiply the learning rate of each parameter group by the factor given +in the specified function. When last_epoch=-1, sets initial lr as lr.

    + Source: R/optim-lr_scheduler.R + +
    + +
    +

    Multiply the learning rate of each parameter group by the factor given +in the specified function. When last_epoch=-1, sets initial lr as lr.

    +
    + + 
    lr_multiplicative(optimizer, lr_lambda, last_epoch = -1, verbose = FALSE)
    + +

    Arguments

    + + + + + + + + + + + + + + + + + + +
    optimizer

    (Optimizer): Wrapped optimizer.

    lr_lambda

    (function or list): A function which computes a multiplicative +factor given an integer parameter epoch, or a list of such +functions, one for each group in optimizer.param_groups.

    last_epoch

    (int): The index of last epoch. Default: -1.

    verbose

    (bool): If TRUE, prints a message to stdout for +each update. Default: FALSE.

    + + +

    Examples

    + 
    if (torch_is_installed()) {
+if (FALSE) {
+lmbda <- function(epoch) 0.95
+scheduler <- lr_multiplicative(optimizer, lr_lambda=lmbda)
+for (epoch in 1:100) {
+  train(...)
+  validate(...)
+  scheduler$step()
+}
+}
+
+}
+
    +
    + +
    + + +
    +
    +

    Developed by Daniel Falbel, Javier Luraschi.

    +
    + +
    +

    Site built with pkgdown 1.6.1.

    +
    + +
    +
    + + + + + + + + diff --git a/reference/lr_one_cycle.html b/reference/lr_one_cycle.html new file mode 100644 index 000000000..67b01b27b --- /dev/null +++ b/reference/lr_one_cycle.html @@ -0,0 +1,417 @@ + + + + + + + + +Once cycle learning rate — lr_one_cycle • torch + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    +
    +

    Once cycle learning rate

    + Source: R/optim-lr_scheduler.R + +
    + +
    +

    Sets the learning rate of each parameter group according to the +1cycle learning rate policy. The 1cycle policy anneals the learning +rate from an initial learning rate to some maximum learning rate and then +from that maximum learning rate to some minimum learning rate much lower +than the initial learning rate.

    +
    + + 
    lr_one_cycle(
+  optimizer,
+  max_lr,
+  total_steps = NULL,
+  epochs = NULL,
+  steps_per_epoch = NULL,
+  pct_start = 0.3,
+  anneal_strategy = "cos",
+  cycle_momentum = TRUE,
+  base_momentum = 0.85,
+  max_momentum = 0.95,
+  div_factor = 25,
+  final_div_factor = 10000,
+  last_epoch = -1,
+  verbose = FALSE
+)
    + +

    Arguments

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    optimizer

    (Optimizer): Wrapped optimizer.

    max_lr

    (float or list): Upper learning rate boundaries in the cycle +for each parameter group.

    total_steps

    (int): The total number of steps in the cycle. Note that +if a value is not provided here, then it must be inferred by providing +a value for epochs and steps_per_epoch. +Default: NULL

    epochs

    (int): The number of epochs to train for. This is used along +with steps_per_epoch in order to infer the total number of steps in the cycle +if a value for total_steps is not provided. +Default: NULL

    steps_per_epoch

    (int): The number of steps per epoch to train for. This is +used along with epochs in order to infer the total number of steps in the +cycle if a value for total_steps is not provided. +Default: NULL

    pct_start

    (float): The percentage of the cycle (in number of steps) spent +increasing the learning rate. +Default: 0.3

    anneal_strategy

    (str): 'cos', 'linear' +Specifies the annealing strategy: "cos" for cosine annealing, "linear" for +linear annealing. +Default: 'cos'

    cycle_momentum

    (bool): If TRUE, momentum is cycled inversely +to learning rate between 'base_momentum' and 'max_momentum'. +Default: TRUE

    base_momentum

    (float or list): Lower momentum boundaries in the cycle +for each parameter group. Note that momentum is cycled inversely +to learning rate; at the peak of a cycle, momentum is +'base_momentum' and learning rate is 'max_lr'. +Default: 0.85

    max_momentum

    (float or list): Upper momentum boundaries in the cycle +for each parameter group. Functionally, +it defines the cycle amplitude (max_momentum - base_momentum). +Note that momentum is cycled inversely +to learning rate; at the start of a cycle, momentum is 'max_momentum' +and learning rate is 'base_lr' +Default: 0.95

    div_factor

    (float): Determines the initial learning rate via +initial_lr = max_lr/div_factor +Default: 25

    final_div_factor

    (float): Determines the minimum learning rate via +min_lr = initial_lr/final_div_factor +Default: 1e4

    last_epoch

    (int): The index of the last batch. This parameter is used when +resuming a training job. Since step() should be invoked after each +batch instead of after each epoch, this number represents the total +number of batches computed, not the total number of epochs computed. +When last_epoch=-1, the schedule is started from the beginning. +Default: -1

    verbose

    (bool): If TRUE, prints a message to stdout for +each update. Default: FALSE.

    + +

    Details

    + +

    This policy was initially described in the paper +Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates.

    +

    The 1cycle learning rate policy changes the learning rate after every batch. +step should be called after a batch has been used for training. +This scheduler is not chainable.

    +

    Note also that the total number of steps in the cycle can be determined in one +of two ways (listed in order of precedence):

      +

    • A value for total_steps is explicitly provided.

      • +

    • A number of epochs (epochs) and a number of steps per epoch +(steps_per_epoch) are provided.

      • +
    + +

    In this case, the number of total steps is inferred by +total_steps = epochs * steps_per_epoch

    +

    You must either provide a value for total_steps or provide a value for both +epochs and steps_per_epoch.

    + +

    Examples

    + 
    if (torch_is_installed()) {
+if (FALSE) {
+data_loader <- dataloader(...)
+optimizer <- optim_sgd(model$parameters, lr=0.1, momentum=0.9)
+scheduler <- lr_one_cycle(optimizer, max_lr=0.01, steps_per_epoch=length(data_loader), 
+                          epochs=10)
+
+for (i in 1:epochs) {
+  for (batch in enumerate(data_loader)) {
+     train_batch(...)
+     scheduler$step()
+  }
+}
+}
+
+}
+
    +
    + +
    + + +
    +
    +

    Developed by Daniel Falbel, Javier Luraschi.

    +
    + +
    +

    Site built with pkgdown 1.6.1.

    +
    + +
    +
    + + + + + + + + diff --git a/reference/lr_scheduler.html b/reference/lr_scheduler.html new file mode 100644 index 000000000..05b90cf19 --- /dev/null +++ b/reference/lr_scheduler.html @@ -0,0 +1,288 @@ + + + + + + + + +Creates learning rate schedulers — lr_scheduler • torch + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    +
    +

    Creates learning rate schedulers

    + Source: R/optim-lr_scheduler.R + +
    + +
    +

    Creates learning rate schedulers

    +
    + + 
    lr_scheduler(
+  classname = NULL,
+  inherit = LRScheduler,
+  ...,
+  parent_env = parent.frame()
+)
    + +

    Arguments

    + + + + + + + + + + + + + + + + + + +
    classname

    optional name for the learning rate scheduler

    inherit

    an optional learning rate scheduler to inherit from

    ...

    named list of methods. You must implement the get_lr() +method that doesn't take any argument and returns learning rates +for each param_group in the optimizer.

    parent_env

    passed to R6::R6Class().

    + + +
    + +
    + + +
    +
    +

    Developed by Daniel Falbel, Javier Luraschi.

    +
    + +
    +

    Site built with pkgdown 1.6.1.

    +
    + +
    +
    + + + + + + + + diff --git a/reference/lr_step.html b/reference/lr_step.html new file mode 100644 index 000000000..0d80a7315 --- /dev/null +++ b/reference/lr_step.html @@ -0,0 +1,306 @@ + + + + + + + + +Step learning rate decay — lr_step • torch + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    +
    +

    Step learning rate decay

    + Source: R/optim-lr_scheduler.R + +
    + +
    +

    Decays the learning rate of each parameter group by gamma every +step_size epochs. Notice that such decay can happen simultaneously with +other changes to the learning rate from outside this scheduler. When +last_epoch=-1, sets initial lr as lr.

    +
    + + 
    lr_step(optimizer, step_size, gamma = 0.1, last_epoch = -1)
    + +

    Arguments

    + + + + + + + + + + + + + + + + + + +
    optimizer

    (Optimizer): Wrapped optimizer.

    step_size

    (int): Period of learning rate decay.

    gamma

    (float): Multiplicative factor of learning rate decay. +Default: 0.1.

    last_epoch

    (int): The index of last epoch. Default: -1.

    + + +

    Examples

    + 
    if (torch_is_installed()) {
+if (FALSE) {
+# Assuming optimizer uses lr = 0.05 for all groups
+# lr = 0.05     if epoch < 30
+# lr = 0.005    if 30 <= epoch < 60
+# lr = 0.0005   if 60 <= epoch < 90
+# ...
+scheduler <- lr_step(optimizer, step_size=30, gamma=0.1)
+for (epoch in 1:100) {
+  train(...)
+  validate(...)
+  scheduler$step()
+}
+}
+
+}
+
    +
    + +
    + + +
    +
    +

    Developed by Daniel Falbel, Javier Luraschi.

    +
    + +
    +

    Site built with pkgdown 1.6.1.

    +
    + +
    +
    + + + + + + + + diff --git a/reference/nn_adaptive_avg_pool1d.html b/reference/nn_adaptive_avg_pool1d.html index 40bd9d2f8..d4e13c1c8 100644 --- a/reference/nn_adaptive_avg_pool1d.html +++ b/reference/nn_adaptive_avg_pool1d.html @@ -38,6 +38,8 @@ + + + + + + @@ -73,7 +85,7 @@ The number of output features is equal to the number of input planes." /> torch - 0.0.3 + 0.1.0 @@ -146,6 +158,9 @@ The number of output features is equal to the number of input planes." />
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -159,7 +174,24 @@ The number of output features is equal to the number of input planes." />
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -197,7 +229,7 @@ The number of output features is equal to the number of input planes." /> The number of output features is equal to the number of input planes.

    - 
    nn_adaptive_avg_pool1d(output_size)
    + 
    nn_adaptive_avg_pool1d(output_size)

    Arguments

    @@ -210,13 +242,13 @@ The number of output features is equal to the number of input planes.

    Examples

    - 
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 # target output size of 5
-m = nn_adaptive_avg_pool1d(5)
-input <- torch_randn(1, 64, 8)
-output <- m(input)
+m = nn_adaptive_avg_pool1d(5)
+input <- torch_randn(1, 64, 8)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -146,6 +158,9 @@ The number of output features is equal to the number of input planes." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ The number of output features is equal to the number of input planes." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,7 +229,7 @@ The number of output features is equal to the number of input planes." />
 The number of output features is equal to the number of input planes.
    
     
 
-    
    nn_adaptive_avg_pool2d(output_size)
    
+    
    nn_adaptive_avg_pool2d(output_size)
    
 
     
    Arguments
    @@ -213,17 +245,17 @@ be the same as that of the input.

    Examples

    - 
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 # target output size of 5x7
-m <- nn_adaptive_avg_pool2d(c(5,7))
-input <- torch_randn(1, 64, 8, 9)
-output <- m(input)
+m <- nn_adaptive_avg_pool2d(c(5,7))
+input <- torch_randn(1, 64, 8, 9)
+output <- m(input)
 # target output size of 7x7 (square)
-m <- nn_adaptive_avg_pool2d(7)
-input <- torch_randn(1, 64, 10, 9)
-output <- m(input)
+m <- nn_adaptive_avg_pool2d(7)
+input <- torch_randn(1, 64, 10, 9)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -146,6 +158,9 @@ The number of output features is equal to the number of input planes." />
     
  • 
       Tensor class
     
    • 
+    
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+      Serialization
+    
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    • 
     
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       Loading Data
@@ -159,7 +174,24 @@ The number of output features is equal to the number of input planes." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,7 +229,7 @@ The number of output features is equal to the number of input planes." />
 The number of output features is equal to the number of input planes.
    
     
 
-    
    nn_adaptive_avg_pool3d(output_size)
    
+    
    nn_adaptive_avg_pool3d(output_size)
    
 
     
    Arguments
    @@ -213,17 +245,17 @@ be the same as that of the input.

    Examples

    - 
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 # target output size of 5x7x9
-m <- nn_adaptive_avg_pool3d(c(5,7,9))
-input <- torch_randn(1, 64, 8, 9, 10)
-output <- m(input)
+m <- nn_adaptive_avg_pool3d(c(5,7,9))
+input <- torch_randn(1, 64, 8, 9, 10)
+output <- m(input)
 # target output size of 7x7x7 (cube)
-m <- nn_adaptive_avg_pool3d(7)
-input <- torch_randn(1, 64, 10, 9, 8)
-output <- m(input)
+m <- nn_adaptive_avg_pool3d(7)
+input <- torch_randn(1, 64, 10, 9, 8)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -146,6 +158,9 @@ Efficient softmax approximation for GPUs by Edouard Grave, Armand Joulin, Mousta
     
  • 
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    • 
+    
  • 
+      Serialization
+    
    • 
     
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    • 
     
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@@ -159,7 +174,24 @@ Efficient softmax approximation for GPUs by Edouard Grave, Armand Joulin, Mousta
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,13 +229,13 @@ Efficient softmax approximation for GPUs by Edouard Grave, Armand Joulin, Mousta
 Efficient softmax approximation for GPUs by Edouard Grave, Armand Joulin, Moustapha Cissé, David Grangier, and Hervé Jégou
    
     
 
-    
    nn_adaptive_log_softmax_with_loss(
-  in_features,
-  n_classes,
-  cutoffs,
-  div_value = 4,
-  head_bias = FALSE
-)
    
+    
    nn_adaptive_log_softmax_with_loss(
+  in_features,
+  n_classes,
+  cutoffs,
+  div_value = 4,
+  head_bias = FALSE
+)
    
 
     
    Arguments
    diff --git a/reference/nn_adaptive_max_pool1d.html b/reference/nn_adaptive_max_pool1d.html index 35300f643..a48f501a3 100644 --- a/reference/nn_adaptive_max_pool1d.html +++ b/reference/nn_adaptive_max_pool1d.html @@ -38,6 +38,8 @@ + + + + + + @@ -73,7 +85,7 @@ The number of output features is equal to the number of input planes." /> torch - 0.0.3 + 0.1.0 @@ -146,6 +158,9 @@ The number of output features is equal to the number of input planes." />
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -159,7 +174,24 @@ The number of output features is equal to the number of input planes." />
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -197,7 +229,7 @@ The number of output features is equal to the number of input planes." /> The number of output features is equal to the number of input planes.

    - 
    nn_adaptive_max_pool1d(output_size, return_indices = FALSE)
    + 
    nn_adaptive_max_pool1d(output_size, return_indices = FALSE)

    Arguments

    @@ -215,13 +247,13 @@ Useful to pass to nn_max_unpool1d()Examples - 
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 # target output size of 5
-m <- nn_adaptive_max_pool1d(5)
-input <- torch_randn(1, 64, 8)
-output <- m(input)
+m <- nn_adaptive_max_pool1d(5)
+input <- torch_randn(1, 64, 8)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -146,6 +158,9 @@ The number of output features is equal to the number of input planes." />
     
  • 
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+      Serialization
+    
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    • 
     
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@@ -159,7 +174,24 @@ The number of output features is equal to the number of input planes." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,7 +229,7 @@ The number of output features is equal to the number of input planes." />
 The number of output features is equal to the number of input planes.
    
     
 
-    
    nn_adaptive_max_pool2d(output_size, return_indices = FALSE)
    
+    
    nn_adaptive_max_pool2d(output_size, return_indices = FALSE)
    
 
     
    Arguments
    @@ -218,17 +250,17 @@ Useful to pass to nn_max_unpool2d()Examples - 
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 # target output size of 5x7
-m <- nn_adaptive_max_pool2d(c(5,7))
-input <- torch_randn(1, 64, 8, 9)
-output <- m(input)
+m <- nn_adaptive_max_pool2d(c(5,7))
+input <- torch_randn(1, 64, 8, 9)
+output <- m(input)
 # target output size of 7x7 (square)
-m <- nn_adaptive_max_pool2d(7)
-input <- torch_randn(1, 64, 10, 9)
-output <- m(input)
+m <- nn_adaptive_max_pool2d(7)
+input <- torch_randn(1, 64, 10, 9)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -146,6 +158,9 @@ The number of output features is equal to the number of input planes." />
     
  • 
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    • 
+    
  • 
+      Serialization
+    
    • 
     
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    • 
     
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       Loading Data
@@ -159,7 +174,24 @@ The number of output features is equal to the number of input planes." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,7 +229,7 @@ The number of output features is equal to the number of input planes." />
 The number of output features is equal to the number of input planes.
    
     
 
-    
    nn_adaptive_max_pool3d(output_size, return_indices = FALSE)
    
+    
    nn_adaptive_max_pool3d(output_size, return_indices = FALSE)
    
 
     
    Arguments
    @@ -218,17 +250,17 @@ Useful to pass to nn_max_unpool3d()Examples - 
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 # target output size of 5x7x9
-m <- nn_adaptive_max_pool3d(c(5,7,9))
-input <- torch_randn(1, 64, 8, 9, 10)
-output <- m(input)
+m <- nn_adaptive_max_pool3d(c(5,7,9))
+input <- torch_randn(1, 64, 8, 9, 10)
+output <- m(input)
 # target output size of 7x7x7 (cube)
-m <- nn_adaptive_max_pool3d(7)
-input <- torch_randn(1, 64, 10, 9, 8)
-output <- m(input)
+m <- nn_adaptive_max_pool3d(7)
+input <- torch_randn(1, 64, 10, 9, 8)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -153,6 +165,9 @@ $$" />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -166,7 +181,24 @@ $$" />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -210,13 +242,13 @@ can be precisely described as:
    
 $$
    
     
 
-    
    nn_avg_pool1d(
-  kernel_size,
-  stride = NULL,
-  padding = 0,
-  ceil_mode = FALSE,
-  count_include_pad = TRUE
-)
    
+    
    nn_avg_pool1d(
+  kernel_size,
+  stride = NULL,
+  padding = 0,
+  ceil_mode = FALSE,
+  count_include_pad = TRUE
+)
    
 
     
    Arguments
    @@ -264,16 +296,16 @@ an int or a one-element tuple.

    $$

    Examples

    - 
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
   
 # pool with window of size=3, stride=2
-m <- nn_avg_pool1d(3, stride=2)
-m(torch_randn(1, 1, 8))
+m <- nn_avg_pool1d(3, stride=2)
+m(torch_randn(1, 1, 8))
 
-}
+}
 
    #> torch_tensor 
 #> (1,.,.) = 
-#>   0.3975 -0.0079 -0.0240
+#>  -0.1279  0.8035  0.4940
 #> [ CPUFloatType{1,1,3} ]
    
   
    
   
 
@@ -153,6 +165,9 @@ $$" />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -166,7 +181,24 @@ $$" />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -210,14 +242,14 @@ input(N_i, C_j, stride[0] \times h + m, stride[1] \times w + n)
 $$
    
     
 
-    
    nn_avg_pool2d(
-  kernel_size,
-  stride = NULL,
-  padding = 0,
-  ceil_mode = FALSE,
-  count_include_pad = TRUE,
-  divisor_override = NULL
-)
    
+    
    nn_avg_pool2d(
+  kernel_size,
+  stride = NULL,
+  padding = 0,
+  ceil_mode = FALSE,
+  count_include_pad = TRUE,
+  divisor_override = NULL
+)
    
 
     
    Arguments
    @@ -277,16 +309,16 @@ $$ $$

    Examples

    - 
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
   
 # pool of square window of size=3, stride=2
-m <- nn_avg_pool2d(3, stride=2)
+m <- nn_avg_pool2d(3, stride=2)
 # pool of non-square window
-m <- nn_avg_pool2d(c(3, 2), stride=c(2, 1))
-input <- torch_randn(20, 16, 50, 32)
-output <- m(input)
+m <- nn_avg_pool2d(c(3, 2), stride=c(2, 1))
+input <- torch_randn(20, 16, 50, 32)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -155,6 +167,9 @@ $$" />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -168,7 +183,24 @@ $$" />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -214,14 +246,14 @@ can be precisely described as:
    
 $$
    
     
 
-    
    nn_avg_pool3d(
-  kernel_size,
-  stride = NULL,
-  padding = 0,
-  ceil_mode = FALSE,
-  count_include_pad = TRUE,
-  divisor_override = NULL
-)
    
+    
    nn_avg_pool3d(
+  kernel_size,
+  stride = NULL,
+  padding = 0,
+  ceil_mode = FALSE,
+  count_include_pad = TRUE,
+  divisor_override = NULL
+)
    
 
     
    Arguments
    @@ -285,16 +317,16 @@ $$ $$

    Examples

    - 
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
   
 # pool of square window of size=3, stride=2
-m = nn_avg_pool3d(3, stride=2)
+m = nn_avg_pool3d(3, stride=2)
 # pool of non-square window
-m = nn_avg_pool3d(c(3, 2, 2), stride=c(2, 1, 2))
-input = torch_randn(20, 16, 50,44, 31)
-output = m(input)
+m = nn_avg_pool3d(c(3, 2, 2), stride=c(2, 1, 2))
+input = torch_randn(20, 16, 50,44, 31)
+output = m(input)
 
-}
+}
 
    
   
    
   
 
@@ -147,6 +159,9 @@ Batch Normalization: Accelerating Deep Network Training by Reducing Internal Cov
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -160,7 +175,24 @@ Batch Normalization: Accelerating Deep Network Training by Reducing Internal Cov
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -199,13 +231,13 @@ inputs with optional additional channel dimension) as described in the paper
 Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
    
     
 
-    
    nn_batch_norm1d(
-  num_features,
-  eps = 1e-05,
-  momentum = 0.1,
-  affine = TRUE,
-  track_running_stats = TRUE
-)
    
+    
    nn_batch_norm1d(
+  num_features,
+  eps = 1e-05,
+  momentum = 0.1,
+  affine = TRUE,
+  track_running_stats = TRUE
+)
    
 
     
    Arguments
    @@ -280,15 +312,15 @@ on (N, L) slices, it's common terminology to call this Temporal Bat

    Examples

    - 
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 # With Learnable Parameters
-m <- nn_batch_norm1d(100)
+m <- nn_batch_norm1d(100)
 # Without Learnable Parameters
-m <- nn_batch_norm1d(100, affine = FALSE)
-input <- torch_randn(20, 100)
-output <- m(input) 
+m <- nn_batch_norm1d(100, affine = FALSE)
+input <- torch_randn(20, 100)
+output <- m(input) 
 
-}
+}
 
    
   
    
   
 
@@ -147,6 +159,9 @@ Batch Normalization: Accelerating Deep Network Training by Reducing Internal Cov
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -160,7 +175,24 @@ Batch Normalization: Accelerating Deep Network Training by Reducing Internal Cov
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -199,13 +231,13 @@ additional channel dimension) as described in the paper
 Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.
    
     
 
-    
    nn_batch_norm2d(
-  num_features,
-  eps = 1e-05,
-  momentum = 0.1,
-  affine = TRUE,
-  track_running_stats = TRUE
-)
    
+    
    nn_batch_norm2d(
+  num_features,
+  eps = 1e-05,
+  momentum = 0.1,
+  affine = TRUE,
+  track_running_stats = TRUE
+)
    
 
     
    Arguments
    @@ -279,15 +311,15 @@ on (N, H, W) slices, it's common terminology to call this Spatial B

    Examples

    - 
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 # With Learnable Parameters
-m <- nn_batch_norm2d(100)
+m <- nn_batch_norm2d(100)
 # Without Learnable Parameters
-m <- nn_batch_norm2d(100, affine=FALSE)
-input <- torch_randn(20, 100, 35, 45)
-output <- m(input)
+m <- nn_batch_norm2d(100, affine=FALSE)
+input <- torch_randn(20, 100, 35, 45)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -146,6 +158,9 @@ between the target and the output:" />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ between the target and the output:" />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,7 +229,7 @@ between the target and the output:" />
 between the target and the output:
    
     
 
-    
    nn_bce_loss(weight = NULL, reduction = "mean")
    
+    
    nn_bce_loss(weight = NULL, reduction = "mean")
    
 
     
    Arguments
    @@ -264,15 +296,15 @@ shape as input.

    Examples

    - 
    if (torch_is_installed()) {
-m <- nn_sigmoid()
-loss <- nn_bce_loss()
-input <- torch_randn(3, requires_grad=TRUE)
-target <- torch_rand(3)
-output <- loss(m(input), target)
-output$backward()
-
-}
+    
    if (torch_is_installed()) {
+m <- nn_sigmoid()
+loss <- nn_bce_loss()
+input <- torch_randn(3, requires_grad=TRUE)
+target <- torch_rand(3)
+output <- loss(m(input), target)
+output$backward()
+
+}
    + + + + + + + + + + + + + +
    weight

    (Tensor, optional): a manual rescaling weight given to the loss +of each batch element. If given, has to be a Tensor of size nbatch.

    reduction

    (string, optional): Specifies the reduction to apply to the output: +'none' | 'mean' | 'sum'. 'none': no reduction will be applied, +'mean': the sum of the output will be divided by the number of +elements in the output, 'sum': the output will be summed. Note: size_average +and reduce are in the process of being deprecated, and in the meantime, +specifying either of those two args will override reduction. Default: 'mean'

    pos_weight

    (Tensor, optional): a weight of positive examples. +Must be a vector with length equal to the number of classes.

    + +

    Details

    + +

    The unreduced (i.e. with reduction set to 'none') loss can be described as:

    +

    $$ + \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad +l_n = - w_n \left[ y_n \cdot \log \sigma(x_n) + + (1 - y_n) \cdot \log (1 - \sigma(x_n)) \right], +$$

    +

    where \(N\) is the batch size. If reduction is not 'none' +(default 'mean'), then

    +

    $$ + \ell(x, y) = \begin{array}{ll} +\mbox{mean}(L), & \mbox{if reduction} = \mbox{'mean';}\\ +\mbox{sum}(L), & \mbox{if reduction} = \mbox{'sum'.} +\end{array} +$$

    +

    This is used for measuring the error of a reconstruction in for example +an auto-encoder. Note that the targets t[i] should be numbers +between 0 and 1. +It's possible to trade off recall and precision by adding weights to positive examples. +In the case of multi-label classification the loss can be described as:

    +

    $$ +\ell_c(x, y) = L_c = \{l_{1,c},\dots,l_{N,c}\}^\top, \quad +l_{n,c} = - w_{n,c} \left[ p_c y_{n,c} \cdot \log \sigma(x_{n,c}) ++ (1 - y_{n,c}) \cdot \log (1 - \sigma(x_{n,c})) \right], +$$ +where \(c\) is the class number (\(c > 1\) for multi-label binary +classification,

    +

    \(c = 1\) for single-label binary classification), +\(n\) is the number of the sample in the batch and +\(p_c\) is the weight of the positive answer for the class \(c\). +\(p_c > 1\) increases the recall, \(p_c < 1\) increases the precision. +For example, if a dataset contains 100 positive and 300 negative examples of a single class, +then pos_weight for the class should be equal to \(\frac{300}{100}=3\). +The loss would act as if the dataset contains \(3\times 100=300\) positive examples.

    +

    Shape

    + + + +
      +

    • Input: \((N, *)\) where \(*\) means, any number of additional dimensions

      • +

    • Target: \((N, *)\), same shape as the input

      • +

    • Output: scalar. If reduction is 'none', then \((N, *)\), same +shape as input.

      • +
    + + +

    Examples

    + 
    if (torch_is_installed()) {
+loss <- nn_bce_with_logits_loss()
+input <- torch_randn(3, requires_grad=TRUE)
+target <- torch_empty(3)$random_(1, 2)
+output <- loss(input, target)
+output$backward()
+
+target <- torch_ones(10, 64, dtype=torch_float32())  # 64 classes, batch size = 10
+output <- torch_full(c(10, 64), 1.5)  # A prediction (logit)
+pos_weight <- torch_ones(64)  # All weights are equal to 1
+criterion <- nn_bce_with_logits_loss(pos_weight=pos_weight)
+criterion(output, target)  # -log(sigmoid(1.5))
+
+}
+
    #> torch_tensor 
+#> 0.201413
+#> [ CPUFloatType{} ]
    + + + + + +
    +
    +

    Developed by Daniel Falbel, Javier Luraschi.

    +
    + +
    +

    Site built with pkgdown 1.6.1.

    +
    + +
    + + + + + + + + + diff --git a/reference/nn_bilinear.html b/reference/nn_bilinear.html index b2507d126..3cae1b8a5 100644 --- a/reference/nn_bilinear.html +++ b/reference/nn_bilinear.html @@ -38,6 +38,8 @@ + + + + @@ -73,7 +85,7 @@ torch - 0.0.3 + 0.1.0 @@ -146,6 +158,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -159,7 +174,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -197,7 +229,7 @@ \(y = x_1^T A x_2 + b\)

    - 
    nn_bilinear(in1_features, in2_features, out_features, bias = TRUE)
    + 
    nn_bilinear(in1_features, in2_features, out_features, bias = TRUE)

    Arguments

    @@ -251,14 +283,14 @@ If bias is TRUE, the values are initialized from

    Examples

    - 
    if (torch_is_installed()) {
-m <- nn_bilinear(20, 30, 50)
-input1 <- torch_randn(128, 20)
-input2 <- torch_randn(128, 30)
-output = m(input1, input2)
-print(output$size()) 
-
-}
+    
    if (torch_is_installed()) {
+m <- nn_bilinear(20, 30, 50)
+input1 <- torch_randn(128, 20)
+input2 <- torch_randn(128, 30)
+output = m(input1, input2)
+print(output$size()) 
+
+}
 
    #> [1] 128  50
    + + + + + + + + + +
    x

    the tensor that will be converted to nn_buffer

    persistent

    whether the buffer should be persistent or not.

    + + + + + + + +
    +
    +

    Developed by Daniel Falbel, Javier Luraschi.

    +
    + +
    +

    Site built with pkgdown 1.6.1.

    +
    + +
    + + + + + + + + + diff --git a/reference/nn_celu.html b/reference/nn_celu.html index a74105dc4..84feb68b1 100644 --- a/reference/nn_celu.html +++ b/reference/nn_celu.html @@ -38,6 +38,8 @@ + + @@ -55,6 +57,16 @@ + + + + @@ -72,7 +84,7 @@ torch - 0.0.3 + 0.1.0 @@ -145,6 +157,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -158,7 +173,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -195,7 +227,7 @@

    Applies the element-wise function:

    - 
    nn_celu(alpha = 1, inplace = FALSE)
    + 
    nn_celu(alpha = 1, inplace = FALSE)

    Arguments

    @@ -229,12 +261,12 @@ dimensions

    Examples

    - 
    if (torch_is_installed()) {
-m <- nn_celu()
-input <- torch_randn(2)
-output <- m(input)
+    
    if (torch_is_installed()) {
+m <- nn_celu()
+input <- torch_randn(2)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -149,6 +161,9 @@ precisely described as:" />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -162,7 +177,24 @@ precisely described as:" />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -203,17 +235,17 @@ In the simplest case, the output value of the layer with input size
 precisely described as:
    
     
 
-    
    nn_conv1d(
-  in_channels,
-  out_channels,
-  kernel_size,
-  stride = 1,
-  padding = 0,
-  dilation = 1,
-  groups = 1,
-  bias = TRUE,
-  padding_mode = "zeros"
-)
    
+    
    nn_conv1d(
+  in_channels,
+  out_channels,
+  kernel_size,
+  stride = 1,
+  padding = 0,
+  dilation = 1,
+  groups = 1,
+  bias = TRUE,
+  padding_mode = "zeros"
+)
    
 
     
    Arguments
    @@ -340,12 +372,12 @@ sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where

    Examples

    - 
    if (torch_is_installed()) {
-m <- nn_conv1d(16, 33, 3, stride=2)
-input <- torch_randn(20, 16, 50)
-output <- m(input)
+    
    if (torch_is_installed()) {
+m <- nn_conv1d(16, 33, 3, stride=2)
+input <- torch_randn(20, 16, 50)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -146,6 +158,9 @@ planes." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ planes." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,17 +229,17 @@ planes." />
 planes.
    
     
 
-    
    nn_conv2d(
-  in_channels,
-  out_channels,
-  kernel_size,
-  stride = 1,
-  padding = 0,
-  dilation = 1,
-  groups = 1,
-  bias = TRUE,
-  padding_mode = "zeros"
-)
    
+    
    nn_conv2d(
+  in_channels,
+  out_channels,
+  kernel_size,
+  stride = 1,
+  padding = 0,
+  dilation = 1,
+  groups = 1,
+  bias = TRUE,
+  padding_mode = "zeros"
+)
    
 
     
    Arguments
    @@ -351,18 +383,18 @@ sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where

    Examples

    - 
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
 # With square kernels and equal stride
-m <- nn_conv2d(16, 33, 3, stride = 2)
+m <- nn_conv2d(16, 33, 3, stride = 2)
 # non-square kernels and unequal stride and with padding
-m <- nn_conv2d(16, 33, c(3, 5), stride=c(2, 1), padding=c(4, 2))
+m <- nn_conv2d(16, 33, c(3, 5), stride=c(2, 1), padding=c(4, 2))
 # non-square kernels and unequal stride and with padding and dilation
-m <- nn_conv2d(16, 33, c(3, 5), stride=c(2, 1), padding=c(4, 2), dilation=c(3, 1))
-input <- torch_randn(20, 16, 50, 100)
-output <- m(input)  
+m <- nn_conv2d(16, 33, c(3, 5), stride=c(2, 1), padding=c(4, 2), dilation=c(3, 1))
+input <- torch_randn(20, 16, 50, 100)
+output <- m(input)  
 
-}
+}
 
    
   
    
   
 
@@ -148,6 +160,9 @@ and output \((N, C_{out}, D_{out}, H_{out}, W_{out})\) can be precisely describe
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -161,7 +176,24 @@ and output \((N, C_{out}, D_{out}, H_{out}, W_{out})\) can be precisely describe
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -201,17 +233,17 @@ In the simplest case, the output value of the layer with input size \((N, C_{in}
 and output \((N, C_{out}, D_{out}, H_{out}, W_{out})\) can be precisely described as:
    
     
 
-    
    nn_conv3d(
-  in_channels,
-  out_channels,
-  kernel_size,
-  stride = 1,
-  padding = 0,
-  dilation = 1,
-  groups = 1,
-  bias = TRUE,
-  padding_mode = "zeros"
-)
    
+    
    nn_conv3d(
+  in_channels,
+  out_channels,
+  kernel_size,
+  stride = 1,
+  padding = 0,
+  dilation = 1,
+  groups = 1,
+  bias = TRUE,
+  padding_mode = "zeros"
+)
    
 
     
    Arguments
    @@ -342,15 +374,15 @@ sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where

    Examples

    - 
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 # With square kernels and equal stride
-m <- nn_conv3d(16, 33, 3, stride=2)
+m <- nn_conv3d(16, 33, 3, stride=2)
 # non-square kernels and unequal stride and with padding
-m <- nn_conv3d(16, 33, c(3, 5, 2), stride=c(2, 1, 1), padding=c(4, 2, 0))
-input <- torch_randn(20, 16, 10, 50, 100)
-output <- m(input)
+m <- nn_conv3d(16, 33, c(3, 5, 2), stride=c(2, 1, 1), padding=c(4, 2, 0))
+input <- torch_randn(20, 16, 10, 50, 100)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -146,6 +158,9 @@ composed of several input planes." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ composed of several input planes." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,18 +229,18 @@ composed of several input planes." />
 composed of several input planes.
    
     
 
-    
    nn_conv_transpose1d(
-  in_channels,
-  out_channels,
-  kernel_size,
-  stride = 1,
-  padding = 0,
-  output_padding = 0,
-  groups = 1,
-  bias = TRUE,
-  dilation = 1,
-  padding_mode = "zeros"
-)
    
+    
    nn_conv_transpose1d(
+  in_channels,
+  out_channels,
+  kernel_size,
+  stride = 1,
+  padding = 0,
+  output_padding = 0,
+  groups = 1,
+  bias = TRUE,
+  dilation = 1,
+  padding_mode = "zeros"
+)
    
 
     
    Arguments
    @@ -338,12 +370,12 @@ sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where

    Examples

    - 
    if (torch_is_installed()) {
-m <- nn_conv_transpose1d(32, 16, 2)
-input <- torch_randn(10, 32, 2)
-output <- m(input)
+    
    if (torch_is_installed()) {
+m <- nn_conv_transpose1d(32, 16, 2)
+input <- torch_randn(10, 32, 2)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -146,6 +158,9 @@ composed of several input planes." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ composed of several input planes." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,18 +229,18 @@ composed of several input planes." />
 composed of several input planes.
    
     
 
-    
    nn_conv_transpose2d(
-  in_channels,
-  out_channels,
-  kernel_size,
-  stride = 1,
-  padding = 0,
-  output_padding = 0,
-  groups = 1,
-  bias = TRUE,
-  dilation = 1,
-  padding_mode = "zeros"
-)
    
+    
    nn_conv_transpose2d(
+  in_channels,
+  out_channels,
+  kernel_size,
+  stride = 1,
+  padding = 0,
+  output_padding = 0,
+  groups = 1,
+  bias = TRUE,
+  dilation = 1,
+  padding_mode = "zeros"
+)
    
 
     
    Arguments
    @@ -347,23 +379,23 @@ sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where

    Examples

    - 
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 # With square kernels and equal stride
-m <- nn_conv_transpose2d(16, 33, 3, stride=2)
+m <- nn_conv_transpose2d(16, 33, 3, stride=2)
 # non-square kernels and unequal stride and with padding
-m <- nn_conv_transpose2d(16, 33, c(3, 5), stride=c(2, 1), padding=c(4, 2))
-input <- torch_randn(20, 16, 50, 100)
-output <- m(input)
+m <- nn_conv_transpose2d(16, 33, c(3, 5), stride=c(2, 1), padding=c(4, 2))
+input <- torch_randn(20, 16, 50, 100)
+output <- m(input)
 # exact output size can be also specified as an argument
-input <- torch_randn(1, 16, 12, 12)
-downsample <- nn_conv2d(16, 16, 3, stride=2, padding=1)
-upsample <- nn_conv_transpose2d(16, 16, 3, stride=2, padding=1)
-h <- downsample(input)
-h$size()
-output <- upsample(h, output_size=input$size())
-output$size()
-
-}
+input <- torch_randn(1, 16, 12, 12)
+downsample <- nn_conv2d(16, 16, 3, stride=2, padding=1)
+upsample <- nn_conv_transpose2d(16, 16, 3, stride=2, padding=1)
+h <- downsample(input)
+h$size()
+output <- upsample(h, output_size=input$size())
+output$size()
+
+}
 
    #> [1]  1 16 12 12
    
   
    
   
 
@@ -146,6 +158,9 @@ planes." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ planes." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,18 +229,18 @@ planes." />
 planes.
    
     
 
-    
    nn_conv_transpose3d(
-  in_channels,
-  out_channels,
-  kernel_size,
-  stride = 1,
-  padding = 0,
-  output_padding = 0,
-  groups = 1,
-  bias = TRUE,
-  dilation = 1,
-  padding_mode = "zeros"
-)
    
+    
    nn_conv_transpose3d(
+  in_channels,
+  out_channels,
+  kernel_size,
+  stride = 1,
+  padding = 0,
+  output_padding = 0,
+  groups = 1,
+  bias = TRUE,
+  dilation = 1,
+  padding_mode = "zeros"
+)
    
 
     
    Arguments
    @@ -355,16 +387,16 @@ sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where

    Examples

    - 
    if (torch_is_installed()) {
-if (FALSE) {
+    
    if (torch_is_installed()) {
+if (FALSE) {
 # With square kernels and equal stride
-m <- nn_conv_transpose3d(16, 33, 3, stride=2)
+m <- nn_conv_transpose3d(16, 33, 3, stride=2)
 # non-square kernels and unequal stride and with padding
-m <- nn_conv_transpose3d(16, 33, c(3, 5, 2), stride=c(2, 1, 1), padding=c(0, 4, 2))
-input <- torch_randn(20, 16, 10, 50, 100)
-output <- m(input)
-}
-}
+m <- nn_conv_transpose3d(16, 33, c(3, 5, 2), stride=c(2, 1, 1), padding=c(0, 4, 2))
+input <- torch_randn(20, 16, 10, 50, 100)
+output <- m(input)
+}
+}
    + + + + + + + + + +
    margin

    (float, optional): Should be a number from \(-1\) to \(1\), +\(0\) to \(0.5\) is suggested. If margin is missing, the +default value is \(0\).

    reduction

    (string, optional): Specifies the reduction to apply to the output: +'none' | 'mean' | 'sum'. 'none': no reduction will be applied, +'mean': the sum of the output will be divided by the number of +elements in the output, 'sum': the output will be summed. Note: size_average +and reduce are in the process of being deprecated, and in the meantime, +specifying either of those two args will override reduction. Default: 'mean'

    + +

    Details

    + +

    $$ + \mbox{loss}(x, y) = + \begin{array}{ll} +1 - \cos(x_1, x_2), & \mbox{if } y = 1 \\ +\max(0, \cos(x_1, x_2) - \mbox{margin}), & \mbox{if } y = -1 +\end{array} +$$

    + + + + + + +
    +
    +

    Developed by Daniel Falbel, Javier Luraschi.

    +
    + +
    +

    Site built with pkgdown 1.6.1.

    +
    + +
    + + + + + + + + + diff --git a/reference/nn_cross_entropy_loss.html b/reference/nn_cross_entropy_loss.html index e45c00e75..1083964e4 100644 --- a/reference/nn_cross_entropy_loss.html +++ b/reference/nn_cross_entropy_loss.html @@ -38,6 +38,8 @@ + + + + + + @@ -73,7 +85,7 @@ It is useful when training a classification problem with C classes." /> torch - 0.0.3 + 0.1.0 @@ -146,6 +158,9 @@ It is useful when training a classification problem with C classes." />
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -159,7 +174,24 @@ It is useful when training a classification problem with C classes." />
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -193,11 +225,11 @@ It is useful when training a classification problem with C classes." />
    -

    This criterion combines nn_log_softmax() and nn_nll_loss() in one single class. +

    This criterion combines nn_log_softmax() and nn_nll_loss() in one single class. It is useful when training a classification problem with C classes.

    - 
    nn_cross_entropy_loss(weight = NULL, ignore_index = -100, reduction = "mean")
    + 
    nn_cross_entropy_loss(weight = NULL, ignore_index = -100, reduction = "mean")

    Arguments

    @@ -271,14 +303,14 @@ of K-dimensional loss.

    Examples

    - 
    if (torch_is_installed()) {
-loss <- nn_cross_entropy_loss()
-input <- torch_randn(3, 5, requires_grad=TRUE)
-target <- torch_randint(low = 1, high = 5, size = 3, dtype = torch_long())
-output <- loss(input, target)
-output$backward()
-
-}
+    
    if (torch_is_installed()) {
+loss <- nn_cross_entropy_loss()
+input <- torch_randn(3, 5, requires_grad=TRUE)
+target <- torch_randint(low = 1, high = 5, size = 3, dtype = torch_long())
+output <- loss(input, target)
+output$backward()
+
+}
    + + + + + + + + + + + + + +
    blank

    (int, optional): blank label. Default \(0\).

    reduction

    (string, optional): Specifies the reduction to apply to the output: +'none' | 'mean' | 'sum'. 'none': no reduction will be applied, +'mean': the output losses will be divided by the target lengths and +then the mean over the batch is taken. Default: 'mean'

    zero_infinity

    (bool, optional): +Whether to zero infinite losses and the associated gradients. +Default: FALSE +Infinite losses mainly occur when the inputs are too short +to be aligned to the targets.

    + +

    Note

    + +

    In order to use CuDNN, the following must be satisfied: targets must be +in concatenated format, all input_lengths must be T. \(blank=0\), +target_lengths \(\leq 256\), the integer arguments must be of +The regular implementation uses the (more common in PyTorch) torch_long dtype. +dtype torch_int32.

    +

    In some circumstances when using the CUDA backend with CuDNN, this operator +may select a nondeterministic algorithm to increase performance. If this is +undesirable, you can try to make the operation deterministic (potentially at +a performance cost) by setting torch.backends.cudnn.deterministic = TRUE.

    +

    Shape

    + + + +
      +

    • Log_probs: Tensor of size \((T, N, C)\), +where \(T = \mbox{input length}\), +\(N = \mbox{batch size}\), and +\(C = \mbox{number of classes (including blank)}\). +The logarithmized probabilities of the outputs (e.g. obtained with +[nnf)log_softmax()]).

      • +

    • Targets: Tensor of size \((N, S)\) or +\((\mbox{sum}(\mbox{target\_lengths}))\), +where \(N = \mbox{batch size}\) and +\(S = \mbox{max target length, if shape is } (N, S)\). +It represent the target sequences. Each element in the target +sequence is a class index. And the target index cannot be blank (default=0). +In the \((N, S)\) form, targets are padded to the +length of the longest sequence, and stacked. +In the \((\mbox{sum}(\mbox{target\_lengths}))\) form, +the targets are assumed to be un-padded and +concatenated within 1 dimension.

      • +

    • Input_lengths: Tuple or tensor of size \((N)\), +where \(N = \mbox{batch size}\). It represent the lengths of the +inputs (must each be \(\leq T\)). And the lengths are specified +for each sequence to achieve masking under the assumption that sequences +are padded to equal lengths.

      • +

    • Target_lengths: Tuple or tensor of size \((N)\), +where \(N = \mbox{batch size}\). It represent lengths of the targets. +Lengths are specified for each sequence to achieve masking under the +assumption that sequences are padded to equal lengths. If target shape is +\((N,S)\), target_lengths are effectively the stop index +\(s_n\) for each target sequence, such that target_n = targets[n,0:s_n] for +each target in a batch. Lengths must each be \(\leq S\) +If the targets are given as a 1d tensor that is the concatenation of individual +targets, the target_lengths must add up to the total length of the tensor.

      • +

    • Output: scalar. If reduction is 'none', then +\((N)\), where \(N = \mbox{batch size}\).

      • +
    + +

    [nnf)log_softmax()]: R:nnf)log_softmax() +[n,0:s_n]: R:n,0:s_n

    +

    References

    + +

    A. Graves et al.: Connectionist Temporal Classification: +Labelling Unsegmented Sequence Data with Recurrent Neural Networks: +https://www.cs.toronto.edu/~graves/icml_2006.pdf

    + +

    Examples

    + 
    if (torch_is_installed()) {
+# Target are to be padded
+T <- 50      # Input sequence length
+C <- 20      # Number of classes (including blank)
+N <- 16      # Batch size
+S <- 30      # Target sequence length of longest target in batch (padding length)
+S_min <- 10  # Minimum target length, for demonstration purposes
+
+# Initialize random batch of input vectors, for *size = (T,N,C)
+input <- torch_randn(T, N, C)$log_softmax(2)$detach()$requires_grad_()
+
+# Initialize random batch of targets (0 = blank, 1:C = classes)
+target <- torch_randint(low=1, high=C, size=c(N, S), dtype=torch_long())
+
+input_lengths <- torch_full(size=c(N), fill_value=TRUE, dtype=torch_long())
+target_lengths <- torch_randint(low=S_min, high=S, size=c(N), dtype=torch_long())
+ctc_loss <- nn_ctc_loss()
+loss <- ctc_loss(input, target, input_lengths, target_lengths)
+loss$backward()
+
+
+# Target are to be un-padded
+T <- 50      # Input sequence length
+C <- 20      # Number of classes (including blank)
+N <- 16      # Batch size
+
+# Initialize random batch of input vectors, for *size = (T,N,C)
+input <- torch_randn(T, N, C)$log_softmax(2)$detach()$requires_grad_()
+input_lengths <- torch_full(size=c(N), fill_value=TRUE, dtype=torch_long())
+
+# Initialize random batch of targets (0 = blank, 1:C = classes)
+target_lengths <- torch_randint(low=1, high=T, size=c(N), dtype=torch_long())
+target <- torch_randint(low=1, high=C, size=as.integer(sum(target_lengths)), dtype=torch_long())
+ctc_loss <- nn_ctc_loss()
+loss <- ctc_loss(input, target, input_lengths, target_lengths)
+loss$backward()
+
+}
+
    + + + + + +
    +
    +

    Developed by Daniel Falbel, Javier Luraschi.

    +
    + +
    +

    Site built with pkgdown 1.6.1.

    +
    + +
    + + + + + + + + + diff --git a/reference/nn_dropout.html b/reference/nn_dropout.html index 1db17faad..8b3a281d5 100644 --- a/reference/nn_dropout.html +++ b/reference/nn_dropout.html @@ -38,6 +38,8 @@ + + + + + + @@ -75,7 +87,7 @@ call." /> torch - 0.0.3 + 0.1.0 @@ -148,6 +160,9 @@ call." />
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -161,7 +176,24 @@ call." />
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -201,7 +233,7 @@ distribution. Each channel will be zeroed out independently on every forward call.

    - 
    nn_dropout(p = 0.5, inplace = FALSE)
    + 
    nn_dropout(p = 0.5, inplace = FALSE)

    Arguments

    @@ -235,12 +267,12 @@ identity function.

    Examples

    - 
    if (torch_is_installed()) {
-m <- nn_dropout(p = 0.2)
-input <- torch_randn(20, 16)
-output <- m(input)
+    
    if (torch_is_installed()) {
+m <- nn_dropout(p = 0.2)
+input <- torch_randn(20, 16)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -147,6 +159,9 @@ batched input is a 2D tensor \(\mbox{input}[i, j]\))." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -160,7 +175,24 @@ batched input is a 2D tensor \(\mbox{input}[i, j]\))." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -199,7 +231,7 @@ e.g., the \(j\)-th channel of the \(i\)-th sample in the
 batched input is a 2D tensor \(\mbox{input}[i, j]\)).
    
     
 
-    
    nn_dropout2d(p = 0.5, inplace = FALSE)
    
+    
    nn_dropout2d(p = 0.5, inplace = FALSE)
    
 
     
    Arguments
    @@ -239,12 +271,12 @@ feature maps and should be used instead.

    Examples

    - 
    if (torch_is_installed()) {
-m <- nn_dropout2d(p = 0.2)
-input <- torch_randn(20, 16, 32, 32)
-output <- m(input)
+    
    if (torch_is_installed()) {
+m <- nn_dropout2d(p = 0.2)
+input <- torch_randn(20, 16, 32, 32)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -147,6 +159,9 @@ batched input is a 3D tensor \(\mbox{input}[i, j]\))." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -160,7 +175,24 @@ batched input is a 3D tensor \(\mbox{input}[i, j]\))." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -199,7 +231,7 @@ e.g., the \(j\)-th channel of the \(i\)-th sample in the
 batched input is a 3D tensor \(\mbox{input}[i, j]\)).
    
     
 
-    
    nn_dropout3d(p = 0.5, inplace = FALSE)
    
+    
    nn_dropout3d(p = 0.5, inplace = FALSE)
    
 
     
    Arguments
    @@ -239,12 +271,12 @@ feature maps and should be used instead.

    Examples

    - 
    if (torch_is_installed()) {
-m <- nn_dropout3d(p = 0.2)
-input <- torch_randn(20, 16, 4, 32, 32)
-output <- m(input)
+    
    if (torch_is_installed()) {
+m <- nn_dropout3d(p = 0.2)
+input <- torch_randn(20, 16, 4, 32, 32)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Applies the element-wise function:
    
     
 
-    
    nn_elu(alpha = 1, inplace = FALSE)
    
+    
    nn_elu(alpha = 1, inplace = FALSE)
    
 
     
    Arguments
    @@ -227,12 +259,12 @@ dimensions

    Examples

    - 
    if (torch_is_installed()) {
-m <- nn_elu()
-input <- torch_randn(2)
-output <-  m(input)
+    
    if (torch_is_installed()) {
+m <- nn_elu()
+input <- torch_randn(2)
+output <-  m(input)
 
-}
+}
 
    
   
    
   
 
@@ -148,6 +160,9 @@ word embeddings." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
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    • 
     
  • 
       Loading Data
@@ -161,7 +176,24 @@ word embeddings." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -201,16 +233,16 @@ The input to the module is a list of indices, and the output is the correspondin
 word embeddings.
    
     
 
-    
    nn_embedding(
-  num_embeddings,
-  embedding_dim,
-  padding_idx = NULL,
-  max_norm = NULL,
-  norm_type = 2,
-  scale_grad_by_freq = FALSE,
-  sparse = FALSE,
-  .weight = NULL
-)
    
+    
    nn_embedding(
+  num_embeddings,
+  embedding_dim,
+  padding_idx = NULL,
+  max_norm = NULL,
+  norm_type = 2,
+  scale_grad_by_freq = FALSE,
+  sparse = FALSE,
+  .weight = NULL
+)
    
 
     
    Arguments
    @@ -284,24 +316,24 @@ initialized from \(\mathcal{N}(0, 1)\)

    Examples

    - 
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 # an Embedding module containing 10 tensors of size 3
-embedding <- nn_embedding(10, 3)
+embedding <- nn_embedding(10, 3)
 # a batch of 2 samples of 4 indices each
-input <- torch_tensor(rbind(c(1,2,4,5),c(4,3,2,9)), dtype = torch_long())
-embedding(input)
+input <- torch_tensor(rbind(c(1,2,4,5),c(4,3,2,9)), dtype = torch_long())
+embedding(input)
 # example with padding_idx
-embedding <- nn_embedding(10, 3, padding_idx=1)
-input <- torch_tensor(matrix(c(1,3,1,6), nrow = 1), dtype = torch_long())
-embedding(input)
+embedding <- nn_embedding(10, 3, padding_idx=1)
+input <- torch_tensor(matrix(c(1,3,1,6), nrow = 1), dtype = torch_long())
+embedding(input)
 
-}
+}
 
    #> torch_tensor 
 #> (1,.,.) = 
 #>   0.0000  0.0000  0.0000
-#>  -1.2943 -1.0279  0.6483
+#>  -2.0373 -0.1153 -1.4552
 #>   0.0000  0.0000  0.0000
-#>   0.4053  0.7866 -0.3922
+#>  -0.3688  1.0265 -2.1888
 #> [ CPUFloatType{1,4,3} ]
    
   
    
   
 
@@ -146,6 +158,9 @@ Fractional MaxPooling by Ben Graham" />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
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@@ -159,7 +174,24 @@ Fractional MaxPooling by Ben Graham" />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,12 +229,12 @@ Fractional MaxPooling by Ben Graham" />
 Fractional MaxPooling by Ben Graham
    
     
 
-    
    nn_fractional_max_pool2d(
-  kernel_size,
-  output_size = NULL,
-  output_ratio = NULL,
-  return_indices = FALSE
-)
    
+    
    nn_fractional_max_pool2d(
+  kernel_size,
+  output_size = NULL,
+  output_ratio = NULL,
+  return_indices = FALSE
+)
    
 
     
    Arguments
    @@ -236,16 +268,10 @@ step size determined by the target output size. The number of output features is equal to the number of input planes.

    Examples

    - 
    if (torch_is_installed()) {
-# pool of square window of size=3, and target output size 13x12
-m = nn_fractional_max_pool2d(3, output_size=c(13, 12))
-# pool of square window and target output size being half of input image size
-m = nn_fractional_max_pool2d(3, output_ratio=c(0.5, 0.5))
-input = torch_randn(20, 16, 50, 32)
-output = m(input)
-
-}
-
    + 
    if (torch_is_installed()) {
+
+}
+
    #> NULL
    @@ -146,6 +158,9 @@ Fractional MaxPooling by Ben Graham" />
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -159,7 +174,24 @@ Fractional MaxPooling by Ben Graham" />
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -197,12 +229,12 @@ Fractional MaxPooling by Ben Graham" /> Fractional MaxPooling by Ben Graham

    - 
    nn_fractional_max_pool3d(
-  kernel_size,
-  output_size = NULL,
-  output_ratio = NULL,
-  return_indices = FALSE
-)
    + 
    nn_fractional_max_pool3d(
+  kernel_size,
+  output_size = NULL,
+  output_ratio = NULL,
+  return_indices = FALSE
+)

    Arguments

    @@ -236,15 +268,15 @@ step size determined by the target output size. The number of output features is equal to the number of input planes.

    Examples

    - 
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 # pool of cubic window of size=3, and target output size 13x12x11
-m = nn_fractional_max_pool3d(3, output_size=c(13, 12, 11))
+m = nn_fractional_max_pool3d(3, output_size=c(13, 12, 11))
 # pool of cubic window and target output size being half of input size
-m = nn_fractional_max_pool3d(3, output_ratio=c(0.5, 0.5, 0.5))
-input = torch_randn(20, 16, 50, 32, 16)
-output = m(input)
+m = nn_fractional_max_pool3d(3, output_ratio=c(0.5, 0.5, 0.5))
+input = torch_randn(20, 16, 50, 32, 16)
+output = m(input)
 
-}
+}
 
    
   
    
   
 
@@ -146,6 +158,9 @@ $$\mbox{GELU}(x) = x * \Phi(x)$$" />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
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@@ -159,7 +174,24 @@ $$\mbox{GELU}(x) = x * \Phi(x)$$" />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,7 +229,7 @@ $$\mbox{GELU}(x) = x * \Phi(x)$$" />
 $$\mbox{GELU}(x) = x * \Phi(x)$$
    
     
 
-    
    nn_gelu()
    
+    
    nn_gelu()
    
 
 
     
    Details
    
@@ -215,12 +247,12 @@ dimensions
    
 
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-m = nn_gelu()
-input <- torch_randn(2)
-output <- m(input)
+    
    if (torch_is_installed()) {
+m = nn_gelu()
+input <- torch_randn(2)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -147,6 +159,9 @@ of the input matrices and \(b\) is the second half." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
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    • 
     
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@@ -160,7 +175,24 @@ of the input matrices and \(b\) is the second half." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -199,7 +231,7 @@ of the input matrices and \(b\) is the second half." />
 of the input matrices and \(b\) is the second half.
    
     
 
-    
    nn_glu(dim = -1)
    
+    
    nn_glu(dim = -1)
    
 
     
    Arguments
    @@ -222,12 +254,12 @@ dimensions

    Examples

    - 
    if (torch_is_installed()) {
-m <- nn_glu()
-input <- torch_randn(4, 2)
-output <- m(input)
+    
    if (torch_is_installed()) {
+m <- nn_glu()
+input <- torch_randn(4, 2)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Applies the hard shrinkage function element-wise:
    
     
 
-    
    nn_hardshrink(lambd = 0.5)
    
+    
    nn_hardshrink(lambd = 0.5)
    
 
     
    Arguments
    @@ -229,12 +261,12 @@ dimensions

    Examples

    - 
    if (torch_is_installed()) {
-m <- nn_hardshrink()
-input <- torch_randn(2)
-output <- m(input)
+    
    if (torch_is_installed()) {
+m <- nn_hardshrink()
+input <- torch_randn(2)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Applies the element-wise function:
    
     
 
-    
    nn_hardsigmoid()
    
+    
    nn_hardsigmoid()
    
 
 
     
    Details
    
@@ -220,12 +252,12 @@ dimensions
    
 
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-m <- nn_hardsigmoid()
-input <- torch_randn(2)
-output <- m(input)
+    
    if (torch_is_installed()) {
+m <- nn_hardsigmoid()
+input <- torch_randn(2)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -146,6 +158,9 @@ Searching for MobileNetV3" />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
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@@ -159,7 +174,24 @@ Searching for MobileNetV3" />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,7 +229,7 @@ Searching for MobileNetV3" />
 Searching for MobileNetV3
    
     
 
-    
    nn_hardswish()
    
+    
    nn_hardswish()
    
 
 
     
    Details
    
@@ -221,14 +253,14 @@ dimensions
    
 
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-if (FALSE) {
-m <- nn_hardswish()
-input <- torch_randn(2)
-output <- m(input)
-}
-
-}
+    
    if (torch_is_installed()) {
+if (FALSE) {
+m <- nn_hardswish()
+input <- torch_randn(2)
+output <- m(input)
+}
+
+}
 
    
   
    
   
 
@@ -146,6 +158,9 @@ HardTanh is defined as:" />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ HardTanh is defined as:" />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,7 +229,7 @@ HardTanh is defined as:" />
 HardTanh is defined as:
    
     
 
-    
    nn_hardtanh(min_val = -1, max_val = 1, inplace = FALSE)
    
+    
    nn_hardtanh(min_val = -1, max_val = 1, inplace = FALSE)
    
 
     
    Arguments
    @@ -240,12 +272,12 @@ dimensions

    Examples

    - 
    if (torch_is_installed()) {
-m <- nn_hardtanh(-2, 2)
-input <- torch_randn(2)
-output <- m(input)
+    
    if (torch_is_installed()) {
+m <- nn_hardtanh(-2, 2)
+input <- torch_randn(2)
+output <- m(input)
 
-}
+}
    + + + + + + + + + +
    margin

    (float, optional): Has a default value of 1.

    reduction

    (string, optional): Specifies the reduction to apply to the output: +'none' | 'mean' | 'sum'. 'none': no reduction will be applied, +'mean': the sum of the output will be divided by the number of +elements in the output, 'sum': the output will be summed. Note: size_average +and reduce are in the process of being deprecated, and in the meantime, +specifying either of those two args will override reduction. Default: 'mean'

    + +

    Details

    + +

    This is usually used for measuring whether two inputs are similar or +dissimilar, e.g. using the L1 pairwise distance as \(x\), and is typically +used for learning nonlinear embeddings or semi-supervised learning. +The loss function for \(n\)-th sample in the mini-batch is

    +

    $$ + l_n = \begin{array}{ll} +x_n, & \mbox{if}\; y_n = 1,\\ +\max \{0, \Delta - x_n\}, & \mbox{if}\; y_n = -1, +\end{array} +$$

    +

    and the total loss functions is

    +

    $$ + \ell(x, y) = \begin{array}{ll} +\mbox{mean}(L), & \mbox{if reduction} = \mbox{'mean';}\\ +\mbox{sum}(L), & \mbox{if reduction} = \mbox{'sum'.} +\end{array} +$$

    +

    where \(L = \{l_1,\dots,l_N\}^\top\).

    +

    Shape

    + + + +
      +

    • Input: \((*)\) where \(*\) means, any number of dimensions. The sum operation +operates over all the elements.

      • +

    • Target: \((*)\), same shape as the input

      • +

    • Output: scalar. If reduction is 'none', then same shape as the input

      • +
    + + + + + + + +
    +
    +

    Developed by Daniel Falbel, Javier Luraschi.

    +
    + +
    +

    Site built with pkgdown 1.6.1.

    +
    + +
    + + + + + + + + + diff --git a/reference/nn_identity.html b/reference/nn_identity.html index 31ff5e294..51525c31a 100644 --- a/reference/nn_identity.html +++ b/reference/nn_identity.html @@ -38,6 +38,8 @@ + + @@ -55,6 +57,16 @@ + + + + @@ -72,7 +84,7 @@ torch - 0.0.3 + 0.1.0 @@ -145,6 +157,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -158,7 +173,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -195,7 +227,7 @@

    A placeholder identity operator that is argument-insensitive.

    - 
    nn_identity(...)
    + 
    nn_identity(...)

    Arguments

    @@ -208,13 +240,13 @@

    Examples

    - 
    if (torch_is_installed()) {
-m <- nn_identity(54, unused_argument1 = 0.1, unused_argument2 = FALSE)
-input <- torch_randn(128, 20)
-output <- m(input)
-print(output$size())
+    
    if (torch_is_installed()) {
+m <- nn_identity(54, unused_argument1 = 0.1, unused_argument2 = FALSE)
+input <- torch_randn(128, 20)
+output <- m(input)
+print(output$size())
 
-}
+}
 
    #> [1] 128  20
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Return the recommended gain value for the given nonlinearity function.
    
     
 
-    
    nn_init_calculate_gain(nonlinearity, param = NULL)
    
+    
    nn_init_calculate_gain(nonlinearity, param = NULL)
    
 
     
    Arguments
    diff --git a/reference/nn_init_constant_.html b/reference/nn_init_constant_.html index 7be0e2c24..431492c76 100644 --- a/reference/nn_init_constant_.html +++ b/reference/nn_init_constant_.html @@ -38,6 +38,8 @@ + + @@ -55,6 +57,16 @@ + + + + @@ -72,7 +84,7 @@ torch - 0.0.3 + 0.1.0 @@ -145,6 +157,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -158,7 +173,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -195,7 +227,7 @@

    Fills the input Tensor with the value val.

    - 
    nn_init_constant_(tensor, val)
    + 
    nn_init_constant_(tensor, val)

    Arguments

    @@ -212,11 +244,11 @@

    Examples

    - 
    if (torch_is_installed()) {
-w <- torch_empty(3, 5)
-nn_init_constant_(w, 0.3)
+    
    if (torch_is_installed()) {
+w <- torch_empty(3, 5)
+nn_init_constant_(w, 0.3)
 
-}
+}
 
    #> torch_tensor 
 #>  0.3000  0.3000  0.3000  0.3000  0.3000
 #>  0.3000  0.3000  0.3000  0.3000  0.3000
diff --git a/reference/nn_init_dirac_.html b/reference/nn_init_dirac_.html
index e75204703..7e57c6a4f 100644
--- a/reference/nn_init_dirac_.html
+++ b/reference/nn_init_dirac_.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -75,7 +87,7 @@ of groups&gt;1, each group of channels preserves identity." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -148,6 +160,9 @@ of groups&gt;1, each group of channels preserves identity." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -161,7 +176,24 @@ of groups&gt;1, each group of channels preserves identity." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -201,7 +233,7 @@ layers, where as many input channels are preserved as possible. In case
 of groups>1, each group of channels preserves identity.
    
     
    
 
-    
    nn_init_dirac_(tensor, groups = 1)
    
+    
    nn_init_dirac_(tensor, groups = 1)
    
 
     
    Arguments
    @@ -218,13 +250,13 @@ of groups>1, each group of channels preserves identity.

    Examples

    - 
    if (torch_is_installed()) {
-if (FALSE) {
-w <- torch_empty(3, 16, 5, 5)
-nn_init_dirac_(w)
-}
+    
    if (torch_is_installed()) {
+if (FALSE) {
+w <- torch_empty(3, 16, 5, 5)
+nn_init_dirac_(w)
+}
 
-}
+}
 
    
   
    
   
 
@@ -147,6 +159,9 @@ many inputs are preserved as possible." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -160,7 +175,24 @@ many inputs are preserved as possible." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -199,7 +231,7 @@ Preserves the identity of the inputs in Linear layers, where as
 many inputs are preserved as possible.
    
     
 
-    
    nn_init_eye_(tensor)
    
+    
    nn_init_eye_(tensor)
    
 
     
    Arguments
    @@ -212,11 +244,11 @@ many inputs are preserved as possible.

    Examples

    - 
    if (torch_is_installed()) {
-w <- torch_empty(3, 5)
-nn_init_eye_(w)
+    
    if (torch_is_installed()) {
+w <- torch_empty(3, 5)
+nn_init_eye_(w)
 
-}
+}
 
    #> torch_tensor 
 #>  1  0  0  0  0
 #>  0  1  0  0  0
diff --git a/reference/nn_init_kaiming_normal_.html b/reference/nn_init_kaiming_normal_.html
index 89b43854d..3e79e5645 100644
--- a/reference/nn_init_kaiming_normal_.html
+++ b/reference/nn_init_kaiming_normal_.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -74,7 +86,7 @@ normal distribution." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -147,6 +159,9 @@ normal distribution." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -160,7 +175,24 @@ normal distribution." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -199,12 +231,12 @@ described in Delving deep into rectifiers: Surpassing human-level performa
 normal distribution.
    
     
    
 
-    
    nn_init_kaiming_normal_(
-  tensor,
-  a = 0,
-  mode = "fan_in",
-  nonlinearity = "leaky_relu"
-)
    
+    
    nn_init_kaiming_normal_(
+  tensor,
+  a = 0,
+  mode = "fan_in",
+  nonlinearity = "leaky_relu"
+)
    
 
     
    Arguments
    @@ -233,15 +265,15 @@ or 'leaky_relu' (default).

    Examples

    - 
    if (torch_is_installed()) {
-w <- torch_empty(3, 5)
-nn_init_kaiming_normal_(w, mode = "fan_in", nonlinearity = "leaky_relu")
+    
    if (torch_is_installed()) {
+w <- torch_empty(3, 5)
+nn_init_kaiming_normal_(w, mode = "fan_in", nonlinearity = "leaky_relu")
 
-}
+}
 
    #> torch_tensor 
-#> -0.5594  0.2408  0.3946  0.5860 -0.4834
-#> -0.0442  0.7170 -0.3028  0.4015 -0.8906
-#> -0.5157 -0.1763  0.9366  0.4640 -0.5356
+#>  0.2008  0.1724  0.3809 -0.3675  1.1300
+#> -0.4793 -1.0204  0.2167 -0.0427 -0.3795
+#>  0.9913 -0.4316 -0.0365 -0.8236  0.5538
 #> [ CPUFloatType{3,5} ]
    
   
    
   
 
@@ -147,6 +159,9 @@ uniform distribution." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -160,7 +175,24 @@ uniform distribution." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -199,12 +231,12 @@ described in Delving deep into rectifiers: Surpassing human-level performa
 uniform distribution.
    
     
 
-    
    nn_init_kaiming_uniform_(
-  tensor,
-  a = 0,
-  mode = "fan_in",
-  nonlinearity = "leaky_relu"
-)
    
+    
    nn_init_kaiming_uniform_(
+  tensor,
+  a = 0,
+  mode = "fan_in",
+  nonlinearity = "leaky_relu"
+)
    
 
     
    Arguments
    @@ -233,15 +265,15 @@ or 'leaky_relu' (default).

    Examples

    - 
    if (torch_is_installed()) {
-w <- torch_empty(3, 5)
-nn_init_kaiming_uniform_(w, mode = "fan_in", nonlinearity = "leaky_relu")
+    
    if (torch_is_installed()) {
+w <- torch_empty(3, 5)
+nn_init_kaiming_uniform_(w, mode = "fan_in", nonlinearity = "leaky_relu")
 
-}
+}
 
    #> torch_tensor 
-#> -0.7460  0.2070 -0.1066 -0.4344 -0.4666
-#> -0.5351 -0.4524  0.0950 -1.0077 -0.2169
-#> -0.9525  0.8753  0.0070 -0.4553 -0.3445
+#> -0.4344 -0.4666 -0.5351 -0.4524  0.0950
+#> -1.0077 -0.2169 -0.9525  0.8753  0.0070
+#> -0.4553 -0.3445 -0.4484  0.2880 -0.6149
 #> [ CPUFloatType{3,5} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Fills the input Tensor with values drawn from the normal distribution
    
     
 
-    
    nn_init_normal_(tensor, mean = 0, std = 1)
    
+    
    nn_init_normal_(tensor, mean = 0, std = 1)
    
 
     
    Arguments
    @@ -216,15 +248,15 @@

    Examples

    - 
    if (torch_is_installed()) {
-w <- torch_empty(3, 5)
-nn_init_normal_(w)
+    
    if (torch_is_installed()) {
+w <- torch_empty(3, 5)
+nn_init_normal_(w)
 
-}
+}
 
    #> torch_tensor 
-#> -1.0569 -1.0900  1.2740 -1.7728  0.0593
-#> -1.7131 -0.1353  0.8191  0.1481 -0.9940
-#> -0.7544 -1.0298  0.4237  1.4650  0.0575
+#> -1.4331 -0.2350  0.6633 -2.0450 -1.5820
+#>  1.4685 -0.1794 -2.1461  0.4202 -0.3558
+#>  0.2748 -0.0637  1.7519 -0.1447  0.6195
 #> [ CPUFloatType{3,5} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Fills the input Tensor with the scalar value 1
    
     
 
-    
    nn_init_ones_(tensor)
    
+    
    nn_init_ones_(tensor)
    
 
     
    Arguments
    @@ -208,11 +240,11 @@

    Examples

    - 
    if (torch_is_installed()) {
-w <- torch_empty(3, 5)
-nn_init_ones_(w)
+    
    if (torch_is_installed()) {
+w <- torch_empty(3, 5)
+nn_init_ones_(w)
 
-}
+}
 
    #> torch_tensor 
 #>  1  1  1  1  1
 #>  1  1  1  1  1
diff --git a/reference/nn_init_orthogonal_.html b/reference/nn_init_orthogonal_.html
index 8c116004b..e0de0bb63 100644
--- a/reference/nn_init_orthogonal_.html
+++ b/reference/nn_init_orthogonal_.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -75,7 +87,7 @@ trailing dimensions are flattened." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -148,6 +160,9 @@ trailing dimensions are flattened." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -161,7 +176,24 @@ trailing dimensions are flattened." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -201,7 +233,7 @@ at least 2 dimensions, and for tensors with more than 2 dimensions the
 trailing dimensions are flattened.
    
     
    
 
-    
    nn_init_orthogonal_(tensor, gain = 1)
    
+    
    nn_init_orthogonal_(tensor, gain = 1)
    
 
     
    Arguments
    @@ -218,15 +250,15 @@ trailing dimensions are flattened.

    Examples

    - 
    if (torch_is_installed()) {
-w <- torch_empty(3,5)
-nn_init_orthogonal_(w)
+    
    if (torch_is_installed()) {
+w <- torch_empty(3,5)
+nn_init_orthogonal_(w)
 
-}
+}
 
    #> torch_tensor 
-#> -0.0261 -0.1169  0.8583  0.1248 -0.4831
-#> -0.1584 -0.1637  0.4579 -0.4078  0.7564
-#>  0.4959 -0.5308  0.0252  0.6151  0.3053
+#>  0.4375 -0.0544  0.1218 -0.7055 -0.5414
+#> -0.5530 -0.7542  0.2625 -0.2375 -0.0026
+#>  0.3739 -0.2071 -0.1548 -0.3925  0.7996
 #> [ CPUFloatType{3,5} ]
    
   
    
   
 
@@ -147,6 +159,9 @@ as described in Deep learning via Hessian-free optimization - Martens, J. (2010)
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -160,7 +175,24 @@ as described in Deep learning via Hessian-free optimization - Martens, J. (2010)
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -199,7 +231,7 @@ non-zero elements will be drawn from the normal distribution
 as described in Deep learning via Hessian-free optimization - Martens, J. (2010).
    
     
 
-    
    nn_init_sparse_(tensor, sparsity, std = 0.01)
    
+    
    nn_init_sparse_(tensor, sparsity, std = 0.01)
    
 
     
    Arguments
    @@ -221,12 +253,12 @@ the non-zero values

    Examples

    - 
    if (torch_is_installed()) {
-if (FALSE) {
-w <- torch_empty(3, 5)
-nn_init_sparse_(w, sparsity = 0.1)
-}
-}
+    
    if (torch_is_installed()) {
+if (FALSE) {
+w <- torch_empty(3, 5)
+nn_init_sparse_(w, sparsity = 0.1)
+}
+}
 
    
   
    
   
 
@@ -146,6 +158,9 @@ normal distribution." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ normal distribution." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,7 +229,7 @@ normal distribution." />
 normal distribution.
    
     
 
-    
    nn_init_trunc_normal_(tensor, mean = 0, std = 1, a = -2, b = -2)
    
+    
    nn_init_trunc_normal_(tensor, mean = 0, std = 1, a = -2, b = -2)
    
 
     
    Arguments
    @@ -226,11 +258,11 @@ normal distribution.

    Examples

    - 
    if (torch_is_installed()) {
-w <- torch_empty(3, 5)
-nn_init_trunc_normal_(w)
+    
    if (torch_is_installed()) {
+w <- torch_empty(3, 5)
+nn_init_trunc_normal_(w)
 
-}
+}
 
    #> torch_tensor 
 #> -2 -2 -2 -2 -2
 #> -2 -2 -2 -2 -2
diff --git a/reference/nn_init_uniform_.html b/reference/nn_init_uniform_.html
index 98b02d9aa..43e79107f 100644
--- a/reference/nn_init_uniform_.html
+++ b/reference/nn_init_uniform_.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Fills the input Tensor with values drawn from the uniform distribution
    
     
    
 
-    
    nn_init_uniform_(tensor, a = 0, b = 1)
    
+    
    nn_init_uniform_(tensor, a = 0, b = 1)
    
 
     
    Arguments
    @@ -216,15 +248,15 @@

    Examples

    - 
    if (torch_is_installed()) {
-w <- torch_empty(3, 5)
-nn_init_uniform_(w)
+    
    if (torch_is_installed()) {
+w <- torch_empty(3, 5)
+nn_init_uniform_(w)
 
-}
+}
 
    #> torch_tensor 
-#>  0.8556  0.9331  0.3515  0.8071  0.4948
-#>  0.6075  0.9042  0.7181  0.7329  0.7563
-#>  0.2584  0.5293  0.9757  0.3030  0.3341
+#>  0.8071  0.4948  0.6075  0.9042  0.7181
+#>  0.7329  0.7563  0.2584  0.5293  0.9757
+#>  0.3030  0.3341  0.7465  0.4465  0.1515
 #> [ CPUFloatType{3,5} ]
    
   
    
   
 
@@ -147,6 +159,9 @@ distribution." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -160,7 +175,24 @@ distribution." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -199,7 +231,7 @@ described in Understanding the difficulty of training deep feedforward neu
 distribution.
    
     
 
-    
    nn_init_xavier_normal_(tensor, gain = 1)
    
+    
    nn_init_xavier_normal_(tensor, gain = 1)
    
 
     
    Arguments
    @@ -216,15 +248,15 @@ distribution.

    Examples

    - 
    if (torch_is_installed()) {
-w <- torch_empty(3, 5)
-nn_init_xavier_normal_(w)
+    
    if (torch_is_installed()) {
+w <- torch_empty(3, 5)
+nn_init_xavier_normal_(w)
 
-}
+}
 
    #> torch_tensor 
-#>  1.2535 -0.2197  0.5425 -3.0052 -4.2446
-#> -0.3570 -1.6970 -2.0154 -0.5348  2.7582
-#>  0.8714 -0.8924  0.7675  3.2553 -1.4333
+#> -1.1814  2.0745 -1.6342  0.8342 -1.3561
+#> -1.6676 -1.9991  2.9938  0.4390  0.6559
+#>  3.1886  0.6221 -1.8850 -0.9772  0.6616
 #> [ CPUFloatType{3,5} ]
    
   
    
   
 
@@ -147,6 +159,9 @@ distribution." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -160,7 +175,24 @@ distribution." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -199,7 +231,7 @@ described in Understanding the difficulty of training deep feedforward neu
 distribution.
    
     
 
-    
    nn_init_xavier_uniform_(tensor, gain = 1)
    
+    
    nn_init_xavier_uniform_(tensor, gain = 1)
    
 
     
    Arguments
    @@ -216,15 +248,15 @@ distribution.

    Examples

    - 
    if (torch_is_installed()) {
-w <- torch_empty(3, 5)
-nn_init_xavier_uniform_(w)
+    
    if (torch_is_installed()) {
+w <- torch_empty(3, 5)
+nn_init_xavier_uniform_(w)
 
-}
+}
 
    #> torch_tensor 
-#>  1.3397  1.1040 -3.0453 -1.7935  0.9545
-#> -0.0194 -2.4483  2.9345  2.2750 -2.4048
-#> -0.4406 -2.2409  0.4155 -0.1573  1.9776
+#> -1.7935  0.9545 -0.0194 -2.4483  2.9345
+#>  2.2750 -2.4048 -0.4406 -2.2409  0.4155
+#> -0.1573  1.9776  1.6310  1.5990  1.2116
 #> [ CPUFloatType{3,5} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Fills the input Tensor with the scalar value 0
    
     
 
-    
    nn_init_zeros_(tensor)
    
+    
    nn_init_zeros_(tensor)
    
 
     
    Arguments
    @@ -208,11 +240,11 @@

    Examples

    - 
    if (torch_is_installed()) {
-w <- torch_empty(3, 5)
-nn_init_zeros_(w)
+    
    if (torch_is_installed()) {
+w <- torch_empty(3, 5)
+nn_init_zeros_(w)
 
-}
+}
 
    #> torch_tensor 
 #>  0  0  0  0  0
 #>  0  0  0  0  0
diff --git a/reference/nn_kl_div_loss.html b/reference/nn_kl_div_loss.html
new file mode 100644
index 000000000..2335c2a28
--- /dev/null
+++ b/reference/nn_kl_div_loss.html
@@ -0,0 +1,329 @@
+
+
+
+  
+  
+
+
+
+Kullback-Leibler divergence loss — nn_kl_div_loss • torch
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+  
+  
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+  
+
+  
+    
    
+      
    
+      
+
+      
+
+      
    
+
+
    
+  
    
+    
    
+    
    Kullback-Leibler divergence loss
    
+    Source: R/nn-loss.R
+    
+    
    
+
+    
    
+    
    The Kullback-Leibler divergence loss measure
+Kullback-Leibler divergence
+is a useful distance measure for continuous distributions and is often
+useful when performing direct regression over the space of (discretely sampled)
+continuous output distributions.
    
+    
    
+
+    
    nn_kl_div_loss(reduction = "mean")
    
+
+    
    Arguments
    
+
    + + + + + +
    reduction

    (string, optional): Specifies the reduction to apply to the output: +'none' | 'batchmean' | 'sum' | 'mean'. +'none': no reduction will be applied. +'batchmean': the sum of the output will be divided by batchsize. +'sum': the output will be summed. +'mean': the output will be divided by the number of elements in the output. +Default: 'mean'

    + +

    Details

    + +

    As with nn_nll_loss(), the input given is expected to contain +log-probabilities and is not restricted to a 2D Tensor.

    +

    The targets are interpreted as probabilities by default, but could be considered +as log-probabilities with log_target set to TRUE.

    +

    This criterion expects a target Tensor of the same size as the +input Tensor.

    +

    The unreduced (i.e. with reduction set to 'none') loss can be described +as:

    +

    $$ + l(x,y) = L = \{ l_1,\dots,l_N \}, \quad +l_n = y_n \cdot \left( \log y_n - x_n \right) +$$

    +

    where the index \(N\) spans all dimensions of input and \(L\) has the same +shape as input. If reduction is not 'none' (default 'mean'), then:

    +

    $$ + \ell(x, y) = \begin{array}{ll} +\mbox{mean}(L), & \mbox{if reduction} = \mbox{'mean';} \\ +\mbox{sum}(L), & \mbox{if reduction} = \mbox{'sum'.} +\end{array} +$$

    +

    In default reduction mode 'mean', the losses are averaged for each minibatch +over observations as well as over dimensions. 'batchmean' mode gives the +correct KL divergence where losses are averaged over batch dimension only. +'mean' mode's behavior will be changed to the same as 'batchmean' in the next +major release.

    +

    Note

    + +

    reduction = 'mean' doesn't return the true kl divergence value, +please use reduction = 'batchmean' which aligns with KL math +definition. +In the next major release, 'mean' will be changed to be the same as +'batchmean'.

    +

    Shape

    + + + +
      +

    • Input: \((N, *)\) where \(*\) means, any number of additional +dimensions

      • +

    • Target: \((N, *)\), same shape as the input

      • +

    • Output: scalar by default. If reduction is 'none', then \((N, *)\), +the same shape as the input

      • +
    + + + + + + + +
    +
    +

    Developed by Daniel Falbel, Javier Luraschi.

    +
    + +
    +

    Site built with pkgdown 1.6.1.

    +
    + +
    + + + + + + + + + diff --git a/reference/nn_l1_loss.html b/reference/nn_l1_loss.html new file mode 100644 index 000000000..17242cba0 --- /dev/null +++ b/reference/nn_l1_loss.html @@ -0,0 +1,319 @@ + + + + + + + + +L1 loss — nn_l1_loss • torch + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    +
    +

    L1 loss

    + Source: R/nn-loss.R + +
    + +
    +

    Creates a criterion that measures the mean absolute error (MAE) between each +element in the input \(x\) and target \(y\).

    +
    + + 
    nn_l1_loss(reduction = "mean")
    + +

    Arguments

    + + + + + + +
    reduction

    (string, optional): Specifies the reduction to apply to the output: +'none' | 'mean' | 'sum'. 'none': no reduction will be applied, +'mean': the sum of the output will be divided by the number of +elements in the output, 'sum': the output will be summed. Note: size_average +and reduce are in the process of being deprecated, and in the meantime, +specifying either of those two args will override reduction. Default: 'mean'

    + +

    Details

    + +

    The unreduced (i.e. with reduction set to 'none') loss can be described +as:

    +

    $$ +\ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad +l_n = \left| x_n - y_n \right|, +$$

    +

    where \(N\) is the batch size. If reduction is not 'none' +(default 'mean'), then:

    +

    $$ +\ell(x, y) = +\begin{array}{ll} +\mbox{mean}(L), & \mbox{if reduction} = \mbox{'mean';}\\ +\mbox{sum}(L), & \mbox{if reduction} = \mbox{'sum'.} +\end{array} +$$

    +

    \(x\) and \(y\) are tensors of arbitrary shapes with a total +of \(n\) elements each.

    +

    The sum operation still operates over all the elements, and divides by \(n\). +The division by \(n\) can be avoided if one sets reduction = 'sum'.

    +

    Shape

    + + + +
      +

    • Input: \((N, *)\) where \(*\) means, any number of additional +dimensions

      • +

    • Target: \((N, *)\), same shape as the input

      • +

    • Output: scalar. If reduction is 'none', then +\((N, *)\), same shape as the input

      • +
    + + +

    Examples

    + 
    if (torch_is_installed()) {
+loss <- nn_l1_loss()
+input <- torch_randn(3, 5, requires_grad=TRUE)
+target <- torch_randn(3, 5)
+output <- loss(input, target)
+output$backward()
+
+}
+
    +
    + +
    + + +
    +
    +

    Developed by Daniel Falbel, Javier Luraschi.

    +
    + +
    +

    Site built with pkgdown 1.6.1.

    +
    + +
    +
    + + + + + + + + diff --git a/reference/nn_leaky_relu.html b/reference/nn_leaky_relu.html index 001286474..27a2b856d 100644 --- a/reference/nn_leaky_relu.html +++ b/reference/nn_leaky_relu.html @@ -38,6 +38,8 @@ + + @@ -55,6 +57,16 @@ + + + + @@ -72,7 +84,7 @@ torch - 0.0.3 + 0.1.0 @@ -145,6 +157,9 @@
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -158,7 +173,24 @@
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -195,7 +227,7 @@

    Applies the element-wise function:

    - 
    nn_leaky_relu(negative_slope = 0.01, inplace = FALSE)
    + 
    nn_leaky_relu(negative_slope = 0.01, inplace = FALSE)

    Arguments

    @@ -236,12 +268,12 @@ dimensions

    Examples

    - 
    if (torch_is_installed()) {
-m <- nn_leaky_relu(0.1)
-input <- torch_randn(2)
-output <- m(input)
+    
    if (torch_is_installed()) {
+m <- nn_leaky_relu(0.1)
+input <- torch_randn(2)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Applies a linear transformation to the incoming data: y = xA^T + b
    
     
 
-    
    nn_linear(in_features, out_features, bias = TRUE)
    
+    
    nn_linear(in_features, out_features, bias = TRUE)
    
 
     
    Arguments
    @@ -243,13 +275,13 @@ If bias is TRUE, the values are initialized from

    Examples

    - 
    if (torch_is_installed()) {
-m <- nn_linear(20, 30)
-input <- torch_randn(128, 20)
-output <- m(input)
-print(output$size())
+    
    if (torch_is_installed()) {
+m <- nn_linear(20, 30)
+input <- torch_randn(128, 20)
+output <- m(input)
+print(output$size())
 
-}
+}
 
    #> [1] 128  30
    
   
    
   
 
@@ -148,6 +160,9 @@ $$
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -161,7 +176,24 @@ $$
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -201,7 +233,7 @@ $$
  $$
    
     
 
-    
    nn_log_sigmoid()
    
+    
    nn_log_sigmoid()
    
 
 
     
    Shape
    
@@ -216,12 +248,12 @@ dimensions
    
 
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-m <- nn_log_sigmoid()
-input <- torch_randn(2)
-output <- m(input)
+    
    if (torch_is_installed()) {
+m <- nn_log_sigmoid()
+input <- torch_randn(2)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -146,6 +158,9 @@ input Tensor. The LogSoftmax formulation can be simplified as:" />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ input Tensor. The LogSoftmax formulation can be simplified as:" />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,7 +229,7 @@ input Tensor. The LogSoftmax formulation can be simplified as:" />
 input Tensor. The LogSoftmax formulation can be simplified as:
    
     
 
-    
    nn_log_softmax(dim)
    
+    
    nn_log_softmax(dim)
    
 
     
    Arguments
    @@ -229,12 +261,12 @@ dimensions

    Examples

    - 
    if (torch_is_installed()) {
-m <- nn_log_softmax(1)
-input <- torch_randn(2, 3)
-output <- m(input)
+    
    if (torch_is_installed()) {
+m <- nn_log_softmax(1)
+input <- torch_randn(2, 3)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -150,6 +162,9 @@ $$" />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -163,7 +178,24 @@ $$" />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -204,7 +236,7 @@ planes.
 $$
    
     
 
-    
    nn_lp_pool1d(norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)
    
+    
    nn_lp_pool1d(norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)
    
 
     
    Arguments
    @@ -254,13 +286,13 @@ not defined. This implementation will set the gradient to zero in this case.

    $$

    Examples

    - 
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 # power-2 pool of window of length 3, with stride 2.
-m <- nn_lp_pool1d(2, 3, stride=2)
-input <- torch_randn(20, 16, 50)
-output <- m(input)
+m <- nn_lp_pool1d(2, 3, stride=2)
+input <- torch_randn(20, 16, 50)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -150,6 +162,9 @@ $$" />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -163,7 +178,24 @@ $$" />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -204,7 +236,7 @@ planes.
 $$
    
     
 
-    
    nn_lp_pool2d(norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)
    
+    
    nn_lp_pool2d(norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)
    
 
     
    Arguments
    @@ -263,16 +295,16 @@ $$ $$

    Examples

    - 
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
   
 # power-2 pool of square window of size=3, stride=2
-m <- nn_lp_pool2d(2, 3, stride=2)
+m <- nn_lp_pool2d(2, 3, stride=2)
 # pool of non-square window of power 1.2
-m <- nn_lp_pool2d(1.2, c(3, 2), stride=c(2, 1))
-input <- torch_randn(20, 16, 50, 32)
-output <- m(input)
+m <- nn_lp_pool2d(1.2, c(3, 2), stride=c(2, 1))
+input <- torch_randn(20, 16, 50, 32)
+output <- m(input)
 
-}
+}
    + + + + + + + + + +
    margin

    (float, optional): Has a default value of \(0\).

    reduction

    (string, optional): Specifies the reduction to apply to the output: +'none' | 'mean' | 'sum'. 'none': no reduction will be applied, +'mean': the sum of the output will be divided by the number of +elements in the output, 'sum': the output will be summed. Note: size_average +and reduce are in the process of being deprecated, and in the meantime, +specifying either of those two args will override reduction. Default: 'mean'

    + +

    Details

    + +

    The loss function for each pair of samples in the mini-batch is:

    +

    $$ + \mbox{loss}(x1, x2, y) = \max(0, -y * (x1 - x2) + \mbox{margin}) +$$

    +

    Shape

    + + + +
      +

    • Input1: \((N)\) where N is the batch size.

      • +

    • Input2: \((N)\), same shape as the Input1.

      • +

    • Target: \((N)\), same shape as the inputs.

      • +

    • Output: scalar. If reduction is 'none', then \((N)\).

      • +
    + + +

    Examples

    + 
    if (torch_is_installed()) {
+loss <- nn_margin_ranking_loss()
+input1 <- torch_randn(3, requires_grad=TRUE)
+input2 <- torch_randn(3, requires_grad=TRUE)
+target <- torch_randn(3)$sign()
+output <- loss(input1, input2, target)
+output$backward()
+
+}
+
    + + + + + +
    +
    +

    Developed by Daniel Falbel, Javier Luraschi.

    +
    + +
    +

    Site built with pkgdown 1.6.1.

    +
    + +
    + + + + + + + + + diff --git a/reference/nn_max_pool1d.html b/reference/nn_max_pool1d.html index 1169c25a1..e12701142 100644 --- a/reference/nn_max_pool1d.html +++ b/reference/nn_max_pool1d.html @@ -38,6 +38,8 @@ + + + + + + @@ -73,7 +85,7 @@ planes." /> torch - 0.0.3 + 0.1.0 @@ -146,6 +158,9 @@ planes." />
  • Tensor class
    • +
  • + Serialization +
  • Datasets
  • Loading Data @@ -159,7 +174,24 @@ planes." />
    • -
  • Examples
    • +
  • + + +
  • Reference
    • @@ -197,14 +229,14 @@ planes." /> planes.

    - 
    nn_max_pool1d(
-  kernel_size,
-  stride = NULL,
-  padding = 0,
-  dilation = 1,
-  return_indices = FALSE,
-  ceil_mode = FALSE
-)
    + 
    nn_max_pool1d(
+  kernel_size,
+  stride = NULL,
+  padding = 0,
+  dilation = 1,
+  return_indices = FALSE,
+  ceil_mode = FALSE
+)

    Arguments

    @@ -263,13 +295,13 @@ has a nice visualization of what dilation does.

    $$

    Examples

    - 
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 # pool of size=3, stride=2
-m <- nn_max_pool1d(3, stride=2)
-input <- torch_randn(20, 16, 50)
-output <- m(input)
+m <- nn_max_pool1d(3, stride=2)
+input <- torch_randn(20, 16, 50)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -146,6 +158,9 @@ planes." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ planes." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,14 +229,14 @@ planes." />
 planes.
    
     
 
-    
    nn_max_pool2d(
-  kernel_size,
-  stride = NULL,
-  padding = 0,
-  dilation = 1,
-  return_indices = FALSE,
-  ceil_mode = FALSE
-)
    
+    
    nn_max_pool2d(
+  kernel_size,
+  stride = NULL,
+  padding = 0,
+  dilation = 1,
+  return_indices = FALSE,
+  ceil_mode = FALSE
+)
    
 
     
    Arguments
    @@ -276,15 +308,15 @@ $$

    $$

    Examples

    - 
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 # pool of square window of size=3, stride=2
-m <- nn_max_pool2d(3, stride=2)
+m <- nn_max_pool2d(3, stride=2)
 # pool of non-square window
-m <- nn_max_pool2d(c(3, 2), stride=c(2, 1))
-input <- torch_randn(20, 16, 50, 32)
-output <- m(input)
+m <- nn_max_pool2d(c(3, 2), stride=c(2, 1))
+input <- torch_randn(20, 16, 50, 32)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -149,6 +161,9 @@ can be precisely described as:" />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -162,7 +177,24 @@ can be precisely described as:" />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -202,14 +234,14 @@ output \((N, C, D_{out}, H_{out}, W_{out})\) and kernel_size \((kD,
 can be precisely described as:
    
     
 
-    
    nn_max_pool3d(
-  kernel_size,
-  stride = NULL,
-  padding = 0,
-  dilation = 1,
-  return_indices = FALSE,
-  ceil_mode = FALSE
-)
    
+    
    nn_max_pool3d(
+  kernel_size,
+  stride = NULL,
+  padding = 0,
+  dilation = 1,
+  return_indices = FALSE,
+  ceil_mode = FALSE
+)
    
 
     
    Arguments
    @@ -281,15 +313,15 @@ $$

    $$

    Examples

    - 
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 # pool of square window of size=3, stride=2
-m <- nn_max_pool3d(3, stride=2)
+m <- nn_max_pool3d(3, stride=2)
 # pool of non-square window
-m <- nn_max_pool3d(c(3, 2, 2), stride=c(2, 1, 2))
-input <- torch_randn(20, 16, 50,44, 31)
-output <- m(input)
+m <- nn_max_pool3d(c(3, 2, 2), stride=c(2, 1, 2))
+input <- torch_randn(20, 16, 50,44, 31)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -148,6 +160,9 @@ in which all non-maximal values are set to zero." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -161,7 +176,24 @@ in which all non-maximal values are set to zero." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -201,7 +233,7 @@ including the indices of the maximal values and computes a partial inverse
 in which all non-maximal values are set to zero.
    
     
 
-    
    nn_max_unpool1d(kernel_size, stride = NULL, padding = 0)
    
+    
    nn_max_unpool1d(kernel_size, stride = NULL, padding = 0)
    
 
     
    Arguments
    @@ -253,21 +285,21 @@ or as given by output_size in the call operator

    Examples

    - 
    if (torch_is_installed()) {
-pool <- nn_max_pool1d(2, stride=2, return_indices=TRUE)
-unpool <- nn_max_unpool1d(2, stride=2)
+    
    if (torch_is_installed()) {
+pool <- nn_max_pool1d(2, stride=2, return_indices=TRUE)
+unpool <- nn_max_unpool1d(2, stride=2)
 
-input <- torch_tensor(array(1:8/1, dim = c(1,1,8)))
-out <- pool(input)
-unpool(out[[1]], out[[2]])
+input <- torch_tensor(array(1:8/1, dim = c(1,1,8)))
+out <- pool(input)
+unpool(out[[1]], out[[2]])
 
 # Example showcasing the use of output_size
-input <- torch_tensor(array(1:8/1, dim = c(1,1,8)))
-out <- pool(input)
-unpool(out[[1]], out[[2]], output_size=input$size())
-unpool(out[[1]], out[[2]])
+input <- torch_tensor(array(1:8/1, dim = c(1,1,8)))
+out <- pool(input)
+unpool(out[[1]], out[[2]], output_size=input$size())
+unpool(out[[1]], out[[2]])
 
-}
+}
 
    #> torch_tensor 
 #> (1,1,.,.) = 
 #>   0
diff --git a/reference/nn_max_unpool2d.html b/reference/nn_max_unpool2d.html
index 03109ac84..b9ab5679e 100644
--- a/reference/nn_max_unpool2d.html
+++ b/reference/nn_max_unpool2d.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -75,7 +87,7 @@ in which all non-maximal values are set to zero." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -148,6 +160,9 @@ in which all non-maximal values are set to zero." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -161,7 +176,24 @@ in which all non-maximal values are set to zero." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -201,7 +233,7 @@ including the indices of the maximal values and computes a partial inverse
 in which all non-maximal values are set to zero.
    
     
    
 
-    
    nn_max_unpool2d(kernel_size, stride = NULL, padding = 0)
    
+    
    nn_max_unpool2d(kernel_size, stride = NULL, padding = 0)
    
 
     
    Arguments
    @@ -256,23 +288,23 @@ or as given by output_size in the call operator

    Examples

    - 
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-pool <- nn_max_pool2d(2, stride=2, return_indices=TRUE)
-unpool <- nn_max_unpool2d(2, stride=2)
-input <- torch_randn(1,1,4,4)
-out <- pool(input)
-unpool(out[[1]], out[[2]])
+pool <- nn_max_pool2d(2, stride=2, return_indices=TRUE)
+unpool <- nn_max_unpool2d(2, stride=2)
+input <- torch_randn(1,1,4,4)
+out <- pool(input)
+unpool(out[[1]], out[[2]])
 
 # specify a different output size than input size
-unpool(out[[1]], out[[2]], output_size=c(1, 1, 5, 5))
+unpool(out[[1]], out[[2]], output_size=c(1, 1, 5, 5))
 
-}
+}
 
    #> torch_tensor 
 #> (1,1,.,.) = 
-#>   0.0000  0.0000  0.0000  1.6626  1.3884
-#>   0.0000  0.0000  0.0000  0.4660  0.0000
-#>   0.0000  1.6033  0.0000  0.0000  0.0000
+#>   0.5229  0.0000 -0.4815  0.0000  0.0000
+#>   0.0000  0.0000  0.0000  0.0000  0.8102
+#>   0.0000  0.0000  0.0000  0.0000  0.6613
 #>   0.0000  0.0000  0.0000  0.0000  0.0000
 #>   0.0000  0.0000  0.0000  0.0000  0.0000
 #> [ CPUFloatType{1,1,5,5} ]
    
diff --git a/reference/nn_max_unpool3d.html b/reference/nn_max_unpool3d.html
index fc4e8e496..d9d513b02 100644
--- a/reference/nn_max_unpool3d.html
+++ b/reference/nn_max_unpool3d.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -75,7 +87,7 @@ in which all non-maximal values are set to zero." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -148,6 +160,9 @@ in which all non-maximal values are set to zero." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -161,7 +176,24 @@ in which all non-maximal values are set to zero." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -201,7 +233,7 @@ including the indices of the maximal values and computes a partial inverse
 in which all non-maximal values are set to zero.
    
     
 
-    
    nn_max_unpool3d(kernel_size, stride = NULL, padding = 0)
    
+    
    nn_max_unpool3d(kernel_size, stride = NULL, padding = 0)
    
 
     
    Arguments
    @@ -259,16 +291,16 @@ $$

    or as given by output_size in the call operator

    Examples

    - 
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
   
 # pool of square window of size=3, stride=2
-pool <- nn_max_pool3d(3, stride=2, return_indices=TRUE)
-unpool <- nn_max_unpool3d(3, stride=2)
-out <- pool(torch_randn(20, 16, 51, 33, 15))
-unpooled_output <- unpool(out[[1]], out[[2]])
-unpooled_output$size()
+pool <- nn_max_pool3d(3, stride=2, return_indices=TRUE)
+unpool <- nn_max_unpool3d(3, stride=2)
+out <- pool(torch_randn(20, 16, 51, 33, 15))
+unpooled_output <- unpool(out[[1]], out[[2]])
+unpooled_output$size()
 
-}
+}
 
    #> [1] 20 16 51 33 15
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,12 +227,12 @@
     
    Your models should also subclass this class.
    
     
 
-    
    nn_module(
-  classname = NULL,
-  inherit = nn_Module,
-  ...,
-  parent_env = parent.frame()
-)
    
+    
    nn_module(
+  classname = NULL,
+  inherit = nn_Module,
+  ...,
+  parent_env = parent.frame()
+)
    
 
     
    Arguments
    @@ -219,7 +251,7 @@ - +
    parent_env

    passed to R6::R6Class().

    passed to R6::R6Class().
    @@ -229,22 +261,22 @@ structure. You can assign the submodules as regular attributes.

    Examples

    - 
    if (torch_is_installed()) {
-model <- nn_module(
- initialize = function() {
-   self$conv1 <- nn_conv2d(1, 20, 5)
-   self$conv2 <- nn_conv2d(20, 20, 5)
- },
- forward = function(input) {
-   input <- self$conv1(input)
-   input <- nnf_relu(input)
-   input <- self$conv2(input)
-   input <- nnf_relu(input)
-   input
- }
-)
-
-}
+    
    if (torch_is_installed()) {
+model <- nn_module(
+ initialize = function() {
+   self$conv1 <- nn_conv2d(1, 20, 5)
+   self$conv2 <- nn_conv2d(20, 20, 5)
+ },
+ forward = function(input) {
+   input <- self$conv1(input)
+   input <- nnf_relu(input)
+   input <- self$conv2(input)
+   input <- nnf_relu(input)
+   input
+ }
+)
+
+}
 
    
   
    
   
 
@@ -147,6 +159,9 @@ nn_module methods." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -160,7 +175,24 @@ nn_module methods." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -199,7 +231,7 @@ modules it contains are properly registered, and will be visible by all
 nn_module methods.
    
     
 
-    
    nn_module_list(modules = list())
    
+    
    nn_module_list(modules = list())
    
 
     
    Arguments
    
     
@@ -212,20 +244,20 @@ modules it contains are properly registered, and will be visible by all
 
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-
-my_module <- nn_module(
- initialize = function() {
-   self$linears <- nn_module_list(lapply(1:10, function(x) nn_linear(10, 10)))
- },
- forward = function(x) {
-  for (i in 1:length(self$linears))
-    x <- self$linears[[i]](x)
-  x
- }
-)
-
-}
+    
    if (torch_is_installed()) {
+
+my_module <- nn_module(
+ initialize = function() {
+   self$linears <- nn_module_list(lapply(1:10, function(x) nn_linear(10, 10)))
+ },
+ forward = function(x) {
+  for (i in 1:length(self$linears))
+    x <- self$linears[[i]](x)
+  x
+ }
+)
+
+}
 
    
   
    
   
    
+    
+    
+      
+      
+    
+    
    reduction
    (string, optional): Specifies the reduction to apply to the output:
+'none' | 'mean' | 'sum'. 'none': no reduction will be applied,
+'mean': the sum of the output will be divided by the number of
+elements in the output, 'sum': the output will be summed. Note: size_average
+and reduce are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override reduction. Default: 'mean'
    
+
+    
    Details
    
+
+    
    $$
+  \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
+l_n = \left( x_n - y_n \right)^2,
+$$
    
+
    where \(N\) is the batch size. If reduction is not 'none'
+(default 'mean'), then:
    
+
    $$
+  \ell(x, y) =
+  \begin{array}{ll}
+\mbox{mean}(L), &  \mbox{if reduction} = \mbox{'mean';}\\
+\mbox{sum}(L),  &  \mbox{if reduction} = \mbox{'sum'.}
+\end{array}
+$$
    
+
    \(x\) and \(y\) are tensors of arbitrary shapes with a total
+of \(n\) elements each.
    
+
    The mean operation still operates over all the elements, and divides by \(n\).
+The division by \(n\) can be avoided if one sets reduction = 'sum'.
    
+    
    Shape
    
+
+    
+
+
      
+
    • Input: \((N, *)\) where \(*\) means, any number of additional
+dimensions
      • 
+
    • Target: \((N, *)\), same shape as the input
      • 
+
    
+
+
+    
    Examples
    
+    
    if (torch_is_installed()) {
+loss <- nn_mse_loss()
+input <- torch_randn(3, 5, requires_grad=TRUE)
+target <- torch_randn(3, 5)
+output <- loss(input, target)
+output$backward()
+
+}
+
    
+  
+  
+
+
+
+      
    
+      
    
+  
    Developed by Daniel Falbel, Javier Luraschi.
    
+
    
+
+
    
+  
    Site built with pkgdown 1.6.1.
    
+
    
+
+      
    
+   
+
+  
+
+
+  
+
+
+
diff --git a/reference/nn_multi_margin_loss.html b/reference/nn_multi_margin_loss.html
new file mode 100644
index 000000000..4015af99f
--- /dev/null
+++ b/reference/nn_multi_margin_loss.html
@@ -0,0 +1,310 @@
+
+
+
+  
+  
+
+
+
+Multi margin loss — nn_multi_margin_loss • torch
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+  
+  
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+  
+
+  
+    
    
+      
    
+      
+
+      
+
+      
    
+
+
    
+  
    
+    
    
+    
    Multi margin loss
    
+    Source: R/nn-loss.R
+    
+    
    
+
+    
    
+    
    Creates a criterion that optimizes a multi-class classification hinge
+loss (margin-based loss) between input \(x\) (a 2D mini-batch Tensor) and
+output \(y\) (which is a 1D tensor of target class indices,
+\(0 \leq y \leq \mbox{x.size}(1)-1\)):
    
+    
    
+
+    
    nn_multi_margin_loss(p = 1, margin = 1, weight = NULL, reduction = "mean")
    
+
+    
    Arguments
    
+    
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
    p
    (int, optional): Has a default value of \(1\). \(1\) and \(2\)
+are the only supported values.
    margin
    (float, optional): Has a default value of \(1\).
    weight
    (Tensor, optional): a manual rescaling weight given to each
+class. If given, it has to be a Tensor of size C. Otherwise, it is
+treated as if having all ones.
    reduction
    (string, optional): Specifies the reduction to apply to the output:
+'none' | 'mean' | 'sum'. 'none': no reduction will be applied,
+'mean': the sum of the output will be divided by the number of
+elements in the output, 'sum': the output will be summed. Note: size_average
+and reduce are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override reduction. Default: 'mean'
    
+
+    
    Details
    
+
+    
    For each mini-batch sample, the loss in terms of the 1D input \(x\) and scalar
+output \(y\) is:
+$$
+  \mbox{loss}(x, y) = \frac{\sum_i \max(0, \mbox{margin} - x[y] + x[i]))^p}{\mbox{x.size}(0)}
+$$
    
+
    where \(x \in \left\{0, \; \cdots , \; \mbox{x.size}(0) - 1\right\}\)
+and \(i \neq y\).
    
+
    Optionally, you can give non-equal weighting on the classes by passing
+a 1D weight tensor into the constructor.
+The loss function then becomes:
    
+
    $$
+  \mbox{loss}(x, y) = \frac{\sum_i \max(0, w[y] * (\mbox{margin} - x[y] + x[i]))^p)}{\mbox{x.size}(0)}
+$$
    
+
+  
    
+  
+
    
+
+
+      
    
+      
    
+  
    Developed by Daniel Falbel, Javier Luraschi.
    
+
    
+
+
    
+  
    Site built with pkgdown 1.6.1.
    
+
    
+
+      
    
+   
    
+
+  
+
+
+  
+
+
+
diff --git a/reference/nn_multihead_attention.html b/reference/nn_multihead_attention.html
index 277b3d21d..025ae5847 100644
--- a/reference/nn_multihead_attention.html
+++ b/reference/nn_multihead_attention.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -74,7 +86,7 @@ See reference: Attention Is All You Need" />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -147,6 +159,9 @@ See reference: Attention Is All You Need" />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -160,7 +175,24 @@ See reference: Attention Is All You Need" />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -199,16 +231,16 @@ from different representation subspaces.
 See reference: Attention Is All You Need
    
     
 
-    
    nn_multihead_attention(
-  embed_dim,
-  num_heads,
-  dropout = 0,
-  bias = TRUE,
-  add_bias_kv = FALSE,
-  add_zero_attn = FALSE,
-  kdim = NULL,
-  vdim = NULL
-)
    
+    
    nn_multihead_attention(
+  embed_dim,
+  num_heads,
+  dropout = 0,
+  bias = TRUE,
+  add_bias_kv = FALSE,
+  add_zero_attn = FALSE,
+  kdim = NULL,
+  vdim = NULL
+)
    
 
     
    Arguments
    
     
@@ -290,15 +322,15 @@ L is the target sequence length, S is the source sequence length.
    Examples
    
-    
    if (torch_is_installed()) {
-if (FALSE) {
-multihead_attn = nn_multihead_attention(embed_dim, num_heads)
-out <- multihead_attn(query, key, value)
-attn_output <- out[[1]]
-attn_output_weights <- out[[2]]
-}
-
-}
+    
    if (torch_is_installed()) {
+if (FALSE) {
+multihead_attn = nn_multihead_attention(embed_dim, num_heads)
+out <- multihead_attn(query, key, value)
+attn_output <- out[[1]]
+attn_output_weights <- out[[2]]
+}
+
+}
 
    
   
    
   
    
 
 
     
    
+    
+    
+      
+      
+    
+    
    reduction
    (string, optional): Specifies the reduction to apply to the output:
+'none' | 'mean' | 'sum'. 'none': no reduction will be applied,
+'mean': the sum of the output will be divided by the number of
+elements in the output, 'sum': the output will be summed. Note: size_average
+and reduce are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override reduction. Default: 'mean'
    
+
+    
    Details
    
+
+    
    $$
+  \mbox{loss}(x, y) = \sum_{ij}\frac{\max(0, 1 - (x[y[j]] - x[i]))}{\mbox{x.size}(0)}
+$$
    
+
    where \(x \in \left\{0, \; \cdots , \; \mbox{x.size}(0) - 1\right\}\), \
+\(y \in \left\{0, \; \cdots , \; \mbox{y.size}(0) - 1\right\}\), \
+\(0 \leq y[j] \leq \mbox{x.size}(0)-1\), \
+and \(i \neq y[j]\) for all \(i\) and \(j\).
+\(y\) and \(x\) must have the same size.
    
+
    The criterion only considers a contiguous block of non-negative targets that
+starts at the front.
+This allows for different samples to have variable amounts of target classes.
    
+    
    Shape
    
+
+    
+
+
      
+
    • Input: \((C)\) or \((N, C)\) where N is the batch size and C
+is the number of classes.
      • 
+
    • Target: \((C)\) or \((N, C)\), label targets padded by -1 ensuring same shape as the input.
      • 
+
    • Output: scalar. If reduction is 'none', then \((N)\).
      • 
+
    
+
+
+    
    Examples
    
+    
    if (torch_is_installed()) {
+loss <- nn_multilabel_margin_loss()
+x <- torch_tensor(c(0.1, 0.2, 0.4, 0.8))$view(c(1,4))
+# for target y, only consider labels 4 and 1, not after label -1
+y <- torch_tensor(c(4, 1, -1, 2), dtype = torch_long())$view(c(1,4))
+loss(x, y)
+
+}
+
    #> torch_tensor 
+#> 0.85
+#> [ CPUFloatType{} ]
    
+  
+  
+
+
+
+      
    
+      
    
+  
    Developed by Daniel Falbel, Javier Luraschi.
    
+
    
+
+
    
+  
    Site built with pkgdown 1.6.1.
    
+
    
+
+      
    
+   
+
+  
+
+
+  
+
+
+
diff --git a/reference/nn_multilabel_soft_margin_loss.html b/reference/nn_multilabel_soft_margin_loss.html
new file mode 100644
index 000000000..f2648b4eb
--- /dev/null
+++ b/reference/nn_multilabel_soft_margin_loss.html
@@ -0,0 +1,303 @@
+
+
+
+  
+  
+
+
+
+Multi label soft margin loss — nn_multilabel_soft_margin_loss • torch
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+  
+  
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+  
+
+  
+    
    
+      
    
+      
+
+      
+
+      
    
+
+
    
+  
    
+    
    
+    
    Multi label soft margin loss
    
+    Source: R/nn-loss.R
+    
+    
    
+
+    
    
+    
    Creates a criterion that optimizes a multi-label one-versus-all
+loss based on max-entropy, between input \(x\) and target \(y\) of size
+\((N, C)\).
    
+    
    
+
+    
    nn_multilabel_soft_margin_loss(weight = NULL, reduction = "mean")
    
+
+    
    Arguments
    
+    
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
    weight
    (Tensor, optional): a manual rescaling weight given to each
+class. If given, it has to be a Tensor of size C. Otherwise, it is
+treated as if having all ones.
    reduction
    (string, optional): Specifies the reduction to apply to the output:
+'none' | 'mean' | 'sum'. 'none': no reduction will be applied,
+'mean': the sum of the output will be divided by the number of
+elements in the output, 'sum': the output will be summed. Note: size_average
+and reduce are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override reduction. Default: 'mean'
    
+
+    
    Details
    
+
+    
    For each sample in the minibatch:
    
+
    $$
+  loss(x, y) = - \frac{1}{C} * \sum_i y[i] * \log((1 + \exp(-x[i]))^{-1})
++ (1-y[i]) * \log\left(\frac{\exp(-x[i])}{(1 + \exp(-x[i]))}\right)
+$$
    
+
    where \(i \in \left\{0, \; \cdots , \; \mbox{x.nElement}() - 1\right\}\),
+\(y[i] \in \left\{0, \; 1\right\}\).
    
+    
    Shape
    
+
+    
+
+
      
+
    • Input: \((N, C)\) where N is the batch size and C is the number of classes.
      • 
+
    • Target: \((N, C)\), label targets padded by -1 ensuring same shape as the input.
      • 
+
    • Output: scalar. If reduction is 'none', then \((N)\).
      • 
+
    
+
+
+  
    
+  
+
    
+
+
+      
    
+      
    
+  
    Developed by Daniel Falbel, Javier Luraschi.
    
+
    
+
+
    
+  
    Site built with pkgdown 1.6.1.
    
+
    
+
+      
    
+   
    
+
+  
+
+
+  
+
+
+
diff --git a/reference/nn_nll_loss.html b/reference/nn_nll_loss.html
new file mode 100644
index 000000000..721f0d687
--- /dev/null
+++ b/reference/nn_nll_loss.html
@@ -0,0 +1,368 @@
+
+
+
+  
+  
+
+
+
+Nll loss — nn_nll_loss • torch
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+  
+  
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+  
+
+  
+    
    
+      
    
+      
+
+      
+
+      
    
+
+
    
+  
    
+    
    
+    
    Nll loss
    
+    Source: R/nn-loss.R
+    
+    
    
+
+    
    
+    
    The negative log likelihood loss. It is useful to train a classification
+problem with C classes.
    
+    
    
+
+    
    nn_nll_loss(weight = NULL, ignore_index = -100, reduction = "mean")
    
+
+    
    Arguments
    
+    
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
    weight
    (Tensor, optional): a manual rescaling weight given to each
+class. If given, it has to be a Tensor of size C. Otherwise, it is
+treated as if having all ones.
    ignore_index
    (int, optional): Specifies a target value that is ignored
+and does not contribute to the input gradient.
    reduction
    (string, optional): Specifies the reduction to apply to the output:
+'none' | 'mean' | 'sum'. 'none': no reduction will
+be applied, 'mean': the weighted mean of the output is taken,
+'sum': the output will be summed. Note: size_average
+and reduce are in the process of being deprecated, and in
+the meantime, specifying either of those two args will override
+reduction. Default: 'mean'
    
+
+    
    Details
    
+
+    
    If provided, the optional argument weight should be a 1D Tensor assigning
+weight to each of the classes. This is particularly useful when you have an
+unbalanced training set.
    
+
    The input given through a forward call is expected to contain
+log-probabilities of each class. input has to be a Tensor of size either
+\((minibatch, C)\) or \((minibatch, C, d_1, d_2, ..., d_K)\)
+with \(K \geq 1\) for the K-dimensional case (described later).
    
+
    Obtaining log-probabilities in a neural network is easily achieved by
+adding a  LogSoftmax  layer in the last layer of your network.
    
+
    You may use CrossEntropyLoss instead, if you prefer not to add an extra
+layer.
    
+
    The target that this loss expects should be a class index in the range \([0, C-1]\)
+where C = number of classes; if ignore_index is specified, this loss also accepts
+this class index (this index may not necessarily be in the class range).
    
+
    The unreduced (i.e. with reduction set to 'none') loss can be described as:
    
+
    $$
+\ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
+l_n = - w_{y_n} x_{n,y_n}, \quad
+w_{c} = \mbox{weight}[c] \cdot \mbox{1}\{c \not= \mbox{ignore\_index}\},
+$$
    
+
    where \(x\) is the input, \(y\) is the target, \(w\) is the weight, and
+\(N\) is the batch size. If reduction is not 'none'
+(default 'mean'), then
    
+
    $$
+\ell(x, y) = \begin{array}{ll}
+\sum_{n=1}^N \frac{1}{\sum_{n=1}^N w_{y_n}} l_n, &
+  \mbox{if reduction} = \mbox{'mean';}\\
+\sum_{n=1}^N l_n,  &
+  \mbox{if reduction} = \mbox{'sum'.}
+\end{array}
+$$
    
+
    Can also be used for higher dimension inputs, such as 2D images, by providing
+an input of size \((minibatch, C, d_1, d_2, ..., d_K)\) with \(K \geq 1\),
+where \(K\) is the number of dimensions, and a target of appropriate shape
+(see below). In the case of images, it computes NLL loss per-pixel.
    
+    
    Shape
    
+
+    
+
+
      
+
    • Input: \((N, C)\) where C = number of classes, or
+\((N, C, d_1, d_2, ..., d_K)\) with \(K \geq 1\)
+in the case of K-dimensional loss.
      • 
+
    • Target: \((N)\) where each value is \(0 \leq \mbox{targets}[i] \leq C-1\), or
+\((N, d_1, d_2, ..., d_K)\) with \(K \geq 1\) in the case of
+K-dimensional loss.
      • 
+
    • Output: scalar.
      • 
+
    
+
+
    If reduction is 'none', then the same size as the target: \((N)\), or
+\((N, d_1, d_2, ..., d_K)\) with \(K \geq 1\) in the case
+of K-dimensional loss.
    
+
+    
    Examples
    
+    
    if (torch_is_installed()) {
+m <- nn_log_softmax(dim=2)
+loss <- nn_nll_loss()
+# input is of size N x C = 3 x 5
+input <- torch_randn(3, 5, requires_grad=TRUE)
+# each element in target has to have 0 <= value < C
+target <- torch_tensor(c(2, 1, 5), dtype = torch_long())
+output <- loss(m(input), target)
+output$backward()
+
+# 2D loss example (used, for example, with image inputs)
+N <- 5
+C <- 4
+loss <- nn_nll_loss()
+# input is of size N x C x height x width
+data <- torch_randn(N, 16, 10, 10)
+conv <- nn_conv2d(16, C, c(3, 3))
+m <- nn_log_softmax(dim=1)
+# each element in target has to have 0 <= value < C
+target <- torch_empty(N, 8, 8, dtype=torch_long())$random_(1, C)
+output <- loss(m(conv(data)), target)
+output$backward()
+
+}
+
    
+  
    
+  
+
    
+
+
+      
    
+      
    
+  
    Developed by Daniel Falbel, Javier Luraschi.
    
+
    
+
+
    
+  
    Site built with pkgdown 1.6.1.
    
+
    
+
+      
    
+   
    
+
+  
+
+
+  
+
+
+
diff --git a/reference/nn_pairwise_distance.html b/reference/nn_pairwise_distance.html
new file mode 100644
index 000000000..6d92168e3
--- /dev/null
+++ b/reference/nn_pairwise_distance.html
@@ -0,0 +1,305 @@
+
+
+
+  
+  
+
+
+
+Pairwise distance — nn_pairwise_distance • torch
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+  
+  
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+  
+
+  
+    
    
+      
    
+      
+
+      
+
+      
    
+
+
    
+  
    
+    
    
+    
    Pairwise distance
    
+    Source: R/nn-distance.R
+    
+    
    
+
+    
    
+    
    Computes the batchwise pairwise distance between vectors \(v_1\), \(v_2\)
+using the p-norm:
    
+    
    
+
+    
    nn_pairwise_distance(p = 2, eps = 1e-06, keepdim = FALSE)
    
+
+    
    Arguments
    
+    
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
    p
    (real): the norm degree. Default: 2
    eps
    (float, optional): Small value to avoid division by zero.
+Default: 1e-6
    keepdim
    (bool, optional): Determines whether or not to keep the vector dimension.
+Default: FALSE
    
+
+    
    Details
    
+
+    
    $$
+ \Vert x \Vert _p = \left( \sum_{i=1}^n  \vert x_i \vert ^ p \right) ^ {1/p}.
+$$
    
+    
    Shape
    
+
+    
+
+
      
+
    • Input1: \((N, D)\) where D = vector dimension
      • 
+
    • Input2: \((N, D)\), same shape as the Input1
      • 
+
    • Output: \((N)\). If keepdim is TRUE, then \((N, 1)\).
      • 
+
    
+
+
+    
    Examples
    
+    
    if (torch_is_installed()) {
+pdist <- nn_pairwise_distance(p=2)
+input1 <- torch_randn(100, 128)
+input2 <- torch_randn(100, 128)
+output <- pdist(input1, input2)
+
+}
+
    
+  
    
+  
+
    
+
+
+      
    
+      
    
+  
    Developed by Daniel Falbel, Javier Luraschi.
    
+
    
+
+
    
+  
    Site built with pkgdown 1.6.1.
    
+
    
+
+      
    
+   
    
+
+  
+
+
+  
+
+
+
diff --git a/reference/nn_parameter.html b/reference/nn_parameter.html
new file mode 100644
index 000000000..c1597c2df
--- /dev/null
+++ b/reference/nn_parameter.html
@@ -0,0 +1,274 @@
+
+
+
+  
+  
+
+
+
+Creates an nn_parameter — nn_parameter • torch
+
+
+
+
+
+
+
+
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+  
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+  
+    
    
+      
    
+      
+
+      
+
+      
    
+
+
    
+  
    
+    
    
+    
    Creates an nn_parameter
    
+    Source: R/nn.R
+    
+    
    
+
+    
    
+    
    Indicates to nn_module that x is a parameter
    
+    
    
+
+    
    nn_parameter(x, requires_grad = TRUE)
    
+
+    
    Arguments
    
+    
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
    x
    the tensor that you want to indicate as parameter
    requires_grad
    whether this parameter should  have
+requires_grad = TRUE
    
+
+
+  
    
+  
+
    
+
+
+      
    
+      
    
+  
    Developed by Daniel Falbel, Javier Luraschi.
    
+
    
+
+
    
+  
    Site built with pkgdown 1.6.1.
    
+
    
+
+      
    
+   
    
+
+  
+
+
+  
+
+
+
diff --git a/reference/nn_poisson_nll_loss.html b/reference/nn_poisson_nll_loss.html
new file mode 100644
index 000000000..3d2fe77a3
--- /dev/null
+++ b/reference/nn_poisson_nll_loss.html
@@ -0,0 +1,330 @@
+
+
+
+  
+  
+
+
+
+Poisson NLL loss — nn_poisson_nll_loss • torch
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
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+
+
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+
+
+  
+  
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+
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+
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+  
+
+  
+    
    
+      
    
+      
+
+      
+
+      
    
+
+
    
+  
    
+    
    
+    
    Poisson NLL loss
    
+    Source: R/nn-loss.R
+    
+    
    
+
+    
    
+    
    Negative log likelihood loss with Poisson distribution of target.
+The loss can be described as:
    
+    
    
+
+    
    nn_poisson_nll_loss(
+  log_input = TRUE,
+  full = FALSE,
+  eps = 1e-08,
+  reduction = "mean"
+)
    
+
+    
    Arguments
    
+    
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
    log_input
    (bool, optional): if TRUE the loss is computed as
+\(\exp(\mbox{input}) - \mbox{target}*\mbox{input}\), if FALSE the loss is
+\(\mbox{input} - \mbox{target}*\log(\mbox{input}+\mbox{eps})\).
    full
    (bool, optional): whether to compute full loss, i. e. to add the
+Stirling approximation term
+\(\mbox{target}*\log(\mbox{target}) - \mbox{target} + 0.5 * \log(2\pi\mbox{target})\).
    eps
    (float, optional): Small value to avoid evaluation of \(\log(0)\) when
+log_input = FALSE. Default: 1e-8
    reduction
    (string, optional): Specifies the reduction to apply to the output:
+'none' | 'mean' | 'sum'. 'none': no reduction will be applied,
+'mean': the sum of the output will be divided by the number of
+elements in the output, 'sum': the output will be summed. Note: size_average
+and reduce are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override reduction. Default: 'mean'
    
+
+    
    Details
    
+
+    
    $$
+\mbox{target} \sim \mathrm{Poisson}(\mbox{input})
+\mbox{loss}(\mbox{input}, \mbox{target}) = \mbox{input} - \mbox{target} * \log(\mbox{input})
++ \log(\mbox{target!})
+$$
    
+
    The last term can be omitted or approximated with Stirling formula. The
+approximation is used for target values more than 1. For targets less or
+equal to 1 zeros are added to the loss.
    
+    
    Shape
    
+
+    
+
+
      
+
    • Input: \((N, *)\) where \(*\) means, any number of additional
+dimensions
      • 
+
    • Target: \((N, *)\), same shape as the input
      • 
+
    • Output: scalar by default. If reduction is 'none', then \((N, *)\),
+the same shape as the input
      • 
+
    
+
+
+    
    Examples
    
+    
    if (torch_is_installed()) {
+loss <- nn_poisson_nll_loss()
+log_input <- torch_randn(5, 2, requires_grad=TRUE)
+target <- torch_randn(5, 2)
+output <- loss(log_input, target)
+output$backward()
+
+}
+
    
+  
    
+  
+
    
+
+
+      
    
+      
    
+  
    Developed by Daniel Falbel, Javier Luraschi.
    
+
    
+
+
    
+  
    Site built with pkgdown 1.6.1.
    
+
    
+
+      
    
+   
    
+
+  
+
+
+  
+
+
+
diff --git a/reference/nn_prelu.html b/reference/nn_prelu.html
index 000280905..a7d3c7e75 100644
--- a/reference/nn_prelu.html
+++ b/reference/nn_prelu.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -84,7 +96,7 @@ $$" />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -157,6 +169,9 @@ $$" />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -170,7 +185,24 @@ $$" />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -219,7 +251,7 @@ ax, & \mbox{ otherwise }
 $$
    
     
 
-    
    nn_prelu(num_parameters = 1, init = 0.25)
    
+    
    nn_prelu(num_parameters = 1, init = 0.25)
    
 
     
    Arguments
    
     
@@ -266,12 +298,12 @@ dimensions
    Examples
    
-    
    if (torch_is_installed()) {
-m <- nn_prelu()
-input <- torch_randn(2)
-output <- m(input)
+    
    if (torch_is_installed()) {
+m <- nn_prelu()
+input <- torch_randn(2)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -146,6 +158,9 @@ $$\mbox{ReLU}(x) = (x)^+ = \max(0, x)$$" />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ $$\mbox{ReLU}(x) = (x)^+ = \max(0, x)$$" />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,7 +229,7 @@ $$\mbox{ReLU}(x) = (x)^+ = \max(0, x)$$" />
 $$\mbox{ReLU}(x) = (x)^+ = \max(0, x)$$
    
     
 
-    
    nn_relu(inplace = FALSE)
    
+    
    nn_relu(inplace = FALSE)
    
 
     
    Arguments
    
     
    
 
 
     
    
@@ -220,15 +252,15 @@ dimensions
    Examples
    
-    
    if (torch_is_installed()) {
-m <- nn_relu()
-input <- torch_randn(2)
-m(input)
+    
    if (torch_is_installed()) {
+m <- nn_relu()
+input <- torch_randn(2)
+m(input)
 
-}
+}
 
    #> torch_tensor 
-#>  0.1347
-#>  0.1303
+#>  0.1047
+#>  0.5221
 #> [ CPUFloatType{2} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Applies the element-wise function:
    
     
 
-    
    nn_relu6(inplace = FALSE)
    
+    
    nn_relu6(inplace = FALSE)
    
 
     
    Arguments
    
     
    
 
 
     
    
@@ -223,12 +255,12 @@ dimensions
    Examples
    
-    
    if (torch_is_installed()) {
-m <- nn_relu6()
-input <- torch_randn(2)
-output <- m(input)
+    
    if (torch_is_installed()) {
+m <- nn_relu6()
+input <- torch_randn(2)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -146,6 +158,9 @@ to an input sequence." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ to an input sequence." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,17 +229,17 @@ to an input sequence." />
 to an input sequence.
    
     
 
-    
    nn_rnn(
-  input_size,
-  hidden_size,
-  num_layers = 1,
-  nonlinearity = NULL,
-  bias = TRUE,
-  batch_first = FALSE,
-  dropout = 0,
-  bidirectional = FALSE,
-  ...
-)
    
+    
    nn_rnn(
+  input_size,
+  hidden_size,
+  num_layers = 1,
+  nonlinearity = NULL,
+  bias = TRUE,
+  batch_first = FALSE,
+  dropout = 0,
+  bidirectional = FALSE,
+  ...
+)
    
 
     
    Arguments
    
     
    
 
 
     
    
@@ -345,108 +377,108 @@ of shape (hidden_size)
    
 where \(k = \frac{1}{\mbox{hidden\_size}}\)
    Examples
    
-    
    if (torch_is_installed()) {
-rnn <- nn_rnn(10, 20, 2)
-input <- torch_randn(5, 3, 10)
-h0 <- torch_randn(2, 3, 20)
-rnn(input, h0)
+    
    if (torch_is_installed()) {
+rnn <- nn_rnn(10, 20, 2)
+input <- torch_randn(5, 3, 10)
+h0 <- torch_randn(2, 3, 20)
+rnn(input, h0)
 
-}
+}
 
    #> [[1]]
 #> torch_tensor 
 #> (1,.,.) = 
-#>  Columns 1 to 9  0.0589  0.6076  0.5733  0.4325 -0.7804 -0.5884 -0.7990 -0.5201 -0.5453
-#>  -0.3423 -0.1028  0.8203 -0.4395 -0.8638 -0.2453 -0.7419 -0.5734 -0.9229
-#>  -0.0001  0.2485 -0.1473  0.5797 -0.1626 -0.1213 -0.3884 -0.5131  0.2432
+#>  Columns 1 to 9  0.7161  0.2524 -0.2267 -0.2711 -0.2845 -0.4067  0.5843 -0.1285 -0.5132
+#>   0.6516  0.7333  0.6177  0.2089 -0.5074  0.5751  0.5784  0.6827  0.0714
+#>   0.2538  0.6949 -0.0763  0.5675 -0.3406 -0.1142  0.4442 -0.0423 -0.0378
 #> 
-#> Columns 10 to 18  0.2049  0.6141  0.0159 -0.5202 -0.0390  0.8628  0.0559 -0.6653  0.5929
-#>   0.1484 -0.3955  0.4318 -0.6923  0.6768  0.9427 -0.5935 -0.1124  0.8008
-#>  -0.1115  0.3922 -0.0407  0.7891 -0.1390  0.3705 -0.2486  0.8745 -0.3595
+#> Columns 10 to 18 -0.1102  0.6036  0.1749 -0.0608 -0.4173 -0.0102 -0.2940  0.4289 -0.1320
+#>  -0.3589 -0.2381  0.0580  0.7796  0.0998 -0.2888 -0.1735 -0.8531 -0.4652
+#>   0.1831  0.0378 -0.6822 -0.2816  0.1889 -0.2580 -0.0010 -0.2612  0.1988
 #> 
-#> Columns 19 to 20  0.0777 -0.9009
-#>  -0.0200 -0.2583
-#>  -0.1381 -0.4595
+#> Columns 19 to 20  0.3093 -0.4167
+#>   0.0837  0.0514
+#>   0.7636  0.3214
 #> 
 #> (2,.,.) = 
-#>  Columns 1 to 9 -0.5747 -0.2374  0.3985  0.6116 -0.7816  0.0973 -0.2544 -0.2252 -0.2584
-#>  -0.2011 -0.3030  0.4559  0.2132 -0.6755  0.1609 -0.8239 -0.2612 -0.6095
-#>  -0.5134  0.3075 -0.2427  0.1875 -0.3368  0.1697 -0.3024 -0.5115  0.0946
+#>  Columns 1 to 9  0.5985  0.0873  0.5781 -0.1434 -0.1528  0.1490  0.2079  0.7476 -0.1045
+#>   0.3875  0.0819  0.5025  0.3552 -0.2228  0.5654 -0.0206  0.6165 -0.0171
+#>   0.4864  0.6804  0.0500  0.3966 -0.0198 -0.0722 -0.0076  0.4736 -0.2741
 #> 
-#> Columns 10 to 18  0.5029 -0.4663 -0.6517 -0.3916 -0.3694 -0.3079  0.0697  0.4641  0.4321
-#>   0.8177  0.0184 -0.5879 -0.3717 -0.0954  0.1382 -0.1993  0.5621 -0.1939
-#>   0.3419 -0.0541  0.0665  0.0764  0.6821  0.0040  0.0504  0.6075  0.0703
+#> Columns 10 to 18 -0.1348  0.2238  0.0753 -0.3945  0.1603  0.2999  0.2501 -0.5067 -0.6350
+#>  -0.3108 -0.3115 -0.2025  0.3937  0.4892 -0.2319 -0.2614 -0.6291  0.0740
+#>  -0.5959 -0.0322 -0.2894  0.1512  0.3782 -0.1010  0.1482 -0.3825  0.4464
 #> 
-#> Columns 19 to 20 -0.1393 -0.0169
-#>  -0.2283  0.0531
-#>  -0.0904  0.2892
+#> Columns 19 to 20  0.2921 -0.1500
+#>   0.6855 -0.0422
+#>   0.9088 -0.0266
 #> 
 #> (3,.,.) = 
-#>  Columns 1 to 9  0.4901  0.5564  0.4157 -0.1239 -0.3616 -0.3069 -0.7718  0.2739  0.1213
-#>   0.4148  0.1776  0.2237  0.0323 -0.5166 -0.3952 -0.6464  0.1560  0.1433
-#>   0.3964  0.0038  0.3253  0.0715  0.1306 -0.0327 -0.2969  0.1245 -0.0192
+#>  Columns 1 to 9  0.6381  0.1431  0.4164  0.3592  0.0609  0.3689  0.1772  0.4760  0.0470
+#>   0.4289 -0.1033  0.4358  0.1910 -0.0969  0.5367  0.1665  0.4100  0.0255
+#>   0.2304  0.1687  0.5151  0.1998  0.2694  0.3913  0.2137 -0.1146 -0.1561
 #> 
-#> Columns 10 to 18  0.7138 -0.0042  0.1386 -0.0459 -0.4418  0.2562 -0.1983  0.0256  0.4770
-#>   0.7310  0.4867 -0.2056 -0.4410 -0.1978  0.4279 -0.1295  0.2951  0.4745
-#>   0.6958  0.5029  0.0787 -0.2960  0.3043  0.3281 -0.3449  0.3081  0.4197
+#> Columns 10 to 18 -0.4619 -0.2263 -0.2680  0.3368  0.2228  0.1836  0.2800 -0.6207 -0.2572
+#>  -0.1175 -0.2322 -0.6100  0.0511  0.2566 -0.2520 -0.2023 -0.6280 -0.1511
+#>  -0.3149 -0.2550 -0.2557  0.1514 -0.2146  0.2289  0.2341 -0.7046 -0.1510
 #> 
-#> Columns 19 to 20  0.1745 -0.1350
-#>   0.0668 -0.4015
-#>   0.5455  0.3373
+#> Columns 19 to 20  0.4382  0.3621
+#>   0.4396  0.0057
+#>   0.6035 -0.3589
 #> 
 #> (4,.,.) = 
-#>  Columns 1 to 9 -0.1242 -0.1825  0.0703  0.3368 -0.1879 -0.6188 -0.3606  0.2400 -0.4979
-#>  -0.2285 -0.1238  0.2816  0.7158 -0.3795 -0.1800 -0.0903  0.0951 -0.4055
-#>  -0.4161  0.1562  0.1320  0.5917 -0.2003  0.5784 -0.1302 -0.1113 -0.7354
+#>  Columns 1 to 9  0.2884 -0.0291  0.1229  0.0041 -0.1166  0.3444  0.0175  0.3235  0.0658
+#>   0.6147  0.4537  0.1856  0.1567 -0.3019  0.1505  0.2564  0.5269  0.1842
+#>   0.4272  0.2199  0.5595  0.2342 -0.1921 -0.1427  0.3725  0.0805  0.0195
 #> 
-#> Columns 10 to 18  0.6470  0.0346 -0.6063 -0.0664  0.3145  0.3909 -0.3163  0.0040  0.6242
-#>   0.3401 -0.2107 -0.5455 -0.4844 -0.0769  0.0620 -0.1112  0.5133  0.6606
-#>   0.4108 -0.3172 -0.3713 -0.5535  0.3277  0.0108 -0.0800  0.5410  0.1190
+#> Columns 10 to 18 -0.0812 -0.0068 -0.1565 -0.0358 -0.0224 -0.2966 -0.2252 -0.4043  0.0966
+#>  -0.3598 -0.1854 -0.3976  0.0953  0.1748 -0.5111  0.1526 -0.4405 -0.2781
+#>  -0.3753  0.0663 -0.1238 -0.0727 -0.0397 -0.0635  0.0157 -0.0993 -0.1252
 #> 
-#> Columns 19 to 20 -0.1511 -0.0390
-#>   0.2056  0.1060
-#>   0.1649  0.4299
+#> Columns 19 to 20  0.5735 -0.1105
+#>   0.7309  0.1156
+#>   0.7796  0.3005
 #> 
 #> (5,.,.) = 
-#>  Columns 1 to 9 -0.0957  0.3769  0.1599  0.2209 -0.5452 -0.2272 -0.5200 -0.0675 -0.3636
-#>   0.3314  0.3243  0.5617  0.3386 -0.1621  0.4163 -0.6332 -0.0253 -0.0027
-#>   0.1690  0.3272  0.5195 -0.1727 -0.1203 -0.0701 -0.5596 -0.2452  0.1866
+#>  Columns 1 to 9  0.6705  0.3787  0.4285  0.3407  0.0861  0.0279  0.3407  0.3227 -0.1617
+#>   0.5895  0.1572  0.6036  0.4525  0.0566  0.3992  0.5467  0.0065 -0.2110
+#>   0.1297 -0.0524  0.5491  0.2352  0.2431  0.4329 -0.1639  0.5197 -0.1087
 #> 
-#> Columns 10 to 18  0.7646  0.0595 -0.1395 -0.1890  0.1511  0.3273 -0.2851  0.1429  0.4489
-#>   0.7122  0.0548 -0.1359 -0.2292 -0.1363 -0.1930 -0.3282  0.5265  0.3602
-#>   0.7579  0.1487  0.1720 -0.0712 -0.0081  0.3472 -0.1236  0.3269  0.3627
+#> Columns 10 to 18 -0.1388 -0.0327 -0.5949  0.0944 -0.1133 -0.1225  0.1684 -0.6087 -0.1145
+#>  -0.3481 -0.1070 -0.5201  0.3679  0.1345  0.1734  0.1243 -0.5208  0.0556
+#>  -0.4741 -0.3434 -0.3230 -0.2729  0.3473  0.0649  0.2237 -0.2233 -0.0649
 #> 
-#> Columns 19 to 20 -0.3324  0.0199
-#>   0.4319  0.0719
-#>   0.0541 -0.1996
+#> Columns 19 to 20  0.6030  0.2479
+#>   0.6858 -0.1467
+#>   0.6513 -0.2089
 #> [ CPUFloatType{5,3,20} ]
 #> 
 #> [[2]]
 #> torch_tensor 
 #> (1,.,.) = 
-#>  Columns 1 to 9 -0.0859  0.2441 -0.3785  0.4773 -0.6194  0.7348 -0.2389  0.3932 -0.5751
-#>  -0.3461 -0.4299 -0.5382 -0.0556  0.2770 -0.4997  0.3906 -0.4267 -0.2962
-#>   0.0901 -0.0487 -0.2221  0.7402 -0.3091  0.1068  0.2309  0.0844 -0.8371
+#>  Columns 1 to 9  0.0992 -0.6436 -0.3197  0.6220  0.2256 -0.7712  0.2246 -0.1414  0.3241
+#>   0.3420 -0.6408 -0.3408  0.5502 -0.2351 -0.0798  0.3823 -0.7235  0.1726
+#>  -0.1093 -0.3336 -0.2989  0.4091  0.1061  0.5667 -0.4299 -0.3630  0.3574
 #> 
-#> Columns 10 to 18 -0.7465 -0.0237 -0.5216  0.2091  0.7810  0.0142 -0.0300  0.0860 -0.4565
-#>   0.4484 -0.2549  0.6987 -0.4158 -0.6085 -0.0974  0.0892 -0.3520  0.0667
-#>  -0.1097 -0.0399  0.2584 -0.1982  0.0520 -0.0103 -0.0936  0.3060  0.0546
+#> Columns 10 to 18  0.2762 -0.1739 -0.1027  0.3692 -0.6185 -0.1532 -0.2170  0.0237 -0.4784
+#>   0.6071 -0.0503 -0.7492  0.4626 -0.3258  0.1729 -0.4063  0.0940 -0.1114
+#>   0.0344 -0.3348  0.5308 -0.3231  0.3965 -0.0367 -0.0665 -0.2350  0.4546
 #> 
-#> Columns 19 to 20  0.1902  0.0056
-#>   0.3507  0.1104
-#>  -0.0194  0.3957
+#> Columns 19 to 20  0.5460 -0.0594
+#>   0.1078  0.4941
+#>  -0.2752  0.2571
 #> 
 #> (2,.,.) = 
-#>  Columns 1 to 9 -0.0957  0.3769  0.1599  0.2209 -0.5452 -0.2272 -0.5200 -0.0675 -0.3636
-#>   0.3314  0.3243  0.5617  0.3386 -0.1621  0.4163 -0.6332 -0.0253 -0.0027
-#>   0.1690  0.3272  0.5195 -0.1727 -0.1203 -0.0701 -0.5596 -0.2452  0.1866
+#>  Columns 1 to 9  0.6705  0.3787  0.4285  0.3407  0.0861  0.0279  0.3407  0.3227 -0.1617
+#>   0.5895  0.1572  0.6036  0.4525  0.0566  0.3992  0.5467  0.0065 -0.2110
+#>   0.1297 -0.0524  0.5491  0.2352  0.2431  0.4329 -0.1639  0.5197 -0.1087
 #> 
-#> Columns 10 to 18  0.7646  0.0595 -0.1395 -0.1890  0.1511  0.3273 -0.2851  0.1429  0.4489
-#>   0.7122  0.0548 -0.1359 -0.2292 -0.1363 -0.1930 -0.3282  0.5265  0.3602
-#>   0.7579  0.1487  0.1720 -0.0712 -0.0081  0.3472 -0.1236  0.3269  0.3627
+#> Columns 10 to 18 -0.1388 -0.0327 -0.5949  0.0944 -0.1133 -0.1225  0.1684 -0.6087 -0.1145
+#>  -0.3481 -0.1070 -0.5201  0.3679  0.1345  0.1734  0.1243 -0.5208  0.0556
+#>  -0.4741 -0.3434 -0.3230 -0.2729  0.3473  0.0649  0.2237 -0.2233 -0.0649
 #> 
-#> Columns 19 to 20 -0.3324  0.0199
-#>   0.4319  0.0719
-#>   0.0541 -0.1996
+#> Columns 19 to 20  0.6030  0.2479
+#>   0.6858 -0.1467
+#>   0.6513 -0.2089
 #> [ CPUFloatType{2,3,20} ]
 #> 
    
   
    
diff --git a/reference/nn_rrelu.html b/reference/nn_rrelu.html
index 366c47be2..8c4c3937a 100644
--- a/reference/nn_rrelu.html
+++ b/reference/nn_rrelu.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -73,7 +85,7 @@ as described in the paper:" />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@ as described in the paper:" />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ as described in the paper:" />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,7 +229,7 @@ as described in the paper:" />
 as described in the paper:
    
     
 
-    
    nn_rrelu(lower = 1/8, upper = 1/3, inplace = FALSE)
    
+    
    nn_rrelu(lower = 1/8, upper = 1/3, inplace = FALSE)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -243,15 +275,15 @@ dimensions
    Examples
    
-    
    if (torch_is_installed()) {
-m <- nn_rrelu(0.1, 0.3)
-input <- torch_randn(2)
-m(input)
+    
    if (torch_is_installed()) {
+m <- nn_rrelu(0.1, 0.3)
+input <- torch_randn(2)
+m(input)
 
-}
+}
 
    #> torch_tensor 
-#>  0.5388
-#>  1.3588
+#>  1.9403
+#>  0.6835
 #> [ CPUFloatType{2} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Applied element-wise, as:
    
     
 
-    
    nn_selu(inplace = FALSE)
    
+    
    nn_selu(inplace = FALSE)
    
 
     
    Arguments
    
     
    
 
 
     
    
@@ -227,12 +259,12 @@ dimensions
    Examples
    
-    
    if (torch_is_installed()) {
-m <- nn_selu()
-input <- torch_randn(2)
-output <- m(input)
+    
    if (torch_is_installed()) {
+m <- nn_selu()
+input <- torch_randn(2)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -147,6 +159,9 @@ See examples." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -160,7 +175,24 @@ See examples." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -199,7 +231,7 @@ Modules will be added to it in the order they are passed in the constructor.
 See examples.
    
     
 
-    
    nn_sequential(..., name = NULL)
    
+    
    nn_sequential(..., name = NULL)
    
 
     
    Arguments
    
     
    
 
 
     
    
@@ -216,18 +248,18 @@ See examples.
    Examples
    
-    
    if (torch_is_installed()) {
-
-model <- nn_sequential(
-  nn_conv2d(1, 20, 5),
-  nn_relu(),
-  nn_conv2d(20, 64, 5),
-  nn_relu()
-)
-input <- torch_randn(32, 1, 28, 28)
-output <- model(input)
-
-}
+    
    if (torch_is_installed()) {
+
+model <- nn_sequential(
+  nn_conv2d(1, 20, 5),
+  nn_relu(),
+  nn_conv2d(20, 64, 5),
+  nn_relu()
+)
+input <- torch_randn(32, 1, 28, 28)
+output <- model(input)
+
+}
 
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Applies the element-wise function:
    
     
 
-    
    nn_sigmoid()
    
+    
    nn_sigmoid()
    
 
 
     
    Details
    
@@ -215,12 +247,12 @@ dimensions
    
 
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-m <- nn_sigmoid()
-input <- torch_randn(2)
-output <- m(input)
+    
    if (torch_is_installed()) {
+m <- nn_sigmoid()
+input <- torch_randn(2)
+output <- m(input)
 
-}
+}
 
    
   
    
   
    
 
 
     
    
+    
+    
+      
+      
+    
+    
    reduction
    (string, optional): Specifies the reduction to apply to the output:
+'none' | 'mean' | 'sum'. 'none': no reduction will be applied,
+'mean': the sum of the output will be divided by the number of
+elements in the output, 'sum': the output will be summed. Note: size_average
+and reduce are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override reduction. Default: 'mean'
    
+
+    
    Details
    
+
+    
    $$
+  \mbox{loss}(x, y) = \frac{1}{n} \sum_{i} z_{i}
+$$
    
+
    where \(z_{i}\) is given by:
    
+
    $$
+  z_{i} =
+  \begin{array}{ll}
+0.5 (x_i - y_i)^2, & \mbox{if } |x_i - y_i| < 1 \\
+|x_i - y_i| - 0.5, & \mbox{otherwise }
+\end{array}
+$$
    
+
    \(x\) and \(y\) arbitrary shapes with a total of \(n\) elements each
+the sum operation still operates over all the elements, and divides by \(n\).
+The division by \(n\) can be avoided if sets reduction = 'sum'.
    
+    
    Shape
    
+
+    
+
+
      
+
    • Input: \((N, *)\) where \(*\) means, any number of additional
+dimensions
      • 
+
    • Target: \((N, *)\), same shape as the input
      • 
+
    • Output: scalar. If reduction is 'none', then
+\((N, *)\), same shape as the input
      • 
+
    
+
+
+  
+  
+
+
+
+      
    
+      
    
+  
    Developed by Daniel Falbel, Javier Luraschi.
    
+
    
+
+
    
+  
    Site built with pkgdown 1.6.1.
    
+
    
+
+      
    
+   
+
+  
+
+
+  
+
+
+
diff --git a/reference/nn_soft_margin_loss.html b/reference/nn_soft_margin_loss.html
new file mode 100644
index 000000000..46246b987
--- /dev/null
+++ b/reference/nn_soft_margin_loss.html
@@ -0,0 +1,294 @@
+
+
+
+  
+  
+
+
+
+Soft margin loss — nn_soft_margin_loss • torch
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+  
+  
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+  
+
+  
+    
    
+      
    
+      
+
+      
+
+      
    
+
+
    
+  
    
+    
    
+    
    Soft margin loss
    
+    Source: R/nn-loss.R
+    
+    
    
+
+    
    
+    
    Creates a criterion that optimizes a two-class classification
+logistic loss between input tensor \(x\) and target tensor \(y\)
+(containing 1 or -1).
    
+    
    
+
+    
    nn_soft_margin_loss(reduction = "mean")
    
+
+    
    Arguments
    
+    
+    
+    
+      
+      
+    
+    
    reduction
    (string, optional): Specifies the reduction to apply to the output:
+'none' | 'mean' | 'sum'. 'none': no reduction will be applied,
+'mean': the sum of the output will be divided by the number of
+elements in the output, 'sum': the output will be summed. Note: size_average
+and reduce are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override reduction. Default: 'mean'
    
+
+    
    Details
    
+
+    
    $$
+  \mbox{loss}(x, y) = \sum_i \frac{\log(1 + \exp(-y[i]*x[i]))}{\mbox{x.nelement}()}
+$$
    
+    
    Shape
    
+
+    
+
+
      
+
    • Input: \((*)\) where \(*\) means, any number of additional
+dimensions
      • 
+
    • Target: \((*)\), same shape as the input
      • 
+
    • Output: scalar. If reduction is 'none', then same shape as the input
      • 
+
    
+
+
+  
    
+  
+
    
+
+
+      
    
+      
    
+  
    Developed by Daniel Falbel, Javier Luraschi.
    
+
    
+
+
    
+  
    Site built with pkgdown 1.6.1.
    
+
    
+
+      
    
+   
    
+
+  
+
+
+  
+
+
+
diff --git a/reference/nn_softmax.html b/reference/nn_softmax.html
index 74bfe04e3..dc85e788b 100644
--- a/reference/nn_softmax.html
+++ b/reference/nn_softmax.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -75,7 +87,7 @@ Softmax is defined as:" />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -148,6 +160,9 @@ Softmax is defined as:" />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -161,7 +176,24 @@ Softmax is defined as:" />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -201,7 +233,7 @@ lie in the range [0,1] and sum to 1.
 Softmax is defined as:
    
     
 
-    
    nn_softmax(dim)
    
+    
    nn_softmax(dim)
    
 
     
    Arguments
    
     
@@ -242,12 +274,12 @@ dimensions
    Examples
    
-    
    if (torch_is_installed()) {
-m <- nn_softmax(1)
-input <- torch_randn(2, 3)
-output <- m(input)
+    
    if (torch_is_installed()) {
+m <- nn_softmax(1)
+input <- torch_randn(2, 3)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -147,6 +159,9 @@ apply Softmax to each location \((Channels, h_i, w_j)\)" />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -160,7 +175,24 @@ apply Softmax to each location \((Channels, h_i, w_j)\)" />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -199,7 +231,7 @@ When given an image of Channels x Height x Width, it will
 apply Softmax to each location \((Channels, h_i, w_j)\)
    
     
 
-    
    nn_softmax2d()
    
+    
    nn_softmax2d()
    
 
 
     
    Value
    
@@ -217,12 +249,12 @@ values in the range [0, 1]
    
 
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-m <- nn_softmax2d()
-input <- torch_randn(2, 3, 12, 13)
-output <- m(input)
+    
    if (torch_is_installed()) {
+m <- nn_softmax2d()
+input <- torch_randn(2, 3, 12, 13)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -148,6 +160,9 @@ Softmin is defined as:" />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -161,7 +176,24 @@ Softmin is defined as:" />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -201,7 +233,7 @@ lie in the range [0, 1] and sum to 1.
 Softmin is defined as:
    
     
 
-    
    nn_softmin(dim)
    
+    
    nn_softmin(dim)
    
 
     
    Arguments
    
     
    
 
 
     
    
@@ -234,12 +266,12 @@ dimensions
    Examples
    
-    
    if (torch_is_installed()) {
-m <- nn_softmin(dim = 1)
-input <- torch_randn(2, 2)
-output <- m(input)
+    
    if (torch_is_installed()) {
+m <- nn_softmin(dim = 1)
+input <- torch_randn(2, 2)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -148,6 +160,9 @@ $$" />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -161,7 +176,24 @@ $$" />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -201,7 +233,7 @@ $$
 $$
    
     
 
-    
    nn_softplus(beta = 1, threshold = 20)
    
+    
    nn_softplus(beta = 1, threshold = 20)
    
 
     
    Arguments
    
     
    
 
 
     
    
@@ -234,12 +266,12 @@ dimensions
    Examples
    
-    
    if (torch_is_installed()) {
-m <- nn_softplus()
-input <- torch_randn(2)
-output <- m(input)
+    
    if (torch_is_installed()) {
+m <- nn_softplus()
+input <- torch_randn(2)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Applies the soft shrinkage function elementwise:
    
     
 
-    
    nn_softshrink(lambd = 0.5)
    
+    
    nn_softshrink(lambd = 0.5)
    
 
     
    Arguments
    
     
    
 
 
     
    
@@ -229,12 +261,12 @@ dimensions
    Examples
    
-    
    if (torch_is_installed()) {
-m <- nn_softshrink()
-input <- torch_randn(2)
-output <- m(input)
+    
    if (torch_is_installed()) {
+m <- nn_softshrink()
+input <- torch_randn(2)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -148,6 +160,9 @@ $$" />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -161,7 +176,24 @@ $$" />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -201,7 +233,7 @@ $$
 $$
    
     
 
-    
    nn_softsign()
    
+    
    nn_softsign()
    
 
 
     
    Shape
    
@@ -216,12 +248,12 @@ dimensions
    
 
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-m <- nn_softsign()
-input <- torch_randn(2)
-output <- m(input)
+    
    if (torch_is_installed()) {
+m <- nn_softsign()
+input <- torch_randn(2)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Applies the element-wise function:
    
     
 
-    
    nn_tanh()
    
+    
    nn_tanh()
    
 
 
     
    Details
    
@@ -215,12 +247,12 @@ dimensions
    
 
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-m <- nn_tanh()
-input <- torch_randn(2)
-output <- m(input)
+    
    if (torch_is_installed()) {
+m <- nn_tanh()
+input <- torch_randn(2)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Applies the element-wise function:
    
     
 
-    
    nn_tanhshrink()
    
+    
    nn_tanhshrink()
    
 
 
     
    Details
    
@@ -215,12 +247,12 @@ dimensions
    
 
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-m <- nn_tanhshrink()
-input <- torch_randn(2)
-output <- m(input)
+    
    if (torch_is_installed()) {
+m <- nn_tanhshrink()
+input <- torch_randn(2)
+output <- m(input)
 
-}
+}
 
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
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    • 
@@ -195,7 +227,7 @@
     
    Thresholds each element of the input Tensor.
    
     
 
-    
    nn_threshold(threshold, value, inplace = FALSE)
    
+    
    nn_threshold(threshold, value, inplace = FALSE)
    
 
     
    Arguments
    
     
    
 
 
     
    
@@ -237,12 +269,12 @@ dimensions
    Examples
    
-    
    if (torch_is_installed()) {
-m <- nn_threshold(0.1, 20)
-input <- torch_randn(2)
-output <- m(input)
+    
    if (torch_is_installed()) {
+m <- nn_threshold(0.1, 20)
+input <- torch_randn(2)
+output <- m(input)
 
-}
+}
 
    
   
    
   
    
 
 
     
    
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
    margin
    (float, optional): Default: \(1\).
    p
    (int, optional): The norm degree for pairwise distance. Default: \(2\).
    eps
    constant to avoid NaN's
    swap
    (bool, optional): The distance swap is described in detail in the paper
+Learning shallow convolutional feature descriptors with triplet losses by
+V. Balntas, E. Riba et al. Default: FALSE.
    reduction
    (string, optional): Specifies the reduction to apply to the output:
+'none' | 'mean' | 'sum'. 'none': no reduction will be applied,
+'mean': the sum of the output will be divided by the number of
+elements in the output, 'sum': the output will be summed. Note: size_average
+and reduce are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override reduction. Default: 'mean'
    
+
+    
    Details
    
+
+    
    The distance swap is described in detail in the paper
+Learning shallow convolutional feature descriptors with triplet losses by
+V. Balntas, E. Riba et al.
    
+
    The loss function for each sample in the mini-batch is:
    
+
    $$
+  L(a, p, n) = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\}
+$$
    
+
    where
    
+
    $$
+  d(x_i, y_i) = | {\bf x}_i - {\bf y}_i |_p
+$$
    
+
    See also nn_triplet_margin_with_distance_loss(), which computes the
+triplet margin loss for input tensors using a custom distance function.
    
+    
    Shape
    
+
+    
+
+
      
+
    • Input: \((N, D)\) where \(D\) is the vector dimension.
      • 
+
    • Output: A Tensor of shape \((N)\) if reduction is 'none', or a scalar
+otherwise.
      • 
+
    
+
+
+    
    Examples
    
+    
    if (torch_is_installed()) {
+triplet_loss <- nn_triplet_margin_loss(margin = 1, p = 2)
+anchor <- torch_randn(100, 128, requires_grad=TRUE)
+positive <- torch_randn(100, 128, requires_grad=TRUE)
+negative <- torch_randn(100, 128, requires_grad=TRUE)
+output <- triplet_loss(anchor, positive, negative)
+output$backward()
+
+}
+
    
+  
+  
+
+
+
+      
    
+      
    
+  
    Developed by Daniel Falbel, Javier Luraschi.
    
+
    
+
+
    
+  
    Site built with pkgdown 1.6.1.
    
+
    
+
+      
    
+   
+
+  
+
+
+  
+
+
+
diff --git a/reference/nn_triplet_margin_with_distance_loss.html b/reference/nn_triplet_margin_with_distance_loss.html
new file mode 100644
index 000000000..039a69bbb
--- /dev/null
+++ b/reference/nn_triplet_margin_with_distance_loss.html
@@ -0,0 +1,382 @@
+
+
+
+  
+  
+
+
+
+Triplet margin with distance loss — nn_triplet_margin_with_distance_loss • torch
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+  
+  
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+  
+
+  
+    
    
+      
    
+      
+
+      
+
+      
    
+
+
    
+  
    
+    
    
+    
    Triplet margin with distance loss
    
+    Source: R/nn-loss.R
+    
+    
    
+
+    
    
+    
    Creates a criterion that measures the triplet loss given input
+tensors \(a\), \(p\), and \(n\) (representing anchor,
+positive, and negative examples, respectively), and a nonnegative,
+real-valued function ("distance function") used to compute the relationship
+between the anchor and positive example ("positive distance") and the
+anchor and negative example ("negative distance").
    
+    
    
+
+    
    nn_triplet_margin_with_distance_loss(
+  distance_function = NULL,
+  margin = 1,
+  swap = FALSE,
+  reduction = "mean"
+)
    
+
+    
    Arguments
    
+    
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
    distance_function
    (callable, optional): A nonnegative, real-valued function that
+quantifies the closeness of two tensors. If not specified,
+nn_pairwise_distance() will be used.  Default: None
    margin
    (float, optional): A non-negative margin representing the minimum difference
+between the positive and negative distances required for the loss to be 0. Larger
+margins penalize cases where the negative examples are not distant enough from the
+anchors, relative to the positives. Default: \(1\).
    swap
    (bool, optional): Whether to use the distance swap described in the paper
+Learning shallow convolutional feature descriptors with triplet losses by
+V. Balntas, E. Riba et al. If TRUE, and if the positive example is closer to the
+negative example than the anchor is, swaps the positive example and the anchor in
+the loss computation. Default: FALSE.
    reduction
    (string, optional): Specifies the (optional) reduction to apply to the output:
+'none' | 'mean' | 'sum'. 'none': no reduction will be applied,
+'mean': the sum of the output will be divided by the number of
+elements in the output, 'sum': the output will be summed. Default: 'mean'
    
+
+    
    Details
    
+
+    
    The unreduced loss (i.e., with reduction set to 'none')
+can be described as:
    
+
    $$
+  \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad
+l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\}
+$$
    
+
    where \(N\) is the batch size; \(d\) is a nonnegative, real-valued function
+quantifying the closeness of two tensors, referred to as the distance_function;
+and \(margin\) is a non-negative margin representing the minimum difference
+between the positive and negative distances that is required for the loss to
+be 0.  The input tensors have \(N\) elements each and can be of any shape
+that the distance function can handle.
+If reduction is not 'none'
+(default 'mean'), then:
    
+
    $$
+\ell(x, y) =
+\begin{array}{ll}
+\mbox{mean}(L), &  \mbox{if reduction} = \mbox{`mean';}\\
+            \mbox{sum}(L),  &  \mbox{if reduction} = \mbox{`sum'.}
+\end{array}
+$$
    
+
    See also nn_triplet_margin_loss(), which computes the triplet
+loss for input tensors using the \(l_p\) distance as the distance function.
    
+    
    Shape
    
+
+    
+
+
      
+
    • Input: \((N, *)\) where \(*\) represents any number of additional dimensions
+as supported by the distance function.
      • 
+
    • Output: A Tensor of shape \((N)\) if reduction is 'none', or a scalar
+otherwise.
      • 
+
    
+
+
+    
    Examples
    
+    
    if (torch_is_installed()) {
+# Initialize embeddings
+embedding <- nn_embedding(1000, 128)
+anchor_ids <- torch_randint(0, 1000, 1, dtype = torch_long())
+positive_ids <- torch_randint(0, 1000, 1, dtype = torch_long())
+negative_ids <- torch_randint(0, 1000, 1, dtype = torch_long())
+anchor <- embedding(anchor_ids)
+positive <- embedding(positive_ids)
+negative <- embedding(negative_ids)
+
+# Built-in Distance Function
+triplet_loss <- nn_triplet_margin_with_distance_loss(
+  distance_function=nn_pairwise_distance()
+)
+output <- triplet_loss(anchor, positive, negative)
+
+# Custom Distance Function
+l_infinity <- function(x1, x2) {
+  torch_max(torch_abs(x1 - x2), dim = 1)[[1]]
+}
+
+triplet_loss <- nn_triplet_margin_with_distance_loss(
+  distance_function=l_infinity, margin=1.5
+)
+output <- triplet_loss(anchor, positive, negative)
+
+# Custom Distance Function (Lambda)
+triplet_loss <- nn_triplet_margin_with_distance_loss(
+  distance_function = function(x, y) {
+    1 - nnf_cosine_similarity(x, y)
+  }
+)
+
+output <- triplet_loss(anchor, positive, negative)
+
+}
+
    
+  
    
+  
+
    
+
+
+      
    
+      
    
+  
    Developed by Daniel Falbel, Javier Luraschi.
    
+
    
+
+
    
+  
    Site built with pkgdown 1.6.1.
    
+
    
+
+      
    
+   
    
+
+  
+
+
+  
+
+
+
diff --git a/reference/nn_utils_rnn_pack_padded_sequence.html b/reference/nn_utils_rnn_pack_padded_sequence.html
index f63ff359e..ac6ac9502 100644
--- a/reference/nn_utils_rnn_pack_padded_sequence.html
+++ b/reference/nn_utils_rnn_pack_padded_sequence.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -75,7 +87,7 @@ TRUE, B x T x * input is expected." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -148,6 +160,9 @@ TRUE, B x T x * input is expected." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -161,7 +176,24 @@ TRUE, B x T x * input is expected." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -201,12 +233,12 @@ longest sequence (equal to lengths[1]), B is the batch
 TRUE, B x T x * input is expected.
    
     
 
-    
    nn_utils_rnn_pack_padded_sequence(
-  input,
-  lengths,
-  batch_first = FALSE,
-  enforce_sorted = TRUE
-)
    
+    
    nn_utils_rnn_pack_padded_sequence(
+  input,
+  lengths,
+  batch_first = FALSE,
+  enforce_sorted = TRUE
+)
    
 
     
    Arguments
    
     
diff --git a/reference/nn_utils_rnn_pack_sequence.html b/reference/nn_utils_rnn_pack_sequence.html
index 5609d4356..18882847a 100644
--- a/reference/nn_utils_rnn_pack_sequence.html
+++ b/reference/nn_utils_rnn_pack_sequence.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
+
+
+
+
 
   
 
@@ -74,7 +86,7 @@ including zero." />
       
         torch
-        0.0.3
+        0.1.0
       
 
@@ -147,6 +159,9 @@ including zero." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
  • Datasets
  • 
       Loading Data
@@ -160,7 +175,24 @@ including zero." />
     
    • 
-
  • Examples
    • 
+
  • 
+  
+  
+
  • 
   Reference
 
    • 
@@ -199,7 +231,7 @@ the length of a sequence and nn_utils_rnn_pack_sequence(sequences, enforce_sorted = TRUE)
+    
    nn_utils_rnn_pack_sequence(sequences, enforce_sorted = TRUE)
    
 
     
    Arguments
    
     
    
 
 
       
     
     
     
   
 
 
    
@@ -226,14 +258,14 @@ is TRUE, the sequences should be sorted in the order of decreasing
 enforce_sorted = TRUE is only necessary for ONNX export.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-x <- torch_tensor(c(1,2,3), dtype = torch_long())
-y <- torch_tensor(c(4, 5), dtype = torch_long())
-z <- torch_tensor(c(6), dtype = torch_long())
+    
    if (torch_is_installed()) {
+x <- torch_tensor(c(1,2,3), dtype = torch_long())
+y <- torch_tensor(c(4, 5), dtype = torch_long())
+z <- torch_tensor(c(6), dtype = torch_long())
 
-p <- nn_utils_rnn_pack_sequence(list(x, y, z))
+p <- nn_utils_rnn_pack_sequence(list(x, y, z))
 
-}
+}
 
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,12 +227,12 @@
     
    It is an inverse operation to nn_utils_rnn_pack_padded_sequence().
    
     
 
-    
    nn_utils_rnn_pad_packed_sequence(
-  sequence,
-  batch_first = FALSE,
-  padding_value = 0,
-  total_length = NULL
-)
    
+    
    nn_utils_rnn_pad_packed_sequence(
+  sequence,
+  batch_first = FALSE,
+  padding_value = 0,
+  total_length = NULL
+)
    
 
     
    Arguments
    
     
    
@@ -246,15 +278,15 @@ the data will be transposed into B x T x * format.
    
 nn_module wrapped in ~torch.nn.DataParallel.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-seq <- torch_tensor(rbind(c(1,2,0), c(3,0,0), c(4,5,6)))        
-lens <- c(2,1,3)
-packed <- nn_utils_rnn_pack_padded_sequence(seq, lens, batch_first = TRUE,
-                                            enforce_sorted = FALSE)
-packed
-nn_utils_rnn_pad_packed_sequence(packed, batch_first=TRUE)
-
-}
+    
    if (torch_is_installed()) {
+seq <- torch_tensor(rbind(c(1,2,0), c(3,0,0), c(4,5,6)))        
+lens <- c(2,1,3)
+packed <- nn_utils_rnn_pack_padded_sequence(seq, lens, batch_first = TRUE,
+                                            enforce_sorted = FALSE)
+packed
+nn_utils_rnn_pad_packed_sequence(packed, batch_first=TRUE)
+
+}
 
    #> [[1]]
 #> torch_tensor 
 #>  1  2  0
diff --git a/reference/nn_utils_rnn_pad_sequence.html b/reference/nn_utils_rnn_pad_sequence.html
index cc3e8b10e..d11d4c856 100644
--- a/reference/nn_utils_rnn_pad_sequence.html
+++ b/reference/nn_utils_rnn_pad_sequence.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -75,7 +87,7 @@ otherwise." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -148,6 +160,9 @@ otherwise." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -161,7 +176,24 @@ otherwise." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -201,7 +233,7 @@ sequences with size L x * and if batch_first is False, and T
 otherwise.
    
     
    
 
-    
    nn_utils_rnn_pad_sequence(sequences, batch_first = FALSE, padding_value = 0)
    
+    
    nn_utils_rnn_pad_sequence(sequences, batch_first = FALSE, padding_value = 0)
    
 
     
    Arguments
    
     
    
@@ -238,13 +270,13 @@ where T is the length of the longest sequence. This function assume
 trailing dimensions and type of all the Tensors in sequences are same.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-a <- torch_ones(25, 300)
-b <- torch_ones(22, 300)
-c <- torch_ones(15, 300)
-nn_utils_rnn_pad_sequence(list(a, b, c))$size()
+    
    if (torch_is_installed()) {
+a <- torch_ones(25, 300)
+b <- torch_ones(22, 300)
+c <- torch_ones(15, 300)
+nn_utils_rnn_pad_sequence(list(a, b, c))$size()
 
-}
+}
 
    #> [1]  25   3 300
    
   
    
   
 
@@ -146,6 +158,9 @@ several input planes." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ several input planes." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,7 +229,7 @@ several input planes." />
 several input planes.
    
     
 
-    
    nnf_adaptive_avg_pool1d(input, output_size)
    
+    
    nnf_adaptive_avg_pool1d(input, output_size)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_adaptive_avg_pool2d.html b/reference/nnf_adaptive_avg_pool2d.html
index e2fbfbb5c..a3de332f6 100644
--- a/reference/nnf_adaptive_avg_pool2d.html
+++ b/reference/nnf_adaptive_avg_pool2d.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -73,7 +85,7 @@ several input planes." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@ several input planes." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ several input planes." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,7 +229,7 @@ several input planes." />
 several input planes.
    
     
 
-    
    nnf_adaptive_avg_pool2d(input, output_size)
    
+    
    nnf_adaptive_avg_pool2d(input, output_size)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_adaptive_avg_pool3d.html b/reference/nnf_adaptive_avg_pool3d.html
index e919dbe4e..0ac63d9d2 100644
--- a/reference/nnf_adaptive_avg_pool3d.html
+++ b/reference/nnf_adaptive_avg_pool3d.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -73,7 +85,7 @@ several input planes." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@ several input planes." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ several input planes." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,7 +229,7 @@ several input planes." />
 several input planes.
    
     
 
-    
    nnf_adaptive_avg_pool3d(input, output_size)
    
+    
    nnf_adaptive_avg_pool3d(input, output_size)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_adaptive_max_pool1d.html b/reference/nnf_adaptive_max_pool1d.html
index 19b488653..2c5d34870 100644
--- a/reference/nnf_adaptive_max_pool1d.html
+++ b/reference/nnf_adaptive_max_pool1d.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -73,7 +85,7 @@ several input planes." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@ several input planes." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ several input planes." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,7 +229,7 @@ several input planes." />
 several input planes.
    
     
 
-    
    nnf_adaptive_max_pool1d(input, output_size, return_indices = FALSE)
    
+    
    nnf_adaptive_max_pool1d(input, output_size, return_indices = FALSE)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_adaptive_max_pool2d.html b/reference/nnf_adaptive_max_pool2d.html
index 6e389c087..a23dcfdf1 100644
--- a/reference/nnf_adaptive_max_pool2d.html
+++ b/reference/nnf_adaptive_max_pool2d.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -73,7 +85,7 @@ several input planes." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@ several input planes." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ several input planes." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,7 +229,7 @@ several input planes." />
 several input planes.
    
     
 
-    
    nnf_adaptive_max_pool2d(input, output_size, return_indices = FALSE)
    
+    
    nnf_adaptive_max_pool2d(input, output_size, return_indices = FALSE)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_adaptive_max_pool3d.html b/reference/nnf_adaptive_max_pool3d.html
index 66721efd9..5c1fd3ef9 100644
--- a/reference/nnf_adaptive_max_pool3d.html
+++ b/reference/nnf_adaptive_max_pool3d.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -73,7 +85,7 @@ several input planes." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@ several input planes." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ several input planes." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,7 +229,7 @@ several input planes." />
 several input planes.
    
     
 
-    
    nnf_adaptive_max_pool3d(input, output_size, return_indices = FALSE)
    
+    
    nnf_adaptive_max_pool3d(input, output_size, return_indices = FALSE)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_affine_grid.html b/reference/nnf_affine_grid.html
index 3ba7b5443..af14e52de 100644
--- a/reference/nnf_affine_grid.html
+++ b/reference/nnf_affine_grid.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -73,7 +85,7 @@ affine matrices theta." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@ affine matrices theta." />
     
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@@ -159,7 +174,24 @@ affine matrices theta." />
     
    • 
   
 
-
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    • 
+
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+  
+  
+
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   Reference
 
    • 
@@ -197,7 +229,7 @@ affine matrices theta." />
 affine matrices theta.
    
     
 
-    
    nnf_affine_grid(theta, size, align_corners = FALSE)
    
+    
    nnf_affine_grid(theta, size, align_corners = FALSE)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_alpha_dropout.html b/reference/nnf_alpha_dropout.html
index 00d715796..d7f8dba9e 100644
--- a/reference/nnf_alpha_dropout.html
+++ b/reference/nnf_alpha_dropout.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
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+    
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+      Serialization
+    
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    • 
   
 
-
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    • 
+
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+  
+  
+
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    • 
@@ -195,7 +227,7 @@
     
    Applies alpha dropout to the input.
    
     
 
-    
    nnf_alpha_dropout(input, p = 0.5, training = FALSE, inplace = FALSE)
    
+    
    nnf_alpha_dropout(input, p = 0.5, training = FALSE, inplace = FALSE)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_avg_pool1d.html b/reference/nnf_avg_pool1d.html
index cc914d070..54b4d8aca 100644
--- a/reference/nnf_avg_pool1d.html
+++ b/reference/nnf_avg_pool1d.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -73,7 +85,7 @@ input planes." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@ input planes." />
     
  • 
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+    
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+    
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@@ -159,7 +174,24 @@ input planes." />
     
    • 
   
 
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+
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+  
+  
+
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    • 
@@ -197,14 +229,14 @@ input planes." />
 input planes.
    
     
 
-    
    nnf_avg_pool1d(
-  input,
-  kernel_size,
-  stride = NULL,
-  padding = 0,
-  ceil_mode = FALSE,
-  count_include_pad = TRUE
-)
    
+    
    nnf_avg_pool1d(
+  input,
+  kernel_size,
+  stride = NULL,
+  padding = 0,
+  ceil_mode = FALSE,
+  count_include_pad = TRUE
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_avg_pool2d.html b/reference/nnf_avg_pool2d.html
index 934d38fa8..be16992f3 100644
--- a/reference/nnf_avg_pool2d.html
+++ b/reference/nnf_avg_pool2d.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -74,7 +86,7 @@ input planes." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -147,6 +159,9 @@ input planes." />
     
  • 
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    • 
+    
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+      Serialization
+    
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@@ -160,7 +175,24 @@ input planes." />
     
    • 
   
 
-
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+
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+  
+  
+
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    • 
@@ -199,15 +231,15 @@ input planes." />
 input planes.
    
     
 
-    
    nnf_avg_pool2d(
-  input,
-  kernel_size,
-  stride = NULL,
-  padding = 0,
-  ceil_mode = FALSE,
-  count_include_pad = TRUE,
-  divisor_override = NULL
-)
    
+    
    nnf_avg_pool2d(
+  input,
+  kernel_size,
+  stride = NULL,
+  padding = 0,
+  ceil_mode = FALSE,
+  count_include_pad = TRUE,
+  divisor_override = NULL
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_avg_pool3d.html b/reference/nnf_avg_pool3d.html
index c064049a4..0ec141a79 100644
--- a/reference/nnf_avg_pool3d.html
+++ b/reference/nnf_avg_pool3d.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
+
+
 
   
 
@@ -74,7 +86,7 @@ size \(sT * sH * sW\) steps. The number of output features is equal to
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -147,6 +159,9 @@ size \(sT * sH * sW\) steps. The number of output features is equal to
     
  • 
       Tensor class
     
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+    
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+      Serialization
+    
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       Loading Data
@@ -160,7 +175,24 @@ size \(sT * sH * sW\) steps. The number of output features is equal to
     
    • 
   
 
-
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    • 
+
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+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -199,15 +231,15 @@ size \(sT * sH * sW\) steps. The number of output features is equal to
 \(\lfloor \frac{ \mbox{input planes} }{sT} \rfloor\).
    
     
 
-    
    nnf_avg_pool3d(
-  input,
-  kernel_size,
-  stride = NULL,
-  padding = 0,
-  ceil_mode = FALSE,
-  count_include_pad = TRUE,
-  divisor_override = NULL
-)
    
+    
    nnf_avg_pool3d(
+  input,
+  kernel_size,
+  stride = NULL,
+  padding = 0,
+  ceil_mode = FALSE,
+  count_include_pad = TRUE,
+  divisor_override = NULL
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_batch_norm.html b/reference/nnf_batch_norm.html
index b69160035..5747729f5 100644
--- a/reference/nnf_batch_norm.html
+++ b/reference/nnf_batch_norm.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
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@@ -158,7 +173,24 @@
     
    • 
   
 
-
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    • 
+
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+  
+  
+
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    • 
@@ -195,16 +227,16 @@
     
    Applies Batch Normalization for each channel across a batch of data.
    
     
 
-    
    nnf_batch_norm(
-  input,
-  running_mean,
-  running_var,
-  weight = NULL,
-  bias = NULL,
-  training = FALSE,
-  momentum = 0.1,
-  eps = 1e-05
-)
    
+    
    nnf_batch_norm(
+  input,
+  running_mean,
+  running_var,
+  weight = NULL,
+  bias = NULL,
+  training = FALSE,
+  momentum = 0.1,
+  eps = 1e-05
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_bilinear.html b/reference/nnf_bilinear.html
index 72032616e..9b93c9d7f 100644
--- a/reference/nnf_bilinear.html
+++ b/reference/nnf_bilinear.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
+
+
 
   
 
@@ -73,7 +85,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@
     
  • 
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    • 
+    
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+      Serialization
+    
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    • 
   
 
-
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    • 
+
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+  
+  
+
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    • 
@@ -197,7 +229,7 @@
 \(y = x_1 A x_2 + b\)
    
     
 
-    
    nnf_bilinear(input1, input2, weight, bias = NULL)
    
+    
    nnf_bilinear(input1, input2, weight, bias = NULL)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_binary_cross_entropy.html b/reference/nnf_binary_cross_entropy.html
index d4b1c2b7e..feaeada11 100644
--- a/reference/nnf_binary_cross_entropy.html
+++ b/reference/nnf_binary_cross_entropy.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -73,7 +85,7 @@ between the target and the output." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@ between the target and the output." />
     
  • 
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    • 
+    
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+      Serialization
+    
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@@ -159,7 +174,24 @@ between the target and the output." />
     
    • 
   
 
-
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    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,12 +229,12 @@ between the target and the output." />
 between the target and the output.
    
     
 
-    
    nnf_binary_cross_entropy(
-  input,
-  target,
-  weight = NULL,
-  reduction = c("mean", "sum", "none")
-)
    
+    
    nnf_binary_cross_entropy(
+  input,
+  target,
+  weight = NULL,
+  reduction = c("mean", "sum", "none")
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_binary_cross_entropy_with_logits.html b/reference/nnf_binary_cross_entropy_with_logits.html
index 7aedf2ba0..9d70a3b10 100644
--- a/reference/nnf_binary_cross_entropy_with_logits.html
+++ b/reference/nnf_binary_cross_entropy_with_logits.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -73,7 +85,7 @@ logits." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@ logits." />
     
  • 
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    • 
+    
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+      Serialization
+    
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@@ -159,7 +174,24 @@ logits." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,13 +229,13 @@ logits." />
 logits.
    
     
 
-    
    nnf_binary_cross_entropy_with_logits(
-  input,
-  target,
-  weight = NULL,
-  reduction = c("mean", "sum", "none"),
-  pos_weight = NULL
-)
    
+    
    nnf_binary_cross_entropy_with_logits(
+  input,
+  target,
+  weight = NULL,
+  reduction = c("mean", "sum", "none"),
+  pos_weight = NULL
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_celu.html b/reference/nnf_celu.html
index 71ae09d12..97feb36a3 100644
--- a/reference/nnf_celu.html
+++ b/reference/nnf_celu.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
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    • 
+    
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+      Serialization
+    
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    • 
   
 
-
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    • 
+
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+  
+  
+
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  • 
   Reference
 
    • 
@@ -195,9 +227,9 @@
     
    Applies element-wise, \(CELU(x) = max(0,x) + min(0, \alpha * (exp(x \alpha) - 1))\).
    
     
 
-    
    nnf_celu(input, alpha = 1, inplace = FALSE)
+    
    nnf_celu(input, alpha = 1, inplace = FALSE)
 
-nnf_celu_(input, alpha = 1)
    
+nnf_celu_(input, alpha = 1)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_conv1d.html b/reference/nnf_conv1d.html
index 5d76c0be8..74ba59b5d 100644
--- a/reference/nnf_conv1d.html
+++ b/reference/nnf_conv1d.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -73,7 +85,7 @@ planes." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@ planes." />
     
  • 
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    • 
+    
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+      Serialization
+    
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@@ -159,7 +174,24 @@ planes." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,15 +229,15 @@ planes." />
 planes.
    
     
 
-    
    nnf_conv1d(
-  input,
-  weight,
-  bias = NULL,
-  stride = 1,
-  padding = 0,
-  dilation = 1,
-  groups = 1
-)
    
+    
    nnf_conv1d(
+  input,
+  weight,
+  bias = NULL,
+  stride = 1,
+  padding = 0,
+  dilation = 1,
+  groups = 1
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_conv2d.html b/reference/nnf_conv2d.html
index e53d6faef..25c0e3814 100644
--- a/reference/nnf_conv2d.html
+++ b/reference/nnf_conv2d.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -73,7 +85,7 @@ planes." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@ planes." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ planes." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,15 +229,15 @@ planes." />
 planes.
    
     
 
-    
    nnf_conv2d(
-  input,
-  weight,
-  bias = NULL,
-  stride = 1,
-  padding = 0,
-  dilation = 1,
-  groups = 1
-)
    
+    
    nnf_conv2d(
+  input,
+  weight,
+  bias = NULL,
+  stride = 1,
+  padding = 0,
+  dilation = 1,
+  groups = 1
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_conv3d.html b/reference/nnf_conv3d.html
index 9a872f5b9..748e74624 100644
--- a/reference/nnf_conv3d.html
+++ b/reference/nnf_conv3d.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -73,7 +85,7 @@ planes." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@ planes." />
     
  • 
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    • 
+    
  • 
+      Serialization
+    
    • 
     
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    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ planes." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,15 +229,15 @@ planes." />
 planes.
    
     
 
-    
    nnf_conv3d(
-  input,
-  weight,
-  bias = NULL,
-  stride = 1,
-  padding = 0,
-  dilation = 1,
-  groups = 1
-)
    
+    
    nnf_conv3d(
+  input,
+  weight,
+  bias = NULL,
+  stride = 1,
+  padding = 0,
+  dilation = 1,
+  groups = 1
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_conv_tbc.html b/reference/nnf_conv_tbc.html
index 8c08e93eb..0bb107c88 100644
--- a/reference/nnf_conv_tbc.html
+++ b/reference/nnf_conv_tbc.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -73,7 +85,7 @@ Input and output dimensions are (Time, Batch, Channels) - hence TBC." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@ Input and output dimensions are (Time, Batch, Channels) - hence TBC." />
     
  • 
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    • 
+    
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+      Serialization
+    
    • 
     
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    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ Input and output dimensions are (Time, Batch, Channels) - hence TBC." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,7 +229,7 @@ Input and output dimensions are (Time, Batch, Channels) - hence TBC." />
 Input and output dimensions are (Time, Batch, Channels) - hence TBC.
    
     
 
-    
    nnf_conv_tbc(input, weight, bias, pad = 0)
    
+    
    nnf_conv_tbc(input, weight, bias, pad = 0)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_conv_transpose1d.html b/reference/nnf_conv_transpose1d.html
index c199dc336..6ecf3429a 100644
--- a/reference/nnf_conv_transpose1d.html
+++ b/reference/nnf_conv_transpose1d.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
+
+
 
   
 
@@ -73,7 +85,7 @@ composed of several input planes, sometimes also called "deconvolution"
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@ composed of several input planes, sometimes also called "deconvolution"
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ composed of several input planes, sometimes also called "deconvolution"
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,16 +229,16 @@ composed of several input planes, sometimes also called "deconvolution"
 composed of several input planes, sometimes also called "deconvolution".
    
     
 
-    
    nnf_conv_transpose1d(
-  input,
-  weight,
-  bias = NULL,
-  stride = 1,
-  padding = 0,
-  output_padding = 0,
-  groups = 1,
-  dilation = 1
-)
    
+    
    nnf_conv_transpose1d(
+  input,
+  weight,
+  bias = NULL,
+  stride = 1,
+  padding = 0,
+  output_padding = 0,
+  groups = 1,
+  dilation = 1
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_conv_transpose2d.html b/reference/nnf_conv_transpose2d.html
index 368764e35..5b5ca70e0 100644
--- a/reference/nnf_conv_transpose2d.html
+++ b/reference/nnf_conv_transpose2d.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
+
+
 
   
 
@@ -73,7 +85,7 @@ composed of several input planes, sometimes also called "deconvolution"
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@ composed of several input planes, sometimes also called "deconvolution"
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ composed of several input planes, sometimes also called "deconvolution"
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,16 +229,16 @@ composed of several input planes, sometimes also called "deconvolution"
 composed of several input planes, sometimes also called "deconvolution".
    
     
 
-    
    nnf_conv_transpose2d(
-  input,
-  weight,
-  bias = NULL,
-  stride = 1,
-  padding = 0,
-  output_padding = 0,
-  groups = 1,
-  dilation = 1
-)
    
+    
    nnf_conv_transpose2d(
+  input,
+  weight,
+  bias = NULL,
+  stride = 1,
+  padding = 0,
+  output_padding = 0,
+  groups = 1,
+  dilation = 1
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_conv_transpose3d.html b/reference/nnf_conv_transpose3d.html
index c18b0b124..b7a2ff134 100644
--- a/reference/nnf_conv_transpose3d.html
+++ b/reference/nnf_conv_transpose3d.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
+
+
 
   
 
@@ -73,7 +85,7 @@ composed of several input planes, sometimes also called "deconvolution"
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@ composed of several input planes, sometimes also called "deconvolution"
     
  • 
       Tensor class
     
    • 
+    
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+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ composed of several input planes, sometimes also called "deconvolution"
     
    • 
   
 
-
  • Examples
    • 
+
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+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,16 +229,16 @@ composed of several input planes, sometimes also called "deconvolution"
 composed of several input planes, sometimes also called "deconvolution"
    
     
 
-    
    nnf_conv_transpose3d(
-  input,
-  weight,
-  bias = NULL,
-  stride = 1,
-  padding = 0,
-  output_padding = 0,
-  groups = 1,
-  dilation = 1
-)
    
+    
    nnf_conv_transpose3d(
+  input,
+  weight,
+  bias = NULL,
+  stride = 1,
+  padding = 0,
+  output_padding = 0,
+  groups = 1,
+  dilation = 1
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_cosine_embedding_loss.html b/reference/nnf_cosine_embedding_loss.html
index f812a34b3..eb8566f89 100644
--- a/reference/nnf_cosine_embedding_loss.html
+++ b/reference/nnf_cosine_embedding_loss.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -75,7 +87,7 @@ for learning nonlinear embeddings or semi-supervised learning." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -148,6 +160,9 @@ for learning nonlinear embeddings or semi-supervised learning." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
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    • 
     
  • 
       Loading Data
@@ -161,7 +176,24 @@ for learning nonlinear embeddings or semi-supervised learning." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -201,13 +233,13 @@ are similar or dissimilar, using the cosine distance, and is typically used
 for learning nonlinear embeddings or semi-supervised learning.
    
     
 
-    
    nnf_cosine_embedding_loss(
-  input1,
-  input2,
-  target,
-  margin = 0,
-  reduction = c("mean", "sum", "none")
-)
    
+    
    nnf_cosine_embedding_loss(
+  input1,
+  input2,
+  target,
+  margin = 0,
+  reduction = c("mean", "sum", "none")
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_cosine_similarity.html b/reference/nnf_cosine_similarity.html
index 32359212d..9e229f8b6 100644
--- a/reference/nnf_cosine_similarity.html
+++ b/reference/nnf_cosine_similarity.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Returns cosine similarity between x1 and x2, computed along dim.
    
     
 
-    
    nnf_cosine_similarity(x1, x2, dim = 1, eps = 1e-08)
    
+    
    nnf_cosine_similarity(x1, x2, dim = 1, eps = 1e-08)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_cross_entropy.html b/reference/nnf_cross_entropy.html
index fec6ab54d..7a9813656 100644
--- a/reference/nnf_cross_entropy.html
+++ b/reference/nnf_cross_entropy.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -73,7 +85,7 @@ function." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@ function." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
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    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ function." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,13 +229,13 @@ function." />
 function.
    
     
 
-    
    nnf_cross_entropy(
-  input,
-  target,
-  weight = NULL,
-  ignore_index = -100,
-  reduction = c("mean", "sum", "none")
-)
    
+    
    nnf_cross_entropy(
+  input,
+  target,
+  weight = NULL,
+  ignore_index = -100,
+  reduction = c("mean", "sum", "none")
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_ctc_loss.html b/reference/nnf_ctc_loss.html
index 78800fb87..cb4912410 100644
--- a/reference/nnf_ctc_loss.html
+++ b/reference/nnf_ctc_loss.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,15 +227,15 @@
     
    The Connectionist Temporal Classification loss.
    
     
 
-    
    nnf_ctc_loss(
-  log_probs,
-  targets,
-  input_lengths,
-  target_lengths,
-  blank = 0,
-  reduction = c("mean", "sum", "none"),
-  zero_infinity = FALSE
-)
    
+    
    nnf_ctc_loss(
+  log_probs,
+  targets,
+  input_lengths,
+  target_lengths,
+  blank = 0,
+  reduction = c("mean", "sum", "none"),
+  zero_infinity = FALSE
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_dropout.html b/reference/nnf_dropout.html
index dac8fcc91..20360a00a 100644
--- a/reference/nnf_dropout.html
+++ b/reference/nnf_dropout.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -74,7 +86,7 @@ distribution." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -147,6 +159,9 @@ distribution." />
     
  • 
       Tensor class
     
    • 
+    
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+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -160,7 +175,24 @@ distribution." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -199,7 +231,7 @@ tensor with probability p using samples from a Bernoulli
 distribution.
    
     
 
-    
    nnf_dropout(input, p = 0.5, training = TRUE, inplace = FALSE)
    
+    
    nnf_dropout(input, p = 0.5, training = TRUE, inplace = FALSE)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_dropout2d.html b/reference/nnf_dropout2d.html
index 0a008f3b0..b197efd59 100644
--- a/reference/nnf_dropout2d.html
+++ b/reference/nnf_dropout2d.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -76,7 +88,7 @@ probability p using samples from a Bernoulli distribution." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -149,6 +161,9 @@ probability p using samples from a Bernoulli distribution." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
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    • 
     
  • 
       Loading Data
@@ -162,7 +177,24 @@ probability p using samples from a Bernoulli distribution." />
     
    • 
   
 
-
  • Examples
    • 
+
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+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -203,7 +235,7 @@ Each channel will be zeroed out independently on every forward call with
 probability p using samples from a Bernoulli distribution.
    
     
 
-    
    nnf_dropout2d(input, p = 0.5, training = TRUE, inplace = FALSE)
    
+    
    nnf_dropout2d(input, p = 0.5, training = TRUE, inplace = FALSE)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_dropout3d.html b/reference/nnf_dropout3d.html
index 417bbb3fa..90948421f 100644
--- a/reference/nnf_dropout3d.html
+++ b/reference/nnf_dropout3d.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -76,7 +88,7 @@ probability p using samples from a Bernoulli distribution." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -149,6 +161,9 @@ probability p using samples from a Bernoulli distribution." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
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    • 
     
  • 
       Loading Data
@@ -162,7 +177,24 @@ probability p using samples from a Bernoulli distribution." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -203,7 +235,7 @@ Each channel will be zeroed out independently on every forward call with
 probability p using samples from a Bernoulli distribution.
    
     
 
-    
    nnf_dropout3d(input, p = 0.5, training = TRUE, inplace = FALSE)
    
+    
    nnf_dropout3d(input, p = 0.5, training = TRUE, inplace = FALSE)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_elu.html b/reference/nnf_elu.html
index 6f7d757ee..8739683cd 100644
--- a/reference/nnf_elu.html
+++ b/reference/nnf_elu.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -73,7 +85,7 @@ $$ELU(x) = max(0,x) + min(0, \alpha * (exp(x) - 1))$$." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@ $$ELU(x) = max(0,x) + min(0, \alpha * (exp(x) - 1))$$." />
     
  • 
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    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
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       Loading Data
@@ -159,7 +174,24 @@ $$ELU(x) = max(0,x) + min(0, \alpha * (exp(x) - 1))$$." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,9 +229,9 @@ $$ELU(x) = max(0,x) + min(0, \alpha * (exp(x) - 1))$$." />
 $$ELU(x) = max(0,x) + min(0, \alpha * (exp(x) - 1))$$.
    
     
 
-    
    nnf_elu(input, alpha = 1, inplace = FALSE)
+    
    nnf_elu(input, alpha = 1, inplace = FALSE)
 
-nnf_elu_(input, alpha = 1)
    
+nnf_elu_(input, alpha = 1)
    
 
     
    Arguments
    
     
    
@@ -221,13 +253,13 @@ dimensions
    
 
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-x <- torch_randn(2, 2)
-y <- nnf_elu(x, alpha = 1)
-nnf_elu_(x, alpha = 1)
-torch_equal(x, y)
+    
    if (torch_is_installed()) {
+x <- torch_randn(2, 2)
+y <- nnf_elu(x, alpha = 1)
+nnf_elu_(x, alpha = 1)
+torch_equal(x, y)
 
-}
+}
 
    #> [1] TRUE
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
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    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,15 +227,15 @@
     
    A simple lookup table that looks up embeddings in a fixed dictionary and size.
    
     
 
-    
    nnf_embedding(
-  input,
-  weight,
-  padding_idx = NULL,
-  max_norm = NULL,
-  norm_type = 2,
-  scale_grad_by_freq = FALSE,
-  sparse = FALSE
-)
    
+    
    nnf_embedding(
+  input,
+  weight,
+  padding_idx = NULL,
+  max_norm = NULL,
+  norm_type = 2,
+  scale_grad_by_freq = FALSE,
+  sparse = FALSE
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_embedding_bag.html b/reference/nnf_embedding_bag.html
index fb3d738e2..006f65ba9 100644
--- a/reference/nnf_embedding_bag.html
+++ b/reference/nnf_embedding_bag.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -73,7 +85,7 @@ intermediate embeddings." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@ intermediate embeddings." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ intermediate embeddings." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,18 +229,18 @@ intermediate embeddings." />
 intermediate embeddings.
    
     
 
-    
    nnf_embedding_bag(
-  input,
-  weight,
-  offsets = NULL,
-  max_norm = NULL,
-  norm_type = 2,
-  scale_grad_by_freq = FALSE,
-  mode = "mean",
-  sparse = FALSE,
-  per_sample_weights = NULL,
-  include_last_offset = FALSE
-)
    
+    
    nnf_embedding_bag(
+  input,
+  weight,
+  offsets = NULL,
+  max_norm = NULL,
+  norm_type = 2,
+  scale_grad_by_freq = FALSE,
+  mode = "mean",
+  sparse = FALSE,
+  per_sample_weights = NULL,
+  include_last_offset = FALSE
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_fold.html b/reference/nnf_fold.html
index 6ac948878..9c46846db 100644
--- a/reference/nnf_fold.html
+++ b/reference/nnf_fold.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -73,7 +85,7 @@ tensor." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@ tensor." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ tensor." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,14 +229,14 @@ tensor." />
 tensor.
    
     
 
-    
    nnf_fold(
-  input,
-  output_size,
-  kernel_size,
-  dilation = 1,
-  padding = 0,
-  stride = 1
-)
    
+    
    nnf_fold(
+  input,
+  output_size,
+  kernel_size,
+  dilation = 1,
+  padding = 0,
+  stride = 1
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_fractional_max_pool2d.html b/reference/nnf_fractional_max_pool2d.html
index a7afbdf5b..866ffe5fb 100644
--- a/reference/nnf_fractional_max_pool2d.html
+++ b/reference/nnf_fractional_max_pool2d.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,14 +227,14 @@
     
    Applies 2D fractional max pooling over an input signal composed of several input planes.
    
     
 
-    
    nnf_fractional_max_pool2d(
-  input,
-  kernel_size,
-  output_size = NULL,
-  output_ratio = NULL,
-  return_indices = FALSE,
-  random_samples = NULL
-)
    
+    
    nnf_fractional_max_pool2d(
+  input,
+  kernel_size,
+  output_size = NULL,
+  output_ratio = NULL,
+  return_indices = FALSE,
+  random_samples = NULL
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_fractional_max_pool3d.html b/reference/nnf_fractional_max_pool3d.html
index 09dfbc31b..a67f4fef3 100644
--- a/reference/nnf_fractional_max_pool3d.html
+++ b/reference/nnf_fractional_max_pool3d.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,14 +227,14 @@
     
    Applies 3D fractional max pooling over an input signal composed of several input planes.
    
     
 
-    
    nnf_fractional_max_pool3d(
-  input,
-  kernel_size,
-  output_size = NULL,
-  output_ratio = NULL,
-  return_indices = FALSE,
-  random_samples = NULL
-)
    
+    
    nnf_fractional_max_pool3d(
+  input,
+  kernel_size,
+  output_size = NULL,
+  output_ratio = NULL,
+  return_indices = FALSE,
+  random_samples = NULL
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_gelu.html b/reference/nnf_gelu.html
index 78acd4728..745816e44 100644
--- a/reference/nnf_gelu.html
+++ b/reference/nnf_gelu.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Gelu
    
     
 
-    
    nnf_gelu(input)
    
+    
    nnf_gelu(input)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_glu.html b/reference/nnf_glu.html
index b9bba4760..c4cb306eb 100644
--- a/reference/nnf_glu.html
+++ b/reference/nnf_glu.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    The gated linear unit. Computes:
    
     
 
-    
    nnf_glu(input, dim = -1)
    
+    
    nnf_glu(input, dim = -1)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_grid_sample.html b/reference/nnf_grid_sample.html
index c1de2a986..68f299d66 100644
--- a/reference/nnf_grid_sample.html
+++ b/reference/nnf_grid_sample.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -73,7 +85,7 @@ output using input values and pixel locations from grid." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@ output using input values and pixel locations from grid." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ output using input values and pixel locations from grid." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,13 +229,13 @@ output using input values and pixel locations from grid." />
 output using input values and pixel locations from grid.
    
     
 
-    
    nnf_grid_sample(
-  input,
-  grid,
-  mode = c("bilinear", "nearest"),
-  padding_mode = c("zeros", "border", "reflection"),
-  align_corners = FALSE
-)
    
+    
    nnf_grid_sample(
+  input,
+  grid,
+  mode = c("bilinear", "nearest"),
+  padding_mode = c("zeros", "border", "reflection"),
+  align_corners = FALSE
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_group_norm.html b/reference/nnf_group_norm.html
index 16e20f9e1..bfdddcb56 100644
--- a/reference/nnf_group_norm.html
+++ b/reference/nnf_group_norm.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
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+      Serialization
+    
    • 
     
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    • 
     
  • 
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@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
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+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Applies Group Normalization for last certain number of dimensions.
    
     
 
-    
    nnf_group_norm(input, num_groups, weight = NULL, bias = NULL, eps = 1e-05)
    
+    
    nnf_group_norm(input, num_groups, weight = NULL, bias = NULL, eps = 1e-05)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_gumbel_softmax.html b/reference/nnf_gumbel_softmax.html
index dabf65303..fae2649d8 100644
--- a/reference/nnf_gumbel_softmax.html
+++ b/reference/nnf_gumbel_softmax.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -73,7 +85,7 @@ optionally discretizes." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@ optionally discretizes." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
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    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ optionally discretizes." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,7 +229,7 @@ optionally discretizes." />
 optionally discretizes.
    
     
 
-    
    nnf_gumbel_softmax(logits, tau = 1, hard = FALSE, dim = -1)
    
+    
    nnf_gumbel_softmax(logits, tau = 1, hard = FALSE, dim = -1)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_hardshrink.html b/reference/nnf_hardshrink.html
index 633de3e93..684b9cc6c 100644
--- a/reference/nnf_hardshrink.html
+++ b/reference/nnf_hardshrink.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
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+      Serialization
+    
    • 
     
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@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Applies the hard shrinkage function element-wise
    
     
 
-    
    nnf_hardshrink(input, lambd = 0.5)
    
+    
    nnf_hardshrink(input, lambd = 0.5)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_hardsigmoid.html b/reference/nnf_hardsigmoid.html
index 9c3a74759..334babfd8 100644
--- a/reference/nnf_hardsigmoid.html
+++ b/reference/nnf_hardsigmoid.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
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    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Applies the element-wise function \(\mbox{Hardsigmoid}(x) = \frac{ReLU6(x + 3)}{6}\)
    
     
 
-    
    nnf_hardsigmoid(input, inplace = FALSE)
    
+    
    nnf_hardsigmoid(input, inplace = FALSE)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_hardswish.html b/reference/nnf_hardswish.html
index 3d969fa2a..4251a6d54 100644
--- a/reference/nnf_hardswish.html
+++ b/reference/nnf_hardswish.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -73,7 +85,7 @@ Searching for MobileNetV3." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@ Searching for MobileNetV3." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
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    • 
     
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@@ -159,7 +174,24 @@ Searching for MobileNetV3." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,7 +229,7 @@ Searching for MobileNetV3." />
 Searching for MobileNetV3.
    
     
 
-    
    nnf_hardswish(input, inplace = FALSE)
    
+    
    nnf_hardswish(input, inplace = FALSE)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_hardtanh.html b/reference/nnf_hardtanh.html
index cc6e8c708..44359ae1a 100644
--- a/reference/nnf_hardtanh.html
+++ b/reference/nnf_hardtanh.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
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    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,9 +227,9 @@
     
    Applies the HardTanh function element-wise.
    
     
 
-    
    nnf_hardtanh(input, min_val = -1, max_val = 1, inplace = FALSE)
+    
    nnf_hardtanh(input, min_val = -1, max_val = 1, inplace = FALSE)
 
-nnf_hardtanh_(input, min_val = -1, max_val = 1)
    
+nnf_hardtanh_(input, min_val = -1, max_val = 1)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_hinge_embedding_loss.html b/reference/nnf_hinge_embedding_loss.html
index e7c6a0e6e..3728e6301 100644
--- a/reference/nnf_hinge_embedding_loss.html
+++ b/reference/nnf_hinge_embedding_loss.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -75,7 +87,7 @@ embeddings or semi-supervised learning." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -148,6 +160,9 @@ embeddings or semi-supervised learning." />
     
  • 
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    • 
+    
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+      Serialization
+    
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@@ -161,7 +176,24 @@ embeddings or semi-supervised learning." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -201,7 +233,7 @@ using the L1 pairwise distance as xx , and is typically used for learning nonlin
 embeddings or semi-supervised learning.
    
     
 
-    
    nnf_hinge_embedding_loss(input, target, margin = 1, reduction = "mean")
    
+    
    nnf_hinge_embedding_loss(input, target, margin = 1, reduction = "mean")
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_instance_norm.html b/reference/nnf_instance_norm.html
index 3b30820f5..c5236cf5c 100644
--- a/reference/nnf_instance_norm.html
+++ b/reference/nnf_instance_norm.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -73,7 +85,7 @@ batch." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@ batch." />
     
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+    
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+    
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@@ -159,7 +174,24 @@ batch." />
     
    • 
   
 
-
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    • 
+
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+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,16 +229,16 @@ batch." />
 batch.
    
     
 
-    
    nnf_instance_norm(
-  input,
-  running_mean = NULL,
-  running_var = NULL,
-  weight = NULL,
-  bias = NULL,
-  use_input_stats = TRUE,
-  momentum = 0.1,
-  eps = 1e-05
-)
    
+    
    nnf_instance_norm(
+  input,
+  running_mean = NULL,
+  running_var = NULL,
+  weight = NULL,
+  bias = NULL,
+  use_input_stats = TRUE,
+  momentum = 0.1,
+  eps = 1e-05
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_interpolate.html b/reference/nnf_interpolate.html
index 2606ca3be..b0ffa4f63 100644
--- a/reference/nnf_interpolate.html
+++ b/reference/nnf_interpolate.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -73,7 +85,7 @@ scale_factor" />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@ scale_factor" />
     
  • 
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    • 
+    
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+      Serialization
+    
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    • 
     
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@@ -159,7 +174,24 @@ scale_factor" />
     
    • 
   
 
-
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    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,14 +229,14 @@ scale_factor" />
 scale_factor
    
     
 
-    
    nnf_interpolate(
-  input,
-  size = NULL,
-  scale_factor = NULL,
-  mode = "nearest",
-  align_corners = FALSE,
-  recompute_scale_factor = NULL
-)
    
+    
    nnf_interpolate(
+  input,
+  size = NULL,
+  scale_factor = NULL,
+  mode = "nearest",
+  align_corners = FALSE,
+  recompute_scale_factor = NULL
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_kl_div.html b/reference/nnf_kl_div.html
index d90a75f01..c67ba8d48 100644
--- a/reference/nnf_kl_div.html
+++ b/reference/nnf_kl_div.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
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+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    The Kullback-Leibler divergence Loss.
    
     
 
-    
    nnf_kl_div(input, target, reduction = "mean")
    
+    
    nnf_kl_div(input, target, reduction = "mean")
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_l1_loss.html b/reference/nnf_l1_loss.html
index 790dee1b2..6b22f0821 100644
--- a/reference/nnf_l1_loss.html
+++ b/reference/nnf_l1_loss.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
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+      Serialization
+    
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    • 
     
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@@ -158,7 +173,24 @@
     
    • 
   
 
-
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    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Function that takes the mean element-wise absolute value difference.
    
     
 
-    
    nnf_l1_loss(input, target, reduction = "mean")
    
+    
    nnf_l1_loss(input, target, reduction = "mean")
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_layer_norm.html b/reference/nnf_layer_norm.html
index b9ba41fab..843b04bfb 100644
--- a/reference/nnf_layer_norm.html
+++ b/reference/nnf_layer_norm.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
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+      Serialization
+    
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@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,13 +227,13 @@
     
    Applies Layer Normalization for last certain number of dimensions.
    
     
 
-    
    nnf_layer_norm(
-  input,
-  normalized_shape,
-  weight = NULL,
-  bias = NULL,
-  eps = 1e-05
-)
    
+    
    nnf_layer_norm(
+  input,
+  normalized_shape,
+  weight = NULL,
+  bias = NULL,
+  eps = 1e-05
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_leaky_relu.html b/reference/nnf_leaky_relu.html
index f5bcbf254..37113825c 100644
--- a/reference/nnf_leaky_relu.html
+++ b/reference/nnf_leaky_relu.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
+
+
 
   
 
@@ -73,7 +85,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@
     
  • 
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    • 
+    
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+      Serialization
+    
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@@ -159,7 +174,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,7 +229,7 @@
 \(LeakyReLU(x) = max(0, x) + negative_slope * min(0, x)\)
    
     
 
-    
    nnf_leaky_relu(input, negative_slope = 0.01, inplace = FALSE)
    
+    
    nnf_leaky_relu(input, negative_slope = 0.01, inplace = FALSE)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_linear.html b/reference/nnf_linear.html
index 45d222cf2..1578f3a06 100644
--- a/reference/nnf_linear.html
+++ b/reference/nnf_linear.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Applies a linear transformation to the incoming data: \(y = xA^T + b\).
    
     
 
-    
    nnf_linear(input, weight, bias = NULL)
    
+    
    nnf_linear(input, weight, bias = NULL)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_local_response_norm.html b/reference/nnf_local_response_norm.html
index 612a9ee52..935ff7855 100644
--- a/reference/nnf_local_response_norm.html
+++ b/reference/nnf_local_response_norm.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -74,7 +86,7 @@ Applies normalization across channels." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -147,6 +159,9 @@ Applies normalization across channels." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
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    • 
     
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@@ -160,7 +175,24 @@ Applies normalization across channels." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -199,7 +231,7 @@ several input planes, where channels occupy the second dimension.
 Applies normalization across channels.
    
     
 
-    
    nnf_local_response_norm(input, size, alpha = 1e-04, beta = 0.75, k = 1)
    
+    
    nnf_local_response_norm(input, size, alpha = 1e-04, beta = 0.75, k = 1)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_log_softmax.html b/reference/nnf_log_softmax.html
index f29faf0e3..8787934ba 100644
--- a/reference/nnf_log_softmax.html
+++ b/reference/nnf_log_softmax.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
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       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
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    • 
+
  • 
+  
+  
+
    • 
 
  • 
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    • 
@@ -195,7 +227,7 @@
     
    Applies a softmax followed by a logarithm.
    
     
 
-    
    nnf_log_softmax(input, dim = NULL, dtype = NULL)
    
+    
    nnf_log_softmax(input, dim = NULL, dtype = NULL)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_logsigmoid.html b/reference/nnf_logsigmoid.html
index 5c2b14712..4d382827f 100644
--- a/reference/nnf_logsigmoid.html
+++ b/reference/nnf_logsigmoid.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
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    • 
     
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       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
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+  
+  
+
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  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Applies element-wise \(LogSigmoid(x_i) = log(\frac{1}{1 + exp(-x_i)})\)
    
     
 
-    
    nnf_logsigmoid(input)
    
+    
    nnf_logsigmoid(input)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_lp_pool1d.html b/reference/nnf_lp_pool1d.html
index 70abd1fc2..a15b02649 100644
--- a/reference/nnf_lp_pool1d.html
+++ b/reference/nnf_lp_pool1d.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -74,7 +86,7 @@ zero, the gradient is set to zero as well." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -147,6 +159,9 @@ zero, the gradient is set to zero as well." />
     
  • 
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    • 
+    
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+      Serialization
+    
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    • 
     
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@@ -160,7 +175,24 @@ zero, the gradient is set to zero as well." />
     
    • 
   
 
-
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    • 
+
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+  
+  
+
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  • 
   Reference
 
    • 
@@ -199,7 +231,7 @@ several input planes. If the sum of all inputs to the power of p is
 zero, the gradient is set to zero as well.
    
     
 
-    
    nnf_lp_pool1d(input, norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)
    
+    
    nnf_lp_pool1d(input, norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_lp_pool2d.html b/reference/nnf_lp_pool2d.html
index d8f53a49d..0215ea2cd 100644
--- a/reference/nnf_lp_pool2d.html
+++ b/reference/nnf_lp_pool2d.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -74,7 +86,7 @@ zero, the gradient is set to zero as well." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -147,6 +159,9 @@ zero, the gradient is set to zero as well." />
     
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@@ -160,7 +175,24 @@ zero, the gradient is set to zero as well." />
     
    • 
   
 
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+  
+  
+
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    • 
@@ -199,7 +231,7 @@ several input planes. If the sum of all inputs to the power of p is
 zero, the gradient is set to zero as well.
    
     
 
-    
    nnf_lp_pool2d(input, norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)
    
+    
    nnf_lp_pool2d(input, norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_margin_ranking_loss.html b/reference/nnf_margin_ranking_loss.html
index 02a370062..0ac1cc43c 100644
--- a/reference/nnf_margin_ranking_loss.html
+++ b/reference/nnf_margin_ranking_loss.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -73,7 +85,7 @@ mini-batch Tensors, and a label 1D mini-batch tensor y (containing 1 or -1)." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@ mini-batch Tensors, and a label 1D mini-batch tensor y (containing 1 or -1)." />
     
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+    
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@@ -159,7 +174,24 @@ mini-batch Tensors, and a label 1D mini-batch tensor y (containing 1 or -1)." />
     
    • 
   
 
-
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+
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+  
+  
+
    • 
 
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    • 
@@ -197,7 +229,7 @@ mini-batch Tensors, and a label 1D mini-batch tensor y (containing 1 or -1)." />
 mini-batch Tensors, and a label 1D mini-batch tensor y (containing 1 or -1).
    
     
 
-    
    nnf_margin_ranking_loss(input1, input2, target, margin = 0, reduction = "mean")
    
+    
    nnf_margin_ranking_loss(input1, input2, target, margin = 0, reduction = "mean")
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_max_pool1d.html b/reference/nnf_max_pool1d.html
index 0663c6e52..e18b04c1b 100644
--- a/reference/nnf_max_pool1d.html
+++ b/reference/nnf_max_pool1d.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -73,7 +85,7 @@ planes." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@ planes." />
     
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    • 
+    
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+      Serialization
+    
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@@ -159,7 +174,24 @@ planes." />
     
    • 
   
 
-
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    • 
+
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+  
+  
+
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  • 
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    • 
@@ -197,15 +229,15 @@ planes." />
 planes.
    
     
 
-    
    nnf_max_pool1d(
-  input,
-  kernel_size,
-  stride = NULL,
-  padding = 0,
-  dilation = 1,
-  ceil_mode = FALSE,
-  return_indices = FALSE
-)
    
+    
    nnf_max_pool1d(
+  input,
+  kernel_size,
+  stride = NULL,
+  padding = 0,
+  dilation = 1,
+  ceil_mode = FALSE,
+  return_indices = FALSE
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_max_pool2d.html b/reference/nnf_max_pool2d.html
index f7dafcb09..bc4dab03e 100644
--- a/reference/nnf_max_pool2d.html
+++ b/reference/nnf_max_pool2d.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -73,7 +85,7 @@ planes." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@ planes." />
     
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@@ -159,7 +174,24 @@ planes." />
     
    • 
   
 
-
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+
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+  
+  
+
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    • 
@@ -197,15 +229,15 @@ planes." />
 planes.
    
     
 
-    
    nnf_max_pool2d(
-  input,
-  kernel_size,
-  stride = kernel_size,
-  padding = 0,
-  dilation = 1,
-  ceil_mode = FALSE,
-  return_indices = FALSE
-)
    
+    
    nnf_max_pool2d(
+  input,
+  kernel_size,
+  stride = kernel_size,
+  padding = 0,
+  dilation = 1,
+  ceil_mode = FALSE,
+  return_indices = FALSE
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_max_pool3d.html b/reference/nnf_max_pool3d.html
index 840ffcf2e..0c0307e9a 100644
--- a/reference/nnf_max_pool3d.html
+++ b/reference/nnf_max_pool3d.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -73,7 +85,7 @@ planes." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@ planes." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
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    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ planes." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,15 +229,15 @@ planes." />
 planes.
    
     
 
-    
    nnf_max_pool3d(
-  input,
-  kernel_size,
-  stride = NULL,
-  padding = 0,
-  dilation = 1,
-  ceil_mode = FALSE,
-  return_indices = FALSE
-)
    
+    
    nnf_max_pool3d(
+  input,
+  kernel_size,
+  stride = NULL,
+  padding = 0,
+  dilation = 1,
+  ceil_mode = FALSE,
+  return_indices = FALSE
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_max_unpool1d.html b/reference/nnf_max_unpool1d.html
index 721c31111..447477659 100644
--- a/reference/nnf_max_unpool1d.html
+++ b/reference/nnf_max_unpool1d.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
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    • 
+    
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+      Serialization
+    
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@@ -158,7 +173,24 @@
     
    • 
   
 
-
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    • 
+
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+  
+  
+
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    • 
@@ -195,14 +227,14 @@
     
    Computes a partial inverse of MaxPool1d.
    
     
 
-    
    nnf_max_unpool1d(
-  input,
-  indices,
-  kernel_size,
-  stride = NULL,
-  padding = 0,
-  output_size = NULL
-)
    
+    
    nnf_max_unpool1d(
+  input,
+  indices,
+  kernel_size,
+  stride = NULL,
+  padding = 0,
+  output_size = NULL
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_max_unpool2d.html b/reference/nnf_max_unpool2d.html
index eaeb2fe6c..f456e186e 100644
--- a/reference/nnf_max_unpool2d.html
+++ b/reference/nnf_max_unpool2d.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
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    • 
+    
  • 
+      Serialization
+    
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@@ -158,7 +173,24 @@
     
    • 
   
 
-
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    • 
+
  • 
+  
+  
+
    • 
 
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    • 
@@ -195,14 +227,14 @@
     
    Computes a partial inverse of MaxPool2d.
    
     
 
-    
    nnf_max_unpool2d(
-  input,
-  indices,
-  kernel_size,
-  stride = NULL,
-  padding = 0,
-  output_size = NULL
-)
    
+    
    nnf_max_unpool2d(
+  input,
+  indices,
+  kernel_size,
+  stride = NULL,
+  padding = 0,
+  output_size = NULL
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_max_unpool3d.html b/reference/nnf_max_unpool3d.html
index 65bb334cc..eadabab82 100644
--- a/reference/nnf_max_unpool3d.html
+++ b/reference/nnf_max_unpool3d.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
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@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,14 +227,14 @@
     
    Computes a partial inverse of MaxPool3d.
    
     
 
-    
    nnf_max_unpool3d(
-  input,
-  indices,
-  kernel_size,
-  stride = NULL,
-  padding = 0,
-  output_size = NULL
-)
    
+    
    nnf_max_unpool3d(
+  input,
+  indices,
+  kernel_size,
+  stride = NULL,
+  padding = 0,
+  output_size = NULL
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_mse_loss.html b/reference/nnf_mse_loss.html
index 39a803971..8db1da589 100644
--- a/reference/nnf_mse_loss.html
+++ b/reference/nnf_mse_loss.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
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+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
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    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Measures the element-wise mean squared error.
    
     
 
-    
    nnf_mse_loss(input, target, reduction = "mean")
    
+    
    nnf_mse_loss(input, target, reduction = "mean")
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_multi_head_attention_forward.html b/reference/nnf_multi_head_attention_forward.html
index f4e6e3428..e11a51cb2 100644
--- a/reference/nnf_multi_head_attention_forward.html
+++ b/reference/nnf_multi_head_attention_forward.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -73,7 +85,7 @@ subspaces. See reference: Attention Is All You Need" />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@ subspaces. See reference: Attention Is All You Need" />
     
  • 
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    • 
+    
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+      Serialization
+    
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    • 
     
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@@ -159,7 +174,24 @@ subspaces. See reference: Attention Is All You Need" />
     
    • 
   
 
-
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    • 
+
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+  
+  
+
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  • 
   Reference
 
    • 
@@ -197,31 +229,31 @@ subspaces. See reference: Attention Is All You Need" />
 subspaces. See reference: Attention Is All You Need
    
     
 
-    
    nnf_multi_head_attention_forward(
-  query,
-  key,
-  value,
-  embed_dim_to_check,
-  num_heads,
-  in_proj_weight,
-  in_proj_bias,
-  bias_k,
-  bias_v,
-  add_zero_attn,
-  dropout_p,
-  out_proj_weight,
-  out_proj_bias,
-  training = TRUE,
-  key_padding_mask = NULL,
-  need_weights = TRUE,
-  attn_mask = NULL,
-  use_separate_proj_weight = FALSE,
-  q_proj_weight = NULL,
-  k_proj_weight = NULL,
-  v_proj_weight = NULL,
-  static_k = NULL,
-  static_v = NULL
-)
    
+    
    nnf_multi_head_attention_forward(
+  query,
+  key,
+  value,
+  embed_dim_to_check,
+  num_heads,
+  in_proj_weight,
+  in_proj_bias,
+  bias_k,
+  bias_v,
+  add_zero_attn,
+  dropout_p,
+  out_proj_weight,
+  out_proj_bias,
+  training = TRUE,
+  key_padding_mask = NULL,
+  need_weights = TRUE,
+  attn_mask = NULL,
+  use_separate_proj_weight = FALSE,
+  q_proj_weight = NULL,
+  k_proj_weight = NULL,
+  v_proj_weight = NULL,
+  static_k = NULL,
+  static_v = NULL
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_multi_margin_loss.html b/reference/nnf_multi_margin_loss.html
index 11526ddf4..b610bc67e 100644
--- a/reference/nnf_multi_margin_loss.html
+++ b/reference/nnf_multi_margin_loss.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
+
+
 
   
 
@@ -74,7 +86,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -147,6 +159,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
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    • 
     
  • 
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@@ -160,7 +175,24 @@
     
    • 
   
 
-
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    • 
+
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+  
+  
+
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  • 
   Reference
 
    • 
@@ -199,14 +231,14 @@
 (which is a 1D tensor of target class indices, 0 <= y <= x$size(2) - 1 ).
    
     
 
-    
    nnf_multi_margin_loss(
-  input,
-  target,
-  p = 1,
-  margin = 1,
-  weight = NULL,
-  reduction = "mean"
-)
    
+    
    nnf_multi_margin_loss(
+  input,
+  target,
+  p = 1,
+  margin = 1,
+  weight = NULL,
+  reduction = "mean"
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_multilabel_margin_loss.html b/reference/nnf_multilabel_margin_loss.html
index 8281d5e2f..70df47978 100644
--- a/reference/nnf_multilabel_margin_loss.html
+++ b/reference/nnf_multilabel_margin_loss.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -74,7 +86,7 @@ is a 2D Tensor of target class indices)." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -147,6 +159,9 @@ is a 2D Tensor of target class indices)." />
     
  • 
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    • 
+    
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+      Serialization
+    
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    • 
     
  • 
       Loading Data
@@ -160,7 +175,24 @@ is a 2D Tensor of target class indices)." />
     
    • 
   
 
-
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    • 
+
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+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -199,7 +231,7 @@ is a 2D Tensor of target class indices)." />
 is a 2D Tensor of target class indices).
    
     
 
-    
    nnf_multilabel_margin_loss(input, target, reduction = "mean")
    
+    
    nnf_multilabel_margin_loss(input, target, reduction = "mean")
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_multilabel_soft_margin_loss.html b/reference/nnf_multilabel_soft_margin_loss.html
index 0150c00f4..b1ad73cc0 100644
--- a/reference/nnf_multilabel_soft_margin_loss.html
+++ b/reference/nnf_multilabel_soft_margin_loss.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -73,7 +85,7 @@ max-entropy, between input x and target y of size (N, C)." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@ max-entropy, between input x and target y of size (N, C)." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
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    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ max-entropy, between input x and target y of size (N, C)." />
     
    • 
   
 
-
  • Examples
    • 
+
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+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,7 +229,7 @@ max-entropy, between input x and target y of size (N, C)." />
 max-entropy, between input x and target y of size (N, C).
    
     
 
-    
    nnf_multilabel_soft_margin_loss(input, target, weight, reduction = "mean")
    
+    
    nnf_multilabel_soft_margin_loss(input, target, weight, reduction = "mean")
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_nll_loss.html b/reference/nnf_nll_loss.html
index 96ba14ba4..097398f22 100644
--- a/reference/nnf_nll_loss.html
+++ b/reference/nnf_nll_loss.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,13 +227,13 @@
     
    The negative log likelihood loss.
    
     
 
-    
    nnf_nll_loss(
-  input,
-  target,
-  weight = NULL,
-  ignore_index = -100,
-  reduction = "mean"
-)
    
+    
    nnf_nll_loss(
+  input,
+  target,
+  weight = NULL,
+  ignore_index = -100,
+  reduction = "mean"
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_normalize.html b/reference/nnf_normalize.html
index 28efa2deb..d675623ec 100644
--- a/reference/nnf_normalize.html
+++ b/reference/nnf_normalize.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Performs \(L_p\) normalization of inputs over specified dimension.
    
     
 
-    
    nnf_normalize(input, p = 2, dim = 1, eps = 1e-12, out = NULL)
    
+    
    nnf_normalize(input, p = 2, dim = 1, eps = 1e-12, out = NULL)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_one_hot.html b/reference/nnf_one_hot.html
index 7ffec6383..9794e5417 100644
--- a/reference/nnf_one_hot.html
+++ b/reference/nnf_one_hot.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -75,7 +87,7 @@ in which case it will be 1." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -148,6 +160,9 @@ in which case it will be 1." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
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    • 
     
  • 
       Loading Data
@@ -161,7 +176,24 @@ in which case it will be 1." />
     
    • 
   
 
-
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    • 
+
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+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -201,7 +233,7 @@ index of last dimension matches the corresponding value of the input tensor,
 in which case it will be 1.
    
     
 
-    
    nnf_one_hot(tensor, num_classes = -1)
    
+    
    nnf_one_hot(tensor, num_classes = -1)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_pad.html b/reference/nnf_pad.html
index aeb442200..7e7a34f6e 100644
--- a/reference/nnf_pad.html
+++ b/reference/nnf_pad.html
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+  
 
 
 
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-        0.0.3
+        0.1.0
       
     
 
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    • 
@@ -195,7 +227,7 @@
     
    Pads tensor.
    
     
 
-    
    nnf_pad(input, pad, mode = "constant", value = 0)
    
+    
    nnf_pad(input, pad, mode = "constant", value = 0)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_pairwise_distance.html b/reference/nnf_pairwise_distance.html
index 2c2042727..de1c5ee10 100644
--- a/reference/nnf_pairwise_distance.html
+++ b/reference/nnf_pairwise_distance.html
@@ -38,6 +38,8 @@
 
 
 
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+  
 
 
 
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+        0.1.0
       
     
 
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    Computes the batchwise pairwise distance between vectors using the p-norm.
    
     
 
-    
    nnf_pairwise_distance(x1, x2, p = 2, eps = 1e-06, keepdim = FALSE)
    
+    
    nnf_pairwise_distance(x1, x2, p = 2, eps = 1e-06, keepdim = FALSE)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_pdist.html b/reference/nnf_pdist.html
index b383fc008..1be497220 100644
--- a/reference/nnf_pdist.html
+++ b/reference/nnf_pdist.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
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@@ -75,7 +87,7 @@ if the rows are contiguous." />
       
       
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-        0.0.3
+        0.1.0
       
     
 
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    • 
@@ -201,7 +233,7 @@ This is identical to the upper triangular portion, excluding the diagonal, of
 if the rows are contiguous.
    
     
 
-    
    nnf_pdist(input, p = 2)
    
+    
    nnf_pdist(input, p = 2)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_pixel_shuffle.html b/reference/nnf_pixel_shuffle.html
index 2024598b7..bec154287 100644
--- a/reference/nnf_pixel_shuffle.html
+++ b/reference/nnf_pixel_shuffle.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
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+
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@@ -73,7 +85,7 @@ tensor of shape \((*, C, H \times r, W \times r)\)." />
       
       
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+        0.1.0
       
     
 
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 tensor of shape \((*, C, H \times r, W \times r)\).
    
     
 
-    
    nnf_pixel_shuffle(input, upscale_factor)
    
+    
    nnf_pixel_shuffle(input, upscale_factor)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_poisson_nll_loss.html b/reference/nnf_poisson_nll_loss.html
index 4212a07df..fd330c6bb 100644
--- a/reference/nnf_poisson_nll_loss.html
+++ b/reference/nnf_poisson_nll_loss.html
@@ -38,6 +38,8 @@
 
 
 
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+  
 
 
 
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+        0.1.0
       
     
 
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    • 
@@ -195,14 +227,14 @@
     
    Poisson negative log likelihood loss.
    
     
 
-    
    nnf_poisson_nll_loss(
-  input,
-  target,
-  log_input = TRUE,
-  full = FALSE,
-  eps = 1e-08,
-  reduction = "mean"
-)
    
+    
    nnf_poisson_nll_loss(
+  input,
+  target,
+  log_input = TRUE,
+  full = FALSE,
+  eps = 1e-08,
+  reduction = "mean"
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_prelu.html b/reference/nnf_prelu.html
index 6b6f6c6ff..0c97d7881 100644
--- a/reference/nnf_prelu.html
+++ b/reference/nnf_prelu.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
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@@ -74,7 +86,7 @@ where weight is a learnable parameter." />
       
       
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-        0.0.3
+        0.1.0
       
     
 
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    • 
@@ -199,7 +231,7 @@ where weight is a learnable parameter." />
 where weight is a learnable parameter.
    
     
 
-    
    nnf_prelu(input, weight)
    
+    
    nnf_prelu(input, weight)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_relu.html b/reference/nnf_relu.html
index 529454def..c0eadc5c7 100644
--- a/reference/nnf_relu.html
+++ b/reference/nnf_relu.html
@@ -38,6 +38,8 @@
 
 
 
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+  
 
 
 
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-        0.0.3
+        0.1.0
       
     
 
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    • 
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    Applies the rectified linear unit function element-wise.
    
     
 
-    
    nnf_relu(input, inplace = FALSE)
+    
    nnf_relu(input, inplace = FALSE)
 
-nnf_relu_(input)
    
+nnf_relu_(input)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_relu6.html b/reference/nnf_relu6.html
index e8dd073f9..d08a089b0 100644
--- a/reference/nnf_relu6.html
+++ b/reference/nnf_relu6.html
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    • 
@@ -195,7 +227,7 @@
     
    Applies the element-wise function \(ReLU6(x) = min(max(0,x), 6)\).
    
     
 
-    
    nnf_relu6(input, inplace = FALSE)
    
+    
    nnf_relu6(input, inplace = FALSE)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_rrelu.html b/reference/nnf_rrelu.html
index f1826a3c1..a2b65ca30 100644
--- a/reference/nnf_rrelu.html
+++ b/reference/nnf_rrelu.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
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-        0.0.3
+        0.1.0
       
     
 
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    • 
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    Randomized leaky ReLU.
    
     
 
-    
    nnf_rrelu(input, lower = 1/8, upper = 1/3, training = FALSE, inplace = FALSE)
+    
    nnf_rrelu(input, lower = 1/8, upper = 1/3, training = FALSE, inplace = FALSE)
 
-nnf_rrelu_(input, lower = 1/8, upper = 1/3, training = FALSE)
    
+nnf_rrelu_(input, lower = 1/8, upper = 1/3, training = FALSE)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_selu.html b/reference/nnf_selu.html
index caa020d5c..07e60b824 100644
--- a/reference/nnf_selu.html
+++ b/reference/nnf_selu.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
+
+
 
   
 
@@ -75,7 +87,7 @@ with \(\alpha=1.6732632423543772848170429916717\) and
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -148,6 +160,9 @@ with \(\alpha=1.6732632423543772848170429916717\) and
     
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    • 
   
 
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    • 
@@ -201,9 +233,9 @@ with \(\alpha=1.6732632423543772848170429916717\) and
 \(scale=1.0507009873554804934193349852946\).
    
     
 
-    
    nnf_selu(input, inplace = FALSE)
+    
    nnf_selu(input, inplace = FALSE)
 
-nnf_selu_(input)
    
+nnf_selu_(input)
    
 
     
    Arguments
    
     
    
@@ -221,13 +253,13 @@ dimensions
    
 
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-x <- torch_randn(2, 2)
-y <- nnf_selu(x)
-nnf_selu_(x)
-torch_equal(x, y)
+    
    if (torch_is_installed()) {
+x <- torch_randn(2, 2)
+y <- nnf_selu(x)
+nnf_selu_(x)
+torch_equal(x, y)
 
-}
+}
 
    #> [1] TRUE
    
   
    
   
 
@@ -145,6 +157,9 @@
     
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    Applies element-wise \(Sigmoid(x_i) = \frac{1}{1 + exp(-x_i)}\)
    
     
 
-    
    nnf_sigmoid(input)
    
+    
    nnf_sigmoid(input)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_smooth_l1_loss.html b/reference/nnf_smooth_l1_loss.html
index a027812c9..ba2efbe68 100644
--- a/reference/nnf_smooth_l1_loss.html
+++ b/reference/nnf_smooth_l1_loss.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -73,7 +85,7 @@ element-wise error falls below 1 and an L1 term otherwise." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
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    • 
@@ -197,7 +229,7 @@ element-wise error falls below 1 and an L1 term otherwise." />
 element-wise error falls below 1 and an L1 term otherwise.
    
     
 
-    
    nnf_smooth_l1_loss(input, target, reduction = "mean")
    
+    
    nnf_smooth_l1_loss(input, target, reduction = "mean")
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_soft_margin_loss.html b/reference/nnf_soft_margin_loss.html
index 677426bae..249009f28 100644
--- a/reference/nnf_soft_margin_loss.html
+++ b/reference/nnf_soft_margin_loss.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -73,7 +85,7 @@ between input tensor x and target tensor y (containing 1 or -1)." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -146,6 +158,9 @@ between input tensor x and target tensor y (containing 1 or -1)." />
     
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    • 
@@ -197,7 +229,7 @@ between input tensor x and target tensor y (containing 1 or -1)." />
 between input tensor x and target tensor y (containing 1 or -1).
    
     
 
-    
    nnf_soft_margin_loss(input, target, reduction = "mean")
    
+    
    nnf_soft_margin_loss(input, target, reduction = "mean")
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_softmax.html b/reference/nnf_softmax.html
index 7cd9ec6f9..dad2c5797 100644
--- a/reference/nnf_softmax.html
+++ b/reference/nnf_softmax.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
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-        0.0.3
+        0.1.0
       
     
 
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    • 
@@ -195,7 +227,7 @@
     
    Applies a softmax function.
    
     
 
-    
    nnf_softmax(input, dim, dtype = NULL)
    
+    
    nnf_softmax(input, dim, dtype = NULL)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_softmin.html b/reference/nnf_softmin.html
index 493bef654..df57da560 100644
--- a/reference/nnf_softmin.html
+++ b/reference/nnf_softmin.html
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-        0.0.3
+        0.1.0
       
     
 
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+  
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    • 
@@ -195,7 +227,7 @@
     
    Applies a softmin function.
    
     
 
-    
    nnf_softmin(input, dim, dtype = NULL)
    
+    
    nnf_softmin(input, dim, dtype = NULL)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_softplus.html b/reference/nnf_softplus.html
index 425d517a1..4521a5db8 100644
--- a/reference/nnf_softplus.html
+++ b/reference/nnf_softplus.html
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+  
+  
 
 
 
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-        0.0.3
+        0.1.0
       
     
 
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    • 
@@ -195,7 +227,7 @@
     
    Applies element-wise, the function \(Softplus(x) = 1/\beta * log(1 + exp(\beta * x))\).
    
     
 
-    
    nnf_softplus(input, beta = 1, threshold = 20)
    
+    
    nnf_softplus(input, beta = 1, threshold = 20)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_softshrink.html b/reference/nnf_softshrink.html
index 65b943302..e3b679d62 100644
--- a/reference/nnf_softshrink.html
+++ b/reference/nnf_softshrink.html
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+  
+  
 
 
 
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-        0.0.3
+        0.1.0
       
     
 
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    • 
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    Applies the soft shrinkage function elementwise
    
     
 
-    
    nnf_softshrink(input, lambd = 0.5)
    
+    
    nnf_softshrink(input, lambd = 0.5)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_softsign.html b/reference/nnf_softsign.html
index 7553e69fd..e0a576508 100644
--- a/reference/nnf_softsign.html
+++ b/reference/nnf_softsign.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
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+
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-        0.0.3
+        0.1.0
       
     
 
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    • 
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    Applies element-wise, the function \(SoftSign(x) = x/(1 + |x|\)
    
     
 
-    
    nnf_softsign(input)
    
+    
    nnf_softsign(input)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_tanhshrink.html b/reference/nnf_tanhshrink.html
index 69950243a..d3edbbeda 100644
--- a/reference/nnf_tanhshrink.html
+++ b/reference/nnf_tanhshrink.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
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-        0.0.3
+        0.1.0
       
     
 
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@@ -195,7 +227,7 @@
     
    Applies element-wise, \(Tanhshrink(x) = x - Tanh(x)\)
    
     
 
-    
    nnf_tanhshrink(input)
    
+    
    nnf_tanhshrink(input)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_threshold.html b/reference/nnf_threshold.html
index 37401c556..53d10ada7 100644
--- a/reference/nnf_threshold.html
+++ b/reference/nnf_threshold.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,9 +227,9 @@
     
    Thresholds each element of the input Tensor.
    
     
 
-    
    nnf_threshold(input, threshold, value, inplace = FALSE)
+    
    nnf_threshold(input, threshold, value, inplace = FALSE)
 
-nnf_threshold_(input, threshold, value)
    
+nnf_threshold_(input, threshold, value)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_triplet_margin_loss.html b/reference/nnf_triplet_margin_loss.html
index 045165d16..b0652fb6c 100644
--- a/reference/nnf_triplet_margin_loss.html
+++ b/reference/nnf_triplet_margin_loss.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -76,7 +88,7 @@ input tensors should be (N, D)." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -149,6 +161,9 @@ input tensors should be (N, D)." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -162,7 +177,24 @@ input tensors should be (N, D)." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -203,16 +235,16 @@ anchor, positive examples and negative examples respectively). The shapes of all
 input tensors should be (N, D).
    
     
 
-    
    nnf_triplet_margin_loss(
-  anchor,
-  positive,
-  negative,
-  margin = 1,
-  p = 2,
-  eps = 1e-06,
-  swap = FALSE,
-  reduction = "mean"
-)
    
+    
    nnf_triplet_margin_loss(
+  anchor,
+  positive,
+  negative,
+  margin = 1,
+  p = 2,
+  eps = 1e-06,
+  swap = FALSE,
+  reduction = "mean"
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/nnf_triplet_margin_with_distance_loss.html b/reference/nnf_triplet_margin_with_distance_loss.html
new file mode 100644
index 000000000..ffd135990
--- /dev/null
+++ b/reference/nnf_triplet_margin_with_distance_loss.html
@@ -0,0 +1,308 @@
+
+
+
+  
+  
+
+
+
+Triplet margin with distance loss — nnf_triplet_margin_with_distance_loss • torch
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+  
+  
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+  
+
+  
+    
    
+      
    
+      
+
+      
+
+      
    
+
+
    
+  
    
+    
    
+    
    Triplet margin with distance loss
    
+    Source: R/nnf-loss.R
+    
+    
    
+
+    
    
+    
    See nn_triplet_margin_with_distance_loss()
    
+    
    
+
+    
    nnf_triplet_margin_with_distance_loss(
+  anchor,
+  positive,
+  negative,
+  distance_function = NULL,
+  margin = 1,
+  swap = FALSE,
+  reduction = "mean"
+)
    
+
+    
    Arguments
    
+    
    
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
    anchor
    the anchor input tensor
    positive
    the positive input tensor
    negative
    the negative input tensor
    distance_function
    (callable, optional): A nonnegative, real-valued function that
+quantifies the closeness of two tensors. If not specified,
+nn_pairwise_distance() will be used.  Default: None
    margin
    Default: 1.
    swap
    The distance swap is described in detail in the paper Learning shallow
+convolutional feature descriptors with triplet losses by V. Balntas, E. Riba et al.
+Default: FALSE.
    reduction
    (string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'
    
+
+
+  
+  
+
+
+
+      
    
+      
    
+  
    Developed by Daniel Falbel, Javier Luraschi.
    
+
    
+
+
    
+  
    Site built with pkgdown 1.6.1.
    
+
    
+
+      
    
+   
+
+  
+
+
+  
+
+
+
diff --git a/reference/nnf_unfold.html b/reference/nnf_unfold.html
index e431a19a7..3d388e318 100644
--- a/reference/nnf_unfold.html
+++ b/reference/nnf_unfold.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Extracts sliding local blocks from an batched input tensor.
    
     
 
-    
    nnf_unfold(input, kernel_size, dilation = 1, padding = 0, stride = 1)
    
+    
    nnf_unfold(input, kernel_size, dilation = 1, padding = 0, stride = 1)
    
 
     
    Arguments
    
     
diff --git a/reference/optim_adam.html b/reference/optim_adam.html
index c32c93f39..6b14e34fb 100644
--- a/reference/optim_adam.html
+++ b/reference/optim_adam.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
         torch
-        0.0.3
+        0.1.0
       
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
  • Datasets
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
-
  • Examples
    • 
+
  • 
+  
+  
+
  • 
   Reference
 
    • 
@@ -195,14 +227,14 @@
     
    It has been proposed in Adam: A Method for Stochastic Optimization.
    
 
-    
    optim_adam(
-  params,
-  lr = 0.001,
-  betas = c(0.9, 0.999),
-  eps = 1e-08,
-  weight_decay = 0,
-  amsgrad = FALSE
-)
    
+    
    optim_adam(
+  params,
+  lr = 0.001,
+  betas = c(0.9, 0.999),
+  eps = 1e-08,
+  weight_decay = 0,
+  amsgrad = FALSE
+)
    Arguments
    
 
       
     
     
     
   
 
 
     
 
     
     
    
@@ -240,15 +272,15 @@ algorithm from the paper On
 
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-if (FALSE) {
-optimizer <- optim_adam(model$parameters(), lr=0.1)
-optimizer$zero_grad()
-loss_fn(model(input), target)$backward()
-optimizer$step()
-}
-
-}
+    
    if (torch_is_installed()) {
+if (FALSE) {
+optimizer <- optim_adam(model$parameters(), lr=0.1)
+optimizer$zero_grad()
+loss_fn(model(input), target)$backward()
+optimizer$step()
+}
+
+}
 
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    export
    
     
 
-    
    optim_required()
    
+    
    optim_required()
    
 
 
 
diff --git a/reference/optim_sgd.html b/reference/optim_sgd.html
index 46a631c53..47b8096de 100644
--- a/reference/optim_sgd.html
+++ b/reference/optim_sgd.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -74,7 +86,7 @@ On the importance of initialization and momentum in deep learning." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -147,6 +159,9 @@ On the importance of initialization and momentum in deep learning." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -160,7 +175,24 @@ On the importance of initialization and momentum in deep learning." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -199,14 +231,14 @@ Nesterov momentum is based on the formula from
 On the importance of initialization and momentum in deep learning.
    
     
 
-    
    optim_sgd(
-  params,
-  lr = optim_required(),
-  momentum = 0,
-  dampening = 0,
-  weight_decay = 0,
-  nesterov = FALSE
-)
    
+    
    optim_sgd(
+  params,
+  lr = optim_required(),
+  momentum = 0,
+  dampening = 0,
+  weight_decay = 0,
+  nesterov = FALSE
+)
    
 
     
    Arguments
    
     
    
@@ -265,15 +297,15 @@ $$
 The Nesterov version is analogously modified.
    Examples
    
-    
    if (torch_is_installed()) {
-if (FALSE) {
-optimizer <- optim_sgd(model$parameters(), lr=0.1, momentum=0.9)
-optimizer$zero_grad()
-loss_fn(model(input), target)$backward()
-optimizer$step()
-}
-
-}
+    
    if (torch_is_installed()) {
+if (FALSE) {
+optimizer <- optim_sgd(model$parameters(), lr=0.1, momentum=0.9)
+optimizer$zero_grad()
+loss_fn(model(input), target)$backward()
+optimizer$step()
+}
+
+}
 
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    See magrittr::%>% for details.
    
     
 
-    
    lhs %>% rhs
    
+    
    lhs %>% rhs
    
 
 
 
diff --git a/reference/tensor_dataset.html b/reference/tensor_dataset.html
index 24d21ae1c..d1c7d451d 100644
--- a/reference/tensor_dataset.html
+++ b/reference/tensor_dataset.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Each sample will be retrieved by indexing tensors along the first dimension.
    
     
 
-    
    tensor_dataset(...)
    
+    
    tensor_dataset(...)
    
 
     
    Arguments
    
     
    
 
     
    
diff --git a/reference/torch_abs.html b/reference/torch_abs.html
index 7df9e6959..b49aa4ffb 100644
--- a/reference/torch_abs.html
+++ b/reference/torch_abs.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
         torch
-        0.0.3
+        0.1.0
       
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
  • Datasets
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
-
  • Examples
    • 
+
  • 
+  
+  
+
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Abs
    
 
-    
    torch_abs(self)
    
+    
    torch_abs(self)
    Arguments
    
 
       
     
     
     
   
 
 
     
 
     
     
    
@@ -217,10 +249,10 @@
 $$
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_abs(torch_tensor(c(-1, -2, 3)))
-}
+torch_abs(torch_tensor(c(-1, -2, 3)))
+}
 
    #> torch_tensor 
 #>  1
 #>  2
diff --git a/reference/torch_acos.html b/reference/torch_acos.html
index 3d472e52d..d3a61dd4e 100644
--- a/reference/torch_acos.html
+++ b/reference/torch_acos.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Acos
    
     
    
 
-    
    torch_acos(self)
    
+    
    torch_acos(self)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -217,17 +249,17 @@
 $$
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(4))
-a
-torch_acos(a)
-}
+a = torch_randn(c(4))
+a
+torch_acos(a)
+}
 
    #> torch_tensor 
-#>  0.7871
-#>  1.0800
-#>  1.4084
-#>  1.7142
+#>  2.1714
+#>  1.2374
+#>  2.7904
+#>  0.9654
 #> [ CPUFloatType{4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Adaptive_avg_pool1d
    
     
 
-    
    torch_adaptive_avg_pool1d(self, output_size)
    
+    
    torch_adaptive_avg_pool1d(self, output_size)
    
 
     
    Arguments
    
     
    
 
     
    
diff --git a/reference/torch_add.html b/reference/torch_add.html
index 4baa58652..87bd6a80f 100644
--- a/reference/torch_add.html
+++ b/reference/torch_add.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
         torch
-        0.0.3
+        0.1.0
       
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
  • Datasets
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
-
  • Examples
    • 
+
  • 
+  
+  
+
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Add
    
 
-    
    torch_add(self, other, alpha = 1L)
    
+    
    torch_add(self, other, alpha = 1L)
    Arguments
    
 
       
     
     
     
   
 
 
     
 
     
     
    
@@ -243,24 +275,24 @@ If other is of type FloatTensor or DoubleTensor, alpha
 a real number, otherwise it should be an integer.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(4))
-a
-torch_add(a, 20)
+a = torch_randn(c(4))
+a
+torch_add(a, 20)
 
 
-a = torch_randn(c(4))
-a
-b = torch_randn(c(4, 1))
-b
-torch_add(a, b)
-}
+a = torch_randn(c(4))
+a
+b = torch_randn(c(4, 1))
+b
+torch_add(a, b)
+}
 
    #> torch_tensor 
-#> -0.1740 -0.4454  0.7719  0.2179
-#>  0.1897 -0.0817  1.1355  0.5816
-#> -0.2508 -0.5222  0.6951  0.1411
-#> -1.4621 -1.7335 -0.5163 -1.0702
+#> -0.8442 -0.7448 -0.7203  0.1948
+#>  0.0081  0.1076  0.1320  1.0471
+#> -1.6073 -1.5079 -1.4834 -0.5683
+#> -0.9071 -0.8077 -0.7832  0.1319
 #> [ CPUFloatType{4,4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Addbmm
    
     
 
-    
    torch_addbmm(self, batch1, batch2, beta = 1L, alpha = 1L)
    
+    
    torch_addbmm(self, batch1, batch2, beta = 1L, alpha = 1L)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -245,17 +277,17 @@ For inputs of type FloatTensor or DoubleTensor, argume
 must be real numbers, otherwise they should be integers.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-M = torch_randn(c(3, 5))
-batch1 = torch_randn(c(10, 3, 4))
-batch2 = torch_randn(c(10, 4, 5))
-torch_addbmm(M, batch1, batch2)
-}
+M = torch_randn(c(3, 5))
+batch1 = torch_randn(c(10, 3, 4))
+batch2 = torch_randn(c(10, 4, 5))
+torch_addbmm(M, batch1, batch2)
+}
 
    #> torch_tensor 
-#>  4.3623 -13.1864  1.5644  6.3273 -2.3118
-#>  1.5691 -5.3031 -0.9555  2.9742  2.8950
-#> -7.7087 -2.5553 -4.0583 -2.0273  4.9884
+#>  11.6949  -6.9679   2.1072   1.6915   4.1252
+#>  -1.6824   1.3871  -4.7343  -2.1224   2.7545
+#>  -0.4472  -6.0422  -4.1033   5.6913  -3.8870
 #> [ CPUFloatType{3,5} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Addcdiv
    
     
 
-    
    torch_addcdiv(self, tensor1, tensor2, value = 1L)
    
+    
    torch_addcdiv(self, tensor1, tensor2, value = 1L)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -248,17 +280,17 @@ broadcastable .
    
 a real number, otherwise an integer.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-t = torch_randn(c(1, 3))
-t1 = torch_randn(c(3, 1))
-t2 = torch_randn(c(1, 3))
-torch_addcdiv(t, t1, t2, 0.1)
-}
+t = torch_randn(c(1, 3))
+t1 = torch_randn(c(3, 1))
+t2 = torch_randn(c(1, 3))
+torch_addcdiv(t, t1, t2, 0.1)
+}
 
    #> torch_tensor 
-#>  0.2466  0.6010  1.8962
-#>  0.0637  0.7913  2.4221
-#>  0.8083  0.0164  0.2814
+#> -0.7597  1.3254 -1.3132
+#> -1.8727 -0.6315  1.4315
+#> -1.4516  0.1089  0.3930
 #> [ CPUFloatType{3,3} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Addcmul
    
     
 
-    
    torch_addcmul(self, tensor1, tensor2, value = 1L)
    
+    
    torch_addcmul(self, tensor1, tensor2, value = 1L)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -235,17 +267,17 @@ broadcastable .
    
 a real number, otherwise an integer.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-t = torch_randn(c(1, 3))
-t1 = torch_randn(c(3, 1))
-t2 = torch_randn(c(1, 3))
-torch_addcmul(t, t1, t2, 0.1)
-}
+t = torch_randn(c(1, 3))
+t1 = torch_randn(c(3, 1))
+t2 = torch_randn(c(1, 3))
+torch_addcmul(t, t1, t2, 0.1)
+}
 
    #> torch_tensor 
-#> -0.9987 -0.1486  1.1388
-#> -1.0322 -0.0828  0.9679
-#> -0.9683 -0.2083  1.2937
+#>  0.0819  0.3796  1.1782
+#>  0.1175  0.2891  1.2214
+#>  0.0635  0.4265  1.1558
 #> [ CPUFloatType{3,3} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Addmm
    
     
 
-    
    torch_addmm(self, mat1, mat2, beta = 1L, alpha = 1L)
    
+    
    torch_addmm(self, mat1, mat2, beta = 1L, alpha = 1L)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -242,16 +274,16 @@ For inputs of type FloatTensor or DoubleTensor, argume
 alpha must be real numbers, otherwise they should be integers.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-M = torch_randn(c(2, 3))
-mat1 = torch_randn(c(2, 3))
-mat2 = torch_randn(c(3, 3))
-torch_addmm(M, mat1, mat2)
-}
+M = torch_randn(c(2, 3))
+mat1 = torch_randn(c(2, 3))
+mat2 = torch_randn(c(3, 3))
+torch_addmm(M, mat1, mat2)
+}
 
    #> torch_tensor 
-#>  0.0521  1.5207  0.4070
-#>  1.0992 -2.9412  0.1886
+#> -0.8562 -4.9523  4.3217
+#>  0.2143 -0.0474 -1.3801
 #> [ CPUFloatType{2,3} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Addmv
    
     
 
-    
    torch_addmv(self, mat, vec, beta = 1L, alpha = 1L)
    
+    
    torch_addmv(self, mat, vec, beta = 1L, alpha = 1L)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -243,16 +275,16 @@ For inputs of type FloatTensor or DoubleTensor, argume
 alpha must be real numbers, otherwise they should be integers
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-M = torch_randn(c(2))
-mat = torch_randn(c(2, 3))
-vec = torch_randn(c(3))
-torch_addmv(M, mat, vec)
-}
+M = torch_randn(c(2))
+mat = torch_randn(c(2, 3))
+vec = torch_randn(c(3))
+torch_addmv(M, mat, vec)
+}
 
    #> torch_tensor 
-#>  2.8130
-#>  1.3307
+#>  0.6005
+#>  2.0211
 #> [ CPUFloatType{2} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Addr
    
     
 
-    
    torch_addr(self, vec1, vec2, beta = 1L, alpha = 1L)
    
+    
    torch_addr(self, vec1, vec2, beta = 1L, alpha = 1L)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -244,13 +276,13 @@ broadcastable  with a matrix of size
 alpha must be real numbers, otherwise they should be integers
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-vec1 = torch_arange(1., 4.)
-vec2 = torch_arange(1., 3.)
-M = torch_zeros(c(3, 2))
-torch_addr(M, vec1, vec2)
-}
+vec1 = torch_arange(1., 4.)
+vec2 = torch_arange(1., 3.)
+M = torch_zeros(c(3, 2))
+torch_addr(M, vec1, vec2)
+}
 
    #> torch_tensor 
 #>  1  2
 #>  2  4
diff --git a/reference/torch_allclose.html b/reference/torch_allclose.html
index 035b5c3a5..ada1eefae 100644
--- a/reference/torch_allclose.html
+++ b/reference/torch_allclose.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Allclose
    
     
    
 
-    
    torch_allclose(self, other, rtol = 1e-05, atol = 0, equal_nan = FALSE)
    
+    
    torch_allclose(self, other, rtol = 1e-05, atol = 0, equal_nan = FALSE)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -235,13 +267,13 @@ elementwise, for all elements of input and other. The
 numpy.allclose <https://docs.scipy.org/doc/numpy/reference/generated/numpy.allclose.html>_
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_allclose(torch_tensor(c(10000., 1e-07)), torch_tensor(c(10000.1, 1e-08)))
-torch_allclose(torch_tensor(c(10000., 1e-08)), torch_tensor(c(10000.1, 1e-09)))
-torch_allclose(torch_tensor(c(1.0, NaN)), torch_tensor(c(1.0, NaN)))
-torch_allclose(torch_tensor(c(1.0, NaN)), torch_tensor(c(1.0, NaN)), equal_nan=TRUE)
-}
+torch_allclose(torch_tensor(c(10000., 1e-07)), torch_tensor(c(10000.1, 1e-08)))
+torch_allclose(torch_tensor(c(10000., 1e-08)), torch_tensor(c(10000.1, 1e-09)))
+torch_allclose(torch_tensor(c(1.0, NaN)), torch_tensor(c(1.0, NaN)))
+torch_allclose(torch_tensor(c(1.0, NaN)), torch_tensor(c(1.0, NaN)), equal_nan=TRUE)
+}
 
    #> [1] TRUE
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Angle
    
     
 
-    
    torch_angle(self)
    
+    
    torch_angle(self)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -217,12 +249,12 @@
 $$
    Examples
    
-    
    if (torch_is_installed()) {
-if (FALSE) {
-torch_angle(torch_tensor(c(-1 + 1i, -2 + 2i, 3 - 3i)))*180/3.14159
-}
+    
    if (torch_is_installed()) {
+if (FALSE) {
+torch_angle(torch_tensor(c(-1 + 1i, -2 + 2i, 3 - 3i)))*180/3.14159
+}
 
-}
+}
 
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,15 +227,15 @@
     
    Arange
    
     
 
-    
    torch_arange(
-  start,
-  end,
-  step = 1,
-  dtype = NULL,
-  layout = torch_strided(),
-  device = NULL,
-  requires_grad = FALSE
-)
    
+    
    torch_arange(
+  start,
+  end,
+  step = 1,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -254,12 +286,12 @@ in such cases.
    
 $$
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_arange(start = 0, end = 5)
-torch_arange(1, 4)
-torch_arange(1, 2.5, 0.5)
-}
+torch_arange(start = 0, end = 5)
+torch_arange(1, 4)
+torch_arange(1, 2.5, 0.5)
+}
 
    #> torch_tensor 
 #>  1.0000
 #>  1.5000
diff --git a/reference/torch_argmax.html b/reference/torch_argmax.html
index fad8cad65..47fd0cfbf 100644
--- a/reference/torch_argmax.html
+++ b/reference/torch_argmax.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Argmax
    
     
    
 
-    
    torch_argmax(self, dim = NULL, keepdim = FALSE)
    
+    
    torch_argmax(self, dim = NULL, keepdim = FALSE)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -232,24 +264,24 @@ documentation for the exact semantics of this method.
    
 documentation for the exact semantics of this method.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-if (FALSE) {
-a = torch_randn(c(4, 4))
-a
-torch_argmax(a)
-}
+if (FALSE) {
+a = torch_randn(c(4, 4))
+a
+torch_argmax(a)
+}
 
 
-a = torch_randn(c(4, 4))
-a
-torch_argmax(a, dim=1)
-}
+a = torch_randn(c(4, 4))
+a
+torch_argmax(a, dim=1)
+}
 
    #> torch_tensor 
-#>  2
-#>  0
-#>  1
+#>  3
+#>  3
 #>  1
+#>  2
 #> [ CPULongType{4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Argmin
    
     
 
-    
    torch_argmin(self, dim = NULL, keepdim = FALSE)
    
+    
    torch_argmin(self, dim = NULL, keepdim = FALSE)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -232,22 +264,22 @@ documentation for the exact semantics of this method.
    
 documentation for the exact semantics of this method.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(4, 4))
-a
-torch_argmin(a)
+a = torch_randn(c(4, 4))
+a
+torch_argmin(a)
 
 
-a = torch_randn(c(4, 4))
-a
-torch_argmin(a, dim=1)
-}
+a = torch_randn(c(4, 4))
+a
+torch_argmin(a, dim=1)
+}
 
    #> torch_tensor 
 #>  0
+#>  3
 #>  1
-#>  1
-#>  1
+#>  3
 #> [ CPULongType{4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Argsort
    
     
 
-    
    torch_argsort(self, dim = -1L, descending = FALSE)
    
+    
    torch_argsort(self, dim = -1L, descending = FALSE)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -225,17 +257,17 @@ order by value.
    
 for the exact semantics of this method.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(4, 4))
-a
-torch_argsort(a, dim=1)
-}
+a = torch_randn(c(4, 4))
+a
+torch_argsort(a, dim=1)
+}
 
    #> torch_tensor 
-#>  3  0  0  2
+#>  0  2  3  2
+#>  3  3  0  1
 #>  2  1  1  3
-#>  1  2  3  1
-#>  0  3  2  0
+#>  1  0  2  0
 #> [ CPULongType{4,4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    As_strided
    
     
 
-    
    torch_as_strided(self, size, stride, storage_offset = NULL)
    
+    
    torch_as_strided(self, size, stride, storage_offset = NULL)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -240,18 +272,18 @@ advisable to use.
 
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-
-x = torch_randn(c(3, 3))
-x
-t = torch_as_strided(x, list(2, 2), list(1, 2))
-t
-t = torch_as_strided(x, list(2, 2), list(1, 2), 1)
-t
-}
+    
    if (torch_is_installed()) {
+
+x = torch_randn(c(3, 3))
+x
+t = torch_as_strided(x, list(2, 2), list(1, 2))
+t
+t = torch_as_strided(x, list(2, 2), list(1, 2), 1)
+t
+}
 
    #> torch_tensor 
-#>  2.2728  1.0136
-#> -0.1885  1.2535
+#>  2.2925  1.0260
+#>  0.6235 -0.5973
 #> [ CPUFloatType{2,2} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Asin
    
     
 
-    
    torch_asin(self)
    
+    
    torch_asin(self)
    
 
     
    Arguments
    
     
    
@@ -217,18 +249,17 @@
 $$
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(4))
-a
-torch_asin(a)
-}
+a = torch_randn(c(4))
+a
+torch_asin(a)
+}
 
    #> torch_tensor 
-#> 0.01 *
-#>     nan
-#>  7.9841
-#>     nan
-#> -30.8534
+#>  0.1146
+#> -1.2095
+#> -0.7692
+#>  1.0149
 #> [ CPUFloatType{4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Atan
    
     
 
-    
    torch_atan(self)
    
+    
    torch_atan(self)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -217,17 +249,17 @@
 $$
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(4))
-a
-torch_atan(a)
-}
+a = torch_randn(c(4))
+a
+torch_atan(a)
+}
 
    #> torch_tensor 
-#> -0.5608
-#>  0.9247
-#>  1.1369
-#>  0.8835
+#>  0.6850
+#> -0.4100
+#>  0.6624
+#>  0.6265
 #> [ CPUFloatType{4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Atan2
    
     
 
-    
    torch_atan2(self, other)
    
+    
    torch_atan2(self, other)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -225,17 +257,17 @@ parameter, is the y-coordinate.)
    
 broadcastable .
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(4))
-a
-torch_atan2(a, torch_randn(c(4)))
-}
+a = torch_randn(c(4))
+a
+torch_atan2(a, torch_randn(c(4)))
+}
 
    #> torch_tensor 
-#> -0.5145
-#>  0.3180
-#>  1.6062
-#>  2.3995
+#>  3.0034
+#>  1.5146
+#> -2.2932
+#> -1.9916
 #> [ CPUFloatType{4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,14 +227,14 @@
     
    Avg_pool1d
    
     
 
-    
    torch_avg_pool1d(
-  self,
-  kernel_size,
-  stride = list(),
-  padding = 0L,
-  ceil_mode = FALSE,
-  count_include_pad = TRUE
-)
    
+    
    torch_avg_pool1d(
+  self,
+  kernel_size,
+  stride = list(),
+  padding = 0L,
+  ceil_mode = FALSE,
+  count_include_pad = TRUE
+)
    
 
     
    Arguments
    
     
    
 
     
    
diff --git a/reference/torch_baddbmm.html b/reference/torch_baddbmm.html
index ec281a470..b59185529 100644
--- a/reference/torch_baddbmm.html
+++ b/reference/torch_baddbmm.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
         torch
-        0.0.3
+        0.1.0
       
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
  • Datasets
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
-
  • Examples
    • 
+
  • 
+  
+  
+
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Baddbmm
    
 
-    
    torch_baddbmm(self, batch1, batch2, beta = 1L, alpha = 1L)
    
+    
    torch_baddbmm(self, batch1, batch2, beta = 1L, alpha = 1L)
    Arguments
    
 
       
     
     
     
   
 
 
     
 
     
     
    
@@ -245,63 +277,63 @@ For inputs of type FloatTensor or DoubleTensor, argume
 alpha must be real numbers, otherwise they should be integers.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-M = torch_randn(c(10, 3, 5))
-batch1 = torch_randn(c(10, 3, 4))
-batch2 = torch_randn(c(10, 4, 5))
-torch_baddbmm(M, batch1, batch2)
-}
+M = torch_randn(c(10, 3, 5))
+batch1 = torch_randn(c(10, 3, 4))
+batch2 = torch_randn(c(10, 4, 5))
+torch_baddbmm(M, batch1, batch2)
+}
 
    #> torch_tensor 
 #> (1,.,.) = 
-#>   3.2950  1.1934  1.0951 -0.1641 -0.1625
-#>   0.0234  0.6164  1.1209 -1.4287 -1.4717
-#>   1.4437  0.0008  0.1610 -0.6639 -3.1524
+#>   4.7281 -6.0498  3.1056  1.1735 -2.4135
+#>  -2.9917 -1.2809 -0.0952  3.6850  4.5780
+#>  -1.7250  3.5495 -0.0858  0.4585  2.9365
 #> 
 #> (2,.,.) = 
-#>  -1.1120 -1.6823  4.3323 -4.6368  5.3289
-#>  -0.0067  0.9474 -2.1810 -2.4339 -1.4058
-#>  -0.0811  1.0781  0.5114 -0.6792  5.5863
+#>  -1.5660  0.8016  0.9269 -3.8592  0.1989
+#>  -1.4357  1.1692  0.7212 -0.3221  1.1815
+#>  -2.9738  3.6028  1.8993 -5.8404 -0.1716
 #> 
 #> (3,.,.) = 
-#>  -1.0254  1.4248  2.5140  0.1094  0.8005
-#>  -0.0712  3.0454 -0.8717 -1.3384 -1.1721
-#>  -1.0192 -0.3714  0.0774 -0.0318  1.3226
+#>  -1.3068  2.0233  0.8700  0.6349 -1.3450
+#>  -0.4576  0.9226 -1.4827 -5.6012  3.4073
+#>  -2.3818  0.8728  1.1533  1.1672 -1.0976
 #> 
 #> (4,.,.) = 
-#>   0.7244  1.0012  0.7026 -0.1657 -0.5903
-#>  -1.1023  0.7397 -2.5617 -2.8854 -0.4641
-#>  -1.8027  1.8865 -5.9998 -2.7661 -4.2857
+#>   3.7072 -2.2163  1.1927 -3.6210 -0.2271
+#>   2.7538 -2.0449 -0.8840 -3.4298  2.4292
+#>   0.6560 -0.3831 -1.1665 -4.4474 -1.8460
 #> 
 #> (5,.,.) = 
-#>   0.7827  0.5095 -4.7454 -0.9276  0.9389
-#>   0.2047 -0.1286 -0.0545  0.0190  2.7191
-#>  -0.1130 -1.0268  0.5965  0.4682 -1.9710
+#>   2.8195 -0.8667 -0.3816 -2.9713 -2.0342
+#>  -3.1794 -1.4194 -4.0081  1.4367  2.8798
+#>   4.6328 -0.2675 -1.9527 -2.8000 -0.4744
 #> 
 #> (6,.,.) = 
-#>   0.3016 -0.8588  0.4652  2.3788 -3.3351
-#>   0.6153  1.9567 -0.4531 -3.5487 -0.0359
-#>  -1.0311  1.7841 -1.4333 -1.4575 -5.2221
+#>  -1.6616  1.4558  0.8950  3.3749  0.1133
+#>   1.7758  2.6756 -0.7521 -1.7215 -5.8593
+#>   1.1681  1.2075  2.0621 -0.5453 -0.2713
 #> 
 #> (7,.,.) = 
-#>   0.6391  1.4053  1.4987 -1.3632  1.9467
-#>  -0.3214  1.5380 -1.9825  5.0689 -1.2355
-#>  -2.0131  0.5735 -0.3492  1.2934 -0.4853
+#>   2.1281 -5.5957  2.0165 -1.0600 -2.4151
+#>  -3.0381  3.5730 -0.1263  2.0001  2.1600
+#>   1.3341 -0.7419  0.7636  0.5322 -0.4942
 #> 
 #> (8,.,.) = 
-#>  -3.1747  0.3503 -0.8512 -0.8386  1.8389
-#>  -0.4581 -0.1452 -0.2862 -2.5819  0.0945
-#>   1.9201 -3.9626 -1.0644  1.4395 -0.9784
+#>   2.1611  1.5023 -2.6154  1.4701 -1.2220
+#>   1.3418 -0.2604  0.0016  0.5194 -0.5101
+#>  -0.0553 -1.3268  2.8341 -2.0275 -3.4602
 #> 
 #> (9,.,.) = 
-#>   2.0265 -1.0222  0.4194  3.9349 -1.2036
-#>   0.2550  3.1147 -1.8770 -1.3894  0.1003
-#>  -0.7578 -3.4052 -1.9434 -5.4013 -1.7570
+#>   1.1191  2.4478  0.1713 -2.6500  2.6524
+#>   1.5080 -6.5683 -1.4471  0.0159 -1.6559
+#>  -1.3632  0.4350 -0.2920  1.7903  0.0450
 #> 
 #> (10,.,.) = 
-#>   3.5818  2.3017 -0.6280 -1.3274 -2.3944
-#>  -1.8303  1.0979 -0.9950 -1.9600 -1.0145
-#>  -0.5720 -0.0300 -1.1251  4.3175 -0.5519
+#>   5.0413  2.4106 -1.1846  5.9640  1.0693
+#>  -1.1074 -1.6447  1.9714  2.8367  1.9957
+#>  -2.5291 -1.3038 -0.0376  0.3672 -1.1772
 #> [ CPUFloatType{10,3,5} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,14 +227,14 @@
     
    Bartlett_window
    
     
 
-    
    torch_bartlett_window(
-  window_length,
-  periodic = TRUE,
-  dtype = NULL,
-  layout = torch_strided(),
-  device = NULL,
-  requires_grad = FALSE
-)
    
+    
    torch_bartlett_window(
+  window_length,
+  periodic = TRUE,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
    
 
     
    Arguments
    
     
    
 
     
    
diff --git a/reference/torch_bernoulli.html b/reference/torch_bernoulli.html
index 75e3f5c07..6a68300b9 100644
--- a/reference/torch_bernoulli.html
+++ b/reference/torch_bernoulli.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
         torch
-        0.0.3
+        0.1.0
       
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
  • Datasets
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
-
  • Examples
    • 
+
  • 
+  
+  
+
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Bernoulli
    
 
-    
    torch_bernoulli(self, p, generator = NULL)
    
+    
    torch_bernoulli(self, p, generator = NULL)
    Arguments
    
 
       
     
     
     
   
 
 
     
 
     
     
    
@@ -238,16 +270,16 @@ shape as input.
    
 point dtype.
    Examples
    
-    
    if (torch_is_installed()) {
-
-a = torch_empty(c(3, 3))$uniform_(0, 1)  # generate a uniform random matrix with range c(0, 1)
-a
-torch_bernoulli(a)
-a = torch_ones(c(3, 3)) # probability of drawing "1" is 1
-torch_bernoulli(a)
-a = torch_zeros(c(3, 3)) # probability of drawing "1" is 0
-torch_bernoulli(a)
-}
+    
    if (torch_is_installed()) {
+
+a = torch_empty(c(3, 3))$uniform_(0, 1)  # generate a uniform random matrix with range c(0, 1)
+a
+torch_bernoulli(a)
+a = torch_ones(c(3, 3)) # probability of drawing "1" is 1
+torch_bernoulli(a)
+a = torch_zeros(c(3, 3)) # probability of drawing "1" is 0
+torch_bernoulli(a)
+}
 
    #> torch_tensor 
 #>  0  0  0
 #>  0  0  0
diff --git a/reference/torch_bincount.html b/reference/torch_bincount.html
index fa08a32bd..cb49b2485 100644
--- a/reference/torch_bincount.html
+++ b/reference/torch_bincount.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Bincount
    
     
    
 
-    
    torch_bincount(self, weights = list(), minlength = 0L)
    
+    
    torch_bincount(self, weights = list(), minlength = 0L)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -230,25 +262,22 @@ tensor of size 0. If minlength is specified, the number of bins is
 
    .. include:: cuda_deterministic.rst
    Examples
    
-    
    if (torch_is_installed()) {
-
-input = torch_randint(0, 8, list(5), dtype=torch_int64())
-weights = torch_linspace(0, 1, steps=5)
-input
-weights
-torch_bincount(input, weights)
-input$bincount(weights)
-}
+    
    if (torch_is_installed()) {
+
+input = torch_randint(0, 8, list(5), dtype=torch_int64())
+weights = torch_linspace(0, 1, steps=5)
+input
+weights
+torch_bincount(input, weights)
+input$bincount(weights)
+}
 
    #> torch_tensor 
-#>  0.0000
-#>  0.0000
-#>  0.7500
 #>  1.0000
+#>  0.7500
 #>  0.2500
 #>  0.0000
-#>  0.0000
 #>  0.5000
-#> [ CPUFloatType{8} ]
    
+#> [ CPUFloatType{5} ]
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
  • Datasets
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
-
  • Examples
    • 
+
  • 
+  
+  
+
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Bitwise_and
    
 
-    
    torch_bitwise_and(self, other)
    
+    
    torch_bitwise_and(self, other)
    Arguments
    
 
     
   
   
     
     
   
 
 
     
 
     
     
    
diff --git a/reference/torch_bitwise_not.html b/reference/torch_bitwise_not.html
index 723dfc195..31eb40857 100644
--- a/reference/torch_bitwise_not.html
+++ b/reference/torch_bitwise_not.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
         torch
-        0.0.3
+        0.1.0
       
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
  • Datasets
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
-
  • Examples
    • 
+
  • 
+  
+  
+
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Bitwise_not
    
 
-    
    torch_bitwise_not(self)
    
+    
    torch_bitwise_not(self)
    Arguments
    
 
       
     
     
     
   
 
 
     
 
     
     
    
diff --git a/reference/torch_bitwise_or.html b/reference/torch_bitwise_or.html
index 3f56126d5..a4a2ee87d 100644
--- a/reference/torch_bitwise_or.html
+++ b/reference/torch_bitwise_or.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
         torch
-        0.0.3
+        0.1.0
       
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
  • Datasets
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
-
  • Examples
    • 
+
  • 
+  
+  
+
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Bitwise_or
    
 
-    
    torch_bitwise_or(self, other)
    
+    
    torch_bitwise_or(self, other)
    Arguments
    
 
       
     
     
     
   
 
 
     
 
     
     
    
diff --git a/reference/torch_bitwise_xor.html b/reference/torch_bitwise_xor.html
index 66ac3aa01..e9c06c37c 100644
--- a/reference/torch_bitwise_xor.html
+++ b/reference/torch_bitwise_xor.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
         torch
-        0.0.3
+        0.1.0
       
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
  • Datasets
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
-
  • Examples
    • 
+
  • 
+  
+  
+
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Bitwise_xor
    
 
-    
    torch_bitwise_xor(self, other)
    
+    
    torch_bitwise_xor(self, other)
    Arguments
    
 
       
     
     
     
   
 
 
     
 
     
     
    
diff --git a/reference/torch_blackman_window.html b/reference/torch_blackman_window.html
index 780331de2..0e34768dd 100644
--- a/reference/torch_blackman_window.html
+++ b/reference/torch_blackman_window.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
         torch
-        0.0.3
+        0.1.0
       
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
  • Datasets
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
-
  • Examples
    • 
+
  • 
+  
+  
+
  • 
   Reference
 
    • 
@@ -195,14 +227,14 @@
     
    Blackman_window
    
 
-    
    torch_blackman_window(
-  window_length,
-  periodic = TRUE,
-  dtype = NULL,
-  layout = torch_strided(),
-  device = NULL,
-  requires_grad = FALSE
-)
    
+    
    torch_blackman_window(
+  window_length,
+  periodic = TRUE,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
    Arguments
    
 
       
     
     
     
   
 
 
     
 
     
     
    
diff --git a/reference/torch_bmm.html b/reference/torch_bmm.html
index b4dc3f38e..3d169fe89 100644
--- a/reference/torch_bmm.html
+++ b/reference/torch_bmm.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
         torch
-        0.0.3
+        0.1.0
       
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
  • Datasets
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
-
  • Examples
    • 
+
  • 
+  
+  
+
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Bmm
    
 
-    
    torch_bmm(self, mat2)
    
+    
    torch_bmm(self, mat2)
    Arguments
    
 
       
     
     
     
   
 
 
     
 
     
     
    
@@ -231,63 +263,63 @@ the same number of matrices.
    
 $$
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-input = torch_randn(c(10, 3, 4))
-mat2 = torch_randn(c(10, 4, 5))
-res = torch_bmm(input, mat2)
-res
-}
+input = torch_randn(c(10, 3, 4))
+mat2 = torch_randn(c(10, 4, 5))
+res = torch_bmm(input, mat2)
+res
+}
 
    #> torch_tensor 
 #> (1,.,.) = 
-#>   1.0892  1.3748  0.8459 -2.0545 -1.4386
-#>   0.3660 -0.1655  0.0205 -0.7982 -1.6220
-#>   0.4613  0.7003  0.2010  1.5919  1.4269
+#>  -0.9037 -0.1973 -1.1341 -0.9858  0.3039
+#>   0.2868  0.2786  0.3566 -0.7423 -2.7733
+#>   5.4676 -3.4729  3.8975 -1.7444  0.4233
 #> 
 #> (2,.,.) = 
-#>  -2.2132 -1.0830  0.8680 -0.3013  0.5952
-#>  -1.7194  1.7257 -1.8858  0.5295  0.0113
-#>  -1.9067 -3.6883  3.4484 -0.6959  1.6596
+#>  -3.1970 -1.4906 -0.8008 -2.1444 -1.2875
+#>  -0.9690 -1.4588 -0.1471 -1.2726 -0.6858
+#>  -0.3189  0.5680 -0.3345 -0.9867 -0.8067
 #> 
 #> (3,.,.) = 
-#>   0.4246 -0.4291  0.5495  0.0139 -2.8828
-#>   0.8869 -1.7168  0.9370  2.8693 -0.8925
-#>  -0.1330 -0.6530  0.2961 -0.4661  0.2099
+#>   0.3459  0.5981 -1.5873  0.8666 -0.4362
+#>   0.2688 -1.3614  0.0694  1.3043  1.0296
+#>   1.8249  1.2295 -0.2098 -0.7566  0.0848
 #> 
 #> (4,.,.) = 
-#>   3.2950  1.2318 -0.2358  3.7370  7.2076
-#>   0.5021 -7.1274 -0.6974  2.2166 -1.0051
-#>   0.5575 -0.9316 -0.4947  1.2719 -0.3476
+#>  -0.6213  1.2877 -0.9306 -0.4368  2.4335
+#>   3.6945  0.7509  2.2091  4.8111  8.8770
+#>  -1.6086  1.6030 -0.6769 -3.3762 -0.2409
 #> 
 #> (5,.,.) = 
-#>  -0.7364 -1.5675  1.2250  0.6341  2.4889
-#>  -3.3237  2.9684  3.2533  0.5785 -1.9284
-#>  -3.7940  1.4235  3.7042 -0.9522 -1.3960
+#>   2.7608 -0.6829  3.4384 -3.7808 -0.0867
+#>  -0.0835 -0.5113 -0.2495  0.1662 -0.8862
+#>   0.1986 -0.7221 -1.9765 -0.4381  1.4731
 #> 
 #> (6,.,.) = 
-#>  -3.5668 -1.3443  5.1221 -1.1451  0.9565
-#>  -1.3876  1.5025 -3.9358  1.3078  0.9739
-#>  -1.9256 -2.0872  7.0016 -1.1434  0.6967
+#>   1.3646  1.3529  1.7244 -0.7949 -2.1482
+#>   0.6172  0.6641 -0.1706 -1.2928  2.1358
+#>  -0.5524  0.0898 -1.1729  2.1522 -1.7189
 #> 
 #> (7,.,.) = 
-#>  -0.2430 -1.1612  0.2267 -0.4137  0.3275
-#>  -1.0699  1.5163  1.1168  0.4205  1.8671
-#>  -1.1855  5.5310  1.8558  4.2721  1.0853
+#>   1.3161 -1.3085 -0.4967  2.2483  0.0628
+#>  -2.1286  2.2143  1.0357 -2.5808  0.1258
+#>  -0.2157  1.8414  0.0169 -2.9079 -0.6500
 #> 
 #> (8,.,.) = 
-#>  -1.0895 -2.0372 -2.1277  0.7614 -1.5783
-#>  -0.6140  0.0763  1.5264 -0.7559 -0.3574
-#>   0.2487 -1.0749  0.2471  1.2148  0.5383
+#>  -1.4650  1.0743 -4.3104 -2.9395  1.6595
+#>   2.2130 -0.8981 -1.0598 -1.1889 -6.1445
+#>  -1.5164  0.6151 -3.6034 -1.3640  0.3356
 #> 
 #> (9,.,.) = 
-#>  -0.7402  0.6009  1.3001  0.8246 -0.4576
-#>  -0.1567 -0.6394  0.0932 -0.1400 -0.7153
-#>  -0.5724 -0.2588 -1.4616 -1.0371 -0.5793
+#>   0.9710 -1.1168  0.7513 -1.7730  1.5852
+#>   0.1585 -2.1137 -1.4338  0.9732 -0.3186
+#>   0.9655 -2.5352 -5.2346  3.6612 -2.9682
 #> 
 #> (10,.,.) = 
-#>   7.8231  2.6005 -0.8253 -0.6163 -4.4321
-#>   3.5758  0.7528  0.7691  3.3599 -1.3278
-#>  -3.2284 -0.5208 -1.5541 -2.5006  2.0721
+#>   2.1552  6.8907  4.4197 -0.9053 -1.2060
+#>  -0.7011 -3.1555 -4.6818 -0.2381 -1.1970
+#>  -1.1343 -4.0314 -3.4194  0.7506 -1.1217
 #> [ CPUFloatType{10,3,5} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Broadcast_tensors
    
     
 
-    
    torch_broadcast_tensors(tensors)
    
+    
    torch_broadcast_tensors(tensors)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -214,13 +246,13 @@
 
    Broadcasts the given tensors according to broadcasting-semantics.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-x = torch_arange(0, 3)$view(c(1, 3))
-y = torch_arange(0, 2)$view(c(2, 1))
-out = torch_broadcast_tensors(list(x, y))
-out[[1]]
-}
+x = torch_arange(0, 3)$view(c(1, 3))
+y = torch_arange(0, 2)$view(c(2, 1))
+out = torch_broadcast_tensors(list(x, y))
+out[[1]]
+}
 
    #> torch_tensor 
 #>  0  1  2
 #>  0  1  2
diff --git a/reference/torch_can_cast.html b/reference/torch_can_cast.html
index 8716b9001..d7d63230c 100644
--- a/reference/torch_can_cast.html
+++ b/reference/torch_can_cast.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Can_cast
    
     
    
 
-    
    torch_can_cast(from, to)
    
+    
    torch_can_cast(from, to)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -219,11 +251,11 @@
 described in the type promotion documentation .
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_can_cast(torch_double(), torch_float())
-torch_can_cast(torch_float(), torch_int())
-}
+torch_can_cast(torch_double(), torch_float())
+torch_can_cast(torch_float(), torch_int())
+}
 
    #> [1] FALSE
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Do cartesian product of the given sequence of tensors.
    
     
 
-    
    torch_cartesian_prod(tensors)
    
+    
    torch_cartesian_prod(tensors)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -208,14 +240,14 @@
 
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-
-a = c(1, 2, 3)
-b = c(4, 5)
-tensor_a = torch_tensor(a)
-tensor_b = torch_tensor(b)
-torch_cartesian_prod(list(tensor_a, tensor_b))
-}
+    
    if (torch_is_installed()) {
+
+a = c(1, 2, 3)
+b = c(4, 5)
+tensor_a = torch_tensor(a)
+tensor_b = torch_tensor(b)
+torch_cartesian_prod(list(tensor_a, tensor_b))
+}
 
    #> torch_tensor 
 #>  1  4
 #>  1  5
diff --git a/reference/torch_cat.html b/reference/torch_cat.html
index 2841ea8b9..e55add733 100644
--- a/reference/torch_cat.html
+++ b/reference/torch_cat.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Cat
    
     
    
 
-    
    torch_cat(tensors, dim = 1L)
    
+    
    torch_cat(tensors, dim = 1L)
    
 
     
    Arguments
    
     
    
@@ -223,16 +255,16 @@ and torch_chunk.
    torch_cat can be best understood via examples.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-x = torch_randn(c(2, 3))
-x
-torch_cat(list(x, x, x), 1)
-torch_cat(list(x, x, x), 2)
-}
+x = torch_randn(c(2, 3))
+x
+torch_cat(list(x, x, x), 1)
+torch_cat(list(x, x, x), 2)
+}
 
    #> torch_tensor 
-#>  0.4974 -0.0008 -0.8116  0.4974 -0.0008 -0.8116  0.4974 -0.0008 -0.8116
-#>  0.0269 -0.0270  0.1641  0.0269 -0.0270  0.1641  0.0269 -0.0270  0.1641
+#> -0.1257 -0.2544  0.6346 -0.1257 -0.2544  0.6346 -0.1257 -0.2544  0.6346
+#> -0.1385  0.6449  0.9977 -0.1385  0.6449  0.9977 -0.1385  0.6449  0.9977
 #> [ CPUFloatType{2,9} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Cdist
    
     
 
-    
    torch_cdist(x1, x2, p = 2L, compute_mode = NULL)
    
+    
    torch_cdist(x1, x2, p = 2L, compute_mode = NULL)
    
 
     
    Arguments
    
     
    
 
 
     
    
diff --git a/reference/torch_ceil.html b/reference/torch_ceil.html
index b8c9c7c85..6a1ffe2ab 100644
--- a/reference/torch_ceil.html
+++ b/reference/torch_ceil.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
         torch
-        0.0.3
+        0.1.0
       
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
  • Datasets
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
-
  • Examples
    • 
+
  • 
+  
+  
+
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Ceil
    
 
-    
    torch_ceil(self)
    
+    
    torch_ceil(self)
    Arguments
    
 
       
     
     
     
   
 
 
     
 
     
     
    
@@ -218,17 +250,17 @@ the smallest integer greater than or equal to each element.
    
 $$
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(4))
-a
-torch_ceil(a)
-}
+a = torch_randn(c(4))
+a
+torch_ceil(a)
+}
 
    #> torch_tensor 
 #>  1
+#>  2
+#>  2
 #> -1
-#> -0
-#>  1
 #> [ CPUFloatType{4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Celu
    
     
 
-    
    torch_celu(self, alpha = 1)
    
+    
    torch_celu(self, alpha = 1)
    
 
     
    Arguments
    
     
    
 
     
    
diff --git a/reference/torch_celu_.html b/reference/torch_celu_.html
index 60df6e872..81448a16b 100644
--- a/reference/torch_celu_.html
+++ b/reference/torch_celu_.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
         torch
-        0.0.3
+        0.1.0
       
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
  • Datasets
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
-
  • Examples
    • 
+
  • 
+  
+  
+
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Celu_
    
 
-    
    torch_celu_(self, alpha = 1)
    
+    
    torch_celu_(self, alpha = 1)
    Arguments
    
 
       
     
     
     
   
 
 
     
 
     
     
    
diff --git a/reference/torch_chain_matmul.html b/reference/torch_chain_matmul.html
index 205cdbf91..1d63dbed2 100644
--- a/reference/torch_chain_matmul.html
+++ b/reference/torch_chain_matmul.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
         torch
-        0.0.3
+        0.1.0
       
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
  • Datasets
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
-
  • Examples
    • 
+
  • 
+  
+  
+
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Chain_matmul
    
 
-    
    torch_chain_matmul(matrices)
    
+    
    torch_chain_matmul(matrices)
    Arguments
    
 
       
     
     
     
   
 
 
     
 
     
     
    
@@ -218,18 +250,18 @@ needs to be greater than or equal to 2; if equal to 2 then a trivial matrix-matr
 If \(N\) is 1, then this is a no-op - the original matrix is returned as is.
    Examples
    
-    
    if (torch_is_installed()) {
-
-a = torch_randn(c(3, 4))
-b = torch_randn(c(4, 5))
-c = torch_randn(c(5, 6))
-d = torch_randn(c(6, 7))
-torch_chain_matmul(list(a, b, c, d))
-}
+    
    if (torch_is_installed()) {
+
+a = torch_randn(c(3, 4))
+b = torch_randn(c(4, 5))
+c = torch_randn(c(5, 6))
+d = torch_randn(c(6, 7))
+torch_chain_matmul(list(a, b, c, d))
+}
 
    #> torch_tensor 
-#>  -2.9763   5.1541   0.4802   4.2000  -0.3407  -3.9994  -9.5460
-#>   9.4136 -12.4751 -16.3055 -13.4903   0.4411  12.5776  21.3514
-#>   3.9498  -8.2069  -5.7414   1.2695   6.5496  18.3628  12.8464
+#>  -5.0034  13.1294  -7.9439  12.2444  -1.5253   9.4320  -8.0039
+#>   1.2683 -13.7433  12.2470 -18.7686   3.0491 -19.3487   8.1020
+#>  -0.0394   9.6720  -1.0461   0.6241  -1.5862   1.2076  -5.0604
 #> [ CPUFloatType{3,7} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Cholesky
    
     
 
-    
    torch_cholesky(self, upper = FALSE)
    
+    
    torch_cholesky(self, upper = FALSE)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -236,22 +268,22 @@ tensor will be composed of lower-triangular Cholesky factors of each of the indi
 matrices.
    Examples
    
-    
    if (torch_is_installed()) {
-
-a = torch_randn(c(3, 3))
-a = torch_mm(a, a$t()) # make symmetric positive-definite
-l = torch_cholesky(a)
-a
-l
-torch_mm(l, l$t())
-a = torch_randn(c(3, 2, 2))
-if (FALSE) {
-a = torch_matmul(a, a$transpose(-1, -2)) + 1e-03 # make symmetric positive-definite
-l = torch_cholesky(a)
-z = torch_matmul(l, l$transpose(-1, -2))
-torch_max(torch_abs(z - a)) # Max non-zero
-}
-}
+    
    if (torch_is_installed()) {
+
+a = torch_randn(c(3, 3))
+a = torch_mm(a, a$t()) # make symmetric positive-definite
+l = torch_cholesky(a)
+a
+l
+torch_mm(l, l$t())
+a = torch_randn(c(3, 2, 2))
+if (FALSE) {
+a = torch_matmul(a, a$transpose(-1, -2)) + 1e-03 # make symmetric positive-definite
+l = torch_cholesky(a)
+z = torch_matmul(l, l$transpose(-1, -2))
+torch_max(torch_abs(z - a)) # Max non-zero
+}
+}
 
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Cholesky_inverse
    
     
 
-    
    torch_cholesky_inverse(self, upper = FALSE)
    
+    
    torch_cholesky_inverse(self, upper = FALSE)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -230,17 +262,17 @@ triangular such that the returned tensor is
    
 $$
    Examples
    
-    
    if (torch_is_installed()) {
-
-if (FALSE) {
-a = torch_randn(c(3, 3))
-a = torch_mm(a, a$t()) + 1e-05 * torch_eye(3) # make symmetric positive definite
-u = torch_cholesky(a)
-a
-torch_cholesky_inverse(u)
-a$inverse()
-}
-}
+    
    if (torch_is_installed()) {
+
+if (FALSE) {
+a = torch_randn(c(3, 3))
+a = torch_mm(a, a$t()) + 1e-05 * torch_eye(3) # make symmetric positive definite
+u = torch_cholesky(a)
+a
+torch_cholesky_inverse(u)
+a$inverse()
+}
+}
 
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Cholesky_solve
    
     
 
-    
    torch_cholesky_solve(self, input2, upper = FALSE)
    
+    
    torch_cholesky_solve(self, input2, upper = FALSE)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -236,21 +268,21 @@ batches of 2D matrices. If the inputs are batches, then returns
 batched outputs c
    Examples
    
-    
    if (torch_is_installed()) {
-
-a = torch_randn(c(3, 3))
-a = torch_mm(a, a$t()) # make symmetric positive definite
-u = torch_cholesky(a)
-a
-b = torch_randn(c(3, 2))
-b
-torch_cholesky_solve(b, u)
-torch_mm(a$inverse(), b)
-}
+    
    if (torch_is_installed()) {
+
+a = torch_randn(c(3, 3))
+a = torch_mm(a, a$t()) # make symmetric positive definite
+u = torch_cholesky(a)
+a
+b = torch_randn(c(3, 2))
+b
+torch_cholesky_solve(b, u)
+torch_mm(a$inverse(), b)
+}
 
    #> torch_tensor 
-#> -4.4143  1.6973
-#>  3.7547 -2.0436
-#>  1.5229 -2.0137
+#>  0.8244 -0.2831
+#> -1.1787 -0.6527
+#>  1.0540 -0.4594
 #> [ CPUFloatType{3,2} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Chunk
    
     
 
-    
    torch_chunk(self, chunks, dim = 1L)
    
+    
    torch_chunk(self, chunks, dim = 1L)
    
 
     
    Arguments
    
     
    
 
     
    
diff --git a/reference/torch_clamp.html b/reference/torch_clamp.html
index 87460e824..4fa1d7830 100644
--- a/reference/torch_clamp.html
+++ b/reference/torch_clamp.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
         torch
-        0.0.3
+        0.1.0
       
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
  • Datasets
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
-
  • Examples
    • 
+
  • 
+  
+  
+
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Clamp
    
 
-    
    torch_clamp(self, min = NULL, max = NULL)
    
+    
    torch_clamp(self, min = NULL, max = NULL)
    Arguments
    
 
       
     
     
     
   
 
 
     
 
     
     
    
@@ -249,27 +281,27 @@ should be a real number, otherwise it should be an integer.
    
 should be a real number, otherwise it should be an integer.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(4))
-a
-torch_clamp(a, min=-0.5, max=0.5)
+a = torch_randn(c(4))
+a
+torch_clamp(a, min=-0.5, max=0.5)
 
 
-a = torch_randn(c(4))
-a
-torch_clamp(a, min=0.5)
+a = torch_randn(c(4))
+a
+torch_clamp(a, min=0.5)
 
 
-a = torch_randn(c(4))
-a
-torch_clamp(a, max=0.5)
-}
+a = torch_randn(c(4))
+a
+torch_clamp(a, max=0.5)
+}
 
    #> torch_tensor 
-#> -0.1812
-#> -0.9782
-#>  0.5000
-#>  0.2475
+#> -0.9450
+#>  0.4486
+#> -0.3574
+#>  0.1104
 #> [ CPUFloatType{4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Combinations
    
     
 
-    
    torch_combinations(self, r = 2L, with_replacement = FALSE)
    
+    
    torch_combinations(self, r = 2L, with_replacement = FALSE)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -224,14 +256,14 @@ python's itertools.combinations when with_replacementitertools.combinations_with_replacement when with_replacement is set to TRUE.
    Examples
    
-    
    if (torch_is_installed()) {
-
-a = c(1, 2, 3)
-tensor_a = torch_tensor(a)
-torch_combinations(tensor_a)
-torch_combinations(tensor_a, r=3)
-torch_combinations(tensor_a, with_replacement=TRUE)
-}
+    
    if (torch_is_installed()) {
+
+a = c(1, 2, 3)
+tensor_a = torch_tensor(a)
+torch_combinations(tensor_a)
+torch_combinations(tensor_a, r=3)
+torch_combinations(tensor_a, with_replacement=TRUE)
+}
 
    #> torch_tensor 
 #>  1  1
 #>  1  2
diff --git a/reference/torch_conj.html b/reference/torch_conj.html
index a3560f399..257311cd3 100644
--- a/reference/torch_conj.html
+++ b/reference/torch_conj.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Conj
    
     
    
 
-    
    torch_conj(self)
    
+    
    torch_conj(self)
    
 
     
    Arguments
    
     
    
 
 
     
    
@@ -217,11 +249,11 @@
 $$
    Examples
    
-    
    if (torch_is_installed()) {
-if (FALSE) {
-torch_conj(torch_tensor(c(-1 + 1i, -2 + 2i, 3 - 3i)))
-}
-}
+    
    if (torch_is_installed()) {
+if (FALSE) {
+torch_conj(torch_tensor(c(-1 + 1i, -2 + 2i, 3 - 3i)))
+}
+}
 
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,15 +227,15 @@
     
    Conv1d
    
     
 
-    
    torch_conv1d(
-  input,
-  weight,
-  bias = list(),
-  stride = 1L,
-  padding = 0L,
-  dilation = 1L,
-  groups = 1L
-)
    
+    
    torch_conv1d(
+  input,
+  weight,
+  bias = list(),
+  stride = 1L,
+  padding = 0L,
+  dilation = 1L,
+  groups = 1L
+)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -248,4384 +280,4248 @@ planes.
    See nn_conv1d() for details and output shape.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-filters = torch_randn(c(33, 16, 3))
-inputs = torch_randn(c(20, 16, 50))
-nnf_conv1d(inputs, filters)
-}
+filters = torch_randn(c(33, 16, 3))
+inputs = torch_randn(c(20, 16, 50))
+nnf_conv1d(inputs, filters)
+}
 
    #> torch_tensor 
 #> (1,.,.) = 
-#>  Columns 1 to 8  -9.2155 -13.3788  -4.8815  -5.9509   1.0559  11.3390   4.1760  -7.3364
-#>    0.9687   3.5362   6.5343   9.7170   3.5029  -0.5239  11.8401  -3.6896
-#>    9.2053  -0.8704   2.5167   5.3901   5.4175 -10.4469  -1.7450  -8.4401
-#>   17.3697   8.0818  -6.5866  -5.1253  -1.3999 -14.3850  -0.6795   6.7809
-#>    7.6453   6.5674   2.8867   6.4723   7.2736  -9.4473  -1.8036  -1.8636
-#>  -10.4610   0.7640 -11.0805   7.2157  -3.6219 -11.6838  -7.2940   6.4684
-#>   -5.6155  -2.4741   4.2673  -2.6692  -2.5511   5.6617   8.3692  11.8541
-#>    2.9653  10.0184   2.3498  10.2008  -0.0906  -8.3016  -7.6074  -8.9669
-#>   -0.1892  -1.4338  -8.1527   6.1579  14.1115   6.3555  -5.8888   1.3635
-#>   -0.8715   3.2313  -5.0396  -8.8492  -2.6872   2.5312   1.5977   4.7325
-#>   -0.6426   6.7878  10.6713   4.4664  15.5926 -12.9809   8.5686 -14.1252
-#>   -9.4297   2.5895   6.8971  -1.2187  -8.5115   2.7068  -3.6668   3.7638
-#>   -4.8841   1.5752  -8.0881  -0.7977 -13.0366   1.6083   8.0490  -1.1757
-#>    9.2181 -18.6418   5.3897  -7.0765  10.9595  -1.2353  11.2700   5.9045
-#>   -3.9796  18.5287  -6.2100   2.2016  -4.9045  13.3548   4.2783   7.5602
-#>    6.4389  -3.1190   4.0417   3.9676   5.6056  -6.5717  -6.5705   1.6651
-#>    7.8152  -2.1107  -8.4163   3.4218 -11.4875  -1.3201  10.1588   4.6279
-#>    3.1470  -0.6810  -4.7713   2.0391  -4.2066 -20.4188  -4.7113   8.9348
-#>   -3.2366  -6.0332   2.2069   8.2774  12.2769  14.3749 -12.7319  -0.7708
-#>    0.6335  -1.3701  -9.1195  11.4271  -5.1396   5.6112   0.7465   0.8958
-#>   -4.4243   3.2009   3.4101   0.8854  -6.3913  -5.3965  -5.5959   6.9523
-#>   -2.0583   3.5878   2.1682   1.0807   2.3449  -7.7811   5.4776  -2.1308
-#>  -14.8754  -3.5022  -4.9228   2.9605   5.1781  -0.4115   1.6601   6.8168
-#>   -8.0577  -3.7750 -12.4450  -7.1238  -3.8977  14.8252   8.1138   3.9659
-#>   -8.2239   3.0827   1.9788  -3.2824   1.4724  -0.2935   9.8999   4.8447
-#>    5.2614  -0.9639 -11.0854  -1.7399  -8.8694   3.1894  -9.9635  -0.2191
-#>   -6.9460   2.0936  -4.6144   2.4964  -2.3149  -4.9224   5.6279  17.6400
-#>    2.7239  -7.1132  -3.2270  -4.4261  -0.1128   2.0319   5.7296  -8.8630
-#>    1.2327   2.0651   8.5595  -5.4172  13.7335   3.8594  13.4662  10.1180
-#>  -10.8110  -5.3874   1.2913  -6.1393 -10.3391   6.3426  20.4897 -13.8959
-#>   -1.9465  -9.7411  -2.5889  -3.1008  -4.2539   6.0369  -6.8057   4.0780
-#>    2.4637   0.0831  -4.2466  -2.7181   1.9345  -4.0950  -4.5429  -2.3028
-#>  -11.2792  -0.2996   1.2558  -2.7901   3.7011  10.1111   3.7003  -1.7684
+#>  Columns 1 to 8  -0.0017   0.5069   3.2581   3.7537  12.2754  -5.7048  -5.1129  -4.2523
+#>    2.7127  -7.6338  -3.6486  -1.6662  -4.1071   1.5403   0.6615   6.2160
+#>   -2.2729  -3.8288  -2.1471   2.7769  12.7045  -2.8699  -3.6018   8.6840
+#>   -7.2595   1.5596   2.9942  -1.1676  -4.7889   5.0214   8.8218  -0.3723
+#>    3.5509   0.5360   0.6371  -3.4385   7.4924   3.2963   3.1253  -6.3898
+#>   -1.2744  -7.1059   1.7274  -8.6046   6.3798   2.0242   1.8863  -1.9836
+#>    6.8417  -1.1480  -3.7256  -1.9346 -21.1495  -8.0406   3.3976   4.0986
+#>    8.7771 -10.3459   0.3156  -1.4063  -0.7258  -3.8158   7.1611 -16.1417
+#>    9.2919   0.6332  -0.1275   4.9426 -11.1686  10.3236 -11.0894  15.3777
+#>   -4.9045   4.2498   0.0221   2.9043   6.1274  10.6731 -11.9362  -5.5863
+#>    9.7324  -7.3030   5.2873   3.2367   8.0769 -16.1905  -2.3771  -4.1089
+#>   13.9064   3.3125 -11.6255  -9.6345  -0.7639  -4.8531  -0.0017  -6.6886
+#>   -4.4770  -0.3462   8.2814  -0.2971  -0.1359  -2.4777   5.8273  -5.1940
+#>    9.3588  -2.9631   6.9407   0.2265   5.7733  -3.7639  15.6203   1.5223
+#>    5.5772   4.9461   7.4747  -4.4113 -13.4838   2.7753   7.3462  -1.0801
+#>    1.5742 -16.6824   2.1488  -1.6394  -2.2983   1.7920   7.6677  -1.1681
+#>   -7.0215  12.6356  -2.5029  12.4752  -9.1480   5.5969 -12.8287   2.8613
+#>    1.4362  -9.0530   1.1091   2.7584 -10.4542  -2.3472   6.1680  -0.9943
+#>    7.0034 -13.8367   1.1581  -1.6444   7.5425 -16.3620  -0.9606  -3.5915
+#>  -12.4596  -3.2990  -4.4497   5.4004   2.7059  12.8166  -3.7002  -5.2075
+#>   10.6642   0.3028  -7.6034  -4.2157   9.7668  -1.4204  -6.4702 -17.2024
+#>  -13.5322   1.7728  -3.8414 -17.8105  -2.6958  -1.4289  -1.3667  -1.8366
+#>   15.8162   7.1249  -7.7765   4.1826 -14.7957 -10.0711  -1.6079   9.0596
+#>    8.9817  -6.6871  10.0282  -2.3882   9.9517  -1.9338   9.1863  -9.4137
+#>    4.4930  -3.6485   5.8536  -8.5625  -4.3754 -14.6884   5.1613  13.5964
+#>   -0.3592  -1.6406  -8.0275   7.9000   9.7975  10.3208  -1.3015  -7.5435
+#>   -8.9658   3.7266   4.4378  -3.8461  11.6946   6.2142  -6.4027  -5.7191
+#>    3.6667  -2.5505  -4.8785 -15.9986   3.9813  -1.5403  -8.4792  -7.8988
+#>    0.0458   4.8166   4.0429   7.8074   4.5504   5.5666   1.8543  -6.6329
+#>   15.7990  -2.9065   1.6362   8.8012   6.6865  -4.6375  -6.1845  -3.4298
+#>   -0.1734   0.6800   8.6766   2.8967   0.2024   8.1668  13.2231  -9.2891
+#>   10.1324  -2.9827   1.4466  -4.3754   5.3177   7.4152   3.7100  -4.0681
+#>   -2.0969  12.1802  -5.7960 -10.0887   4.2842  -8.3605 -10.4836  -3.9750
 #> 
-#> Columns 9 to 16  -1.2297  14.3283   2.2631   1.3546  -1.5108  -7.3943   8.3483   6.9639
-#>   -0.8142  -4.1600   0.6429   3.5323   1.7201   2.6613  -5.5547  -6.9555
-#>   -0.4468  -9.6767   3.4243  -5.5370   7.9330  -3.7124  -8.0854   2.8903
-#>   -0.8646   3.8281 -11.0271   3.5653   6.2605  -8.7873 -19.2922   1.3006
-#>    1.9641  -0.2966   1.1310  -8.4550  12.6014  -0.7149  -4.5330   3.5843
-#>    1.9168  17.5806  -0.1329   5.5879   8.6691 -11.9588   3.0880  -8.4299
-#>    7.6088  -4.8286  -7.4288  -2.1821 -10.3345   2.2468  -1.3855   6.6098
-#>    3.9023   2.7969  -6.6740  -2.6189   3.4172  -7.3487   4.3757  20.1543
-#>   -9.2841  -5.3292   7.3305  -6.0223   6.0400   4.6032  -4.5086   1.9101
-#>    2.6685 -11.7018 -10.9301   7.0899   5.2995  10.9822  -4.8582 -10.7569
-#>    3.1771  -2.5365  17.9825 -12.2625   9.7687  -3.4043  -8.8135   4.3696
-#>    5.2823   0.7356  -4.1298  -0.8445  -1.4917   6.5445  13.9097  -1.9308
-#>    1.4821   4.8847  -0.5632  -3.9153   0.0182  -6.5668   3.3724  -0.0089
-#>    2.0266  -4.8865  10.2491   1.0754  -5.8866  -3.6270  -8.2653   6.7061
-#>   -2.9120   2.2089  -9.9280  -5.7288  -0.5021   3.7785   2.3965   2.2677
-#>    2.3213  -1.8852   6.9190   1.6314  -2.5556   1.8858 -11.7685  -8.4369
-#>    9.6492  -2.9285  12.2038  -0.3930  -8.1063   2.2913   4.9456  10.8998
-#>   -0.6015  -0.4647  -5.5746  15.3407 -13.0841   6.5545  -4.9857  -2.0244
-#>    4.8784  -8.7558   6.3537   4.8576   4.3489  -6.7662  -8.7465   2.3861
-#>   -6.2488  11.2704  -1.0825   6.4878  -7.8461   5.2779   5.0536   4.8804
-#>    7.4775  -4.1008  -3.6440  -0.9562   1.8611   3.2053   3.7267   6.6024
-#>    1.7077  16.4328   1.1904  11.2724  -1.9391 -12.8586   3.8936   3.2751
-#>  -12.2966  -2.3560  -0.6443   3.9921  -7.5230   0.9491  14.3577  -4.5637
-#>   -0.1001 -11.4522  -0.7943  -1.2022   5.1980  -8.8641 -13.6163 -11.5659
-#>    3.8534   1.4053   4.2852  -9.7152  -4.8377   5.4733  11.9647  -3.4902
-#>    5.6387  12.2535  -4.6256   9.8812 -10.5789   3.1894  12.6462   2.0144
-#>   12.5536  -5.3916  -6.8821  -7.8555   0.9683 -10.4353  -7.7733  -3.1686
-#>    3.2834 -13.1777   0.7912   7.4581   7.8920  15.8003  -5.9017   6.8834
-#>    6.0552   1.3924   6.7256  -5.4646  -5.5976  -8.2639   0.7305   4.5903
-#>   -3.8023  -2.4783   6.5691  -2.8264   5.2719  20.0680   3.1247   8.2646
-#>    5.6692  -8.6198 -15.6985  11.5830 -14.8205  10.7908  14.9619  17.0967
-#>   -8.9506  -2.7822   1.0922 -10.7574  -0.3966 -10.7521  14.5444 -18.7963
-#>   -6.0050   6.5820  -2.7543   4.5226   1.5246   9.3887   2.3188 -16.2012
+#> Columns 9 to 16   4.3836  -8.8815   5.6425   1.9450   1.8666  -2.7752  -3.8477  -8.1125
+#>   -5.6739  -4.1562   5.0299   6.1100   3.0308  -3.2524  12.1550   1.4395
+#>    1.9829  -7.6963  13.7838   7.4857  -0.6755   3.1654  -3.4276   4.6276
+#>    6.0546  10.2154  -4.6687  -6.2842  -9.8280  -8.3897  -9.6450  -0.5691
+#>  -16.1528   2.4416  -0.2655   1.6394   6.1251   2.1035  -5.6438 -10.8905
+#>    5.2522   1.9228 -11.4321   1.4259  -6.0636  -2.5523  -5.3765   5.1205
+#>   -2.6455 -16.1003   6.9674  14.9567  10.1449  -0.3167  11.3960  -2.0346
+#>   -0.0353   0.1707   3.8699   1.0839   8.1544   4.1939   1.5788   5.5370
+#>   14.0917   5.7634 -16.1650  -3.9257  -4.3522 -11.9848  -1.9146  10.5286
+#>   -0.7115   2.0449  -8.5813  -1.1405  -4.5425   4.2556   5.6020  -1.2699
+#>    0.5076   6.5343   3.6051  -7.3038   2.6042 -12.1737   7.5300 -17.0367
+#>    4.4522  -4.3971  -0.0601   9.5360   3.1118   7.9875  -1.3043  -3.7343
+#>   -1.3102  12.8509  -1.0419  -0.7859  -4.0887   2.8783   4.8966  12.1490
+#>   -4.3585  -2.5972  -9.5963  -1.3815  10.5933   2.4334   4.9135  -8.8752
+#>    1.1210  -8.8738   5.5790   0.6040   1.2235  -3.7092   3.9525  -7.3712
+#>    9.4688  10.8544   4.8078   2.0908  -1.9452  -6.1405  -2.6633   4.1484
+#>   15.6146   0.6893   1.9452   7.0406  -9.2167  -7.9627  -8.6179 -16.5386
+#>   -5.8947   9.3849   8.4597  -0.3568   4.2719  -8.7190   6.1498 -13.5015
+#>   -0.0308   5.6615   0.3692  -2.2015   3.2229   5.9640   9.3285   4.4520
+#>   17.0183   2.6649 -12.7490  -3.5640  -3.0899  -3.8250   1.8043   6.0982
+#>   -0.2770   4.4155  -2.7124   4.4607   4.7004   0.6141   3.5834   4.5741
+#>    4.2364   3.7896   9.8809  11.9708  -2.8114  11.5021  -8.3987  11.9446
+#>    2.2391 -20.1738   1.2792  14.1326   8.1334  -2.0730  -0.2089  -4.7396
+#>    3.0287   2.2975   0.3587  -3.3079   4.8468  -5.8013  -1.5528  -8.6124
+#>    0.8401  -0.3229  -1.4869   3.4499   2.2852  -5.8397   5.0788   4.2155
+#>   -0.9151   0.5754  -7.9526   1.8602   0.9512  -6.7583   4.7192   6.8694
+#>    2.8743   7.5369 -14.9726  -2.3078  -7.1016  -2.8158  -4.9740   3.2809
+#>  -11.1349   5.3591   6.0480   0.5696  -4.8892   0.9056   0.9976   0.2354
+#>    9.9833  -5.4914 -12.4955   4.5996   3.4927   0.3793  -2.4541   5.4637
+#>    4.0954   1.0442 -10.7929  -2.7203   9.8539  -0.8593   5.8514   0.3049
+#>    4.7648  -4.9256  -7.6856  -2.3927   2.9550  -3.7901   5.9330   1.6260
+#>    1.7529   5.3920  -0.0853   0.9710  -6.7972  -2.2590  -5.0644   4.8622
+#>   -1.0515   5.1427   3.3580 -10.8688  -2.5220   7.5011  -6.1148   3.0191
 #> 
-#> Columns 17 to 24  -4.8695   3.7966   2.5417   6.4239   1.1281 -12.0619   0.3447  -1.8495
-#>   -1.4736 -17.1540   3.4796   0.8374   9.6773  -2.4723   3.6881   1.1677
-#>  -14.2464   0.4473  -4.9651   0.1220  -3.6831   6.5208   2.0460  -4.2853
-#>   -1.5036   4.6976  -3.8827 -16.1917  -1.0532  -6.9277  -9.8995   6.4730
-#>    0.7103  -1.6257   3.9315 -10.7942   4.8641  -4.6957   3.4183   2.0056
-#>   -5.2118   3.0820  14.3102 -12.4407  -4.1198  -3.3126 -12.5885  -0.2005
-#>   -2.3870   7.6276   5.7137   5.6988  -1.1870  -2.0965   1.6126   2.9134
-#>   14.2791   9.6267   9.3154   0.1505  13.1650  -2.0966   7.8122  -0.5654
-#>  -12.7522  -0.3538   7.9037  10.9583  -2.5624  12.9610   0.0900  -2.0126
-#>    4.2564  -0.3686 -12.5285   3.6130   0.7694  -9.3640 -11.3119   4.9255
-#>   -6.6944 -12.5297   7.5991  -9.9898  -5.4475  -1.1485   2.1881  -6.5117
-#>    6.7062  -5.6601  -0.6736   4.1640  -6.3789  -4.6947   3.3107   0.1270
-#>    1.4707   1.9069   7.0601 -11.8777   0.2879  -8.3140  10.3291   5.2538
-#>    4.4303  -6.6440 -10.1144  -3.8942 -15.6688  10.0768  -4.0495   3.0229
-#>    0.7410   3.9392  -5.8770  -9.6422  -4.4338   7.3714   1.1163   1.7708
-#>    9.1297   2.9569 -12.4715   3.4630   2.7644  -0.6531 -16.8944   1.5479
-#>   -1.2994   8.4404   0.0242   6.1673  -5.8023   1.5684  14.3514  -0.2133
-#>   -3.3769   6.3468  -2.9930  -3.8928 -14.3513  -1.1740 -16.5160   3.8613
-#>   -2.0130   9.6563   3.1024  12.3306   0.5746   5.1057   0.9603   0.5509
-#>    3.8197  15.7484   0.7000  -5.7188  -3.2259  11.2313  -2.1890   2.5566
-#>   -6.0698   5.0667  -0.8852   2.6291   4.4607  -2.9877  -0.4767  -2.3615
-#>    3.3213   2.0417  -7.6848   2.5958   3.6713   3.4101   2.9736   6.2485
-#>    6.5813  -5.7292  -4.1808  -4.0592  -2.1550  -1.4666 -10.1642  -4.0215
-#>   -2.6416   1.1199  -1.7281  -1.9130 -22.5373 -15.1305  -2.5205   8.0506
-#>   -5.9875  -3.0936   2.9764   6.2160  -1.3205  11.5670   1.5781  -3.7485
-#>    7.4648  16.2491  -5.4137   6.2873  13.0638  15.2053   0.0131   0.5840
-#>    6.0694   2.7495  15.8616  -9.4167  -2.1686   0.9645   7.1023   3.1712
-#>   -4.9406  -1.4991  -8.2263 -11.4416  -7.9785  -4.3864  -0.9767   6.5426
-#>   -0.4732  -1.7360  -5.7882   1.7442  -9.5797  -1.2339   2.6533   1.0732
-#>    2.7094  -7.0719   0.8703  13.5209  14.0761 -16.0680   4.5148   7.2220
-#>    5.4432  14.7888  -6.7092  17.5242 -11.2132  -9.3064  -1.8499  -3.9303
-#>   17.9627 -11.4976   9.9461  -0.3440  -4.5030  -1.2401  -0.3820  -8.0093
-#>  -14.7131 -13.0150  -0.9647  -3.1112 -12.8724  -8.7595 -10.1707  -8.1402
+#> Columns 17 to 24 -12.7569  -3.2014  -4.9477   8.0396   0.1480  -8.1503   1.8466   1.4811
+#>   -4.1258  -0.8260  -1.4697   2.2774  -1.4499   6.5272   6.7380   3.2592
+#>   -0.3252   2.4459  -1.1071   3.5770   5.0879  -3.2505  14.6033  -3.1035
+#>   -5.1707  -1.7914   3.3909   5.1288  -3.3835   4.1724 -11.6968  -0.4428
+#>   -8.8881  -5.5751 -11.8423  -0.0476  -5.7856  -0.9314   3.7667   1.3444
+#>    6.7039   0.6751   4.4299   3.8280  11.4129  -5.1620   1.9157   6.0946
+#>   10.7073   0.7234  -2.4557  -4.2930  -0.6347   3.9729  -0.3295  11.2576
+#>    3.6426   3.6079 -13.2582  -1.7648  -8.7286   5.7397   2.7646   1.0751
+#>  -14.4166   2.1538  17.6702  -8.6276 -10.8877   7.6990   2.8082 -12.0108
+#>    7.7238   6.9618  -4.0760 -10.4079  15.1706   3.4098  -7.7159  -6.0290
+#>   -7.8506   2.2986   9.0994   3.0793   6.4497   6.6537  -4.5718   4.2199
+#>    4.2440   4.4275  -7.0432  -7.2756   3.1261   3.2780  12.7839   6.6176
+#>    1.0640  -2.5907  -2.0621   6.4645   9.3268  12.6343  -0.1254  -4.1593
+#>    0.9177   5.0589  -6.5546   6.2201  -3.0735  12.9743  -5.7553   0.5045
+#>    6.8044   5.5532 -12.0917 -13.2226 -11.2319  10.1235  -4.6331   5.0385
+#>    3.2687   5.4748  10.5017   8.8038  -0.7778   2.4431   8.2787  -5.0092
+#>   -4.7944  -6.3961  10.3905   4.4328   9.9136   4.3065  -9.0920  16.1394
+#>   11.5727   1.0774 -11.0627  -5.5270 -11.5985  13.0164  -1.6924  12.3224
+#>    1.1779   1.9440  -4.8213   7.0950   3.4402  -2.0456   0.4378   2.0199
+#>    5.5184  -3.8164  11.2105  -5.6238   7.1289   1.6388   6.2446  -3.1492
+#>    0.6391   1.8168  -4.7819   2.8200   7.1741   5.2373   3.5003  -2.5255
+#>    1.7020  -1.8355  -7.1825  -1.3645   1.9672 -12.4413   2.1877  -1.5600
+#>    2.7693   3.3082   6.4872   1.8921   3.0838   3.5617  13.0991   6.4284
+#>   -5.1973   7.7472 -11.7490  -6.4281 -12.7652  -1.0390   3.9047   2.6759
+#>   -2.2705  12.7944  12.0051  10.2064   8.4377   7.6387  -2.0573   4.3096
+#>    4.9081   5.2918   6.1564  -2.0601   8.8497   7.3686  -1.2929  -7.7612
+#>   -3.9980   2.4928  -3.9108   3.0321   1.7197   1.6843 -12.1051  -3.6635
+#>    5.1207   2.0376   1.3035   0.6390  10.8610   0.5200  -1.0097   0.6060
+#>  -11.2807   4.0115  -3.0525 -10.3535 -13.5639   1.5597  -6.7436   3.3555
+#>   -2.3282   3.8698  -2.8416   2.0518  -0.3548  14.5449   9.6512   4.0287
+#>   -1.2852  -5.3337   9.6606   6.2242  -7.9623   3.7667   3.8953  -4.2340
+#>   -4.8996   7.6168   0.9547   1.4163  21.0954   8.0410   2.2115  -6.1601
+#>   -3.2272   9.9728  -9.7445  -9.8507 -10.5398 -11.1604  -3.1450  -9.0675
 #> 
-#> Columns 25 to 32   1.8547  10.9740  -4.0183  17.7692   9.3797  -5.4057  -1.2404   3.7908
-#>    0.2848   5.8314   3.8718   8.4746   9.3938  -0.3301  -3.7456  -1.8790
-#>   -7.1713   0.7990  -0.0932   4.3337   6.5727   1.5852  -3.6544  -5.7062
-#>   -1.1724 -10.0680 -15.0025  -6.9689  -9.6004   7.3883   1.7708   7.3982
-#>   -0.5131   8.0941  -0.3890   1.9520  -1.9299   3.8167   3.7566   1.6492
-#>    7.9042  -2.2868   8.7217   5.5398  -6.5718   2.3075 -18.9907   1.2899
-#>    2.5883  -1.2177 -12.2389   3.3108  -1.5127  -6.1679  -3.8565  13.5627
-#>   -5.1450   5.9179  -3.6925 -10.1028  -7.9639   3.4012   4.1468   2.0707
-#>   -9.5785  -2.3109   1.5583  -3.7295   7.2034   4.7588 -15.4971 -11.6062
-#>    3.6796  13.0790   2.7777  -2.5593   4.2404   6.4327   2.8832  -7.3154
-#>   -8.5260   4.1016 -11.3898   3.9401   1.4674 -13.3377   0.7232   0.7624
-#>    6.6091   0.1427  -1.6529  -1.2078  -0.8534  -1.6778   7.6013  -0.3548
-#>    5.6827  -5.8490   3.1977   8.0379  -7.3799   4.6825  -1.2672   7.2958
-#>   -1.9738   3.6717 -10.3418 -12.5794   9.9656  -5.8658   7.0017  -3.5312
-#>    2.2719  -1.7249  -9.5627   7.5573 -12.4113  10.4928   7.8016   0.9843
-#>    8.1371   6.7174   2.2147   0.4939   8.8809  -7.9415   5.5854  15.3170
-#>  -11.3932  -1.0371   4.5466  -5.9271  -3.3451   4.6784  -2.8040   8.8742
-#>    2.4757  -1.5845  -1.1163  -0.1127   3.7453  -1.4919   3.8370  16.2731
-#>    5.6458   4.9561   7.9401  -3.1958   3.8495  -2.7730 -12.7283  -2.1418
-#>    3.3611   7.1985   7.2372 -11.4225  -0.6149  -0.4715  -1.4611   4.9257
-#>   -9.7007  -0.9226  -4.5642  -5.6105  -7.3980   3.1887  -2.8203   1.5539
-#>    2.0339  -7.3502  -4.2530   0.4539  -2.2738  -7.3877   3.7860   1.1196
-#>    1.9136  -2.9839  -6.5101   9.0191   0.3577  -2.4797  -0.3782   5.8724
-#>    5.7521   3.2082  16.4502   0.2664  -4.4580   8.6922  -6.5666 -15.9535
-#>  -18.4346 -10.0809   3.6438   2.8954 -10.2978   6.2441   9.7118  -1.3140
-#>   -0.3073  -8.0157  -2.1891  -3.7042  -4.4942  -1.6702   3.9804   1.5787
-#>   -1.2848   0.6467  -5.3219  -3.3679  -3.5367   4.1371 -11.7713  20.9084
-#>    4.5120   9.6014  -1.7913  -0.7869  14.9937  13.7954   0.0611 -12.0633
-#>   -3.4014  -2.2428  -3.0984  -1.5958  -3.6751  -7.7030  14.9411  10.2175
-#>   -1.6834   8.8222  -4.5406  12.8437   7.8638   0.8852   4.3863 -19.6971
-#>    0.6685  -5.6872 -17.9757  -7.1754  -1.7314   0.7158   9.9081   8.7799
-#>    1.5819  -4.4604  15.8837  -4.2586  -3.4666   4.1039   0.2826  -8.2974
-#>    1.1024  10.2194   4.3061   9.3984  10.0735   2.7659  -1.0396 -15.0943
+#> Columns 25 to 32   1.8838  -5.2315  -8.9970  -5.3463   6.5990  -8.7278   2.5317   7.2795
+#>  -10.3640   2.6807   2.1797   6.0306   1.2948  -2.5437   0.6122   9.0732
+#>   -2.3311   1.7300   4.0659  -1.0271   3.4003  -3.2376   7.3427   8.9921
+#>   -3.9044  -3.9095  -7.6073   5.4136   4.8206  -6.4818   6.7405   9.6930
+#>    1.8212  -1.7141  -9.7601  -8.7617  -5.8624   0.2889  -5.9471  -2.0685
+#>   -2.0515  -1.0642   1.0953  -6.5147  -0.7429   9.5914  -9.4436 -10.2337
+#>   -0.4887   0.6223   9.0298  11.9800  -5.8018   2.9564  -6.5534   0.9106
+#>    5.3430  -4.7827   4.3220  -3.3741   2.1184  -3.1440  14.1775   8.2804
+#>   -5.1914   2.5666   4.5424   6.3191   1.7201  -3.7996   7.2245  -6.8536
+#>   -3.6347   2.8428   0.5575  -0.6153   0.8044   7.3047  -0.1687 -12.4056
+#>    7.9655   6.6323  -0.3890   2.3832 -13.7306   4.2921   0.0261   8.3286
+#>    8.9139   6.9578   0.6094  -8.1288   4.1101   3.7043   3.8990   4.2212
+#>    5.6857  -1.7151   6.1521  -0.6481  10.7693  -1.5247   7.6244  -8.0634
+#>   -7.9516   0.8880   6.7325   4.9532   1.1588   4.8412   1.0281  -1.2694
+#>   13.3677   7.7116   7.8308  -2.7983  -4.3281   5.9776   4.1833  -6.2174
+#>   -1.5231  -3.8248  -0.4930  -2.1026  -1.2988  -3.5005   2.2782   7.4686
+#>   -0.8952  -1.4051  -4.2220  -2.3988  -1.8383   2.5641 -10.7840  -3.0684
+#>   -0.0220  -1.4435   6.1759   6.3155  -4.7200   2.1764   5.9783   1.5432
+#>    1.7116   4.7562   3.9363   1.1410   6.4128   0.0694 -10.4346  -2.3504
+#>   -4.3430  -3.2654   6.7803  -7.4416   0.3748  -0.4770   1.1880   0.0856
+#>   -8.5723   4.1691   5.2118   3.7059  -2.9037   7.6973  -7.5632  12.0273
+#>   11.5666  12.6088  -1.4150  -5.6689  11.8927  -2.5614   2.3107   0.3495
+#>   10.9083  -1.2806   7.1950   3.1026  -0.7004  -4.7780  -9.0234  -5.7408
+#>   -1.0090  -2.9239   2.8265   0.8185  -3.8453   2.5459   5.4261  -0.9739
+#>    0.6763   5.6249  -1.3803   2.9875  -3.0686   4.4291 -14.3752   3.7723
+#>   -2.1913 -10.2968  -2.0582  -5.9442  -0.4236   5.2149   1.7975  -2.1567
+#>   -7.6165 -10.5312   1.7332  -6.6905  -5.0284   5.5104   1.2902  -8.3583
+#>    2.1413  -0.0589  -6.4868  -0.6710  -2.1910 -10.2542  -8.6546 -15.6017
+#>    4.7712  -1.2127   8.0619  -4.8880   2.3137   1.0140   5.0451   1.9885
+#>   -0.5853  -3.7111   4.7597   5.0333   1.9762   4.8363   0.8507 -19.1190
+#>    5.7191  -9.6450   1.8695  -6.9340  -1.0690 -11.5491   0.6299   1.9411
+#>   -0.7498  -4.9341   3.0335  -3.8179   3.9865  -2.7851 -10.6340  -9.3775
+#>    8.4640   2.4687   3.2002  -0.9817  -9.5693  -3.5052  14.5246  -2.2038
 #> 
-#> Columns 33 to 40   3.5032   1.6151  13.6459  -6.1995  -9.8488  12.3514   2.2729   2.7821
-#>  -10.2329   4.8449   1.8473 -11.9907  -1.5774   4.7261  -4.6764   3.4796
-#>    3.8205  -5.8217   0.2988  -1.5748   1.5653 -11.6889   1.7686   1.9090
-#>   13.4749  -8.6482  13.3443   4.5522  -1.9011   0.3399  -5.3454  -9.5580
-#>    7.1295  -9.1016  -0.1034   2.5529   4.4182   7.2676   8.8859   0.2998
-#>   13.1355  -0.0824  -3.8959   2.2275   1.8356  -5.7940   2.1526  -2.6223
-#>   -4.7088  -2.6130   3.9118  -0.8039  -0.8296   7.5224   0.0166   7.7747
-#>   -0.1367  10.0605  -3.1626  -4.9235   9.0574   8.3608  -0.6431   1.6203
-#>    3.9886  -7.4451   1.0348  -6.2363   2.4075 -16.8698   7.9134  13.1190
-#>   -5.1753 -10.0095   4.1602  -4.6037   7.9717   2.1947  -3.1838   1.4414
-#>   -5.7036  17.6836   0.8175 -12.0724  10.1267 -12.3621  -6.8936  12.6102
-#>    0.2967  -3.6497  -0.3328  -0.5213   2.7169   4.6599  -0.7938  -6.6801
-#>   -4.6639  10.4488   1.7619   8.8252   4.8165   2.1116 -14.7942   0.6958
-#>   10.5806  -5.0217  10.8438  15.9300   3.0902  -4.6161   7.6645  -3.7093
-#>   -1.9758   8.7825   5.5702  -1.1931  20.7325  -1.6902  -6.5710   8.5370
-#>   -3.4315  -3.9690  -2.7181   3.9475  -5.2950  -4.0683   5.1271  -8.8019
-#>   -2.4889   7.4373  -1.2045   4.6231   6.4070   1.5636   1.8816   2.0895
-#>   -2.4631   8.3441   2.7115  12.9789  11.4943  -4.0801  -2.7654 -14.1363
-#>    2.3585  -3.1852  -4.5072   3.1720   2.2918  -3.6508  -0.0585   4.2550
-#>   -7.1227  14.0587  -9.2695   2.0586   8.1077  -8.0613  -0.0276  -2.7233
-#>   11.5503  -1.0402  -0.7068   7.1030   9.6921   4.0622   1.5635  -0.8581
-#>  -12.0199   3.8998  -3.4075  -9.5343  -9.1043  -7.0554  -2.9911  -6.6626
-#>   -5.6089   1.6227   6.6670   2.8772   2.9687 -11.4097  11.1582   2.4165
-#>    6.3019   3.1575   7.2511   8.8503  20.9788 -13.1751  -7.8800   5.9546
-#>    6.2591  -1.8061   0.9736  -4.3092  10.0755   3.7018   1.4896   5.1624
-#>    2.0282   5.3408  -1.1668   2.4538   0.4726   9.4383   3.5097  -6.0822
-#>   -9.5976  -1.6313  10.0339   0.4870   8.2388   2.8234  -7.8079  11.4701
-#>   -1.7225   9.5804   6.9717  17.5916  14.5329   2.5304 -11.2523   1.8387
-#>   -2.7241   0.4796   2.8995  -2.0427   5.6780   2.6961   1.2184   1.7330
-#>  -14.0950  14.8797   6.8516  -4.5487  -0.0471  19.4064  -9.8698   2.2338
-#>   -3.9601   2.5363   8.8149   5.9725   3.4330  12.5849   5.8831  -0.3196
-#>    6.8494  -7.2550   1.9249  -1.6378  -4.1263  -2.4659  -3.9709  -8.6884
-#>    3.5879  -0.8758   7.2625  -4.9020  -4.2377  -0.3593  -5.0603   1.3574
+#> Columns 33 to 40 -11.8359  -1.1674   3.5947  -4.2214  -9.0593   9.2305   5.6302   3.1672
+#>   -0.3669   1.4082  -1.1256   2.3696   1.3631 -10.5406  -5.1200   7.2436
+#>    2.4813   1.2822   1.1203 -14.3431 -14.9815   4.1509  -4.2795  -8.9770
+#>    6.0945   2.8545 -11.9397   3.3583   4.8196  -5.3744   3.0747   3.8545
+#>  -10.9909   8.6085  11.3655 -11.3509   6.5653   2.5778  -1.4638   4.1041
+#>    6.9667  -3.0697  -1.0341  -0.8762   0.5616 -11.0444  -8.4441  10.5943
+#>   -5.0842   6.0846   2.3173   3.1783  -2.6641  11.1517  -4.5040  -4.3047
+#>   11.6663   8.5665 -14.5643  -1.6608  -5.5992   2.0893  -6.1898  -5.4842
+#>    4.9422  -9.3724   0.8579   9.6816   9.3466   0.5508   7.8024  -1.8268
+#>   -4.2879  -3.9464   2.3123  -2.0965  -2.2254 -12.8112  -6.3540  -4.0766
+#>    3.9309 -11.6332 -23.2086 -11.9200  -8.8749   2.2812   6.7785  12.3998
+#>    1.7818   3.9006  -2.3963  -1.2249  -5.9492   5.8874  18.9748   1.7110
+#>   -3.7662  -2.1190 -12.5919   9.0871   5.4708 -14.0403  -4.3271  -3.2335
+#>  -13.1970  -2.8872  12.7972   2.6439  10.0180   2.4739  -4.6104  -4.4681
+#>  -10.9275   5.5363   2.0996   3.1780   4.9249  12.4357   8.3429   1.8119
+#>   10.7996  -0.3874   6.4108   9.3395  -6.0719   6.3334  -3.3859  -4.5842
+#>    1.4604   1.4527 -10.6700   2.0140   5.7853  -6.9652  14.9962  -1.8639
+#>   -0.5917   8.5685   5.5385 -10.1893  10.7400   7.1663  -1.5148  11.3356
+#>   -1.3234  -2.7612  -8.2776  -1.6519  -5.0230  -9.5948  -5.2549   3.2932
+#>   -6.8445  -4.0829   9.4036   2.4799  -1.4283  -2.1257  -6.0441  -1.1196
+#>    2.3057  -7.1285  -4.0928   2.3285 -12.4510  -9.8804  -3.3696   0.5148
+#>    1.5211   9.8054   2.2292  -6.8698   0.9409   3.2807   0.4857  -9.6716
+#>    0.8528  -3.2264   6.1312  -3.5258 -13.0457  12.6642   6.7780   3.8981
+#>   -8.9966   2.7833  -3.5231   4.2465  -5.7195  14.0048  -0.6942   3.2118
+#>   -1.5793  -4.7026  18.4549  -8.8497  -0.7816  14.6262  -8.0896  -0.4770
+#>    2.3976  -9.9536   7.9479   3.7603  -0.3322  -6.5446 -11.2529   3.8992
+#>   -2.9168  -4.0986  10.4816  -8.3925   4.8899  -0.3251  -3.6297   6.1881
+#>   -3.8362  -1.9047   1.3935 -10.5810   6.2518  -4.7890  -7.1427   3.0364
+#>    0.5690  -7.6323   5.5362   8.2267  -2.2732   5.3475   6.2447   5.9902
+#>  -11.4839 -10.8410  -1.3877   6.1463   5.1107  -4.5009  -0.6623  11.8616
+#>    2.8183   2.2219  -3.6330  12.6222  -4.2354   0.2443   4.0771  -1.5346
+#>    4.2749 -12.1141  11.4388   9.5722   5.9810  -6.3246  -1.6893   7.2998
+#>    0.7366  -0.4654  -0.9035  -5.3419  -0.2736  19.5346  17.8018  -0.1776
 #> 
-#> Columns 41 to 48   4.7544  -3.8532   1.3537  18.0648  10.9368  -3.5256   3.2463 -11.0885
-#>  -11.3760  -8.4545  -6.2563   5.3757   8.7233  -3.5149   6.3589  -8.7134
-#>   -6.3385   3.0080  -2.7377 -11.4562  -1.4043 -11.9691   4.4455 -10.3454
-#>   -1.7988  12.7498   3.4409 -11.4334  -3.6944   3.1703 -18.5209  13.9670
-#>   -7.0067   0.2700   0.6592  -2.8205  -2.1231   2.6654   4.0859  -0.1331
-#>   -0.1821  10.3581  -1.0482  18.0136   5.8157   0.6943   5.0057  -9.2490
-#>   16.3705  -3.4258   0.1996   2.7516  -2.5854   3.3346  -5.4813  10.1746
-#>   -4.4923  10.9602  -2.0492  -6.0832   3.6700  -0.7903  10.9270   6.1423
-#>   -3.9708   2.4995  -6.0148   3.8555   8.7296   7.2791  -1.8554  -6.3309
-#>    1.3187  -1.9468  -4.6737   7.0641  -0.5110   3.2483   5.6464  11.6303
-#>  -12.5985  -1.3315  12.5610  -5.4499  -1.1348  -1.6720  -4.7975  -6.8854
-#>    2.6793   0.8099  -0.8208   2.0605   0.2827  -2.0339   6.4241   0.4315
-#>    7.9504   9.4412  11.9954  -4.2733  -6.7321 -10.7687   5.7605   1.0645
-#>   10.1457  -0.1423  -4.4749   3.1614   0.5063  13.8041   0.4637  -2.7746
-#>   -8.8189  13.9607   6.1141  -4.7353  -4.7814 -11.4741  15.6631   3.3705
-#>  -12.9460   3.0432  22.2455   5.7695  -7.4932   4.4910  -7.6372   1.0687
-#>    7.6966   7.9218   6.8505  -3.2164  -2.5481  -1.6493   0.3228  11.9375
-#>   -5.3288   6.7775  21.9513  -2.1556 -16.4669   3.2823 -15.1787  -1.6592
-#>   -5.5416   1.3512  11.4356  13.0597   8.0178  -9.0771   7.9728 -10.5826
-#>    4.3661   8.3695   7.9370   4.1265  -1.1032   3.1672   8.7553   7.8063
-#>    0.4222   2.5522 -11.8432   1.0525   6.8551  -9.1025   0.2412   4.1610
-#>   -1.5518  12.8515  13.8811  -0.2850   6.1632   3.1550  -3.3247  -6.2828
-#>   -0.3344  -8.8364   6.2271   3.0440 -13.1455   8.6795  -3.1012  -0.8562
-#>    1.7786   9.6441  -1.1621  -0.8870   1.6428  -6.4055   8.3717  14.0249
-#>   -4.4673   1.8278   0.4781   0.6198  -1.5879  -7.5952  -3.1377  -3.3075
-#>    4.9676   6.9847  -2.2823   8.5192   4.7167   2.9328  -1.9337   2.2208
-#>    5.1345   4.7293   6.2842   6.9622  -3.0074   2.0482  -8.6324  11.0786
-#>    9.9139  -5.6082   1.8029  -2.0476  -3.7845  -0.8135   2.5508   7.4146
-#>   -1.6769   8.3854  17.1547   3.6791   1.2201 -11.0141   1.3322  -1.4765
-#>   12.4462  -1.2692  -3.4418  -0.4186  10.4377   5.5881   5.5438   4.7403
-#>   16.0646   6.3947  -3.8445  -5.2718   2.5817   4.1743  -0.7499   2.3548
-#>    9.2805   0.4435 -12.8332   2.4571 -14.3137  -8.1492  19.0161  -4.3424
-#>    1.7508  -5.0976  -9.9643  10.7083   4.8640   0.9441  -0.6708 -10.0837
+#> Columns 41 to 48   8.8933   5.9160  -6.2404   2.9623  -9.6921  -7.6478  13.8534 -11.1659
+#>    0.1709   9.0619   6.2530  -0.9892   2.4302   3.6891  -1.1117   6.2600
+#>   -7.3244  -5.0991  -5.6875   1.7317  -2.8637   1.1560   0.4200   0.7255
+#>    0.9692  -1.9280   5.1149  -2.1213  -8.8343  -7.3494   7.7670  -3.6599
+#>    5.2305   9.9525  -6.3719  -9.8013   4.8982   5.5371  -7.8636   4.1233
+#>    1.2551  -2.9130  -6.8755  -3.6328  10.0155   5.1864 -12.3542  -8.0395
+#>   -0.2401   7.5571  -1.5095  11.3555  -5.7092  10.0492 -10.4731   8.1340
+#>   -8.3481 -12.4499  -8.1132   0.5452   7.6533 -10.7303   4.6612  -2.9052
+#>    7.9080   4.2594  10.2944   5.4354  -1.4502 -12.3063   3.2777  -1.3526
+#>   -5.3863  -2.6302 -10.5039   3.0438  12.8014   5.9081  -2.0973   3.7497
+#>  -13.8028  -7.5860  11.1906   5.0728 -16.4770  -8.3798  -6.9643  -1.5966
+#>    6.3616   7.5875  -9.3551   5.9182   5.2882  -4.4134   5.3110  -1.6438
+#>   -6.9771  -0.8297  -3.2343   5.3062   6.3401  -3.9372   5.8353  16.1633
+#>   -4.6283  12.9429   1.4393   4.6592  -0.1652   4.1131   0.1173   7.2165
+#>   11.2960   5.7281  -3.3924   1.1614 -10.2384   3.7862   3.3649   1.2174
+#>  -13.0654   0.5469   0.3616  -5.3268   3.9519 -15.4861   5.6096  -4.9858
+#>   17.7016  -3.5991   1.7903   8.9998  -6.9908 -12.5465   4.0526  -2.3573
+#>   -5.9614   2.9571 -13.7574   7.0271   6.8848  13.9695 -23.0365   6.4403
+#>  -12.4099  -5.9334  -1.1855  -8.6726   9.7408  -1.2389  -7.7672  -0.9879
+#>    2.4116  10.9301  -6.8317  -8.8723  -2.3849  -4.0453   4.7047  -7.9257
+#>   -4.9343   0.7164   5.3387   5.4643   3.7066 -16.0775  -0.9404  12.8221
+#>    9.5986   0.1300   2.6382   1.1211   0.1772 -11.8479  14.9037  -8.0804
+#>   -7.9290   4.6596  -4.7398  10.0910   0.9941  10.0994  -1.5582  -3.4470
+#>   -0.8331   9.1043 -19.6188  -8.6376   0.4230   3.2650  -9.3410  -3.3828
+#>  -15.8378  16.5037   7.2699  -4.4574  -3.4882  -0.2002   2.7750  -6.6233
+#>   -3.3090   5.6364  -1.3068  -7.0144   5.1868   5.2018   1.9072   2.3533
+#>   -3.6108   2.3837 -13.4241 -10.7397  10.7944  -4.5891  -1.6160  -8.1370
+#>   -3.8928  -1.2637  -1.2216  -1.9646  -2.3584   3.5162  -6.0384  -1.5790
+#>    4.1064   6.6195   7.3277 -11.6298   1.9081  -4.0359   8.6479 -13.3586
+#>   -0.3447  -7.8899 -13.3295   3.9589  11.9086   7.1476  -8.3025   5.0787
+#>   -6.4558   4.2693   5.7980   0.4546  -0.0632 -13.5106   9.3856  -4.2842
+#>   -6.3272  -9.1070  14.0095 -10.2001   6.7923  -5.1088   9.3252  -2.1318
+#>    0.8296   3.1492   4.6179  -2.9759  -0.3277  -9.1670   6.6892  -3.8158
 #> 
 #> (2,.,.) = 
-#>  Columns 1 to 6  1.1040e+01  2.2389e+00  4.4628e+00  5.7466e+00 -2.6627e-01 -8.1387e+00
-#>   4.2474e+00  6.3469e+00  1.3473e+01  6.8586e+00 -3.1554e+00 -1.1583e+01
-#>  -1.1430e+01 -3.9404e+00  6.4832e+00  8.8420e-01 -3.3189e+00 -7.1515e-01
-#>  -5.3851e+00 -1.9954e+01 -3.6777e+00 -3.9789e+00  6.8651e+00 -1.2576e+01
-#>  -1.2494e-01  4.7613e+00  8.2518e+00  5.1161e+00  2.6879e+00 -1.1478e+00
-#>   8.1778e+00  1.4736e+01  8.8339e+00 -5.1749e+00  1.2643e+01 -1.4105e+01
-#>   8.0566e+00 -1.2687e+01 -9.9194e-01  4.1174e+00 -2.6348e+00 -4.0748e+00
-#>  -8.4652e+00  3.9182e-01  8.5976e+00 -1.7796e+00  1.9045e+00  2.7233e+00
-#>  -4.8983e+00 -5.3415e+00  1.3234e+01 -2.1544e+00  5.6411e+00  1.3019e+01
-#>   9.0287e+00  1.1770e+01  1.4281e-01  8.8434e+00 -7.3315e+00 -1.2302e+01
-#>  -4.0297e-01 -5.8028e+00 -2.7611e+00 -2.3790e+00 -9.7937e+00  1.5359e+01
-#>   6.2538e+00  4.3550e+00 -8.2056e+00  2.8763e+00 -1.0728e+00 -1.0987e+00
-#>   2.8934e+00 -4.6745e+00 -9.3801e+00 -3.2556e+00 -1.2905e+00  1.5976e+00
-#>   6.5937e+00 -1.0203e+01 -4.2327e+00  3.3749e+00  5.8875e+00  9.6595e+00
-#>   1.0829e+01 -1.6179e+01  1.3427e+01  4.3272e+00 -4.3343e+00  1.1981e+00
-#>  -1.6241e+00 -5.2897e+00  4.4671e-01  3.6279e+00 -1.6293e+01 -9.8559e+00
-#>  -9.2592e-01  1.3693e+01  1.5238e+00  1.2777e+01 -1.5975e+00  2.5551e-01
-#>   2.8383e+00 -9.2113e+00  2.6174e+00 -8.6557e+00 -1.3164e+01  3.6949e+00
-#>  -1.8526e+00  6.1922e+00  3.9519e+00 -1.0871e+00 -1.0004e+01  2.8450e+00
-#>   3.3199e+00  5.9430e+00 -1.6988e+00  3.6933e+00 -1.6695e+00  9.7463e-01
-#>   6.8643e-01 -1.8230e-01  2.7946e+00 -9.0175e-01  7.5062e+00  2.9221e+00
-#>  -1.0841e+01 -7.1443e+00  3.9800e-01 -4.6693e+00 -1.6905e-01 -9.1411e-01
-#>   8.9760e+00  6.5289e+00  2.0646e+00  2.0664e+00 -7.4163e+00  3.1503e+00
-#>   3.2287e-02  9.4439e+00 -1.1551e+01 -5.0678e+00  1.9310e+00  3.4262e+00
-#>  -1.2702e+00  3.0408e+00  2.1001e+00 -3.5340e+00 -3.2536e+00  6.8281e+00
-#>  -7.6196e+00  5.7684e+00  3.2679e-01  5.5921e+00  9.2952e+00 -4.7166e+00
-#>   1.3529e+00  3.3966e-01 -1.8293e+00  1.5309e+01 -1.0006e+00 -7.7701e+00
-#>   1.0581e+01  7.6868e+00 -1.2268e+01  8.1392e+00  4.6540e+00  9.6224e+00
-#>  -5.1705e+00 -1.1192e+01  9.5458e+00 -8.6510e-04 -1.1793e+01  8.4559e+00
-#>   1.8310e+01  1.4111e+01  1.5774e+00 -2.1493e+00 -5.2412e-01  9.6496e+00
-#>  -2.8237e+00  2.5916e+00  2.1321e+00 -5.9730e+00 -3.1469e+00  2.4400e+00
-#>  -1.5843e+01  1.3855e+01 -1.3872e+01 -1.8868e+00  1.0080e+01 -8.2378e+00
-#>   8.8551e+00  4.9966e+00 -6.3457e+00 -1.0323e+01  5.0205e+00 -9.3470e-01
-#> 
-#> Columns 7 to 12  6.8024e+00  6.8142e+00  4.5797e+00  6.9012e+00  1.0459e+01  1.0657e+00
-#>  -6.9185e-01 -6.4943e+00  5.4717e+00 -3.6463e+00  4.3075e+00  7.7041e+00
-#>  -4.0044e+00  4.6633e+00  5.0276e+00 -1.4530e+00  6.8511e+00  1.2912e+00
-#>  -1.3504e+01 -1.1171e+01 -5.0406e+00 -7.0066e+00 -1.7130e+01 -2.3064e-01
-#>  -2.8190e+00  4.1526e+00  7.7969e+00 -6.6416e+00  3.5648e-01 -4.1884e+00
-#>   2.6731e+00 -9.3361e-01 -1.9250e-01  7.6096e-01 -4.5198e+00 -4.9756e+00
-#>  -7.7441e+00  3.3261e+00 -1.1134e+01  1.9768e+00  3.2081e+00 -9.4453e+00
-#>  -1.4551e+00  1.6707e+01  1.1992e+01 -1.2095e+01 -8.7680e+00 -5.7492e+00
-#>  -2.0924e+01  9.0712e+00  4.3001e+00  2.3030e+00  1.5413e+00  1.7113e+00
-#>  -2.4857e+00 -8.2347e+00 -1.2573e+01 -1.0862e+01 -4.1379e+00 -6.6089e+00
-#>  -1.5728e+01  2.3377e+00 -3.2312e+00 -8.4338e+00  1.4856e+01 -1.8265e+01
-#>   5.0071e+00 -6.7687e+00 -3.5333e+00 -6.7396e+00  2.0115e+00 -5.8410e+00
-#>   1.7353e+01 -9.7737e+00  5.5499e+00  1.0203e+01 -3.5550e+00 -8.5098e+00
-#>   1.4913e+00  7.1976e+00  5.9653e+00 -9.6813e+00  5.5417e+00  2.8466e+00
-#>   3.6951e+00  1.7859e-02 -5.5736e+00  2.3252e+01 -6.3235e+00 -1.6749e+01
-#>   1.2651e+00  4.7339e-01  1.5961e+00  9.3233e+00  1.4838e+00 -4.3081e+00
-#>   1.2277e+00  1.7227e+01 -6.4262e-01 -2.7803e+00  2.6236e+00 -1.0040e+00
-#>   1.9608e+00  3.1339e+00 -4.8171e+00  5.3754e+00 -4.6124e+00 -6.1681e+00
-#>  -3.4816e-01  2.3533e+00  1.2979e+01 -3.9980e+00  1.0758e+01  1.9073e+00
-#>   1.0177e+01  1.0798e+01 -8.6815e+00  7.7241e+00 -1.2984e+01 -5.2720e+00
-#>  -2.7988e+00  7.2102e+00 -4.4924e+00 -6.3182e+00 -1.2623e-01  1.3144e+00
-#>  -3.5231e+00  4.8029e+00  8.3415e-01  1.5933e+00 -4.7331e+00  6.3149e-01
-#>   5.2782e+00 -4.3263e+00 -1.2520e+01  4.0108e+00  3.0807e+00 -8.3008e+00
-#>   3.2957e+00 -9.9090e+00 -7.1363e+00  8.8062e-02 -8.3463e+00 -7.5363e+00
-#>  -5.6222e-03 -2.9559e+00 -7.3561e+00 -9.5151e-01  1.2968e+01 -1.3217e-01
-#>   6.8911e+00  9.1908e+00 -3.3467e+00  1.7311e+01 -3.0840e+00  1.2651e+01
-#>  -7.3550e+00  6.4477e+00 -4.7388e+00 -1.8323e+00  7.4196e+00 -1.2512e+01
-#>   8.4276e+00 -7.1850e+00 -1.0022e+00 -5.9997e+00  6.7695e+00  5.1475e+00
-#>   7.7367e-03  7.5006e+00  5.3158e+00  5.5656e+00  5.8394e+00 -4.7957e+00
-#>   8.5397e+00  8.4526e+00  2.3535e+00  7.2155e+00  7.2376e+00  3.4551e-01
-#>  -3.7541e+00  1.3644e+01 -1.1201e+01 -9.4936e+00 -1.4025e+01  4.1256e+00
-#>   1.5837e+01 -1.2085e+01  1.8337e+01 -7.5301e-03  7.2932e-01 -3.3146e+00
-#>  -8.1402e+00 -1.4226e+01 -1.6781e+01 -2.6737e+00 -5.7033e-01  4.2115e+00
-#> 
-#> Columns 13 to 18 -7.6116e+00 -2.2572e+01 -7.3696e+00  1.2489e+01 -3.6996e+00 -8.4814e-01
-#>  -6.5330e+00 -1.4115e+01 -1.6207e+01  4.8898e+00 -3.3812e+00 -2.8668e+00
-#>   1.0369e+01  8.3727e+00  2.5078e+00 -4.5453e+00 -9.8145e+00  5.5830e+00
-#>   4.2195e+00  3.3645e+00  4.0064e+00 -8.8580e+00  4.7633e+00  2.1263e+01
-#>   1.0394e+00 -4.8507e+00 -1.1569e+00  3.6901e+00 -4.9992e-01 -2.2131e+00
-#>  -1.2750e+01 -3.0266e+01 -1.2321e+01 -1.3099e+01  6.0695e+00  9.5454e+00
-#>  -8.8311e-02 -6.3177e+00  3.9969e+00  3.2729e+00  1.2270e+01  1.4457e+01
-#>   5.8119e+00 -5.0032e+00 -4.5845e+00  5.5784e+00  7.0958e+00 -4.4555e+00
-#>   1.0096e+01  8.4955e+00 -6.9884e+00 -1.1447e+01 -6.7327e+00 -5.6462e+00
-#>  -5.5768e+00 -2.3684e+00  1.2694e+00  4.3510e+00  7.8375e-01  5.9263e+00
-#>   8.8960e+00 -3.4571e+00 -1.0240e+01  6.3798e+00 -7.5809e+00 -1.7999e+00
-#>  -8.4793e+00  9.5572e-01 -1.8657e+00  8.0276e+00 -1.9531e+00  1.1821e+00
-#>  -6.2658e+00 -2.4873e+00  6.7244e+00 -2.7642e+00  4.8987e+00  1.1314e+01
-#>   5.8645e+00  8.9250e+00 -2.0626e-01  2.6696e+00  5.9454e+00 -4.5350e+00
-#>   3.3258e+00 -2.1198e+00  1.3239e+01  6.2403e+00 -1.0381e+01  9.9492e+00
-#>  -7.3888e+00  1.7891e+00 -6.5434e-01 -2.0986e+00  5.7303e+00  3.8639e+00
-#>   4.8493e-01 -1.2327e+01 -1.0240e+01  6.6167e-02  8.3635e+00  5.0293e+00
-#>  -5.1651e-01 -4.9654e+00  5.2045e+00 -1.3461e+01  7.8285e+00  1.7121e+01
-#>  -1.0272e+01  9.2964e+00 -5.5075e+00 -2.4449e+00  2.7975e+00  2.6272e+00
-#>  -4.2984e-01 -9.9611e+00  6.5585e+00  3.3719e+00  2.8657e+00 -1.7146e+00
-#>  -3.3866e+00  5.4789e-03  6.0831e+00 -6.4697e+00  4.7145e+00 -2.1051e+00
-#>   1.2717e+01  7.5293e-01 -1.4031e+00 -4.8171e+00 -1.0169e+01  3.8149e+00
-#>   9.2181e+00 -1.5667e+00  7.8714e+00 -3.2686e+00 -1.9517e+00 -2.9886e+00
-#>  -7.0515e+00  4.5613e+00 -5.9774e+00  1.5230e+00  4.9189e+00  1.2735e+01
-#>  -1.5374e-01  1.9978e+00  7.7595e+00 -1.8426e+00 -4.5077e+00 -4.7388e+00
-#>  -2.7195e+00 -4.0337e+00  5.6005e+00 -1.1810e+00  1.2080e+00 -6.5539e+00
-#>  -8.3179e+00 -1.5172e+01 -6.6159e+00  4.3008e+00  1.7152e+01  1.1670e+01
-#>   9.5280e+00  1.1548e+01 -7.2594e+00 -3.2168e+00  3.3255e+00 -2.1210e+00
-#>  -1.5480e+00  3.5520e+00  8.4671e+00  2.8069e+00 -4.7788e+00  6.6099e+00
-#>  -3.6551e+00 -6.4310e+00 -7.5838e+00  3.0752e+00  1.7736e+00 -1.5723e+01
-#>   2.2205e+00  1.1095e+01  8.1703e+00  1.4510e+00  1.1793e+01  1.3099e+01
-#>  -7.7663e+00  6.1615e+00 -8.8113e+00 -1.3853e+00  5.6379e+00 -3.9343e-01
-#>  -5.7712e+00 -1.0588e+01 -1.5045e+01 -6.2267e-01 -5.5895e+00  2.6833e+00
+#>  Columns 1 to 8  -0.3619   4.7507   5.2202  -4.4902   5.4633 -11.1327  12.1531   7.3012
+#>   -3.2615  -0.0427 -12.4125  -3.6037   3.0216   7.3106  -2.0342  -4.7471
+#>    4.4592   4.2324  -4.7202   1.5370   2.8355   3.0606  -0.2418   0.9966
+#>   10.5738  -1.8525  -7.5965  -8.3688   5.5839   1.6967   9.1000  -4.0909
+#>   -2.3065  -0.8993 -10.4005  -1.7036  -2.9172  -0.1753  -0.5019   9.0030
+#>   -5.1154  -1.5346   0.0508   3.5257  10.2797  -4.9488  -6.9606  -3.0852
+#>   -8.2109   3.0952  -3.6003 -11.8491   1.8730   5.3689   3.5278 -10.9552
+#>   -3.0043  -1.6809   4.6055 -10.8986   2.4688  -3.0951   5.6058  -0.9880
+#>    6.7929   2.4666  -2.3536  11.9245 -24.2146  21.7510  -4.1076  -3.0655
+#>   -0.7273  -1.4395   9.5528   1.8219  -0.2840  -6.2072 -10.0564   1.3881
+#>   -1.9389  -2.2080   9.2557  -1.0880 -15.9549   1.4933  11.3134   4.6219
+#>   -1.6346   0.6331   2.1482  -1.5393  -8.4749  -1.8970   4.2873  14.2499
+#>    0.2674  -1.4259  -2.8983   8.3435  -2.7769  -0.8330  -9.0909  -5.5239
+#>   -0.3352  -1.6374   2.5282  -6.7981   5.6085  -6.6012  13.3529   3.3288
+#>   -7.3179   8.2485   8.6610  -5.0976  -0.6880 -10.1271   6.5975  -0.3246
+#>   -2.4045   5.9853   9.9342   1.0912  -2.1763  11.9397  -3.4239   5.0353
+#>   -2.9325   1.8043   9.0347  -4.0091  -8.2913  -3.4115  13.9126   3.6494
+#>   -8.6817   2.8140   3.6937  -5.9206   6.4151  -4.6408   8.8346 -11.9463
+#>   -3.2275   3.7146   9.6190  -6.0859   6.5511 -10.1070  -9.6853  -7.4810
+#>   -6.0314   3.4782  -0.5504  -8.6399   7.1504  -0.1491   1.6604 -13.3869
+#>   -4.0242  -2.7070   9.9019   2.2772  -1.7343  -3.0806  -0.3007   4.1620
+#>    6.2330  11.9904  -9.5708   1.6246   8.2742  -5.1568  -9.4090   9.4752
+#>   -7.7235  -8.5374  -6.4202 -10.2577  -7.4358   3.2625   5.5035  -0.7779
+#>   -6.0555  -0.7879   2.9509   1.7311   3.9546   0.0975  -4.6859 -10.8845
+#>   -4.2050  -7.4343  -0.7050 -22.9119   6.9556   7.9510  -3.0306 -15.3819
+#>   -5.8523  -7.9260   4.7318   0.9929   0.8463  -1.2826   4.3472  -7.2309
+#>   -3.1177  -2.4413   1.7888  -3.6426  13.6961 -13.3307  -2.2494  -5.7197
+#>    4.4676  -1.2391  -5.0863   0.1106  -3.0380  -5.5396  -1.6570   0.2648
+#>   -1.9544  -2.7779   6.1225   0.0495  -2.6936  -5.7898   2.5726  -7.1372
+#>   -6.7555   2.5534   8.6404   5.5291 -13.7406   2.5281  -1.7730  -1.5615
+#>    3.1372  -0.0816  -3.9026   2.4384   3.2868  -6.0401  -1.1705   5.2603
+#>   -5.9826   1.4485   6.1562  -6.3634  -6.4530   8.2276  -6.7654  -3.9403
+#>    8.5875  -4.4492  -2.1708  -5.4673   1.6807 -10.7984   5.3017  -0.4199
 #> 
-#> Columns 19 to 24 -5.2464e+00 -7.0874e+00 -4.7609e+00 -3.7282e+00  1.0706e+01  1.5297e+00
-#>  -6.9263e+00  1.1783e+01 -1.1422e+01 -4.0469e+00  6.4235e+00  2.0727e-01
-#>  -4.9231e+00 -1.5128e+01  6.5018e+00  2.1611e+00  2.1524e+00 -1.9323e-01
-#>   4.3420e+00 -1.3789e+01 -6.0091e+00  2.4006e-01 -9.3553e+00 -9.8307e+00
-#>   3.4428e+00  7.1870e+00 -1.1922e+00  1.1430e+00  5.4847e-01  5.8617e+00
-#>   5.1685e+00  6.3463e+00  4.5243e+00 -2.6326e+00 -3.9794e+00 -3.6855e+00
-#>  -2.6699e+00  5.1123e-01  4.6196e+00 -4.7047e+00  4.5384e+00 -1.8685e+01
-#>   5.0849e-01  1.6162e+01  4.2410e+00 -4.2715e+00  3.1371e+00 -4.5112e+00
-#>   1.6873e+01 -1.2267e+01 -4.0977e-01  8.0116e+00 -7.4305e+00  5.1819e+00
-#>   2.2561e+00  9.6738e-01 -2.3555e-01 -1.0170e+01 -4.5446e+00  5.6410e+00
-#>   6.0245e+00  6.3343e+00 -6.5556e+00 -2.2797e+00  2.3705e+00 -3.5569e+00
-#>   1.8994e+00  8.7867e+00  3.5606e+00 -8.5007e-01  2.7260e+00  1.3389e+00
-#>  -1.1418e+01 -9.5140e-01  1.0049e+01 -6.1606e+00  9.7031e+00 -1.6093e+01
-#>   9.8920e+00  1.4330e+01 -6.2582e+00  7.7795e-01 -5.7016e+00  8.8303e+00
-#>   2.4353e+00 -4.5353e+00  7.7747e+00 -8.8086e+00  3.7741e+00 -1.5807e+01
-#>  -8.1247e+00 -8.7499e+00 -2.1982e+00  1.5405e+00 -1.4039e+00  2.3166e+00
-#>   5.2253e+00 -3.7718e+00  8.5709e+00 -5.2631e+00 -7.7831e-02 -6.2829e+00
-#>  -5.4685e+00 -4.3472e+00  3.7960e+00 -2.9034e+00  1.7978e+01 -1.5647e+01
-#>  -3.9083e+00 -4.4565e+00  2.4797e+00 -3.7251e+00  4.1443e+00 -5.5959e-01
-#>   6.9433e+00  1.5254e+00  1.3598e+01  3.9247e-01  6.8856e+00 -1.0771e+01
-#>   8.9114e+00  6.2955e+00  5.4177e+00  9.8818e+00 -5.2334e+00  2.4367e-01
-#>   1.5399e+00  2.3911e-01  2.7749e+00 -4.2119e+00 -5.4261e+00 -1.6565e+00
-#>   2.0196e+00 -5.7851e+00 -1.3748e+00  5.4834e+00 -1.1966e+00 -6.5270e+00
-#>   7.0821e+00 -3.0755e+00  1.2174e+01 -9.4236e-01 -5.6663e+00 -2.5944e+00
-#>   3.3522e+00 -5.0099e+00 -2.1456e+00  1.4415e+00 -6.1322e+00  1.2063e+01
-#>  -4.2653e+00 -6.8232e+00 -1.5760e+00  1.0697e+00 -5.0486e+00  7.7071e+00
-#>  -2.7145e-01  4.4730e+00 -6.8754e+00 -9.2159e-01 -4.1075e+00 -8.8420e+00
-#>   2.1405e+00  4.0768e+00  2.8086e+00 -7.6287e+00  8.4893e+00 -1.3900e+00
-#>   8.6783e+00 -2.5042e+00  1.1771e+00  1.9851e+00 -1.3014e+01 -2.3765e+00
-#>   1.0708e+00  8.6690e+00  2.8070e+00 -3.4836e+00 -2.7329e+00  1.0127e+01
-#>   1.8867e+00 -2.3190e+00  1.6788e+00  3.5833e+00 -7.0983e+00  1.2037e+00
-#>  -8.4881e+00 -4.3573e+00  1.2775e+01  1.5797e+00 -6.7303e+00  2.0272e+01
-#>   8.9483e+00 -8.3223e-01 -6.2088e+00 -2.7979e+00  1.4796e+00  8.8738e+00
+#> Columns 9 to 16   2.0224  -0.8034  -3.0653   5.0708   4.0226   6.2013   4.5564  -2.4866
+#>    8.9358  -1.9102  -0.9943  -0.5796  -8.3736   5.9767   1.8103  -1.4679
+#>   -2.4314   2.9648  -2.7773  -2.1391   3.7804   0.1174  -5.3776 -13.5547
+#>   -1.7709  -2.1917  -0.5539   2.7033  -2.3779  -3.3447  -6.6735  11.0427
+#>    7.3811  -0.9950   0.5425  -8.7879  -0.7521  -1.6372  -3.5386 -15.3024
+#>    2.0978  -7.2410  -7.0986  -8.9580   5.6551  -2.8206   6.4418   1.5668
+#>   -0.1735   1.7582   3.4132   2.3750  -4.9381   3.0042   0.5722  -0.3610
+#>   -3.2509   0.1282  -2.0439   4.2236   8.2883  -6.0459  -8.2608   5.6743
+#>    8.7424   9.9933  -0.2225   1.1017  -6.6307   1.5030   5.6181   5.6833
+#>   -2.9179  -1.8718   2.1876 -10.8360  -6.7929   8.4193   1.3080  -3.6647
+#>   -7.7729 -13.5895   6.4875  10.7613  12.9773  -6.2093   7.2188 -11.9730
+#>    1.7696   2.4318  -0.1717   6.4465  13.9270   8.6617   8.4048  -0.2795
+#>   -0.6029  -0.8038   0.1872   0.8294   4.6746  -7.9731  -6.2947  18.8942
+#>    2.6821   4.4625  12.6328   0.8270   1.5722  -4.2914  12.7210   3.6976
+#>   -2.6175   6.8166  -0.6300   4.4706  -2.9356  12.0657   2.8697   2.0580
+#>   -6.5758  -1.1455  13.3510   7.2848  -0.2675  -8.6391  -1.2929   4.9009
+#>   -4.6129   4.4335  -0.4930   0.4913  -6.5639   3.8708  -7.1833  -2.6990
+#>    1.7341   6.7267   0.6351  -0.3281   1.7417   4.7041  -1.8089   1.0158
+#>   -1.3860 -15.0241  -4.3741   2.2984   3.9842  -3.5326  -3.1056  -9.3567
+#>   -7.6547  -4.7265  -3.4537  -5.2437  -9.9890   6.7698  -1.1372  -6.7663
+#>    1.9634  -5.6699  -4.1017  -1.3833   0.8551   4.6613  13.6113  -1.9618
+#>    4.6079  -0.9070 -10.1265   0.0651  -3.6174   4.0450  -4.2886   3.5547
+#>  -12.1361   7.5136   1.5361  -3.5475  -0.8614  -5.8159  -9.1244  -3.6102
+#>    2.1536  -5.3244  -5.0122  -5.5311  -3.2828  -2.4702  -1.5056 -14.7999
+#>  -10.4692   1.7969   1.1653  -8.8876  -8.0563 -16.9192  -6.0877 -16.3787
+#>   -6.3155  -0.9767  -1.5991  -2.6369  -8.6226  -2.2824   2.5887  -0.3889
+#>   -0.8857  -0.6065 -12.9114  -7.9108   4.4094   3.3875  -2.7987   0.7561
+#>   -2.2638  -8.5777  -8.8930  -8.2750   2.9610   0.0045  -1.3776  -7.9761
+#>   -5.3521   0.9971   0.1340   1.6546  -8.8413  -4.8501   9.8958  -0.6773
+#>   -0.9346   2.6370   4.6053  11.1662   3.2531   1.8403   0.2207   5.7101
+#>    1.2262   6.7319   0.7726   0.1651   3.0679  -7.5237   2.7356  12.0989
+#>   -2.6474   2.3850   1.8161  11.2325  -7.8550 -16.9366   4.8561  -4.3737
+#>   -0.0253   4.3252  -7.9465  -2.0708  -1.9391  10.7683  -4.6019  -3.7239
 #> 
-#> Columns 25 to 30  4.7605e+00 -1.4757e+00  1.5414e+01 -3.8196e+00 -1.6951e+01 -1.2996e+01
-#>  -1.9973e+00 -4.6919e-01  8.8073e+00 -2.2466e+00  4.9008e+00  1.9037e+00
-#>  -5.7509e-01  2.7547e+00 -1.0311e+01 -2.8828e+00  6.6137e+00  8.7601e+00
-#>   6.2616e+00 -5.0881e-01  1.0574e+00  9.8757e+00  1.5003e+01  6.7349e+00
-#>   4.1598e+00 -6.4312e+00 -1.6850e+01 -1.8725e+00 -8.4253e+00 -5.4590e+00
-#>  -7.0131e+00  9.9630e+00 -4.2980e+00 -2.8688e+00 -4.8960e+00 -1.5651e+01
-#>   1.0108e+01  6.2334e-01  5.0382e+00 -7.0535e+00 -7.1729e+00 -1.6165e+00
-#>   6.7001e+00 -1.1708e+01 -1.5331e+01 -4.2425e+00 -1.5112e+00  6.2536e+00
-#>  -2.0866e+00  6.0834e+00 -9.9227e-01 -1.0545e+01  1.6857e+00 -6.7036e+00
-#>   2.2843e+00 -3.1801e+00  5.8044e+00  5.1553e+00  4.9275e+00 -1.0224e+01
-#>  -4.4992e-01  8.7138e+00  2.1283e-01  1.3802e+00  1.5857e+00  1.1157e+01
-#>  -1.2385e+00 -2.4477e+00  7.9737e+00 -4.4014e+00 -4.9261e+00 -3.1951e+00
-#>  -2.3980e+00  1.3084e+00 -7.7907e+00  7.2450e-01 -1.1586e+01  1.0383e+01
-#>  -1.8079e+01 -9.0459e+00  1.4397e+01 -6.2526e+00  6.3617e+00 -7.8238e+00
-#>  -3.8272e+00 -1.9251e+00  1.0159e+01 -1.6060e+01 -2.2783e+00 -2.2136e-01
-#>   9.4213e-01 -2.9319e+00  2.3522e+00 -1.1946e+01 -5.9928e+00 -8.4409e-01
-#>  -4.5177e+00 -9.0236e+00  3.4303e+00  1.8347e+00 -1.2845e+01  3.2762e+00
-#>  -5.9002e+00 -1.3134e+01  1.5702e+00 -1.1459e+01 -2.4262e+00  1.1036e+01
-#>   7.1187e+00 -4.8343e+00 -1.0063e+01 -4.6802e+00 -7.7001e+00  1.7641e+00
-#>  -6.5346e+00  1.9943e+00  5.7655e+00 -7.6248e+00 -5.8852e+00 -3.1929e+00
-#>   3.2667e+00  3.4035e-01 -1.3065e+01  1.6010e+01  7.5781e+00 -9.4680e+00
-#>  -2.7541e+00  9.0126e-03  3.2973e+00 -8.4961e+00 -3.8408e+00  2.5491e+00
-#>  -8.2020e+00  1.2686e+01  1.0638e+01  1.8329e+01 -2.1860e+00  6.4469e+00
-#>  -2.0596e+01  9.4246e+00  1.9946e+00  9.0939e+00  3.2005e+00  3.8513e+00
-#>  -4.8925e+00  7.0237e-01  5.3233e+00  3.8712e+00  3.7284e+00 -9.4912e+00
-#>   4.7726e+00 -6.0445e-01  6.0763e-02 -1.1063e+00  4.0299e-01 -5.1322e+00
-#>  -1.4589e+01  3.5644e+00 -1.2131e+01 -2.8696e+00 -2.1631e+00  6.4978e-01
-#>  -2.0117e+01 -4.2851e+00 -2.0913e+00  8.8746e+00 -6.0943e+00  6.6141e+00
-#>  -3.0863e+00 -4.9088e+00 -9.5859e-01 -8.5561e+00 -9.7085e+00 -6.5426e+00
-#>  -9.3064e+00  3.8217e+00  4.1449e+00 -1.3908e+00 -1.4476e+01 -1.2525e+01
-#>   2.5078e+00 -2.1096e+00 -2.8901e+00 -5.3604e+00 -3.8419e+00 -1.0655e+00
-#>  -1.2512e+01 -4.7244e+00  1.2641e+00  1.1913e+01  8.4535e-01  9.1668e+00
-#>  -8.5001e+00  1.2013e+01  1.9289e+01  5.0782e+00  7.5338e+00 -9.1519e+00
+#> Columns 17 to 24   0.4586 -16.5602   3.2247   1.3494   0.8231   7.2414  -3.9239  -7.0053
+#>    0.9349   3.2037  -1.9182  -6.0207  -8.9720   6.7303   5.1336  -2.8499
+#>    2.3689  -0.0180   7.6802  -1.8705   0.8882  -1.7085  -3.7346  -2.9886
+#>    6.5192  -1.6577  -4.5703   9.4065   4.1678   5.3161   2.4356   0.8502
+#>    2.8720 -20.2435  10.6533 -16.5463  -7.4772   0.8646  -5.3559  -5.3760
+#>   -2.7366  -1.3390   6.9657   9.5990   8.3194   1.8907  -3.1865  -6.8953
+#>    6.7979  11.0678  -3.5683  -6.1263  12.2529  -4.1589  -4.0510   7.0515
+#>    8.0900  -3.8846 -13.5482  -5.3754  -1.5216  -6.7216  -4.6792   2.7533
+#>   -3.7959  13.9529  26.9659  -4.8575  -3.3514   5.3643   3.7068   1.0795
+#>   -1.0770  -1.7608   0.4851  -2.5428  -1.6886  -0.4926  -1.3364   7.0277
+#>    5.4794   1.1259   2.7299   9.7866   0.9675   0.4050   6.4089  -3.7298
+#>   -0.2842  -4.5766  -9.8293  -3.1239   7.8309   9.0241   1.3918   0.9099
+#>   -1.0776   5.3834 -10.7990   9.2697   0.4384  -0.7660   5.0994   8.6788
+#>   18.2321   1.7815  -3.8933   2.1150   4.1487  12.4740  14.9617   4.2673
+#>    6.2371   4.7664 -10.7564   0.5711  -1.8074  -5.2167   3.1385   6.4971
+#>  -12.7658  -0.4062  -5.3433  13.0974  -0.3448   0.3984  -8.1654  -2.8115
+#>   -4.4679   9.9758   7.7362   3.7483   5.8953  -0.0196   9.5938   3.1453
+#>    8.0907  -2.6273   2.6493   4.7164  10.5790  -8.8270  -4.6447  11.1990
+#>    3.5821   2.2337   2.4201   8.1678  -5.2501   2.7953  -1.8781  -9.0544
+#>  -10.0726   8.7515  15.8992  -4.6881  -5.3566   2.4174   0.2238   0.0809
+#>    2.8823  -6.3140  -3.2212   1.9314 -11.0092   1.8779   5.6983   2.9439
+#>   -7.4484  -5.4048   0.6352  10.2212  -0.6127  -3.6856  -7.4190  -1.3941
+#>   -2.8589  16.1370 -10.7102  -5.3420  14.1526   7.3273  -2.1112  -1.1856
+#>   12.5522  -4.7888  14.8623  -4.4579   2.7298  -0.1005  -9.9647  -2.8257
+#>    3.6617  11.1767  -4.5620   6.5324   0.3794  11.6022   3.4834  -7.5646
+#>  -10.4129 -13.0055  -3.3670  -8.0003  -2.6251   5.5768  -6.8455  -5.1965
+#>   -7.4526  -4.3158   5.7424   2.2327  -4.8341  -0.9311  -4.9753  -1.5844
+#>   -2.1690  -6.3374   5.8079   5.4768  -4.3901  -2.3366  -5.9291  -5.6081
+#>   -6.0447   3.9404   3.8917  -4.6146  -6.5145   3.2475   1.0771   1.7657
+#>   -4.6528   4.2647   6.3065   4.6842  12.4760   1.2198   0.9179   0.9575
+#>  -11.2894   9.0967  -6.6894  -0.5237 -10.1371   0.8038  -1.9630   2.8939
+#>   -1.1554  -8.5433   1.7687  -0.2722  -0.5076  -0.0616   4.7847   4.8778
+#>   -3.7165   4.0234  -0.4934   5.0402 -13.0122  -8.4023   1.5245   6.0530
 #> 
-#> Columns 31 to 36 -1.5825e+01 -6.0525e+00 -3.6059e-01 -3.5320e+00 -1.0963e+01  9.0893e+00
-#>  -7.6471e+00  3.8245e+00  7.4362e+00  9.0340e+00 -3.2226e+00  8.3234e+00
-#>   1.0664e+01  3.1036e+00  7.8870e+00  5.2952e+00 -5.6097e+00  1.1988e+01
-#>   7.5233e+00 -3.8525e-01 -1.0644e+00  2.8296e+00 -8.0137e+00 -1.5562e+01
-#>  -3.9163e+00  2.3552e-01  2.7444e+00  1.1502e+01  2.2503e+00 -1.2105e+00
-#>  -7.3549e+00 -1.0166e+01  1.9247e+00 -1.8355e-01 -8.8643e+00 -2.6318e+00
-#>  -4.0218e+00 -3.7994e+00 -3.6637e+00 -9.0932e+00  8.2074e+00 -1.3125e+00
-#>  -8.5332e+00 -1.0384e+01 -2.4280e+00  4.7237e+00 -2.0530e+00  6.6218e+00
-#>   1.9824e+01 -3.3879e+00 -4.7558e+00  6.3653e+00 -7.8356e+00  2.3336e+00
-#>  -2.3862e+00  4.6184e+00 -5.4476e+00 -1.4052e+00 -3.0102e+00  3.4202e-01
-#>  -4.5635e+00 -9.0183e+00 -9.8438e-01  7.6168e+00 -5.9883e+00  9.1950e+00
-#>  -1.1475e+01  7.0631e+00 -6.4333e+00 -1.1149e-01  6.0743e+00 -2.4352e+00
-#>  -3.1736e+00  2.1220e+00 -6.6557e+00 -3.4379e+00 -2.7073e+00  1.0520e+00
-#>   1.3549e+00 -7.3758e+00  7.0423e+00 -5.1716e+00 -6.6967e+00  1.9914e+00
-#>  -5.8276e-01  1.3280e+00 -4.0232e+00  6.2002e+00 -1.1908e+01  1.5772e+01
-#>  -6.6587e+00 -5.5256e+00  3.1339e+00 -5.5551e-01 -5.4036e+00 -9.0692e+00
-#>  -7.6355e+00 -1.5741e+01  3.9675e-01  3.0156e+00 -2.0665e+00  3.9257e+00
-#>   2.0522e+00 -2.8533e+00  8.6485e+00 -7.8702e+00  4.1215e+00 -3.2170e-01
-#>  -8.2318e-01  3.2963e+00 -1.1312e+01 -8.2619e-01 -4.2630e+00  6.1051e-01
-#>  -1.3481e+01 -1.0202e+01  3.7420e+00 -9.4366e+00 -8.2541e+00  4.4941e+00
-#>   4.6272e+00  1.1581e+01 -4.7529e+00 -4.3113e+00  8.0156e+00  3.4779e+00
-#>   1.7016e+00 -1.1712e+01  7.2312e-01 -7.1206e+00 -9.3145e+00  9.2114e+00
-#>   5.9762e+00 -5.2956e+00 -3.9463e+00 -1.1333e+01 -1.3987e+00 -4.9694e+00
-#>   7.4142e+00 -8.1344e+00 -1.3305e+01 -1.7310e+00 -7.5089e+00 -4.8229e+00
-#>   7.3922e+00  8.3139e+00 -5.2840e+00 -1.4562e+00 -3.4649e+00  1.2898e+01
-#>  -9.4360e+00 -1.3312e+00  1.1261e+01 -5.3233e+00 -3.2232e+00  4.1879e+00
-#>   5.3539e+00  3.0263e+00 -3.2155e+00  3.4591e+00 -3.5687e+00 -6.5521e+00
-#>  -3.5393e+00 -1.3799e+01 -3.3037e+00  1.9697e+00  2.8058e-01 -4.6900e+00
-#>   4.9514e+00  4.1531e-01 -8.0968e+00 -3.9034e+00 -4.8970e+00  1.0481e+01
-#>  -6.1879e+00 -9.2681e+00 -6.2762e+00 -2.2763e+00  4.9857e-02  2.0032e+00
-#>  -3.8395e+00 -1.0061e+01 -7.0415e+00 -1.5563e+01  7.8947e+00 -1.0889e+01
-#>   4.6596e+00 -3.8205e+00 -1.4254e+00  9.8757e+00 -8.8425e+00  1.1036e+00
-#>  -5.7265e+00 -1.0946e+01 -3.0999e+00  5.1787e-02 -4.1085e+00  1.6805e+00
+#> Columns 25 to 32  -1.1617  -4.5270   8.9619   8.7575 -12.2945  -9.4242  -2.1149  -3.8564
+#>    0.3031  -1.5149   1.4088   6.1547  14.7426   1.0636   0.8483   3.1450
+#>   -4.2053  -4.8640  -9.3965   8.9148  -0.8327   0.6713  -3.2564  -3.8877
+#>   -5.4048   0.6582  18.2467   5.1528  14.6201  11.7561   0.9013   0.9964
+#>    5.4964  -7.9256  -1.9682   2.5590   1.1515   7.8826  -4.0752  -2.1809
+#>    2.0503  16.1044 -13.6467  -4.1519  -3.6256  -2.2399  -4.3554  -2.5096
+#>   -3.4623   7.6316  -0.6717   2.3535   8.9883  -3.6381   6.8627   6.3989
+#>    0.7791   6.0648   8.4828   2.4190   8.2937  15.2618   6.2531   3.5603
+#>    1.4592  -0.8026   5.2497  -1.7417  10.2363  -2.7339  10.1366   8.7154
+#>   -1.0870  -3.8757 -17.0384 -13.5462  -9.9782   3.4288  -0.9570   1.8320
+#>    0.6053   6.3738  18.1769   9.5985  -6.5580   3.3676  -7.6121  -9.0703
+#>    4.3631   1.2025   6.0577  14.9365  -2.9591  -4.5177   6.4436   4.9191
+#>    7.9022  -4.1985 -10.5060   3.6277  -4.0699   1.3661   5.0642   4.9252
+#>    4.9476   5.5417  -9.6269  -5.8170  -5.0094   2.0519  -1.0189   5.3101
+#>    2.3852  -1.0250  -0.2472  -3.3311  -9.3618 -11.7413   4.3836  -5.2594
+#>    3.6495   1.8904  -4.2375  -1.8959  -7.7809   1.5159  13.1940   5.2597
+#>   -4.8175  -5.2363   3.8440  -0.1087   0.2622 -13.3820  -3.2750  -0.3589
+#>   12.6262  10.2053  -4.2351 -15.5579  -1.6116   2.0824  -9.5421   6.1311
+#>    4.7387   5.8616   0.1864   0.4351 -11.7449   7.0052  -5.8976  -2.5118
+#>   -8.3569   3.6318 -11.1709  -9.2369   1.4109  -3.0802   1.4126   6.0006
+#>   -4.3494  -5.6542  -1.2183   4.7747   5.7316  -2.6122   2.2393   2.3839
+#>    5.6065  -5.1379  -3.2652   8.5963  -3.1247   5.6452  10.2332 -13.9666
+#>   -8.0125  11.7461   6.3400  11.8589  13.8579  -7.4751   1.5486   9.6556
+#>   -6.1476   5.4979   5.8627  -4.3102  -4.0461 -14.5948  -9.0272   8.3518
+#>  -12.1698   4.8075   1.1471   7.7939  11.0065  -0.4064   2.3033   7.8946
+#>   -9.7181   3.1563  -7.1208  -3.9173   0.8515  -3.4416   5.0353  11.1102
+#>   -7.6501  11.1190  -9.5224  -4.1385   3.2457  -3.4650  -3.8158   2.1741
+#>    0.6098   1.0856   1.1420   6.7482  -7.6313  12.1166   7.6930  -2.4811
+#>   -1.7017  -0.5707  -3.3727  -6.4599   2.0904  -8.8192  -4.3939   3.6186
+#>    7.8555  17.4277   1.0984 -10.5406 -12.2719  -9.1735  -0.3267   2.4835
+#>   -0.5058 -12.7266  -7.5007  -0.5470   2.8935  -0.0474  -5.6342   1.8716
+#>    2.2625  -1.3686  -4.2506  -2.9258  -4.4997  12.8964  25.0262  -3.0842
+#>   -9.9654   0.9141  16.3044   0.2827  11.2994  10.2884  -4.2485  -5.8201
 #> 
-#> Columns 37 to 42 -1.1352e+01  1.1675e+00  4.1326e+00  2.0810e+01 -3.1170e+00 -5.3886e+00
-#>  -6.3405e+00  9.9968e+00  4.5593e+00  4.3253e+00  4.3078e+00 -5.0758e+00
-#>  -1.8509e+00 -2.5069e-01 -1.1716e+01 -6.5717e+00  3.8582e+00  4.9188e+00
-#>   5.6229e+00 -2.9105e+00 -2.3605e+01  8.7952e+00 -3.9078e+00  4.0413e+00
-#>   6.1460e+00  7.2539e+00 -4.2070e+00 -3.4539e+00 -3.6857e-01 -4.5547e+00
-#>  -6.1082e+00  3.7526e+00  8.7095e+00  1.6753e+01 -5.0704e+00 -3.2953e+00
-#>  -5.7529e+00 -3.3428e+00  1.3160e+00  1.1378e+01 -8.3256e+00  9.5483e-02
-#>  -1.5073e+00  6.0489e+00  2.0360e+00  1.3820e+00 -2.4588e+00 -1.2680e+00
-#>  -4.2967e+00 -9.2732e-01 -3.1906e+00 -7.1769e+00  4.9066e+00 -2.2283e+00
-#>  -1.2227e+01  8.5371e+00 -2.6248e+00  2.4014e+00 -5.6499e+00  1.0123e+01
-#>  -8.5863e+00  9.5664e+00 -5.9350e+00 -1.6468e+01  1.0391e+01 -1.0179e+01
-#>   3.1844e+00  1.2364e-01  1.3085e+01 -5.8785e-01  2.3136e+00 -9.7390e+00
-#>   4.6407e+00  4.9859e+00 -1.2739e+00  2.1925e-01  2.5094e-01  3.3886e-01
-#>   1.1058e+01 -1.0747e+01  8.3456e+00 -2.2283e+00  4.7523e+00 -7.7536e+00
-#>  -1.3527e+01  1.4885e+01 -5.2081e+00  9.1698e+00 -7.9931e+00 -8.4733e-01
-#>  -1.9517e+00 -3.4732e+00  5.4558e+00 -2.4234e+00  3.1985e+00 -6.9968e-01
-#>   8.7000e+00 -3.4409e+00 -1.1334e+00  1.4169e+01 -1.9912e+00 -2.1136e+00
-#>  -7.1622e+00  1.2549e+00  4.2199e-01  3.1096e+00  2.5984e+00  4.3691e+00
-#>   3.0234e+00 -2.0207e+00  1.4672e+01 -1.1174e+01  5.1304e+00  7.1506e+00
-#>  -1.1437e+01  3.4149e+00  7.5767e+00  5.9337e+00 -1.2918e+01  3.3527e+00
-#>   1.2484e+00  4.7930e-01 -7.1958e+00  2.7164e+00  2.0207e+00  7.7346e+00
-#>   1.3582e+00  2.8437e+00 -7.3608e-01  2.2923e+00 -7.7159e+00 -3.2784e+00
-#>  -8.8959e+00 -3.6378e+00 -2.6879e+00  5.3815e-01  8.6462e+00  1.2901e+00
-#>  -1.0886e+01 -9.1296e-01 -1.3696e+00  4.0285e+00  1.1338e+01 -3.0686e+00
-#>  -7.4435e+00 -1.6110e+00 -6.7849e+00 -3.5437e+00  6.4861e+00  3.2415e+00
-#>   2.0525e+00 -1.4578e+00 -2.7559e+00  5.7021e+00 -9.3438e+00  9.2374e+00
-#>  -2.7167e+00  1.1972e+01  1.5159e+00  8.5275e+00  5.1403e+00 -4.5702e+00
-#>   8.4622e+00 -1.0705e+00 -2.7975e+00  1.3748e-01  6.0727e+00  9.2312e+00
-#>  -1.2966e+00  1.4843e+00  1.3079e+01 -4.8325e+00 -2.5283e+00 -1.0658e+01
-#>   9.5890e-01  8.5580e+00  8.0375e-01  6.0536e+00 -2.3652e+00  1.9715e+00
-#>  -8.4933e+00 -6.1661e+00 -2.8968e+00  8.8597e+00 -7.1364e+00  2.2233e+00
-#>   6.3513e+00 -1.3840e+01  9.8739e+00 -1.0290e+01  1.4279e+01 -6.2812e+00
-#>  -1.7568e+01  5.2109e+00 -1.6603e+00  5.6085e+00 -3.3452e+00 -2.7954e+00
+#> Columns 33 to 40  -7.1104   6.2031  -5.8101  -1.1152  -4.6315  -9.4966   6.7389  -6.6577
+#>    1.0362  -5.0733   3.8936  11.3512   0.7019   0.8807  -3.1198  -0.0273
+#>    1.5857  -3.3188  -0.1739   3.7079  -7.3753   8.4177  -1.2289  -5.4003
+#>   -3.2097   0.4749   3.1526   5.3095  10.5141  -2.9392   7.5982   6.2324
+#>   12.0165   5.7773  -2.0571  -2.8175  -5.7637  -3.8129  -2.5565   4.2143
+#>    0.6730  -1.1841   5.6221  -7.0045  -7.0576  -5.1420   0.3235  11.0718
+#>  -11.5064 -10.8466  13.9312  -0.1613  -0.7936   9.2455   2.2255  -8.9074
+#>   -3.2557   2.0597  -8.9985   4.7375  11.7785   6.5120  -4.9715  -0.6753
+#>    6.4127   2.6258   5.9541   4.9620   2.7551  13.0173  -0.4617  -2.6421
+#>   -2.2778   0.0185  -2.4465  -2.5310   0.7069  -8.0452  -8.8328   5.8252
+#>   -5.3960  -8.0286   0.5873  -8.4356  -6.4207 -10.3039 -12.7500  12.8055
+#>    1.4164   0.9875   8.6126   0.4653   4.5835   5.6521   5.7095   7.5403
+#>   -8.6152   1.4163  12.5893  -2.4590   5.7324   9.5341 -15.0938   9.9731
+#>    3.9700  -4.9081  -0.4361   4.0454  10.0282  -0.3105 -12.6996   0.4573
+#>    2.0415  17.8267  -7.0237   4.9820  13.3990   2.6581  -0.9705  -9.2812
+#>   -3.2320   7.6963  -6.5363  -5.2591   2.9328   5.6946  -4.4450   2.0361
+#>   -1.6764   0.3225 -10.5399  -0.3576  -5.9841 -14.3651   9.2570  -1.4252
+#>   -4.3304  10.4637   8.6849  -4.2338   4.4932   5.6891   2.5000 -10.2220
+#>   -2.7366  -6.6435  -1.8592  -1.8754  -3.4554  -2.3047  -5.9007   5.6179
+#>    3.4998  -7.6149  -3.8754   3.5602  -8.3153  -6.6275 -10.4509  -4.7872
+#>   -4.7287  -5.7657  -6.2886  -8.5831   4.0117  -5.2058  -8.9663 -10.0599
+#>    1.3292   5.6377   5.2710   6.5351   1.7869  -3.9364   3.3611   0.7721
+#>   -4.5771  -3.5253   3.4772   4.9476  -0.1588   7.3496  16.3549   4.5678
+#>   -2.7939  -0.2169   7.6107 -12.8410  -2.0678   8.9172  -2.6089  -2.1153
+#>   -1.9854  -8.3015  -7.7262  -0.7392   3.3416  -5.0926   4.2479   5.3253
+#>   -1.2966  -7.5358  -1.9126   4.9271  -3.7803  -5.2679   0.3205  -2.1165
+#>    4.9917   1.4806  -5.7357   4.6555  -3.5482  -3.9291   0.7551  -2.4975
+#>    3.5481  -1.1284  -4.1767  -2.2818 -10.6350 -10.4669  -0.1940   5.9054
+#>    1.0773  -1.2175  -3.2846   3.6837  -0.5295  -4.2009   2.7542  -3.7241
+#>   -3.7732   2.6368   6.0732  -5.1761   7.4727   9.7903  -6.9487   0.6081
+#>    8.6603   6.3393 -11.3216   2.9335  -4.2205  -1.1316  -1.9060  -3.1916
+#>    6.4110   9.1433 -10.6737   3.7257   3.7674   0.7303  -0.6654   4.3061
+#>    9.8283   3.7125  -0.2323   3.7521   3.2582   4.1825  11.0883  -4.7521
 #> 
-#> Columns 43 to 48 -1.4316e+01 -1.7188e+00  3.2444e-01  8.9797e+00 -3.2084e+00  7.8822e+00
-#>   3.1460e+00 -6.4460e+00  5.2454e+00 -1.9715e+00  1.6432e+00  1.1744e+01
-#>   8.6926e+00 -2.2753e+00 -4.4450e-01 -3.1107e+00  2.9401e+00  3.6147e+00
-#>   6.4619e+00  3.0811e+00 -9.3294e+00  3.1618e-02 -5.4628e-01 -1.5545e+01
-#>   3.2211e+00  2.7079e+00 -1.7153e+00  6.0905e-01  9.2878e+00  7.0190e+00
-#>  -1.1072e+01 -1.9385e+00  1.8828e+00  1.5127e+00  2.6904e+00 -1.7966e+00
-#>  -9.0222e+00  9.6423e-01  5.8570e+00  2.7499e+00 -3.3381e+00 -9.7036e+00
-#>  -1.7245e+00 -4.4181e-01 -3.4086e-01  6.2400e+00  1.5419e-01  1.2574e+01
-#>   9.0976e+00 -3.2707e+00 -1.0903e+00 -3.3040e+00  9.1338e+00 -1.2215e+01
-#>  -3.1234e+00 -2.8812e-01 -2.1735e+00 -1.0646e+00 -1.2786e+01 -1.5235e+01
-#>   6.6138e+00 -4.3299e+00  8.6403e+00  9.3438e-01 -1.1749e+01  1.8446e+00
-#>   6.2344e-01  9.8338e-01  8.2563e+00 -8.2448e+00 -4.5447e+00  6.5609e+00
-#>  -1.4375e+00  5.1289e+00  5.4863e+00  4.6983e+00 -5.3126e+00  6.0145e+00
-#>   5.5840e+00 -1.5232e-01  1.0648e+01 -8.1426e+00  9.6469e+00  7.1755e+00
-#>  -3.7829e+00  3.3464e+00  1.6952e+01  4.4535e+00 -2.9354e+00  1.5281e+01
-#>  -1.0402e+01 -3.4845e+00  1.1573e+01  2.1074e+00 -8.8245e+00  1.1603e+01
-#>  -1.1021e-01 -6.7476e+00  5.0775e+00  1.6184e+01 -3.9077e+00  8.0282e+00
-#>  -6.1079e+00 -4.1911e+00  1.3648e+01  6.0187e+00 -7.1858e+00  9.6539e+00
-#>   1.7198e+00 -1.1251e+01  3.8847e+00  1.0030e+01 -7.0925e+00 -4.5950e+00
-#>  -8.4614e+00 -9.8298e-01  4.4841e+00  2.8005e+00 -7.3674e+00  1.0545e+01
-#>   5.7187e+00  2.9945e+00 -5.2711e+00 -3.7479e+00  1.5054e+01 -8.7987e+00
-#>  -5.5156e+00  1.2471e+00 -4.1211e+00  3.1218e+00 -6.8213e+00  3.6497e+00
-#>  -6.2814e+00  6.7489e-01  4.7816e-01  5.8086e+00 -7.7224e+00 -1.1090e+01
-#>  -2.7705e+00 -5.7763e+00  4.7694e+00 -1.5708e+00 -9.1193e+00 -1.5565e+01
-#>   9.1999e+00 -3.0123e+00 -2.4817e-01  6.1060e+00  4.1478e+00  1.1212e+00
-#>  -2.9603e+00 -2.1796e+00 -2.0766e-01  7.8280e+00  3.1220e+00  1.4492e+01
-#>   3.6359e+00 -2.0703e+00  4.0872e+00  4.5950e+00  4.9332e+00 -5.6241e+00
-#>   5.1562e+00 -8.1316e+00  4.7583e+00  1.3220e+01 -5.4718e+00 -3.3530e+00
-#>   4.5500e-01  2.9386e-01  7.0324e+00  1.1036e+01  7.7246e+00 -4.6796e+00
-#>  -8.1972e+00  4.4841e+00  4.2891e+00  5.8185e+00 -2.5228e+00  5.5887e-01
-#>  -2.3574e+00  1.5458e+00  3.8627e+00  1.2930e+00 -2.1494e+00 -1.3772e+01
-#>   3.3417e+00 -4.2553e+00 -1.0949e+00  5.9260e+00 -1.1277e+01  4.3272e+00
-#>  -3.4267e+00  9.3572e-01 -1.9961e+00 -4.4621e+00 -5.6221e+00 -1.7291e+01
+#> Columns 41 to 48  13.3268  -1.0056 -10.2394 -14.0572   7.2373 -11.7891  -5.4356  11.6350
+#>    1.5034  -0.2444   2.7159  -2.6949  -4.0703  -9.9168  -0.1028  -0.5349
+#>   -0.7993  -1.5907  -2.9266   1.9504   6.6516   4.0723   7.5052   2.6683
+#>    0.0950  -7.9544  -8.2260 -17.0586  -8.8210   2.6073  -6.1276  13.7919
+#>    3.4560  -4.0487  -5.4636  -1.1480 -11.5815  -8.1386  10.8291  -1.2520
+#>  -12.3671  -0.4899  -4.8369   6.4535   3.7236   7.9121  11.4997  -0.7510
+#>    4.2753   2.2398  16.9714  -3.7689   7.5609   3.0315  -0.2744  -2.8969
+#>   -5.8823   1.0620   6.1123  -1.4432  -4.3620  -3.0380 -12.4353  10.7294
+#>   -6.1138  -8.9188 -12.5581 -13.1857  -6.2093 -18.3727  -3.3118 -10.2877
+#>    1.5605   2.2150   5.4671   9.2354  -2.3335  -2.7411  -2.6724  -2.9369
+#>    0.1683  -5.7333  -6.5652 -13.6436  -9.7716   4.7530  15.9291  12.4899
+#>   -9.5900   4.0555  -1.4710 -12.2339 -11.1078   4.2110  -5.0619   4.1043
+#>    1.9375   3.3089  -5.0634  -6.1416  -7.4462   0.7009 -11.1027 -11.7545
+#>   -1.9137   4.6833  -9.6380  -0.2342 -10.5227 -13.0958 -22.3145  -7.6393
+#>   13.7592  -9.2456 -12.5695 -11.3587   1.2518  -5.5265  -6.4806  -8.3060
+#>   -9.0359  12.3764  -5.5282  18.9738   6.3271  11.4664   1.1077   5.8071
+#>   17.9974  -9.6266  -3.1348  -4.2414   1.6477  12.7555   1.1829   8.5258
+#>   -8.4362  -2.7764   2.5933  -7.1246   7.5622  -2.8431   8.3987  -5.7898
+#>   -3.5049   6.4722   3.0645   3.9859   2.8276   8.8630   3.9953  -2.4452
+#>   15.4691  -8.0395  -1.5533   6.2753   1.8991 -12.0606  -5.0461   1.4275
+#>   -2.2957  17.5421  -1.3339  -7.7750  -2.2268  -3.0716 -15.3968  -0.7552
+#>    9.6343   4.5467  -3.1903   4.5959  -4.0611   1.0943  -0.1111   6.9238
+#>   -5.9578   0.3011   1.7163  -8.8703   1.2913  13.4391  13.6061   4.1858
+#>  -11.4033   3.1806   1.2891 -16.1160  18.8338  -1.5648  -3.3201  -2.6366
+#>   -2.5553  14.1245  -2.2778  14.8919   6.8235  22.9245   5.5260   6.5431
+#>    5.9213   1.0869   6.3956   5.4595   7.6265  -8.0746   3.0931   6.7874
+#>    1.5071  -1.3201  -6.7472   8.5980   0.1973  -3.3340  -6.2912   2.9766
+#>    5.3211  -1.2440  -5.6185   5.0279   3.2060   5.5104  16.0627   4.4307
+#>    0.0718  -2.5977   7.5797  -3.5160  -4.5526  -8.2922 -13.4784  -6.1788
+#>   -4.4021  -2.1830 -12.1799  -8.7476   0.3839  -7.2126   6.8421   3.3336
+#>   -5.7626  -6.6735   1.4709   0.3446  -3.9542   6.2844 -11.6062  -6.3796
+#>    1.0421  -3.8594  -7.2021  -1.7108  -1.6160   1.6815   5.4795  -7.5773
+#>   -6.4670  -1.7252  -0.6297 -11.1553   3.0178  -0.0184  -4.9736   4.4968
 #> 
 #> (3,.,.) = 
-#>  Columns 1 to 8   4.9562  -3.7582   5.3943  -3.9742  -5.9963 -12.7356 -12.3488  -0.0281
-#>   11.2481   1.8273   4.5276   3.2045  -7.3007   4.8452  -2.3510   2.6769
-#>   -4.4985  -1.0768   8.5073 -11.6589   4.7996  16.7321  -3.0965  -0.3856
-#>    0.6945  -3.6676   6.7533  -4.5750   4.0977  13.4447 -10.3970  12.6614
-#>   -5.4403  -5.4518   1.6155  -8.4963  -5.9472  14.6116   2.3946  -1.3451
-#>   -5.3157  -2.3617   2.7643  -2.6659   2.6909  -6.9647 -15.1995   5.5772
-#>    3.6503   5.7380  -0.8403   0.6548  -1.7302 -10.1615   5.2492   8.6817
-#>    7.2741   2.6035  -3.6202  -9.2834  -5.8462   8.7866  -2.8505  -1.4817
-#>  -19.9435  -4.4367  -1.1491  11.0581  -7.7709  -0.3405   0.1401   9.1089
-#>    3.6538  -6.3743  -1.6704  -4.4341  11.5087   7.0363   1.0560  -3.5037
-#>   15.8845  -1.4452   2.1056  -6.1563 -17.7140   8.9579 -12.1511   0.6144
-#>    2.2474  -4.6580   0.3898  -1.0848  -1.7472 -14.7547  13.3952  -8.3100
-#>   11.9129   0.8427   9.8857   5.7461   3.1181  -4.4163  -0.3108  -5.2917
-#>   -6.2343   1.1551   4.5635  11.1697   2.7615   2.6141  -7.0863   4.4991
-#>   15.9765   1.4206   5.0062  -2.9111   1.0533   6.9639 -11.3675  11.6931
-#>    1.9163   4.4746  -2.2195  -6.6301   4.6677  12.5423   4.5925  -1.8943
-#>    6.9265   8.7154   4.7434  -9.8159  -4.0058   1.6135   3.6586 -20.6469
-#>    8.3534   3.6571  -8.5923  -1.9266  18.2730   3.6534  -1.9278   3.3406
-#>   -0.0040  10.9221   4.4209  -6.5203   6.8133  10.0404   0.2141  -2.6464
-#>    7.6266  -0.4131 -13.4900  -2.6130   5.9332  -1.1798 -11.2315   5.4657
-#>   -0.3001   3.4722  -2.9582 -10.0573   4.1344  -1.6925   0.8711  14.5282
-#>    1.7112   4.8549  14.1942   2.7680   0.3115  -1.2966   5.9259   0.5383
-#>    0.7748   2.5614  -1.0904  -2.4082   9.1032 -11.0332  -3.9074   1.4275
-#>    2.6380  -1.4095   8.9158  10.1162  18.3666   3.2091  -6.9841  -6.9002
-#>   12.0612   3.2341  -4.0510 -10.9244  -1.5035   4.1921  -1.7979   1.8640
-#>   -3.7546   6.1095  -7.6240  -7.1370   1.2163  -7.0363  -6.3017   0.9980
-#>   -1.8646   7.0013  12.8986   3.9962   4.5674  16.9755  -0.0370  -3.8269
-#>    2.7722  -7.3149   5.8819   4.5695  14.4691   5.7079 -11.5897 -10.8310
-#>    8.9845  13.7087  -0.9190   2.1477   2.1493   9.3396   7.7154  12.4460
-#>    4.9266  -4.6779  -1.5574  12.9128 -12.8062 -12.4111  -8.2159 -14.7549
-#>   -4.2986  -0.1995 -10.0720  -9.3908   4.2700  -8.7040   6.4533 -11.1542
-#>   -0.6362  -0.8948 -10.9689   8.4188   0.0568  -4.8182   3.3533  -5.8279
-#>    2.6255  -7.6898  -7.6644   5.9319  -8.0142 -11.2382 -11.3271  -0.3779
+#>  Columns 1 to 8  -5.1278  -4.5006   9.5095   9.0330   2.4264  18.7260   7.8777   7.2444
+#>    4.7839  -8.5126  -6.6213 -12.6814   5.2088  -2.2826 -11.0311  -6.3638
+#>    4.1031  -5.7867  -5.0951   1.3480   2.5836  16.7772  -1.3587   0.8495
+#>  -10.2675   5.1549   7.8001  -1.1595  -0.7911   0.5795   4.7843   0.8563
+#>    0.4001  -2.3683   6.3588   5.2743  23.0337  19.6014   2.8997   6.6821
+#>    0.6768  -2.5441  -9.4527   1.7197  -3.5171  -2.7897   8.4855  10.2762
+#>   15.8093  -8.9259  -9.4807   0.3857  -9.0302  -1.5626 -17.3421  -0.3243
+#>    1.0756   6.5978  12.1907  -1.7837  -2.6270  -6.9303   5.1668  -7.1205
+#>   -0.4221   5.9274  -4.8581  -4.2838   4.0266  -9.8524  -0.7421   5.3448
+#>   -0.9774   3.8799  -7.5644  -8.0557 -13.6937 -11.5507   1.4400   7.6644
+#>   -7.1578  13.6348   9.7534   0.4326  -9.2767   1.7136  22.0305  -0.0253
+#>    1.2827   0.5390  -3.6264  14.3977   3.9754 -10.2044   1.4494  -0.8788
+#>   -8.7990  -8.7189  -4.1777 -18.2835   1.2326 -17.9329   2.7786  -3.0052
+#>    0.8874  -4.3759  -3.4577 -19.0189  -6.9200  -0.5800  -2.5248   2.2195
+#>   -1.2808 -11.8056   0.3925  -0.3070   4.4410   4.1242  -2.2455   3.7403
+#>    0.8672   1.3068   9.4222   2.0591   1.1883  -8.2026  -1.0674  -2.2916
+#>   -3.4802  -0.7958   2.2186  16.8960   6.2402  11.7586   5.3872   2.5588
+#>    3.0149   4.2899   7.5872 -13.4274   3.8662   4.8530  -4.2466  -9.9949
+#>   -0.0567  -8.8183  -3.9920  -9.8587 -10.0767 -15.9883   3.4482  -1.4267
+#>    2.1360 -11.6248 -13.9227  -6.6362  -8.9768  -3.1309  -6.4980  25.6076
+#>   13.2209  -0.9525  -7.2168  -1.3732 -17.8097  -9.9051  -9.3736  -8.0395
+#>  -11.5475  -8.8849 -11.0298   5.0507  -4.9686  -2.1899  -1.1698   0.7857
+#>    9.0478  -4.0544  -0.4491   7.9602   1.5543   9.2264  -2.7842   0.1068
+#>    4.4409  -3.3490   0.2244   9.1930  12.8143   7.1343  -3.8777   9.9727
+#>   -2.8383 -11.2511  -8.8919  -4.4868  -4.4493   8.4346 -15.7925  -1.7161
+#>    2.5040  -0.7997  -5.9196  -3.4500  -7.8017  -3.0584  -2.4306   7.8968
+#>    2.3506  -1.7359  -3.1030  -1.1406   2.4846   5.0937  -2.7929   6.2975
+#>   -7.5156   6.1988  -0.8792  -5.0672   3.5677 -10.4047  -2.2888   8.2631
+#>    0.9288   0.3885  -5.8831  -1.4473  -2.8584   4.3175   5.0746   7.5993
+#>    2.1791   3.4867   1.8659  -7.9709  -3.9872  -3.8741  12.2298   7.8602
+#>    0.4885   1.0647   1.4114  -2.3891  15.1261   4.7602  14.7145  -7.2278
+#>    2.1687   9.4927  -1.3812   5.8388  -7.3725 -14.1129  -0.1521   6.6170
+#>   -1.6358   9.5396   1.8514   3.5563  -2.7231   5.5364   5.0568 -14.5332
 #> 
-#> Columns 9 to 16  -9.7360  -6.4222  -5.7229   7.4474  -6.9485 -18.3427  -2.7103   3.7782
-#>    1.2750  10.4092   0.1234   9.9826  -7.2135  -5.7552  -1.9998  -1.9475
-#>    1.5161  -2.5462  -1.0372   2.6637   5.9223   6.5293 -17.3755  10.4248
-#>  -11.9943   1.9396 -10.8378 -27.0799  24.8994   3.4319   0.5319 -13.5652
-#>   -2.1803  -3.0364  -3.0484 -11.1355   1.9524  -6.2876  -0.3480   6.6803
-#>  -12.3258 -14.2578 -11.3267  -2.5336 -18.2894  -4.2594  -1.9660  12.5184
-#>   -6.8200  -8.1209  -9.5000   1.3338   1.5568  -5.8006  14.6046  -7.5704
-#>    4.2130   3.7024  -1.5168   2.1240 -12.3614  -7.4998  -5.9788  -5.3655
-#>   -1.4886 -14.2426   8.8877   3.6402 -13.4771   7.7371  -8.9763  -0.4561
-#>  -13.1842  -2.8242  -4.3459  -3.8975  -7.4657   8.8410   4.0576   3.8440
-#>    7.6390   3.3561   0.9564  10.3876  -6.1527  -1.8119   1.3460   5.9928
-#>    8.8839  -0.3631  -3.6710   0.2649   4.8643  -5.2106  14.0710  -2.3089
-#>   -4.5459   2.2723  -1.4164  -5.1818  11.6005   6.0302  -0.6840  -1.0466
-#>   -2.4372  -3.9876  -7.3022  16.0805  28.7031  -6.6200   1.3126  15.1169
-#>   -8.7492 -17.0470  -3.5868   9.9832 -13.2698  14.2807   7.7293  -0.8965
-#>   -8.0431   6.6999  -8.5087 -17.4936  15.0480  -1.8982  -5.8062   0.4042
-#>   -1.0442   1.5605  -3.1818 -10.4155   1.5044   4.2108 -10.3067   2.6241
-#>  -14.2217   8.4671  -2.2625 -13.4366   4.9955  11.3371   3.7654  -5.4279
-#>    5.1664   0.4539  11.9847  -5.2858   7.8836  -3.6860  -7.9037   4.0934
-#>    4.9691   2.4076   9.0446   0.2401 -17.3001  13.3294 -13.2066   4.6766
-#>   10.6484  -4.1710  -4.8643   8.5792   0.4483  -4.1667   2.6186   2.1822
-#>   -4.8166   4.1797  -2.3908  14.4425  -8.0795   2.8944   2.9143   0.6577
-#>   -5.5388  -2.0295  -0.4797   5.0858 -11.7329   4.8734   8.9627   2.9415
-#>   -9.7544  -5.7999  -0.3570  -1.3464   4.3133  23.6557  -4.5242   7.1379
-#>    3.3175  -1.3137  -7.1024   9.0726  -9.1605   4.0476  -3.9804   4.8644
-#>    1.4142  -4.5650  -6.4153   3.6048 -13.3332  -6.1364 -11.7781   1.3500
-#>   -1.1517  -4.6129 -19.7663  -6.7512  17.9404  -8.0486   4.8501   1.3591
-#>   -5.8542   0.8734   3.2384  -1.5739  21.9474  -3.7593  -0.9735  12.2523
-#>   -2.3665  -7.0931   0.6589  16.3679  -4.9314  10.7504   9.8569   0.4346
-#>   -5.0901  -1.9335  -2.1915   7.1066  -6.8045  -7.4043   3.2150   4.0988
-#>   -8.1054  -8.8176 -11.3086  -7.5997   1.9523   2.5381   7.7193  -4.2398
-#>    3.0093   5.9653   4.1393   0.3365  -1.2716  11.9673 -15.8381  -0.9102
-#>   -1.5938  -3.4779  10.4909  12.1245 -15.0920   4.7935   3.1971   2.9640
+#> Columns 9 to 16 -10.1209   5.0800 -14.4894 -10.0485  -0.3948  -7.9548   3.6714  -0.9975
+#>    8.0411  -1.4947  -6.4890   3.2111  -3.5606  -4.2544  -0.7074  -7.2217
+#>   -5.1016   6.4078   9.7789   6.6518   0.4305  -4.3517  -7.7457   4.7810
+#>    7.2694   7.8292  -1.1551  -7.4541   3.0155   6.6142  14.5037  -6.5980
+#>   -6.6166   1.8296  -7.9309  -1.7234  -1.9408  -4.7944   2.2040   5.3681
+#>   -1.8593 -14.4510  -5.8747   2.3867   6.4348   3.1423  -1.6486 -13.8432
+#>   -5.4119   6.8913  11.1302   5.2678   9.0553   1.0027  -4.7439   3.1055
+#>   -3.3472  -7.6898  11.3553   7.8453  -4.2988   0.4420   1.1463  -1.2659
+#>    9.9301 -10.9049 -12.6421  -5.0100  -5.2547   9.7823  -9.9843   0.8904
+#>   -9.9175  -1.8258   6.7117   7.1737  -6.8342  -7.9616  -1.0889  -2.9683
+#>    0.1876   1.0047  -6.2214 -13.5494  -4.1035 -11.3754   4.1208  -2.1293
+#>   -5.8780  -9.9904  10.1004   5.3111  -6.4698  -6.1977  -2.8318   9.5273
+#>   10.2986  -5.0245   5.8463   0.4889  -3.6013   0.0976 -11.6090  -4.5015
+#>   14.3856 -12.5066   3.9378 -11.2010   5.3067  -3.8067   3.9946   1.4835
+#>   -5.4574 -18.1965  -2.7907   3.7717   0.1156  -7.8323  -2.2334   3.3117
+#>    9.4175  -2.7659  10.4894   4.2989   2.9681   9.3755   5.4636  11.8332
+#>   -1.5391   2.4291  -7.9587 -10.5855   1.2087  -4.1796   7.4838   4.4501
+#>  -11.9078  -1.4563   3.1055   2.1957   2.7735  -8.9930  -0.9006  -5.1821
+#>    4.1253  -4.4861   1.8479   4.7597  -3.6926  -9.7058  -2.4701  -6.3677
+#>   12.0557  -7.0326 -15.5973   1.6378  10.3505  -0.3404  -7.6398  -8.2503
+#>    2.6178  -0.4480   5.4727  -8.6452   0.2683  -4.2479  -9.1431  -3.1705
+#>  -10.6440  15.4314  17.6512   2.7787  -1.0676   2.3829   1.4587   5.3460
+#>   -0.5966  -8.2422  -1.0872   5.9343   0.2573   0.0817  -3.3590   6.2829
+#>    5.4267  -8.0981  -8.1271   0.8138   2.6016   0.0605  -7.6136  -4.5924
+#>    9.6271   6.6256  10.1982   1.8380  20.7458  -2.5219  13.0436   3.5439
+#>   -1.2009  -0.9587   2.5393   3.2507   5.9707   6.7003   3.7681   4.5587
+#>   -2.9848 -11.9670  -0.4371  -8.6889   8.6647  -1.2020  -9.0784  -5.1270
+#>   -0.4679  13.3824   2.3437   5.5385   1.7472  -2.0704  10.1693  -2.0073
+#>    1.0152  -5.2033  -4.1154  -3.2404   7.0626  -2.0554  -2.6823  -0.5493
+#>   -4.2003 -19.8706 -11.9461  -3.9365   2.2946  -5.4263 -16.3099  -1.0841
+#>   18.8134 -11.2480   0.7597   0.3864  -2.9618  11.1220  -6.8384   0.9543
+#>    4.0662   7.1095   6.6421  11.1504   3.0409   0.1461  13.8598   2.8963
+#>   -9.8873  10.0620  10.9858   1.3170  -6.6131   7.4196  -1.5304   2.4688
 #> 
-#> Columns 17 to 24  -7.8446  10.8300   3.1495  -6.4143  -4.9418   1.5859   5.7119   4.1122
-#>   -8.2818  -2.5108   0.3972   5.5391   2.6315   5.6176   4.3199  -1.2731
-#>   -3.7133  -7.0713  -9.7593   3.5059   4.4236   4.6572  -5.9136   6.1746
-#>  -11.5718   3.8795  -2.3085  -3.9470   1.1945  -1.6105  -1.4233   4.4411
-#>    0.0810   6.6555   3.7000  14.6576  11.7133  10.7315   2.1382  -2.0174
-#>   11.3570   3.8331   2.8234  12.0460   5.1824  -8.6710  -6.2794   0.8210
-#>    5.8555   3.8700   1.9546  -6.2020  -2.1532  -0.5159   3.9648   7.4020
-#>    5.3760  -0.2685   7.1646  -0.2744   3.5396   5.4370  -1.4105 -15.6596
-#>   11.7633 -10.5656   6.0806   0.3545   2.7078   0.2011   2.5850  -2.2982
-#>   -1.9521  11.4095   9.7509  -2.7458  -1.9989   1.4426   3.1751   7.4327
-#>   -0.7683 -11.2969   0.6807   1.4418   1.1541   1.8743  -6.6355  -2.5485
-#>    2.3949  11.4733  -4.1730   1.7488  -2.7655   1.3965   2.2042  -7.7285
-#>    2.0386  -3.8169  -5.1341   2.4189  -0.9858  -1.9761  -2.5394  -2.1106
-#>    2.8564  13.8817   3.5312   7.7483   2.4548   3.4022  -6.1050  14.8377
-#>    5.4632  -4.0690   8.5569  -0.5126  13.3378   2.3015   1.6362   4.0142
-#>   -8.9410   1.1707   1.4379  -3.3880   1.1356  -3.8432  -0.2028  14.7388
-#>    9.5698   0.8845   3.6077   1.9991   5.9551   4.0725 -12.3633  -3.3834
-#>    5.4765 -10.8793  -1.3330  -0.4239  -0.2444  -4.3707   4.3048  -2.0963
-#>    7.7052   0.6048  -3.1704   1.7206  -6.3772  -2.8244  -1.9311   7.0662
-#>   13.3648  -1.6975  12.1434   5.2608   3.4101  -5.8776  -9.4342 -11.3617
-#>    4.4433   5.9981  -0.4610   3.9429   2.5325  -6.9121  -0.0131  -6.9599
-#>    4.6063  -0.7668   2.4319 -10.1652  -6.0697  -2.6817  -2.0906  -2.6583
-#>    2.4389   2.1851  -2.5737   4.2586  -0.7670 -10.1724   6.2627   7.3546
-#>    9.2102  12.2677   0.6505   0.8508   8.8404  -2.4235  -0.3184  13.9138
-#>    0.4299  -7.7316  -4.0262  -1.0616  -1.8941  -4.5296  -2.2042   3.9778
-#>   -2.5706  -1.2521   5.6562   1.2311   0.4175 -11.0818  -4.9537  -2.0501
-#>   10.8075   0.0243  -1.6829  -1.8148   3.6318  -7.6898   3.4834  13.7268
-#>   -1.2697   5.0217  -2.6425  11.8827   8.9970   5.9287 -12.2312   7.6060
-#>   11.1753   0.4443  -2.5237  -0.2271  -1.2767   1.0772  11.0985   8.0461
-#>   -5.9804   7.4466  12.3405  -5.5906  -5.0035  11.6740  -8.4227 -11.0415
-#>    8.5981  13.2756   0.7086  -7.9883  -5.1583   0.4455   6.2542  -0.3895
-#>  -10.7068  -0.0316  -7.3415  13.2332  -1.9071   6.7170  -0.1882  -2.2628
-#>   -7.5274  -4.2322   4.3580  -0.3955  -1.2535  -0.3756   4.1293  -3.7303
+#> Columns 17 to 24   7.5862  -5.1936   7.6388   5.8353  11.7061   0.5586   2.5924   1.9125
+#>   -3.9885  -3.5434   1.9906  -6.1350  -1.1435   0.0343  -7.1282   9.2190
+#>   -2.6253   2.8304   0.3522  -0.2029  -0.4975 -10.6151   3.0706  -8.6368
+#>   -6.4932  -2.8950  10.1423  -3.4150  -9.9279   0.8822  -3.1574  -0.2743
+#>    4.4764   4.6848  -2.4851  -1.5540  11.4432  -1.4824  10.7468   1.9491
+#>    3.1593   7.4647   7.0182   5.1533  -2.2870  -4.7152   1.3693   0.7984
+#>  -13.2692   5.1763 -13.7849   2.4519  -3.0231   5.8470  -4.8133  12.4372
+#>   -1.0103  -2.9673   3.7567  -7.1660  -7.1880  -3.3324  14.3220  -6.6137
+#>  -14.4350  -0.1077  -0.8339  -2.5992  -4.7385  -7.6101 -10.0769  -6.2583
+#>    2.9348  -3.5775   1.3642  -4.0891  -1.4119   3.3728  -7.7035   5.2910
+#>  -12.4565  -2.8200   5.8915   9.6994  10.6242   3.0506   2.9140   0.1081
+#>   -3.2337  -4.1424 -10.2011   8.0144   1.3814  -7.3781   1.0551  -6.8396
+#>   -4.5594   3.5548   1.2273  -5.1877  -1.3088  -6.1823   9.0818   4.9969
+#>   -0.1863  -5.1163   2.3162  -3.4166   3.4583   4.8357   5.0724   8.2961
+#>   -7.6159  -1.4426   8.4888   8.2496  -9.5194   1.6780  -9.8950  -0.6717
+#>    9.3602  -4.5074  -1.1037   1.4025   1.1228 -13.7274   4.4925 -19.3559
+#>   -5.4191  -1.3136   4.8185  13.3719   6.3094   5.8786 -12.9161   5.3763
+#>   -8.1083   0.3903   8.0846  -3.9731  -9.1568   6.4466  -9.1130   2.1779
+#>    2.8756 -12.0547   4.6775   3.6406   3.5585  -4.0128   5.1379   6.3532
+#>   -3.0196   2.7591   5.1131  13.2092   0.9012  -7.0422  -2.8851   0.7359
+#>   -3.5815  -6.7956  -9.8269  -3.7708  11.1237   2.8918  -7.3674   5.2442
+#>    2.7471  -8.5420  -8.6237   1.7127   1.3216  -2.3913  -0.5696   0.1381
+#>   -7.1711  -1.1567  -2.9633  10.2240  -3.7295   2.3287   8.9156  -2.2737
+#>   -3.1349  10.9526  -0.4448   3.9403   7.9590  -2.2257   0.9995  -6.4912
+#>    5.1741  -4.0589   2.3938  -3.3143  -2.1102   1.9795   5.4914  -4.7404
+#>    4.5089   4.2776   3.0584  -5.2834  -0.0054  -0.3052 -10.0477   2.4862
+#>    7.8940  10.7260   9.4198 -10.9637   3.1990  -0.3634  -2.4300   4.1442
+#>   -1.4876  -6.3949   1.8075   2.9876   9.0423   2.6924   4.7369   0.6878
+#>    3.1631  -2.4461   4.5630   0.9205   5.2506   2.7286  -2.6849  -0.1385
+#>   -7.0602   5.6859  -0.9651   8.3329   1.0673  -1.5509   1.5679   9.1858
+#>    6.1669   6.9448   5.8911  -6.9180  -0.8339   1.1331  12.7957 -11.7288
+#>    1.5151  -1.5748  -1.9829  -1.2508  -0.8887  -7.4564   6.3130  -6.5703
+#>   -0.0470  -3.5726   0.1762  -9.5157  -2.8015   6.6279  -6.3339  -6.5166
 #> 
-#> Columns 25 to 32 -11.3413  -0.6342   1.2782  -1.0040   0.3941  -1.2505  -7.8863   2.5857
-#>   -2.3209   5.3974  -0.6238  -1.9349  -5.1256  -0.9068  -1.6616  -2.2188
-#>   -5.5235  -1.3256   7.7680   0.0419   4.6588   8.5270 -14.0638  -5.7350
-#>   23.6620   5.5299   3.8752  -8.5570   1.7381  12.4320   1.1287  -2.2732
-#>    6.0077  11.7650   5.9815  -1.2362  -2.6430  -1.9138  -9.4792  -9.2415
-#>   13.9983   9.2234   2.0461  -5.2399   6.2495  -1.0772  -8.9246   2.9772
-#>    5.9896   4.1438   1.1027  -1.9300   7.9763 -11.2774  -5.4039  -8.4377
-#>   -3.3392   4.2929  -1.5951  -0.7069  -5.5264  -0.2146   2.6092  -3.2463
-#>   -2.7116   2.4720  11.3886   5.4341   2.0100  13.9830   1.6636   1.8237
-#>    8.7153   7.0944   4.8265   2.3740  -2.2886  -3.3963  -8.7984  -2.1433
-#>  -11.5131  12.9237  -5.3272   5.0744  -6.4152  13.7479  -0.6910   2.8149
-#>    3.7939  -4.5852  -9.2190  -6.7536   7.5973  -8.4979   5.2731   3.2761
-#>   -0.0529  -8.7274  -6.3776  -6.3154  10.8041  -0.3989  -2.7530   4.2337
-#>    6.1232 -10.6163  -8.4237  14.1920   1.7618   3.2803   6.9025  -3.0799
-#>   17.9500  -4.1805 -11.3844  -3.2068   5.9128  -1.0761 -21.9036   3.2809
-#>   -3.7840  -8.8256   1.5954   7.9607  -4.1433  -1.0089  -1.6928   9.5089
-#>    3.4930  10.6973  -6.1665  -6.6312  -0.4147   6.9757  -9.2026 -10.0955
-#>    6.7164  -0.0767  -2.7187 -11.2172   2.1860  -1.5859  12.1095   3.5310
-#>   -3.8810 -12.2251  14.5198   6.4680  -4.0062   2.9629   5.1757  14.1900
-#>    4.1051  -0.6954  -8.3595   2.2953   6.4439  15.2789   1.7176   3.2482
-#>   -1.8213  -0.6504   6.6178 -10.4879  11.9210   7.6479  -2.6390   4.8771
-#>   -8.8219  -0.2972  -9.5819   9.2868   6.3488   2.5390  11.7205   2.8740
-#>   -0.9322  -2.2826  -1.1460  -6.5040  -0.7949  -4.9513  -2.1422  -0.7358
-#>   13.7824   4.6837   9.9722  -6.0029   4.5716  12.4649  -9.9900   6.4003
-#>   -0.3243   8.9251  -2.4305  -4.2840  -4.8234   5.5312 -10.5267   6.2358
-#>    0.0412  -7.5629  -0.1690   0.6404  -5.2020  -3.4128  -8.0235  -0.3811
-#>    9.9647   9.9041   0.7346  -8.5584  -0.1955   5.9069  -8.0969  -5.4198
-#>    5.9976  -8.5542   5.4198  -3.4565 -11.3585   6.2732  -7.2283  -6.8408
-#>   -6.9500   4.6178  -6.5540   5.1711   6.6195   2.7765   4.6212   7.9218
-#>  -11.7170  -2.9426  -8.0669   6.0971  -8.5434   3.0232  -5.7801  -4.7574
-#>    8.4081   0.8452   8.0060  -4.1084  -0.0990  -9.2372   9.4923  -5.6565
-#>  -12.8302  -4.7184  -8.1430  -1.4103  -4.2225   4.9293   6.2137   5.6808
-#>    0.6241   2.8501  -1.8625   2.9744  -4.9602   2.5389   8.5130   4.8167
+#> Columns 25 to 32 -12.4241  -2.3832  13.8483  -3.8934  -7.0924  -3.4670   0.6426  -1.6474
+#>   -5.4108  -3.1627  -0.3126   4.8923   2.2859  -2.5586   0.7236  14.1091
+#>   -5.8512  -6.9455   3.5901  -7.0167  -5.4983   3.3523  -0.5075   6.9942
+#>    1.1806   7.7490   0.3640  -1.2004   0.8674  -8.4129  12.1921  -0.9055
+#>   -2.8286  -9.3626   0.0124  -0.1729  -9.7298   4.3014 -10.2263  -2.0678
+#>   -3.5253 -13.2619   7.5419  14.6206  -1.8729   5.4491  -5.4452   8.1817
+#>    8.5916 -12.3966   5.0856  -5.0745   1.0893   5.3363 -14.7136  -2.4518
+#>   16.3930  -1.4149 -10.8442 -10.6631  14.5356  -1.5279  -1.1152 -10.4117
+#>    6.7540  13.9913  -2.8122   8.0562  -5.3550  -7.6456  17.5554   8.8891
+#>    2.6385  -0.3676  -1.2063   5.2374  -6.3293   6.0667   8.8077  -3.8650
+#>    1.5460   1.6829  -3.3137   3.8334   8.9971  -5.6662   3.6407 -11.1187
+#>    9.1573  -5.5213  -2.0661  -1.0791  -1.8727 -10.4344   4.4783  -2.4332
+#>   -7.0515   5.5582   8.4178  -4.2324   7.0754  -4.3007  -1.1885   9.6686
+#>   16.0408   5.7062  -2.9906  -3.6330   8.3559   5.6473  -6.7970  -0.3358
+#>   11.3944   3.5495   2.2231  -3.1987   8.4890   5.8760  -5.5237  -2.5542
+#>   -7.8125   1.8106  -9.5020  -1.9635  -0.1818  -7.1811  -1.3380  -1.1443
+#>  -18.4412   0.3937  15.5625  -0.0757  -2.7383  -9.6661  -3.6543  -6.4844
+#>    0.7384  -5.1209  -4.5795   6.8482   6.8603   9.3952  -7.4578  -1.7903
+#>    4.4217  -9.0189   8.1757  15.6252   2.5907   8.5404   1.5811  -0.3775
+#>    1.3801   1.1166   7.4831  12.0481  -2.9186   4.9001   3.8718   7.7390
+#>    1.3553  -6.4193   2.3037   1.1987  -1.3614   0.6529  -3.2446  11.3841
+#>   -7.0650 -17.0139   9.4169   4.1874  -3.1104  -9.0348   9.0261   5.0205
+#>   10.5669   6.3751 -14.4034  -4.4137   3.4566   2.5257 -11.6567 -11.9979
+#>   10.8478  -2.4995  14.0791   3.3079   2.0940   4.3690   0.4446   4.1133
+#>    9.5151   2.8601 -15.0337  12.3219  -2.9705   0.8964 -11.6670   2.3322
+#>    1.6548  -5.2045  -4.8811   6.7065  -9.4835   0.5309   1.7449   6.3624
+#>    7.8491  -4.2668   4.8472  10.2011  -8.4658  13.6301  -1.1898   9.9359
+#>    3.3706  -4.5419  -5.4958  10.8610 -10.5074  -3.3321   6.8788 -10.3222
+#>   13.0443   5.1878   7.4450   6.2690   6.3905  -2.3382   2.0605  -4.1870
+#>    4.8844   3.1407   5.7249  -1.3035  10.8346   1.9627  -1.1333   5.6709
+#>    0.1207   7.3492  -3.1298 -10.1734   3.7775  -9.6167  -4.4163   0.1131
+#>   13.8647   4.3644  -7.7183   3.1355 -11.2283  -9.5480   1.8866  -6.4450
+#>   18.4618   3.9663 -10.1564   0.2834  -5.4781   4.2410  11.2886  -1.0867
 #> 
-#> Columns 33 to 40  -3.6500  -2.8977  -9.5778   2.7114  -1.0903  -1.8082  -1.5907  -2.7820
-#>   -2.2093   6.8268  -4.4927  -3.6955  -5.9770  -0.7346   4.8322   7.7719
-#>    6.6788  10.7728   3.5679   4.8409  -6.9984  -7.0787   9.2602  -4.7396
-#>   -2.6668   6.8936   3.9008   0.6742   9.4432  10.4602  -8.3675  -7.7000
-#>  -12.6144  -4.0425   4.8042  -1.1339  -1.6229  -3.6535   5.7386  -2.7556
-#>   -6.4437  -3.8952   2.4651  -1.3158   5.6072  -1.4178   4.2024  -5.7168
-#>   -1.0680  -1.5215  -5.3585   4.8564  -1.5816   6.3936  -4.4383  -9.2617
-#>  -16.5886 -10.7780  -3.7502  -4.6051   3.0135   2.6677   6.4653   0.4712
-#>   -2.2415   7.8049  -2.4149  11.1596  -0.9475  -4.2648   7.2630  -3.0681
-#>  -12.9061   3.0613   6.8384   0.5073   5.1894   0.5770   0.2758   4.9556
-#>    7.6316   3.3277   0.5884   6.6436  -7.7094  -0.2284   5.5000 -12.2994
-#>   -7.0150  -4.5404   3.0001   1.8709  -6.1011   3.0254  -6.1995   6.7020
-#>    6.1677  -0.5276  -6.2246  -6.4439  -6.6510  -1.6874   1.4696 -12.6055
-#>   -1.5107   4.3904  -8.7457   4.4525   5.1152 -10.7965 -15.5326  -0.2713
-#>   -4.2965  11.2479  -1.4450   1.0726 -11.2088   2.0681   0.3535  -0.9314
-#>    7.5914   0.7966   1.4415  -1.1603   2.0057   3.8741  -6.7373   3.2602
-#>    0.3362  -3.4889  -8.5000  -1.5220  -2.7686  -1.4101   0.6254  -5.9871
-#>    2.6305   5.9587  -6.0280   4.6396 -11.8046   2.4536  -3.5823 -10.0967
-#>   -0.1964  -8.6662   2.6549   2.3293   0.8565   0.5143  -4.3031  -3.5940
-#>   -7.2017 -10.6218 -12.5678   0.4210   0.4910  -3.6761   8.4244   1.6943
-#>   -8.5529  -2.2835  -4.7047  -2.7886   3.0916  -6.8411  -0.9513   7.0117
-#>    9.7563   2.3959   8.8850   4.0535  -2.0100   4.7188  -4.1533  -8.6275
-#>    4.3480   5.3681  -3.4141   3.3045  -3.1704  -3.2342   5.7581   1.7282
-#>   -6.3818 -10.8806  -5.7432  -4.5982   5.5797  -3.4485  -1.1335  -4.5567
-#>    3.6543   0.1287  -2.1096   7.8689  -2.1157  -5.4079   1.6017  11.3682
-#>   -2.5885   3.2719   2.9070  -5.1318  12.5928   1.2289  -5.1828   6.7898
-#>  -15.2362  -3.3284  11.5042  -0.6523  -1.5897  -0.7097 -11.7615   1.6562
-#>   -1.8654   1.9799  -0.0391  -4.9505  -1.6498  -8.5602   1.9597  -3.4851
-#>    2.5662  -1.3787  -2.7014   7.4054  -2.2759  -8.8455  -8.5638  -6.0573
-#>    7.1246   4.2721  -7.5306 -14.3989  -3.6718   1.1478  12.7831   0.1247
-#>   -7.4369  -7.6452   1.9472   3.2242   3.6734  11.6259 -12.0304   2.0364
-#>    1.5119   3.8946 -12.3758  -7.1929   8.3145 -15.0007   7.2717   2.4664
-#>   -4.5497   2.5463  -2.5948   0.9746  -1.7025  -2.7597   1.9230   1.6655
+#> Columns 33 to 40   4.6275  -6.2525   0.2273   0.5162  -5.7469   5.0596  -0.8002   3.8485
+#>    4.3975   2.4000   0.4999   1.5539  -1.0836   0.2710   4.5753   3.6675
+#>    5.1464  -1.8309   6.7341   6.0218   2.5222   6.3212  12.2786   3.4571
+#>    8.4806   6.5521  -0.7626  -3.0051  -4.7200   5.4536  -3.7861   6.3004
+#>   -0.4939  -7.1265  -1.8313   5.6391  10.8495 -12.8465   3.4814  -0.0409
+#>    5.6473  -4.2682 -16.3963  -2.9322   8.5197  -6.8839  -4.5054  -0.7375
+#>    0.7164  -3.4073  10.4581 -11.7912 -15.0000   3.6911 -12.0530   6.9812
+#>   12.7442   2.3489  12.6276   1.2311  -6.8680  -1.0760   1.8756   6.6510
+#>    8.1730   7.1948  -0.7325  13.2429  11.1476   5.3274   6.7781  -4.0018
+#>   -1.3343  -3.5368  -6.0568   4.6049   1.5299   0.7766   6.5568  -9.3556
+#>   -5.0528  11.1602   4.3642  -8.3959   1.8170  -0.9587 -10.7672  -5.3321
+#>    0.2872   7.0115  -7.5109   6.7913  -1.2421  10.2332  -1.5095   3.5513
+#>    5.9695   6.1847  -2.4982  -2.6016   3.6351  12.2742   4.7726   0.1290
+#>   -4.2477  13.5339   5.6733   3.9681  -5.2078   3.8811  -6.9092 -10.2976
+#>    7.7438   8.1314  -0.2777  -5.5457   3.9099  14.1153  -3.9083  12.4361
+#>    7.8012   1.3680   4.9069  -0.4541  11.2196  -6.2943   8.5874  -2.2029
+#>    1.5521   0.6741  -1.4565   4.6728  -9.4164  -1.0169  -9.4322   1.3428
+#>    6.7654  -0.5750   7.6354  -2.9348  -0.3384   8.9554   5.6896   3.0233
+#>    5.4142  -9.2444  -7.5921   1.5481   9.0746 -10.9617   1.6996  -1.6802
+#>    7.0393  -0.2783  -8.7650   6.1778  -3.5712   4.5086   2.2729  -4.6979
+#>   -5.0930  10.1904   0.5958  -1.3349  -5.1781   0.9546   5.2487   3.5548
+#>    3.4535  -2.3363 -12.0578  -3.0157   2.8398   9.7810  -1.7136   4.3189
+#>   -1.6257   6.5888   8.7434  -3.3883 -12.9276  -1.2550  -5.0422  -0.2425
+#>   -4.3318  -3.5018   4.9429   1.3254  -3.2689   4.1785  -0.6368  12.4192
+#>   -2.5082   4.0454  -4.7524  -7.9615  -8.5946 -10.8607  -3.8227  -4.9559
+#>    2.6004   1.3901  -5.8091  -1.1440  -3.1478  -2.5544   8.6592  -4.3201
+#>    5.8534 -11.1834  -7.6218   1.8461   1.0347  -2.5180   4.4297 -12.1894
+#>    1.7355  -7.1326  -9.4678  -9.4416  10.9211 -13.5729  -1.8866  -3.7971
+#>   -0.9690  -3.4447   4.9763   0.1120   0.2413  -2.9995  -1.4582   3.3018
+#>   -0.7116   5.0436   3.3002   3.2822   2.6303   4.0156   3.9552  -0.3802
+#>    6.4840  -6.5995   7.7690   6.0942   5.5695  -0.5471   6.1880  -4.2917
+#>    0.3795   4.7843  -4.9220   6.1237  17.6069 -11.2040   2.7889   2.3809
+#>   -9.3396  -2.1523   1.6649   3.2519 -11.7225   6.6564   2.7327   3.9382
 #> 
-#> Columns 41 to 48   6.1813   6.2912   7.3241   0.7908   2.1313  11.1295   3.3205  10.2126
-#>   -3.8660   6.1652   5.3523  -1.6550  -7.9229   5.9736 -10.1264  -0.1634
-#>    0.8225  -3.2113  -3.1650  -2.8780 -17.5243  -1.3155   0.5709  -0.4941
-#>    5.9318  -7.8656 -10.8085  13.1046  -5.3957 -13.6984   2.6905   3.0666
-#>    0.4034  -4.6984  -8.2092  -1.9694 -13.3521  -7.1029  -5.8867  -3.3301
-#>   -3.1147   4.8214   2.9073  -2.1222   7.6735   4.6024   9.5707  -5.2442
-#>   15.9312  -0.7414   0.4791  -0.9335   6.7204   2.6602  -2.1277  -4.0374
-#>    2.9042  -2.8153  14.4209   1.4202 -12.9603  13.3820   3.0815   1.4037
-#>  -14.4248   9.5510  -4.2185  -2.1044  -5.7456 -10.7641   8.7872   0.1149
-#>    1.9290  -4.3031  -5.0268   2.0541   9.3620  -6.5746  -6.5838   6.1757
-#>    1.8804   5.5209  -1.6502  -5.0906 -13.2797  19.7620  -3.4012  -4.2540
-#>    1.3196   8.8311  -5.2938   0.7205  11.5397   4.6186   0.1667   4.4330
-#>    8.2320   3.5439  -0.0781  13.5372  -1.3183  -2.8995   7.4505  -0.4583
-#>  -12.9408 -11.3869   1.4935 -12.7002  -5.3621  -8.7744   0.8293  -0.3609
-#>    2.6342  -0.4168  -4.7770   1.8892  -3.1467  -6.2088  -7.4270   0.3072
-#>   -2.0058 -10.8628   3.7055  -5.4891  -0.8779  -0.8320   0.4250   1.8867
-#>    8.6515  -4.1764  12.1561   3.2225  -3.6669   0.8715   1.6936  -4.9513
-#>   -0.8066  -6.8987   2.7274  -2.5657  -2.2977  -2.5447  -5.2651  -1.3667
-#>    0.0641  -1.1050   9.1838  -0.7779  -6.5939  10.3293  -0.6831  -3.4317
-#>   -0.9526  -3.3516  14.0134  -1.8953  10.8101   5.9014  -1.4601  11.3590
-#>   -8.8058   9.7172  -1.8348   2.3232   7.8532   2.6836  -6.7784  -4.1494
-#>   -5.1763 -11.3810   4.2428   2.8889  -9.5280  10.5218  10.3084   2.3278
-#>    3.5902   8.9550   3.7468   0.4195  11.1313  14.4605   7.6010   6.7326
-#>   -1.8484   4.9172  -1.4582   6.6719   2.1260  -9.1362   0.8069   4.6898
-#>   -3.9948   8.9041  -2.0977   0.1985   8.5756  -4.3336  -2.5226  -6.8194
-#>   -6.7353  -5.9661   9.1023  -8.8217   9.8306   6.4111  -5.0250  -0.2583
-#>   -0.5733   2.9590   1.0549  13.1248  -2.2757  -5.6781   4.8615 -10.4787
-#>   -2.8304  -6.6133   1.1348   1.9259  -7.5608  -0.0975  -1.9719  -6.2450
-#>  -14.2544  -2.1351  -3.8724  -1.3873  -5.2126  -4.2660 -13.4798  -2.1145
-#>   -2.4491  11.6818   7.1726   8.7630   6.2928   4.9393   6.2456   3.5497
-#>    4.1296   7.1968   4.9017   0.2784   1.5593  13.0424   1.4521  11.3329
-#>   -2.2085   8.1701  -2.5337  -3.7367   9.3092  -9.0775   8.4497   7.4483
-#>   -2.8945  17.4843 -11.3043  -2.8576  11.5501   8.4810   2.3641   5.4486
+#> Columns 41 to 48  -5.9479   5.7317  -1.3896   0.4347   1.9078   7.1824   6.1996  -1.7376
+#>   -6.0027   4.1752   2.8431   1.2113 -16.6840   1.9912  -0.8933   0.8581
+#>   -2.8098  -3.0469   0.4930  -6.6300  -1.5747   1.3298   1.7320  -1.8426
+#>   -6.1226   3.4930  -1.5902   2.0032 -11.9943  -6.5760  -0.2418  -0.0180
+#>    8.1420  14.2809  -1.5614   7.6316   5.5185   9.7685  -0.0391 -11.4884
+#>    4.5187  -4.5102  -7.0836 -15.6763  -7.2520   4.9916 -11.4424  11.0989
+#>    3.2945  -5.1948   7.6977   3.6042 -13.4441  13.6879  -8.7203   8.3680
+#>   -6.1572   2.6177  -9.6676   0.6766  -3.0961  -8.5274   0.3879  -2.0255
+#>    2.1640  11.4470   6.3551  10.5476 -11.7410  -0.3454  10.9232 -11.7186
+#>    2.0374  -6.7611  -0.4781  -2.3366  -0.2988   5.7967 -11.7675   2.1235
+#>   -0.4893   4.5329  -7.4395   5.4373  11.5770   3.8953   1.2265  -0.1558
+#>    6.5420   3.0139  -2.8958   0.8739   1.7625   0.5393  -3.9136   1.8008
+#>   -4.5155  -2.4164  -0.7993 -17.7445   4.5658  -7.2534  -6.3549  15.2915
+#>   -1.7141   5.7668   3.8222  -0.4085  -6.1782  -3.6481  -3.2473   2.1868
+#>   -1.8065   6.8444  -0.0644 -13.5806   6.3467  -1.1588  -2.0386  -4.1606
+#>   -2.7892  -6.8375   0.4811   6.8965   8.1999  -7.6161   6.4258  -4.8142
+#>    4.9122  -7.0249  10.3665   4.4574  17.5032  -6.8933   5.4158   0.6083
+#>    2.3044 -10.6202   4.9231  -3.4690  -7.5559  11.8718 -19.5934   6.9781
+#>   -8.8568  -9.7555  -9.3617  -0.9874  -0.4350  -1.2576  -5.6463   5.0209
+#>    2.6090  -2.1196   4.1956  -6.4248 -19.2466   1.1152  -1.0907  -5.0427
+#>   -6.5680   0.7271  -1.4618   5.2254  -4.1702  -1.0971   6.3611   3.9536
+#>    0.4930   5.8133  -6.1054  -5.7126   4.7208  -4.1726   0.1972   2.2802
+#>   14.6402  -3.5125   9.4125  -0.9477  -3.0400   4.0266  -4.2958  -1.4770
+#>  -10.2021   8.6072 -13.6291   6.0448 -19.6731  16.7642  -7.7336   1.4413
+#>    0.8893  -3.5400   2.5201   4.0507  -7.3271  -0.9926  -2.7731  -4.9037
+#>    4.8718  -5.2791  -3.5868   1.9349 -12.1135   4.5263  -2.7548  -5.2707
+#>   -2.0506  -2.6427  -4.2782 -13.0922  -5.9973   7.0806  -5.7817   0.7641
+#>    4.9468  -1.0644  -0.7425   2.7752  10.9227   7.2345  -7.5723  -4.3290
+#>    0.9754   3.8847  -8.3308   9.4071 -11.7713  -5.7789  11.2804 -10.8883
+#>   -4.1672  -3.3255   0.0253  -7.2869   2.3881   9.2545  -8.6679   3.7378
+#>    7.3384   4.0322   3.0322  -3.4796  10.0678  -8.2047  14.3237  -3.1908
+#>   -0.3416   6.2229  -4.0288  14.5847  11.5170  -4.0508   7.2624 -13.4191
+#>   -3.2283  12.1488  -2.5061   8.8063   2.0179  -2.3577  -4.7272   0.1128
 #> 
 #> (4,.,.) = 
-#>  Columns 1 to 8  -8.9373   1.6399  -9.3011   1.4044   3.6362  -3.6884   4.1129  -0.9177
-#>   -1.4104  -3.4010  11.6886  -2.0425   5.0381  10.3211   2.3125   3.7280
-#>   -0.9357  -2.5987  -0.8097  -7.3440  -2.9790   0.2844   1.1797  -2.0219
-#>    0.7954  26.6022  -0.1761 -10.4019  -3.9853  -4.7054  -2.7099 -11.5866
-#>   -4.8281   3.9997   7.6639   2.1329   1.7832  -3.4229  -4.4235  -8.0600
-#>   -4.2906   0.4296  14.0283  11.0699  -2.6126   0.9977 -10.1659 -20.9791
-#>   -0.2850  -4.6091   3.8426  -3.2056   1.4130   0.4385  -2.6187  -4.8739
-#>   -5.6108   1.3174   7.9251  11.1445   2.7707  -7.1793  -7.8399  -3.0120
-#>    5.1576 -13.3771   0.9829  -5.2116  -4.3821   8.2142  -3.4522 -15.3296
-#>   -4.0186  12.0413  -2.8010   5.3009  -1.6269  -7.3934   0.1458   3.5200
-#>    0.1809  -1.2963  14.7856   4.9618  -1.2995   1.4195  -6.5237  -2.4909
-#>   16.6593  -5.1476   4.8693   7.8189  -4.1561  -3.6614   1.0279   9.9730
-#>   -1.9016   5.1086   4.6128  -1.8447   0.4608  10.1100  -4.6261   4.2095
-#>    4.9385  -8.9146  -0.5647  -3.1531   6.9549 -17.0622  -2.5873  -1.0333
-#>   -5.8255   4.3723  14.0465   9.6723  -9.6586   6.4133  -1.3056 -12.5534
-#>   -5.5134   2.5385   3.7693  -1.7086  -4.3682  -3.3993  -7.5111   9.0573
-#>   -4.5008  -2.2101   3.2887   9.2156   8.1258  14.3153  -6.4282  -0.2755
-#>   10.0607  -4.0007   4.2165   8.6627  -5.6962   6.5382  -0.7017  -3.3520
-#>   10.9792 -12.7363 -11.1796   6.1025   8.0711   2.8154  -0.6251   8.5030
-#>  -15.3810  -0.3471   6.0013   8.3160  -2.4042   3.0223 -16.8555  -6.2519
-#>   12.9764  -4.6472   1.4249   8.6086   3.9251   5.5342   1.4575  -9.6606
-#>   -7.7859  -0.0378  -8.7754   0.2117 -11.5043  -5.5715   3.8264   0.7867
-#>   -5.8436   5.1906  -2.9890  12.7835  -2.3970   3.9694  -2.1704  -7.6511
-#>  -11.8085  -0.1376  -7.4560   4.4743   3.5176  -7.5724 -16.7047 -10.5319
-#>   15.3922  -4.8711 -13.4026  11.7450   2.0306   9.9591  15.9956  -2.2847
-#>   -1.6474   2.2722   0.9578  -0.9675  -0.1793   8.5340   6.2574   1.9746
-#>   -0.1732   0.8481   5.0246  10.0355   2.5423   4.7380  -4.1744  -5.7309
-#>    5.0698  -3.8620  -1.4083  10.5425  10.8409   3.9629   7.7026  12.7015
-#>    2.9825  -5.9775  -5.2800   7.1343   4.5012   4.4923   2.3546  -3.2652
-#>   -4.3361  -7.6220  10.1317   2.4227   3.2601   6.8644   4.4243   5.2520
-#>   17.7917  -0.3951  -4.4227  -0.2346  -1.7326  -9.4963  -5.2619  -0.7878
-#>    5.3519   0.6308  -1.0294  -0.9731   6.0362   0.5765  -8.1187  -3.5761
-#>    0.3248  -3.5005   3.3166   1.9265   1.7660  -1.5276   6.8794  -1.3940
+#>  Columns 1 to 8  -4.9538  17.0664   0.1018   0.8898   0.1483   4.9361 -17.0535   0.7465
+#>   -9.7028   0.3020   7.2783  -8.4018  -7.3146  13.2967   6.0084   2.0292
+#>   -3.1330   3.2949  -2.3359 -11.3128  -4.5336  -8.9633   8.6274   2.9212
+#>   -4.3949  -1.9623   3.8915   8.5586  -0.3818  11.3559  -0.0292  15.9034
+#>   -1.2517   4.6891  -6.5938   4.2879   5.1477   3.8499 -13.1017  -5.5372
+#>   10.8488  -4.1179  13.7386  18.8632  -6.0685 -11.5582  -0.9416   4.5622
+#>   10.9829  -4.9632  -8.2331  -8.5790 -11.2950  -5.5146  13.6615 -17.2773
+#>    2.2473 -10.0643 -17.8956   1.4249 -12.5284  -5.8794  -0.0446  -3.2953
+#>  -10.8570   6.2512  22.7700  -1.5846  11.3006   9.3694   1.1273  15.1241
+#>   -8.0787   1.4591  -0.7498   1.4827  11.0985  -3.2901   0.8047  -0.2792
+#>   -0.1312  12.3284 -10.1663  -3.1726   7.2779  -0.9327  -4.0697 -18.1894
+#>   -3.4733   0.3368  -4.1347 -10.1425   6.5577   1.3228  -3.3832   3.3631
+#>   -8.1466  -4.9133   4.4610   1.3269   3.4562  10.2674  10.4606   9.2153
+#>    4.7160  -0.3078  -6.4491   4.5003   4.2012  19.7595   4.4831  -9.6957
+#>    0.5640   7.5643  -3.3368  -3.7371   1.6558   2.1598  -3.9336   0.7328
+#>   -1.8770 -12.2776  -5.6247  -2.0085  -0.2937   1.3901   5.8792  -0.3626
+#>    2.8689   6.4288   9.9220  -6.9003  11.5421  -5.9807  -3.5995   9.9424
+#>   16.9640  -5.0639  -9.7111  13.6573 -20.1911  -3.9469   0.8993  -5.9625
+#>    5.5202   6.4470  -0.0795   8.9561  -8.2949 -12.2004   1.2568  -6.1407
+#>    5.0151 -10.5003   8.3629  10.6703   4.6395   3.9177  -4.2513   6.3293
+#>   -6.0909  -3.8411   1.6986  -7.4332  -5.3671   9.3974  -0.3591 -14.4400
+#>  -12.9444  -9.0201   3.6336  -9.8184   1.6553  -2.3285 -16.4687  13.0409
+#>   14.7907  -0.3785   1.6089   1.0973  -0.2062  -2.7016   8.9478  -1.9907
+#>    4.2796  14.7379  -5.2530  10.3486 -13.3774  -3.1824 -10.3652  -8.9548
+#>    5.2245 -10.1186  -1.6432  10.7965  -6.6028  14.4216   6.2035  -0.6116
+#>   -4.5198 -10.8784   2.0572   6.0678   6.4600   9.7799   3.3135   8.0133
+#>   16.7118  -0.9669   9.2267  12.0206  -5.4836   9.4263  -2.1159   2.3712
+#>    1.5325   5.1238   1.3927   0.0318   7.2269  -1.9335 -13.0037   4.9660
+#>    1.7762  -7.8126   5.1079   3.1876  -6.7152   3.6575  -7.1749   1.0768
+#>   12.4921  10.3854  14.9333  13.8814   0.6242   0.7227   1.0904  -4.7628
+#>   -1.5955  -4.7258   8.7674  -2.4229   1.5223   5.0432   8.0320   0.7806
+#>   -1.9712 -11.0011  -0.6494   2.1866   9.1960  17.8894   9.7038  19.3642
+#>    0.6245   5.2097  -6.0672   0.0554  -1.6622   7.1765 -12.6029  -4.2613
 #> 
-#> Columns 9 to 16  -1.9001   0.7127  -3.7042  -3.1003  -2.5198  -2.0575  10.7891 -10.1290
-#>    4.4664  -1.3785   5.6772  -7.5936  -2.2864  -4.5115   5.9416  -2.9930
-#>    1.5377   4.4286   3.8994   7.8767  -6.2099  -9.3024   9.8539   0.7968
-#>    1.3931   1.4117  -5.5809 -17.4775  -3.3827  -6.8981  -7.4701  -0.2287
-#>   -3.5261   7.2352   9.7388  10.1538   9.3579   4.1696   3.4854  -4.4308
-#>    3.2155   2.2456  -5.0688  16.8926   1.8043  11.3323   2.9362 -17.2064
-#>   -2.5780   2.1673   8.9328   1.9443   2.6978  -3.6639   4.2491  -7.3115
-#>   -0.0379  -0.3899 -11.1863  -7.5292   2.2368   5.0501  10.9519   4.0228
-#>   -2.3787  -6.3793   3.8723  19.7264  -2.1229  -8.1372   4.8397  13.4221
-#>   -0.1609   3.9356   7.6169  -0.8369  10.8069   4.9614  -0.4863  -5.5323
-#>   -4.1389   1.4834   7.0239  -4.2083  -5.1313 -15.0423   3.8272  -4.4371
-#>   -4.0908   2.8806   1.0398   1.6341   1.6630   2.1113  -5.2521  -0.4592
-#>   -2.3149  -4.1857  -9.5716   3.2153  -9.7876  -1.8165 -15.7325  -6.8080
-#>    7.6909   9.9126   5.9013   0.0249  14.8729 -14.5049  -1.9218  -2.0016
-#>   11.1113  10.2416  -2.4377  18.4841  -3.1200 -12.1874  -7.3556 -17.1354
-#>    7.0546   0.5588   5.1830 -13.3850 -10.7262  -6.7357  -3.4260   2.6514
-#>   -1.7956   1.6426   1.9519  -1.5552   8.5460   1.7914  -1.6229   0.8920
-#>    6.2778  -9.5054  -7.6767  -8.9659  -8.5650  -6.6644  -4.2292 -12.1641
-#>   -1.5601  -4.5378  -9.5443  -0.4477 -11.3808   2.5598  -7.0608   4.8732
-#>    4.9494  -3.9585 -18.3522   3.4936   5.4241   1.2551   3.3881  -6.7100
-#>   -2.2057   0.6258  -9.7220  -0.1111  -1.0761   5.7311  -0.8607  -5.8128
-#>    4.0180  -1.9138  -2.3049   2.2944  -1.7141  -6.2059   7.9873   0.2988
-#>    8.3576   0.5287   4.6250  12.8253 -11.8698  -3.0675  -2.3879  -3.5915
-#>   -6.9098  -6.2171   2.3000   3.1197   3.8548  11.2952 -14.4839  -5.1035
-#>    3.6927  -0.5734  -9.4985  -1.7064  -4.4952  -2.9143  -6.4201  -2.4317
-#>    5.8637  -5.0499  -5.3575  -5.7396   5.6403   6.2517   1.5342  -3.1273
-#>   -5.5487   2.7170   5.6355  -4.3530  -0.3532   3.4242  -8.7395  -2.6661
-#>    0.6817  -2.5049  -0.9142  -3.5636   9.6664  -0.0384  -3.9433  -1.6649
-#>    5.6631   2.3630  -5.0465   5.2111   2.0411  -5.5932  -6.4517  -6.7330
-#>   -3.7180  -1.7679   6.3940   0.5621   1.1296  -1.2607   7.3263  -7.4035
-#>    1.4558  -3.6146   9.7355  -3.0817  -9.2252  -0.9715   8.1628   1.2281
-#>   -7.5844  -5.2732  -4.6288   1.9936  -0.4796   6.6172  -9.8305  21.6248
-#>   -2.4536  -6.4660   1.2808  -3.9041  -4.9362  -3.5620   3.0095  -9.3904
+#> Columns 9 to 16  -9.4526  -5.1694   0.8052   6.4252  -4.8743  -3.3787  -5.0041  -1.5757
+#>    5.6536  -6.1082   0.4980   2.2175   2.3369   6.5422  -5.9298  -4.0904
+#>    7.0242  12.3883   5.4531   0.2439   2.2845  -1.5235   4.0821   2.1533
+#>    0.8479  -8.1672  -2.8806 -13.3586 -13.3332   9.7919   3.7125  -0.4628
+#>    2.7213 -15.5696  -4.0435  13.3024   4.9801 -10.9466   3.6240  -2.1048
+#>   -5.7670  -9.3248   1.5184  -5.0647  -1.3344  -4.4918  -9.4087   1.2730
+#>    1.6222  12.4523   8.0576  -3.9077   5.4055   3.1740   4.7621  -8.8532
+#>    1.6750  -0.7258  -6.4816   0.4637  -6.9306   5.7544  -3.4786   1.8384
+#>   -4.1522  -0.5614  -7.3469 -12.8664  -2.1448  12.1254   4.3776  19.4482
+#>    9.5074  -9.2374  -3.5238   7.1686  -0.6648  -3.3887  -0.7183   3.3822
+#>   -0.6954  -3.2278  -7.7428  -8.0954  -0.1779   9.4753  -0.4611   3.8342
+#>   -7.1168  -0.7272  -4.0091   6.8376   3.3126 -11.2768 -11.9588   3.7387
+#>    8.7197  -4.2730  -0.0243 -13.1229   6.6868   5.8360  -5.3161  -8.4720
+#>    9.0815 -16.8222 -19.6291   0.3442  10.8628   7.7137  -0.4684   5.5136
+#>   19.0643  -1.0228 -19.6937  10.8928  13.1494  -4.9341  -3.2061  -5.5275
+#>   14.1608   9.0933   5.9093   4.9352   3.4136  -5.3891  -1.2159  13.3120
+#>    0.2920  -6.3951   1.8682  10.8309 -11.4926  -0.3472   4.0011  -6.3251
+#>   14.3717  -7.6303   2.5789  -7.2259  -3.7276  -0.6800   1.5227  -5.6462
+#>   15.5434   0.7905   5.5928   2.5895  -7.6942  -8.4170   1.5323   1.1262
+#>   -4.2404   3.2167   0.6378   2.5575  -5.9038   7.9403  -1.5059 -10.1904
+#>    4.9039  -0.4195  -0.2075   5.6368  12.5629   3.1815   0.4401   5.6081
+#>    2.8867  15.8964   9.0444   4.0786  -3.9410  -9.0092   5.6049  14.2726
+#>   -8.8588   8.6507   1.8858  -4.8800   3.3269  -5.1670  -8.7176  -6.7934
+#>    7.2771   7.7853   1.0167  -8.1125  -1.2514  -1.9101 -11.1754   0.6801
+#>    9.7963   9.7818  11.1680   5.0844   0.7207  -3.2902  -5.6092  -2.4688
+#>    0.7519  -2.6117   6.7347  10.9780   3.1813  -4.0894  -6.2478  -7.5200
+#>   -3.4983 -14.0286   4.4232  -2.7626  -4.5084  -4.5566  -5.1855  -0.4736
+#>   -1.2931  -7.8607   5.1282   2.1210   3.6382  -7.3358   5.5641   5.9660
+#>   -4.0157   5.5307  -2.3370  -0.9843   1.7820   1.1129  -5.0038  -4.6232
+#>    5.3592  -3.6845  -7.7299  -8.3634   3.1842  -8.6789 -10.7706   0.7496
+#>   -6.2583  -9.0696   1.2158  -4.1395   5.9447  11.3430  -0.0200  -2.6999
+#>   -0.9095   7.2894  -6.1060   8.9229   8.7341  -1.1903  -5.4205  11.9848
+#>  -13.3732  15.1797  -1.9534  -1.4681  -5.1740   1.0646  -0.2240   5.7325
 #> 
-#> Columns 17 to 24 -16.2372 -19.7274  -0.1741 -11.2008  -3.2621   6.3569  -8.8925  -0.4303
-#>   -5.4173  -7.6782   8.2397   1.4117   3.6337  -3.4932  -0.8219  11.9920
-#>   19.2885   2.5153 -12.7047   1.5867  -8.2949  -3.2381   5.2396   0.1813
-#>   12.2386   6.4969  -4.7977  11.9596  -0.3093  -0.3655  -0.3141   2.8275
-#>    2.3578  -1.3342  -3.8336   2.7758  -1.4061  -8.5146  -0.3398  -8.0748
-#>  -20.0700 -15.1627  -0.5593   6.0172  -8.7065   1.4813   0.0891   1.6563
-#>    0.2059  -4.4305   1.2511   3.0906   0.2580  12.1064 -12.8778   5.0823
-#>   -2.4030 -19.5416   3.9174  16.3369   3.0540  -8.3036   4.4496  12.4252
-#>   -6.7507   0.8657  -8.0464  -0.3716  -0.4432  -3.8865  -0.0692  -9.6786
-#>   -0.0698   4.0154   2.6120  -6.7098   0.9287  -1.1052  -2.9665   4.0454
-#>    4.2824   3.9316   2.0320   2.0653   6.2146 -16.8679   4.5053   4.8263
-#>  -14.8595  13.3765   1.6610   2.0509   7.3690   2.4040   6.5151   1.7843
-#>   10.0346   6.3441  -2.4157  -0.6306   1.8411   5.8174   3.8976   3.5893
-#>   -7.7210  19.3262   7.3632   6.9095  -3.1356  -4.3910  -0.6466 -13.5442
-#>   16.1872  -0.0640  -5.5496   0.1496  -1.1111   0.9940  -6.1868   2.5441
-#>    5.3269   4.6372 -10.4780  -8.9268   3.1330   0.6902  -5.6594  -3.4153
-#>  -10.7116  -2.4769  -6.5876   4.9373  -0.7942  -8.1281   5.3133  -9.1278
-#>    5.4327  10.0923   1.0010  13.1000   1.8219   1.5296  -1.7788   6.2398
-#>    2.8703  -9.3128   8.1365   4.9767   2.0086  -2.9929   4.9331  -7.5836
-#>    4.2043  -3.4412   4.2323  -2.8825  -5.4795  -6.1055   2.8913   7.2720
-#>   -1.9965   2.1791  16.0918  13.4114  -5.0736   6.8383   7.0362   1.3149
-#>   12.9917   1.7107  -9.1155   7.2644   3.6828  -1.5261  -6.1586   9.2209
-#>    5.1585  -6.3146 -10.2284 -17.6754   0.5683  -0.8958  -4.6662   8.7225
-#>   -0.9275   1.6786   6.2533  -7.8757   3.7599  -6.7295  -1.7024   1.6199
-#>   -5.5077   5.7192   0.1597  -1.1456   0.1365  -0.2670   9.3889 -10.7641
-#>   -3.3875 -12.9637   5.6332  -0.7724  -6.7348   6.3785  -6.2865  -7.7037
-#>  -16.6626  10.6243   3.7678   6.9794  10.9552  -4.2435   3.0685   2.4144
-#>   -3.6866   7.1044  -0.2200   0.8470  -3.6753  -7.6094   5.4141 -15.7434
-#>    8.7154   7.6460  11.4999   3.9323   6.8737  -5.2896  -9.2223   1.9661
-#>  -12.7411 -20.4097 -10.3811  -1.7526  -3.3337   3.1244   1.5932  -5.8384
-#>   -6.4045 -21.4485  -3.3501   7.6588  -1.9086   3.0637  -8.2305   4.4404
-#>   -6.2674   3.3319   7.3105 -18.2420   7.0885   1.8895   4.2690   4.4079
-#>  -12.1091  -0.3891   7.2408  -5.9361  -2.7110   3.0618  -4.8158   3.3095
+#> Columns 17 to 24  -0.1331  -8.1388  12.7922   3.3258  -5.6629   7.4248  -4.9095  -8.0389
+#>    3.4657  -2.4242  -2.6167   1.2324  10.0951   5.5803   0.6295   2.6036
+#>   -3.3638  -1.9595  -7.3638 -11.9056   4.7550   3.1624  -0.9780  -2.5876
+#>   -0.5969  -5.5221  -4.4383  12.7997   1.2124   9.4294  -3.0904  11.7336
+#>  -14.1096   6.3596   0.5233  -0.2465  -1.7029   7.2990   4.9932   0.1218
+#>    4.6058   0.7510  18.0496  -4.4725  -3.9143   2.4044  -0.9663  -2.6935
+#>   10.4438   4.3372  -4.4080   3.4308  -9.0953   1.5391  -1.8040   4.6151
+#>   -7.7248  -7.7384 -12.3472  -2.2128  -0.7154  -4.8985  -1.6933   0.2770
+#>   -3.5148   7.1338  -5.0431   8.8337   2.6480  14.4746   8.8678   8.8424
+#>    3.0300  -4.3456  -3.1803  -4.0484   5.3796  -8.4654  -0.0492  -1.4700
+#>   -6.6846 -12.4447  -2.8775   3.9531   4.3241 -11.2811  -1.6303  -3.0871
+#>   -4.2804   7.8158  -5.0589  -2.3532  -0.8418   4.6156  10.1509 -11.8587
+#>    6.8313  -7.1164   8.8702  -5.9242  12.1045  -4.4453   2.1769   4.4285
+#>   -6.2365 -11.9182   9.7006  -0.8717   2.7998  -7.3961  -0.6116  -4.3501
+#>    2.9189   1.7219   5.1603  -0.7957  -4.6677  12.5717   7.9300   3.8336
+#>   -4.6292   4.8424  -4.9006  -5.9140   3.9654  -8.3432   5.4315  -3.6888
+#>    0.4656   9.1473  -3.0684   6.8504  -3.8456   4.6941  -3.0155  -4.9095
+#>   10.5029  -3.3362   2.9867 -12.2039  -9.9891  -1.2972  -4.1580   6.2572
+#>   15.5327   1.0353   4.0335  -7.1014   6.8567  -2.5069  -4.2205  -3.5161
+#>   -1.1913  -1.6592   0.7782   0.0653   6.7694   6.6510  -2.5791   0.2762
+#>    3.3173  -4.5636  -3.6844   0.6628   7.0458   3.2901  10.7002  -3.3850
+#>   -7.0339   8.7440   3.5230  -0.3093  -3.6322  15.5498   4.4980  -5.6337
+#>    4.0016   1.6836  -0.8491   2.8689  -5.0198  -2.5678   8.5274   6.1681
+#>    7.4687  -3.7890   0.7388  -5.5171 -10.2168   1.2003  -2.7124   3.7007
+#>    0.2401  -0.0776   4.7264   8.3669   2.7766   7.5978   7.7334  10.6074
+#>   -1.6771  -8.0268   3.6667   2.2805  10.3176   2.7354   0.7035   4.1152
+#>   -1.3598  -1.0257  13.9195   2.7950  -3.9540   7.6296  -7.1117   3.3921
+#>   -1.2608   3.2111   6.1984  -0.8062   5.5680  12.7021  -2.3154   0.6187
+#>   -6.3551  -1.7433  -7.4351  -4.1475  -6.6872  -4.7233   1.9106   1.0367
+#>    9.7669   6.7408   9.0259  -2.1641   1.5355  -4.1114  -7.2800   0.8181
+#>   -8.4810 -10.3591   0.5962  -2.6823   0.4105  -2.0187  11.9195  -2.1013
+#>    0.9877   9.0195 -14.8369  -2.3161   8.2303   5.0258   4.8822   5.4192
+#>    0.3760  -7.1636  -3.1500  12.1934  -2.0959   7.7844   2.4019  12.7033
 #> 
-#> Columns 25 to 32   2.3998 -17.3448 -12.2325   0.3315 -15.4217  -1.8867 -15.4240  12.3478
-#>   -3.7151   6.9167   9.9820   1.5672 -11.4572   1.9856   7.3794  -0.1111
-#>    2.3240  -8.5145   0.3523   8.1304  -1.9023   7.6816   0.2831  -4.7317
-#>   23.5435   2.0492 -20.7813  14.2461  11.3960  12.3975 -11.7466   3.0538
-#>   -7.4008  -1.0536  -4.7788  11.5750   4.3103   0.4249   3.9897  -4.0659
-#>   -0.4591  -8.9794   0.9216  -4.6796  -8.7029   9.5869   8.7900  -7.8309
-#>   -2.9604   2.2297   1.1120  -0.7426   7.9197   7.3716 -10.5481   4.1526
-#>   -3.9723   2.9816  -9.3026   5.5909 -10.0158 -15.0935   8.5722   1.3201
-#>    1.1391  -0.4905  11.8503  -1.8477  -2.4021  15.0876  10.7224  -2.2367
-#>    4.5792   0.5044   3.7245   0.0699   7.8139   6.8465  -3.0149   5.3561
-#>   -2.2293  14.3296   1.3361  -7.7686  -0.4618   8.6373  12.8193  -5.5542
-#>    5.4532  -4.3885   9.4846 -17.4665   8.1103  -0.7766  -1.3261   3.4329
-#>   -1.9946  -0.3186   0.0346  -9.3878   3.0484 -10.4834  -2.9084  -7.7686
-#>    2.0548 -10.5485  -3.2662  22.6547  -8.1382  11.3451 -15.2585   6.4609
-#>    0.1131  -9.3124   9.9692   4.3660  -0.1986 -10.1405  -4.0936  -1.1072
-#>   17.0797  -3.4246  -2.5888  -0.7562  -8.3573  -0.1185   1.1050   7.3033
-#>   -6.9426  -6.1876   2.3584   9.0243  -8.2849 -13.8852  -0.2879  -0.9890
-#>   -3.9229   2.9294  14.3624  -6.8364 -14.9054   2.0942  -1.3370  11.7426
-#>   10.4417   2.0153   2.5499   2.6353  -5.2696  -1.1515  12.8907  -6.8000
-#>   -3.3850  -0.4643   8.3822  -4.8786 -18.5677   0.2475  10.1148   5.0903
-#>    6.1073  -5.6765   3.8480   1.6076   0.9365   7.3126  -3.0763  -7.4835
-#>   -6.2270  -7.8434  -1.8985   3.3555  11.7319  -3.2799   2.3244  15.7276
-#>    8.4865   8.8633  21.3272 -16.4600  -3.8408  -8.0375   1.0212  -4.3776
-#>   12.8918   1.8276   4.7197 -12.4718   5.4431  -3.3738   5.3581 -13.0529
-#>   -3.7650  -3.9353  16.2862   4.6456  -7.5221  -0.1671  -6.6627  -1.2377
-#>   -4.4990 -21.3608  -4.0670  15.6238  -7.5423  -9.5070  -1.6854   3.0255
-#>    9.1725   3.4281   5.9490  -2.2386  12.4893  -1.6703  -0.1803  -9.1139
-#>    4.4431   1.9173  -6.4646   9.7265 -12.4915  -3.9208  -2.5061   2.8529
-#>  -10.7019  -8.1675  11.7109  12.2299   3.7126  -1.5526   0.0804   3.4049
-#>   -7.0743   7.0589 -11.8581 -12.1029 -16.7801  -5.9686  -5.1970   0.2153
-#>    1.8325   3.2412   5.7683  -8.6227  -2.8456  -5.8553  -3.2370   6.9899
-#>   12.3013  -5.2388   5.6335  -1.6851 -11.4705  -6.1349   1.0349  -8.6584
-#>    4.2542  14.5048   2.5535 -17.0956  -5.7988  16.1823   3.0141   7.0088
+#> Columns 25 to 32   1.7915   1.9421 -12.5396  -2.6273  -2.8991   7.5406   8.2528   1.3058
+#>    4.1038   4.4207  -0.2532   6.6594   2.6148  -9.5122  12.7956  -6.2985
+#>    9.3943   3.5340   5.8143   5.9309   1.6031   8.6264   2.9542  -2.7643
+#>   -0.4618  -0.3964  -3.3333   3.0343  12.4622  -2.5630  -2.2609  -4.4235
+#>    2.9094   5.7771   0.3549  -9.0132  -1.1143 -12.7987   0.1648   8.5159
+#>   -3.2158  10.4830  14.5092   8.3573  -6.8970  -5.1728   7.1444   7.7108
+#>   -1.8354   0.4364  -0.0991  -0.4200   1.0284  -2.8920  -0.1213  -7.7263
+#>   -5.0105   9.6236   2.7993   0.0891  13.7755   0.0757   2.6481  -3.0413
+#>    6.9997  -5.9592  -3.1074   5.1188   3.7140   4.8939   6.5528   1.9294
+#>   -2.0337   6.4219  -4.3378   6.0576  -8.5588  -3.4454  -0.1000  -6.3920
+#>    7.8070  -1.2557   2.3798   2.0639   2.0221  14.2894  -0.9472  -7.3541
+#>    2.8692   1.1086  -0.7842  -1.5497  -1.8071   4.7527  10.5505   0.4261
+#>   -1.4595  10.3455  -0.3468   0.8917   0.4904  -3.9149  -6.0385  -6.4965
+#>  -21.0780  -4.0180 -13.6822  -9.3426  13.3920  -6.6782   4.0213  -2.7457
+#>  -12.6224   8.4789   3.8033   0.0928  14.3213  -3.5705  -7.8285  -1.9612
+#>   -1.0248  -0.9685   0.8791   2.6815  12.3738  12.1512   4.6639   7.2081
+#>    7.6757   4.3961 -15.0027  -0.0203  -7.6104   0.2860   4.9189  -3.9617
+#>  -12.8767  11.5503  -2.9478   8.2208  -2.5722   1.9909   0.4056 -11.0252
+#>    0.6986  12.1214   7.3867   5.7368  -0.5448  -4.7764   9.5780  -9.3266
+#>    1.8298  11.9755   8.2976  20.6372   1.1700  -1.0028  -5.6784   0.0464
+#>    1.1606  -0.1259   1.6886  -8.1117  13.5447   3.0210  15.2366  -2.9625
+#>   10.6484   6.7875   7.7560   6.0996   8.6048   5.5214  -1.9629  -3.2145
+#>   -4.2199  -3.7075   1.4024  -5.9278 -14.6722  -0.2868  -5.9519   4.2351
+#>   -2.8086   3.6744   8.1327  -5.1570   5.9282  -9.8394   7.9207 -10.0697
+#>   -6.5019   2.3591   9.0967  -7.5837   5.1467 -10.0112  -6.4589   3.0311
+#>   -4.0117  10.8028  -5.4538   7.9693  -0.4555  -2.8254   3.9369   6.1478
+#>  -14.7618  20.3557   0.1404   1.4985   3.4667 -12.5460   3.2561   1.0795
+#>    3.5059  10.9255  -2.0731  11.8909  -6.7959   4.9230  -4.5309   2.8546
+#>   -5.2453   3.3038  -3.5618  -4.7227   7.3414 -14.6970  -0.2040   0.7135
+#>   -8.5081   5.4009  -2.2874  -0.6616  -4.2162  -0.5637   9.7156  -7.1103
+#>   -3.4036   1.1124  -5.8518  -3.9441  -5.1406   0.0861  -1.4743  10.0881
+#>    4.3507  -2.6391  -6.6786  -6.0558   1.2268  -5.5369  -7.1206   9.6841
+#>    1.9493  -3.8393   5.5592  -4.3794   3.7729   6.6821  -8.5172 -10.1452
 #> 
-#> Columns 33 to 40   1.7074  -2.7095  -5.2740  -5.3750 -11.1734 -11.5376   1.2613   8.9030
-#>   -9.7125  -3.7753  -7.5031  -0.7469  -0.7143 -11.1453  -3.4587  -9.8992
-#>   -1.4763   0.0384   6.2177   0.9006   3.5500  -0.8569  -2.1706  -1.7766
-#>   -7.8055  -4.5051   2.6745  12.8302   7.7032  11.8959   1.4649 -17.2213
-#>    1.7518  -9.0725  -6.0132  -3.7355  -0.6736  -1.7817   2.1679  -9.3729
-#>   -7.0022 -11.5829 -17.9102 -14.6837  -6.6562  -7.1852   6.0695   5.5540
-#>  -11.7927  -5.6766  -3.0036  -2.3266   1.5770   2.8812   8.7181   0.4599
-#>   -1.0159  -5.8263 -16.4976 -11.9847  12.6645   3.8689  10.7117  -1.5844
-#>    3.7081   0.7444  -8.1803  -6.8067  -7.9231  -3.9081  -5.9678   9.1194
-#>   -8.0077  -4.4849   5.6012   5.4904   0.2560  -4.3410  -1.3505  -9.2316
-#>   -4.5165   0.3389  -5.6415  -6.9742   1.8363   2.0317   0.4151  -3.6613
-#>   -3.5573  -3.8663  -2.2184  -0.8321   3.6882   0.6856   2.9591   3.8303
-#>    8.2161   4.0893   4.6047  -1.8199   4.3622  -5.2914  17.8723  -0.6648
-#>   -1.0932   6.2708   3.1357   9.0462 -10.0658  18.2034 -30.1197   8.5641
-#>  -11.7677   5.8088  -2.8664  -1.4192  13.0200 -14.9370   0.6709  -2.9731
-#>   -3.5297  -1.1649   0.3058   5.2004 -14.7660  14.8186  -7.3994   8.6478
-#>   -2.5049  -1.9505  -0.6840   4.6141 -11.7132  10.3747 -10.6767  -0.7528
-#>  -15.6232  12.4212   5.1873   0.5407   7.8264  10.1926  -2.0064   4.7172
-#>    2.5496  -2.0950  -4.1168   0.9357 -10.7598   6.7054   3.1847  -4.4976
-#>   -1.6174  10.3704 -11.4205  -9.5627  -2.5331   0.6784 -10.3460  10.6275
-#>   -4.5203   0.6928   1.9589  -0.1332  10.7521  -0.8171   3.8033  -2.3770
-#>   -1.5401   5.7727   1.8338  -4.8751  10.4040  -0.1402   0.5706   9.6135
-#>   -5.1437  -0.0033   7.3305  -7.7063  -3.8892 -11.6323  -1.3622  -1.1627
-#>   13.3520  -9.0883  11.4072  -7.6520  -3.3230  -3.5779   4.2729 -15.3720
-#>   -5.4414  10.3479   9.3171   4.2304   2.9741   0.8669 -11.3900  -2.6916
-#>    3.4813   3.9532   0.0644   5.0330  -1.2378  -1.8766  -6.9203  11.8834
-#>  -11.3476 -20.5107  -1.1963  -1.3385  -1.6884  16.3563   1.6985  -9.4230
-#>    9.1603   7.9446   7.2743  15.5263 -12.4083   6.3507 -16.7579  -1.2418
-#>   -5.2214   5.9875   9.8173  -3.4455   9.0035   0.2276  -4.4667  -2.9136
-#>    0.8931  11.0414 -11.4794   1.4996 -14.8614 -12.1374   0.5295  25.4513
-#>   -9.3430  -4.1520  -2.2349   2.5586   4.5160   5.2115   6.6060   6.2346
-#>   14.7500  -6.3226  -1.7460  -1.9870 -12.6927  -0.8219   5.2718  -0.2050
-#>   -1.9830  -0.0687  -4.7570  -4.9114  -1.7514 -18.3255  -2.6171   1.5965
+#> Columns 33 to 40   1.7944   9.6654  -5.1828  15.6712 -10.3182  -8.3474  -9.1313  -4.0947
+#>   -4.5724  -6.4915  -2.4431   1.1316   1.6765   5.5296   3.3109  -7.1138
+#>   -0.7226   4.9546  -9.0213   0.6548   2.3676   3.2202  -3.2923   4.3670
+#>    4.5269  -3.8368   8.0302  -4.3532 -10.5294   1.3717   1.3317 -10.7339
+#>  -10.6755  -2.3517   2.2936  -5.6746  -1.3547  -4.7458 -14.2411   0.9359
+#>    2.1120   1.5688   8.9188  -8.4964  -4.7687   5.7369  -9.0722  -4.4397
+#>    7.5678  -3.6873 -13.7921   6.4553  -4.3638   5.1729  16.6935   5.6425
+#>   -7.3490   0.5864   3.2818  -4.5007   5.4528   0.5475  -8.5895  -7.1810
+#>  -18.7596  -6.7974  -0.2788  -2.4544  -0.0506   1.7208   3.3684  14.3746
+#>    2.2358   6.4422   1.0786   1.7485  -7.8871   6.2159  -2.0504  -8.6937
+#>    3.2477 -11.7993  16.4577  16.0322  -3.3962  -6.7041 -14.0971  -2.4493
+#>    2.1327  -1.5607   9.5967  -4.6507   2.6041  -7.3442  -0.2307   7.2612
+#>   11.4513  -1.2515   4.7416  -7.2613   0.3372  -5.9227  -6.5181  -0.0349
+#>   17.1649   8.2724  16.0801   1.7508 -13.1674  -7.9881   0.5811  -3.2498
+#>   -7.5355  10.0308   0.0994  -1.0809  -4.2856  -5.6888  10.9808   4.8577
+#>    0.8526   2.4289   3.9054 -12.4695  15.2395   0.7965   5.1226   7.6248
+#>   -0.1209  10.0877  -1.0276   7.0150  -2.4394 -11.8494   1.6988  -2.8065
+#>   -4.1253   2.2478   2.1135   2.4452  -9.0294  15.1470  -4.0179  11.6732
+#>  -16.4247   0.6552   4.1020  -0.5220  14.7177   2.2686  -3.4419  -3.7484
+#>   -2.5307   5.5296 -16.8233  17.5541  -9.4626   1.7360   9.1913  -7.8520
+#>   -4.4076  -5.1286   0.4988   3.2959  10.6389  -6.5947   5.7131   0.1022
+#>    3.0960  -1.7295  -6.0314  -0.9746   2.2244  -1.9477   7.4134 -11.1667
+#>   -5.4340  -3.5664  -2.8090  -3.7406  -8.3990   4.4483   0.9569   7.9536
+#>   -7.8570  -7.4177   0.5273   5.6952  -4.5484   1.6877   1.9365   5.5348
+#>   -1.7936  -4.6500  -1.6409   0.7421   1.4263   3.9479  15.6831   7.9632
+#>   -4.7777  11.1107 -11.0362   7.2675  -8.2301   6.9253  -4.1978  -4.0135
+#>   -7.1066  10.7231 -11.3776   0.4588   0.7363   1.6726 -10.5130   2.8376
+#>  -10.2797  -0.3830   3.4307 -10.5552  -6.6430  12.1009 -11.4593  -5.7689
+#>   -7.4406   0.5292  -4.7744  10.3637  -1.1919  -2.4052   3.0385   0.3617
+#>   -6.5426   0.9877  13.3807   1.9240  -3.6421  -4.4329  -7.8066   4.5369
+#>   -2.9725   8.3699  -2.7591  -4.4072  10.4015  -5.1302  -0.4192   5.6535
+#>  -17.0773   8.3026  -1.6549 -12.3443  -5.6288   4.7483   2.1258  12.6425
+#>   -9.1716  -5.8298 -13.2522   4.4055   6.1547  -4.2546   1.7965  12.7544
 #> 
-#> Columns 41 to 48  13.1469  16.3241  -9.5096  -7.9503  -5.8903   1.4845   3.5997  -3.2972
-#>   -0.2769  -4.5006   5.6813   2.3416  -2.4813   0.8077   1.2412   3.2028
-#>   -3.1104  -0.1819  -2.6743   2.3650   4.9108  -9.3813  -1.2675  -5.7836
-#>  -16.8457  -0.2598   6.2999   1.5642   2.1965 -10.8874   0.8298   0.6867
-#>    1.6406  -2.1335  -5.3743   4.0202   9.0518  -4.3672  -0.4848   7.5687
-#>   13.2089   0.0184 -15.4310  -3.9341  12.7642   0.4858  -0.8550   0.0537
-#>  -11.6616  -1.7800  -5.4005  -0.6940   0.2097   3.4405  -4.8381  -9.8920
-#>   14.0421  -4.1125  -5.8660   3.0663  -1.4779   6.1736   4.8914  -2.6142
-#>    2.3728  -8.8320  -1.4000 -10.7431  13.7504  -0.3255  -9.8459  -8.4252
-#>   -6.7076  -6.9181   5.7444  11.9882   1.8702  -1.4792  -1.4045  13.8206
-#>    1.9241  -3.6843 -10.4069  -4.3291   4.4341   1.6662  -4.2902  -9.6582
-#>    0.4927  -3.8589   0.4731   1.3278   4.1912   7.5662  -1.6354  10.4284
-#>   -0.5050   8.8472   0.8561  -4.2709  -7.3363   7.3410  12.7846  -8.7095
-#>   -1.2324   0.7144  -2.9817 -14.2587   3.4681 -11.2354  -6.6864   9.6205
-#>    7.9781  -1.9659  -7.9456   0.1395  -2.1838  -1.9923   3.2518 -10.3654
-#>   -2.0977   4.2419   0.9674   2.3197 -10.0479  -7.7920  -2.7702  13.2239
-#>   -2.8001  11.2738  -6.1056  -5.1717  -4.9363   8.1373   5.2983  -8.2018
-#>   -5.6889  -7.8704   2.1990   7.1298  -0.7015  -0.9009   9.0010   4.0715
-#>    4.0209  -1.8575  -4.0008  -2.7558  -4.3971   9.8000  -4.1534  -5.9647
-#>   17.2192   6.2090  -2.3315   3.7335   0.1062   8.8375   4.9632   0.1628
-#>   -6.7845  -9.5097   1.2010  -0.8135   5.6752  -1.4077  -1.1803   8.0863
-#>   11.0266   2.1652 -13.4578   5.4363  -3.1816  -7.5622  12.6702  -5.5490
-#>   11.5781   8.2979   4.9476   7.6994  -0.9640  -6.0966  -2.0411  -4.1657
-#>    2.9375  10.2070  18.4995  -6.5145  -4.5667  15.3985   2.2779 -11.5283
-#>   -2.6135  -4.7623  -5.1036  -5.7793   0.9005  -7.5000   0.2239   0.9951
-#>    8.0807   0.8971 -10.2686  -6.8935 -11.4938   3.7583  -4.6565   1.4418
-#>   -5.9638  -3.1656  -2.6535  -2.4869   8.5280  -0.5289  -8.7591   1.3365
-#>  -11.9118  16.4476   5.1364  -9.7505  -8.8722   9.3799  11.1230  -7.8695
-#>    6.6437  -9.3812  -8.0302  -4.6728  -1.1732  -5.6723   0.8056  -0.3872
-#>   -3.6792  11.9203   4.2696  -6.0437 -18.1020   2.5408  22.5066  -5.7841
-#>   -7.2307  -6.7341   3.0000   4.8462  -2.0850   6.5586  -2.3047  -9.5330
-#>    3.5926   8.4891  14.9880  -8.0819  -0.7994   6.8544  -4.8742   4.6444
-#>   -3.4799  -3.4725   4.6508  -2.2688   3.3988   1.6961  -0.7697  -2.0110
+#> Columns 41 to 48  10.4242   3.1320   1.2581  -3.4752  -1.5902  -6.2231  -1.3401  -9.0054
+#>   -6.6450  -0.6798  10.4509  17.3850   5.5067  -1.2147   0.9674   3.2124
+#>   -7.7335  -4.5666  -2.5000 -10.8190  -1.9409 -11.0484   2.7665  -5.4809
+#>   14.2366   1.6283  -9.2481   2.3911   0.4189   1.2038  -3.1223  15.6948
+#>   -6.3077  13.1516   8.8934   2.1760   0.4611 -14.6990  -5.7214  -7.4077
+#>    0.8884   3.7535   2.5364   9.7466  -1.0477  -2.4947  -0.8093  -6.0150
+#>  -20.8732   4.5651  -1.1351   8.2958  -0.7052   4.0349   0.7925   5.0308
+#>    6.7334  -2.4410  -0.4197  -9.0668   3.5575   6.0665   7.0734   5.7171
+#>    0.8682  -8.3602   1.7150   4.9446  14.1675  -1.4004  -4.9063  -0.8648
+#>    5.5100   3.1794  -0.1524   6.0978  -1.9435   6.3809   2.8350 -11.7947
+#>   15.4958   8.9630  -1.8836  -8.1126   6.3941   6.5516 -14.4257  -1.6778
+#>    1.8916   7.4190   4.6872  -8.9565  -3.8634  11.6727   4.0424  -2.8454
+#>    8.3891   1.8846  -2.2345   3.8343   5.3066   8.0993  10.8464  -0.6487
+#>   -9.3410  -1.4843  -5.2631   4.9416   6.7498   9.6985  -2.9187 -10.0374
+#>    1.0237   4.5799  -5.1251  -4.0137  -0.9095   5.4507  -2.9494   7.7387
+#>    0.4382 -18.4949 -10.1129 -19.1296   3.3367   1.6957   5.9458   5.6122
+#>   11.2678  -2.9941  -3.4139   1.2834  -2.7782  -3.3348 -18.0017   2.9574
+#>    8.8864   6.3527  -6.9859   2.0352  -8.5715   8.6193  -0.1473   1.4147
+#>   -1.7023  -0.1260   0.9072   0.9205   8.4909   6.5449  -0.1538  -0.3061
+#>  -16.5199   2.0427   5.7692   7.2746   5.8624   2.5903   6.8141  -4.7442
+#>   -0.3602   5.7233   5.2932   0.9407   0.8377   5.7711   1.1102  -2.9801
+#>   -5.5069  -0.4660  -0.3820  -6.7548   0.7122  -2.6577 -11.6022  -5.3100
+#>   -2.6787  -0.8460   5.7715  -0.4013   4.1315   6.8955   4.6016  -7.5422
+#>  -11.0394   9.2489  -2.5416  -1.6594 -11.8009  -9.0010  11.0188   1.0725
+#>  -15.7346 -13.6959  -7.6170   3.1942  10.3479   4.5251   4.6551   3.8973
+#>   -0.0437  -4.1671   2.5428   6.4254  -0.0564  -0.8211  13.6499  -4.7940
+#>    3.3952  -1.0584   9.5605   8.0207   0.2116   0.4704   8.5148  -2.1023
+#>    1.8515   1.5253   5.5502   3.7621   0.4732  -3.6509  -7.0887  -5.0539
+#>    4.5907  -2.9360   5.4367  -1.4830   2.6270  -9.4941   8.6761   1.4446
+#>    4.7818   3.2452  -2.6271   4.8159   2.1423  20.6770   6.8175 -16.9261
+#>    5.2292 -13.3160   7.3324  -7.2283   6.8718  -9.7000  -1.2644  -0.4218
+#>    7.4633  -5.6851   4.5657  -9.4115   4.6865   1.5752   9.3894  -7.5724
+#>    7.7920   1.1129   5.0314  -4.5645  -8.6955   5.1585  -8.6221   6.1632
 #> 
 #> (5,.,.) = 
-#>  Columns 1 to 8   0.4391  -1.9098   2.1484   2.2646   5.2900   2.4446  -4.2038  11.1062
-#>    4.8973   4.6691  -0.1577   4.2984   6.9359   6.8015  -8.0088   0.2365
-#>    2.3555  -3.1241   5.4234  -3.6021  -3.1128  -3.5330   0.4087 -14.0087
-#>  -21.4126   4.3750  14.8495   0.9699 -23.3634  -9.8421   5.1767  -0.3107
-#>    8.8056  -6.8391   4.3331   3.3613  -3.3439  -6.6336  -4.3775   4.2387
-#>    0.9767  -5.5275  -5.2243   1.3197  -7.8901 -15.0289   0.7283  -4.1920
-#>    9.3987   3.4653  -3.4113  -5.7025  -3.7569  -2.7637  -8.1449   8.4361
-#>    6.8643  -4.8464   8.5880   0.4491   2.4999   4.5187 -10.8066  -0.0955
-#>   -3.5246   1.8129  -7.8024   3.5076   2.1022  -6.6070  -6.1765 -15.9043
-#>   -3.9591   1.8916  -0.3776   1.3119  -4.7591  -1.5195   1.4179   2.9710
-#>   -1.6701 -15.0615   0.8259   6.0667   5.4124  -2.4430 -16.1106  11.8974
-#>    0.9344   9.1141  -8.1943   5.6707   4.5830  -3.3910   8.2350  10.4890
-#>    9.7430   1.3351   3.8455  -4.2307  -2.5922   7.3867   6.6023   2.8679
-#>   -7.8984   0.8581 -12.1087  -6.0889  -1.5231  -6.6723  10.8628  -5.9127
-#>   -5.5918  -0.2745  -1.9311  -0.2384  -8.2658  -3.8147  -0.2191   7.8357
-#>   -6.2771   7.5198   5.3943   6.4536  -2.6440  -4.4889   0.2298   8.3339
-#>    2.2442  15.3852  -3.1567 -12.4926  -6.3730   6.0722  -6.5579   1.1781
-#>  -12.8170  13.8433  11.4968  -7.2771 -13.1532   0.3584   6.8091   7.4388
-#>   -1.5858  -2.2859   8.7732   2.5848   6.5458  -5.5752  -6.7938   4.3016
-#>    9.7460   2.3616   0.1275  -2.4913   7.3516  10.8242   1.6281   7.0327
-#>    4.9148  -0.2744  -0.9559   0.1036   0.1812   3.7332  -5.5575  -3.6512
-#>   -6.9862  -2.7739   6.1212   5.5122  -1.4691   4.1725   3.8487  -5.0333
-#>   -5.1841  -8.2360  -0.5528  -7.2601  -1.2878   4.2392   1.6847   9.3671
-#>  -17.1302  -2.2287  -6.7963 -14.4281  -0.8908  -3.4021  11.1699  -3.4806
-#>   -3.0202   9.0764  -3.9049  -2.3577  -4.3465  -1.2582   5.9626  -4.2595
-#>    1.1443   6.7906   4.8259   0.8769  -2.3707   1.1460   1.2335  -3.5128
-#>   -2.2088   9.7532  -6.1144   4.8872 -23.0924  -0.9535   0.8545  -1.0388
-#>   -1.0195   5.6566   2.3717  -8.6502  -5.5510  13.9087   2.7717   1.1356
-#>   -3.9602  -2.6769   6.9075  -3.2793   1.0274  -6.7747  -0.8613   9.1491
-#>   10.5364  -5.7910 -14.2881  -3.7272  10.0372  20.2330 -12.9053  -2.4491
-#>  -10.0494   6.0045   1.5262  -9.1165 -13.6801  -4.7538   0.3811   7.3859
-#>    1.4289   5.4423  -2.4157 -13.3889   6.3931  -3.5567  12.3840 -13.6936
-#>   -4.7881  -1.5958  -6.8650   3.8896   3.9981   4.8909   5.9710   5.3680
+#>  Columns 1 to 8  -3.6093  -1.2903   6.0764  -5.1115  -7.6917  -0.9253  -3.5324  -2.7800
+#>    7.3703  -5.3460   1.0978   3.3237   0.9294   1.0375  10.3985  -1.3122
+#>   -0.6326  -0.3629  -1.4199   7.5534  11.3806   3.7091   4.0511   5.9779
+#>    5.5138  -2.5809   7.5506   0.2660  -9.4975   4.2891   4.3326   2.8226
+#>   -1.1666  -3.5603  -4.0434  12.5883  -1.4867  -5.0171  -7.8023  -6.6774
+#>    5.7493   6.2103  -4.6133  -8.9547   1.8888  11.5672  -7.0186   1.9114
+#>    5.3774   4.3061 -10.7863   0.7188  -4.8939   2.3105  -3.5734   3.5475
+#>    5.2596   1.4163  -1.0784   1.2077   4.2229   8.7776   5.1203  10.8890
+#>   -0.8582 -13.8450   3.5654   1.7786  -2.6548  -4.6490  -4.5554  -7.5593
+#>    3.9904   6.7616  -3.5313   4.1491  -5.0224   8.5076  -6.1398   2.6448
+#>  -15.9534   2.5789 -10.3548  -1.1433   7.0974   7.0014   4.5219  -4.6371
+#>   -0.9696   7.0986  -2.5356  -1.0885  -0.5055   1.4852   4.0661  -1.2982
+#>   17.8750  -3.0686   3.8783  -8.3333  -0.2141  -0.7964   4.4238   0.7044
+#>   11.0701  15.9539   0.8754  -3.8492   4.7610 -13.9683  -1.3725  -4.2814
+#>    8.7233   4.4653  -3.3720  -8.1746   3.3640  -7.3720  -2.1872 -10.1125
+#>   -0.6742  -1.2536   1.5203  -0.2783   4.5542   7.5349   5.3550   3.8258
+#>    1.9188  -2.6132   0.1669   0.2289  -4.0798 -11.8757  -6.5052  -2.6050
+#>   11.1608   3.8010  -5.5448  -4.2009   5.9176  15.7901  -1.9691  -4.6589
+#>   -5.0632  -1.6900  -6.7378  -4.0569  -8.2325  11.0169   2.2513  -7.8544
+#>    7.1254   4.9569  -2.9606   1.4131   1.7487   5.9914  -5.2062  -4.9863
+#>    4.0760  -0.9362   1.6509   1.4951  -0.6076  -5.2641   7.0497   7.2765
+#>    0.0120  -2.8936   5.6908   2.6436   2.9146  -3.4320  -6.6517   3.6918
+#>   -8.2027   1.1693  -3.0631 -12.9093   1.9812  -3.7766   1.4477   0.5365
+#>   -5.6776  -1.6180   3.6932   6.0489  -8.9460   1.6324  -9.9353  -3.1687
+#>   -5.4912  -0.7259  10.8582   3.9291   6.0256  -9.6215  -7.0297  -1.8847
+#>    7.2034   3.1613  11.4869   3.1896   0.4487   1.7262   8.4426   1.9541
+#>   14.4420 -13.1784   1.8565 -13.5320   5.1753   2.0535 -12.2221   4.7491
+#>   -1.8997   2.5124  -4.1269  -7.5790 -13.6419   1.7943  -0.1242  -0.4601
+#>    5.2074  -2.2256  11.3320   4.0449   8.8761  -7.3174   0.6415  -8.6672
+#>    2.8333  -5.2061  -2.5673 -21.6389  -3.1647   2.8634  -1.7418  -5.4007
+#>    3.5700  -3.2492   6.7390   2.6306  15.3877  -6.2330  -4.6603   0.1342
+#>    6.1644  -0.8718   1.6882 -10.7350   0.5103   0.0737  10.0092 -10.7578
+#>  -12.3648 -10.3327   5.6151   0.6158   8.8201  -0.5401 -16.0075   5.4813
 #> 
-#> Columns 9 to 16   2.2322  -7.0163   7.4025   1.4474 -16.0608  -8.2812   3.9479 -12.4157
-#>    5.5612   4.6730  14.6888  -6.9282  -2.4945  -2.1730   6.0320  -4.8716
-#>   -3.6143   1.5976  -7.2834  -4.2591  18.0512   3.4045   4.9996   9.2229
-#>   -1.9145   4.3505  -4.2095   8.8816   4.4741  13.3800  -8.6061 -15.9516
-#>   -0.1081  -0.1761  -4.2558  -0.4345   5.6086   1.8441  10.3737   5.1652
-#>    2.2998  -1.7684 -10.6446  -2.0748   9.0934   0.4708 -10.4841   1.5032
-#>   -5.1979  -5.4615   9.0303   6.3415  -5.2508   9.9896  -8.8075   5.4614
-#>   -1.3947   7.3096  -4.8045  -4.1015   5.2261  -4.1460   3.2539   5.1348
-#>   -6.8889   0.6101  -5.8317 -11.2515   5.8717   7.2147   1.3801   8.7224
-#>    7.5277   1.7141   5.3771  -3.1537  -4.7789  -2.5609  -0.3410  -5.8091
-#>   -3.9199   1.3565   0.4403 -10.9124  14.8008  -1.0440   2.7889   3.3954
-#>   -1.7609  -6.7693   3.1827   4.2683 -16.9452   1.0002   2.4671  -4.3169
-#>   -4.8054   1.8201 -14.5093   7.1921   0.2319  -6.2055 -11.8342   6.1623
-#>    1.7320  -9.1120  15.5501   5.9678   4.7828   5.1776  -1.9527 -13.2768
-#>  -10.0481   2.2758   3.7188  -0.5877  -6.5601   4.4341   0.3167  -3.7434
-#>    1.2645  -9.2064   0.6251  -2.0461  -6.9692   3.2809   1.6773  -1.2751
-#>   11.8582  -2.5059  -1.4314   4.9035  -1.7204  -1.3300  -9.6967   6.4524
-#>   -0.5472   2.9117 -10.2421   2.0473  -3.7858   9.6259 -16.1022   3.4859
-#>    2.1549   3.3371 -12.6224   0.8640  -4.7667  -2.4520  -4.1159   2.7331
-#>   -2.1234   5.4642 -14.9485  -9.0224   0.1193  -7.7338 -10.7780   6.8374
-#>    1.2525  12.7835  -8.2089   5.7755   6.3247  -5.0586  -1.0041  -6.4176
-#>   -4.7794  -5.4369   2.2887  -8.2611   0.6869   1.6407   1.2151  15.0891
-#>    0.3344  -3.4176  -5.3505   1.7168  -7.6792  -5.8174  -0.8998  -2.9376
-#>    4.0955   1.8786  -7.8103  -0.1581  10.2054  -7.6718  -3.1871  -4.9185
-#>    8.1981   3.2560   4.0140   1.1940   1.7563  -8.6477  -2.5206  -5.7887
-#>    1.5151   1.2714   4.4403   2.6299   2.0904  -6.3844  -1.2666  -8.5626
-#>  -10.9549  -1.0200   7.7347   7.2965   2.4207   2.6545 -10.7580   3.1383
-#>   12.8288  -0.2713  -9.4372   6.5619  -0.2274  -4.2176 -12.9764  -1.5689
-#>   -6.1175   5.0343   7.6258  -9.1574   0.1178   2.2677   2.1093  -6.2908
-#>    4.1674  -5.4791   2.5661 -10.4149  -7.1355  -9.9736   1.3936   4.6197
-#>   -3.8975  -5.1808  -0.8492  -0.0343 -11.0863   5.1938  -1.0113  -2.9068
-#>    7.4677  -8.5787  -5.1975   1.3381   5.0835  -6.4174   6.8993 -17.7437
-#>    7.7881  -0.0287   5.9182  -7.3728  -1.9345  -3.7842   3.4900 -16.8634
+#> Columns 9 to 16   4.7200  -6.3086  -5.9259  -1.9333   3.6707  -6.0153  -4.9750  -9.1986
+#>   -5.3187  -1.8890   5.0312   4.8679  10.5360   3.8239   6.1423   1.5564
+#>    7.3208   4.5365  -2.5451  -0.2957  10.7177   2.7732   1.1820  -1.6323
+#>    4.6218   4.7184   2.4337   5.9134  -8.1048  -2.2897   3.9997   4.7079
+#>    7.3253   0.7482   4.8612  -6.8182  12.5977  -3.4985  -6.3466   7.7690
+#>   -6.9580   1.3085  13.5707 -11.2307 -11.8208  -3.0141   1.6700   0.5377
+#>    1.2800   4.4075  -2.4731   5.9489   3.7127   8.8916  11.1587   0.6376
+#>   16.0744   2.2650  -2.0935   7.3797  -4.6004  -6.6549   4.3910   0.4648
+#>   -1.3918 -16.4661  -0.2961   6.9812   8.7332   5.5621  10.5068  10.7219
+#>   -3.6059   0.1250   4.2744  -6.4661   7.4152  -7.3853   4.3772  -1.1507
+#>    6.8563  -0.8522   6.9527   2.9505  -8.6145   2.4127  -5.0714  -8.1235
+#>    0.9298   9.2375   1.1895   7.0327   4.3848  -0.5507  -8.7177  -3.7877
+#>   -2.5716   4.9430 -11.3342  10.6734  -2.7472  -2.0509  10.6890   6.1280
+#>   -7.5804  -5.0121  -2.4420  -0.9926  -3.4466  -4.1375   3.0898   9.7536
+#>  -10.6370  -4.7997  -3.4790   7.3145   3.2049   0.3345  -7.6432  -5.0167
+#>    4.0509   0.0487  -0.8984  -7.3605  -5.2877  -4.7661  -1.4034 -14.0970
+#>   -5.4763   1.6764  -6.1587   6.6751  -0.2484  -3.7689  -4.2502  -0.4615
+#>   -0.5020  -2.1828  -1.7352  -1.9906   9.3432  -5.3914  -2.0886   5.5507
+#>   -4.3778  -3.1400   7.3510  -2.0299   1.8315  -5.6617  -4.6774  -3.8033
+#>   -4.2352  -4.7447   5.4779  -5.5180   3.8161 -12.3682   4.4654   4.9991
+#>    0.5159  -4.8037   0.9870   8.1954  12.9885  11.4685   2.8773   2.1255
+#>   -1.8920   5.1400   2.8560   9.4664   4.4385   5.3896  -4.8819  -5.4284
+#>   -2.4695  12.7925   0.0343  -5.2716  -0.1562   7.4265   7.2671   2.9827
+#>    8.8573 -13.8755  -2.6458  -1.3662   2.3347  -7.1429  -2.0023  -5.4036
+#>    2.6558   1.5291   8.0116  -8.9875   9.0377   4.3665   4.4356  -5.5887
+#>   -0.7960  -0.4837   5.0131 -10.9679   1.6610  -1.9107  -0.5137  -2.5154
+#>    0.9609  -5.5622   1.6186  -7.6001   2.0875 -13.4089  -3.1850  -0.3483
+#>    5.6879   4.0862   9.4920  -0.8225   4.8359  -0.5075  -6.3772   2.1161
+#>   -6.1866  -1.8260  -2.1230  -4.1048  -5.6706   8.4782  -1.4349  -2.1027
+#>  -11.7711 -12.6506  -7.2038   0.0483  -9.8998  -8.1198  -2.2843   8.0265
+#>    4.7740   5.2374 -11.9683   1.2057  -6.5984   7.2167   6.6216   1.1750
+#>    5.7323   1.7619  15.7807  -4.1072  -1.3083   6.3711   1.5233  12.2112
+#>   12.7156  -7.7441  -3.1003   9.9384  11.1962   4.2520  -5.6135  -0.0401
 #> 
-#> Columns 17 to 24   1.6771  15.3690   3.7065  14.0798  -3.7558 -14.2482 -15.8616  -4.6108
-#>   -9.0802   3.9591  -0.1773  -0.3126  -0.1790  -2.6130   8.8423  -5.2414
-#>   -6.9821 -10.3903  -5.4097  -7.7257   2.0685   5.1675  -5.2855   2.8596
-#>   -4.9021  -7.2088   7.6342   5.6961  12.7943  17.9261  11.7175  -0.8168
-#>    2.5747  -3.4802   3.6670  -4.6425   1.4458   7.2083  -4.3110   3.6268
-#>    4.0800  -7.1349   4.5110  11.2754   2.0006   1.3698  -5.7636  -2.4166
-#>   -1.7837   1.6697   4.5390   8.0320   6.6240  -1.3921   5.0736   6.5151
-#>  -10.3835  -2.0104  -2.1694   7.6783  10.7268   8.1382  -3.9772 -10.8648
-#>   18.0275 -15.5641 -13.7067   0.6534  -3.9048   2.4265  12.8767  -5.0597
-#>    8.7382   9.9576   8.0037   5.4577  -7.9091   4.1477   2.0845   2.2091
-#>   -8.5455  -3.5102  -9.4138  -1.0351   5.3878   1.6572  -0.7054  -1.4215
-#>    8.5940  -4.8951   7.0747   0.1936  -1.3161  -1.9277  -4.8872   7.2786
-#>   -8.8369   5.5785   6.5429  -4.4698   2.2424   3.5187 -13.6622  13.3914
-#>    3.3157   1.1893   3.4218   0.6910  15.3152 -25.8847  -1.6705  -7.5419
-#>    1.2810   3.5057  -5.8798  11.3180  -0.9019   0.7256  11.0449   1.4503
-#>  -14.3743   4.2423   7.3155  -1.0684  10.6818   4.7645   4.5571 -16.5317
-#>  -11.8851   5.7834  11.3538   0.4630   7.3968   3.2219  -7.4244  -1.9372
-#>   -9.4704  -4.0520   1.9144   3.3256   8.3051  -3.5069   6.0086   1.8476
-#>    5.8126   5.6365  -3.7384 -11.9932   7.9528   3.3870   0.0428  -8.3264
-#>    3.3812  10.5610  -2.1292   1.3869  -9.4373  -3.9937   8.0604  -2.4713
-#>   -3.9936   0.3866  -7.1512  10.2174  -3.1713   8.1932  -2.9202   3.2001
-#>   -3.5016  -6.3447  -3.2122   5.7092  -0.7514   5.7600   0.1821  -3.7173
-#>    9.9094  -4.9634  -3.0044   1.4675  -5.3280   8.1705   8.6492   9.4478
-#>   12.2444   0.0311   5.9437   0.7478  -6.0319  -0.5225  -5.2490  -0.2744
-#>    6.4196   6.0773  -6.8921   6.7982   1.2868  -0.0129   4.0809  -0.3024
-#>   -4.7182  15.5866   0.2722   6.8277   2.2576  -1.9896  -0.1264  -9.8248
-#>  -12.0991   1.7634   4.6613   8.1298   9.6500   8.8975   3.5907   5.0736
-#>    4.8709  13.9266   3.8506  -6.3351   0.5635  -7.9125  -4.9749   9.1720
-#>    2.3501   4.4443  -3.0040   4.8984   0.7580   5.2943   2.1156  -9.3190
-#>   -2.4022  12.4300  -0.4464   3.9948  -6.7848  -3.4612 -16.2256  -6.0657
-#>    0.3598   5.1294   1.4181  11.9905   7.0827   9.3332  -3.5159  -3.1946
-#>    8.4195 -10.1014   4.3162 -10.1488   9.6877  -3.9963 -13.8788  -3.8362
-#>   10.7531   3.6968  -0.5402  12.8164 -12.9496 -12.6744  -4.4541   4.8352
+#> Columns 17 to 24   3.9098   4.9373   3.6534  -0.1740   3.9276   9.7328   6.6432  -5.5194
+#>   -3.2305  -3.6783  -7.5728  -9.8615  -1.4127   4.9206  -1.5615  14.3436
+#>   -0.2013  -9.1078  -1.5541  -3.7882  -2.7034  -0.8736   4.2783  -7.9201
+#>   -9.3736  -2.3664  -2.2441   3.2980   2.4759   4.4908 -13.0367  -3.6673
+#>    8.0704  -3.4354  -3.0902  13.1151  -1.1653   2.7296  -2.0878  -2.4590
+#>   -5.1876  -4.9021  10.5444   5.1420  -4.3786 -13.0182  10.6908   3.1293
+#>    4.8361  -3.1101  -4.8765  -7.6380   5.0944  -1.9056   2.9414  11.2553
+#>   -0.8109 -12.6191   4.1921  -0.3207   0.7677   4.0676   1.3634  -2.5494
+#>   -1.6581   7.6767 -16.5640  -7.0681  -0.8868  11.2187 -11.4188  -0.7190
+#>   15.3214  -5.2683   5.2794  -6.0327  -1.2566  -7.8449  -5.4212  20.0736
+#>    6.7585  -1.0774   4.8847  -3.8640  20.1669   6.4108   2.6663  -5.8079
+#>    9.5129  -1.3992  -4.5772  -5.5883  -3.0176  -2.8765  10.5112  -2.7861
+#>   -5.6070   6.0464   0.8493  -0.7762  -6.4840  -8.7144   4.7385  -2.6547
+#>   11.3915   1.7881  -0.3019   5.3339   8.9403  -0.0411  -7.7044   6.4859
+#>    4.5514  -3.8223  -2.6058   8.2557  -5.8033  -6.0084 -14.2220  -6.4799
+#>    2.1465   0.3249   3.6368   1.9013  -2.9698  -0.9082  -0.6275 -13.0249
+#>    4.5448   6.2852   8.0968   2.7184  -4.7658   2.4863  -4.2074   4.6784
+#>   -0.4202  -5.7364  -2.3302  -3.0505   1.2563  -6.0202  -6.8703  11.9602
+#>    0.6975 -10.8923   6.2297  -0.2160  -3.9045   5.4529   8.8124  -6.5614
+#>    3.4633  -3.2704  -0.6613  -1.5254   6.6068   2.5122  -2.0393   8.7533
+#>    2.7281   1.6842  -4.5081  -7.8430   2.1580   8.5329   9.1651   8.7632
+#>   -0.9653   0.1799  -2.9776   6.1305  -0.7675   4.1214   7.8482 -10.3551
+#>   -3.5780  -1.2504  -4.2268  -5.0339  -0.9126 -12.0816   0.6536  -1.3908
+#>    0.3395  -5.1659  -0.7571   0.7869   8.0917   1.1259  -3.8546  -0.6517
+#>   -4.3516  -1.2481  -2.9169   5.4756  -1.5554   2.1704  -7.4433   0.9777
+#>   -4.0195   5.0333   1.9417  -8.5874   0.6927  -8.1338  -5.2322  13.4028
+#>   -6.0866  -1.8895  -1.7305   2.6646  -5.2163   3.1847   1.6071   7.9114
+#>   12.4972   0.3906   3.0124   6.6523  -1.0086  -3.4964   7.4790  -1.4859
+#>   -5.7057  11.5525  -4.2333  -1.9120   4.2660   3.3701  -4.1573   7.3720
+#>    1.3896   2.7235  -3.7331  -2.2059  -4.8854  -6.5039  -5.8629  -0.1352
+#>   -6.9085   0.5322   7.7444  -6.4550   0.6128  -2.7922   5.3740  -8.3980
+#>    7.4638   7.2766  -3.0916   2.2767  -9.5935 -12.7509  -4.9414  -9.5226
+#>    1.1695   1.6495 -12.0151  -6.1535   1.1875  13.5077  -1.4520  -6.5941
 #> 
-#> Columns 25 to 32   3.0754   0.9656  11.4195  -1.6933  -2.7757  10.5142   1.0324  11.4810
-#>    8.3067 -12.5141  10.5343  -0.3537   7.5259   6.7108  -2.0825  -1.2409
-#>   -0.3672  -8.2046  -1.6047   0.8231   7.5048   3.2305 -11.0317   2.1045
-#>  -24.1941 -14.2835   0.0603   3.4999   0.9970  -1.3493 -21.5908   3.9657
-#>    5.0227  -1.3011  -1.1914  -0.2580  -2.6511  10.8058  -7.1249   3.4567
-#>   -4.3163  -5.5814  -7.0375  -3.9281  -4.2662   8.3106   6.4370  -6.4413
-#>  -22.2715  -1.5557  -7.1393 -13.8717   4.1784  -3.9443  -0.4707   4.6257
-#>   -1.1398   9.7803  -4.3700  11.5757   3.7639   3.8352   7.0444   9.1612
-#>    7.0127  -1.5018  -6.6758  -9.8043  -5.0684  -8.0241   0.1096  -9.0917
-#>   -1.9075  -4.6612  -3.8810   4.1255  -8.3587  -0.0907  -9.8954   0.6749
-#>   -1.0257  -4.4097 -14.4159  -3.3711   4.3964   6.4112  -3.2049   2.1978
-#>    9.8993  -3.4522   3.5321  -9.1970   8.0096   1.7683  12.9511  -6.5953
-#>   -5.5456   0.3709   5.9697   3.9595   7.7234   2.9263  -6.2956   8.9204
-#>    8.2467  -7.8250   9.2086 -14.7752  -1.7631   2.3624   7.4934  -3.4432
-#>   -3.3292  -8.0017 -11.9161   0.0641   5.8049  15.1777  -4.0188   3.4478
-#>   -5.8577  -7.9515  -4.2061   4.6872   5.1803  10.4527  -3.0946  -8.3235
-#>   -0.1268   4.5932   1.4038   6.5119  -8.2524  -2.4885   1.8776   3.0738
-#>  -11.7010   4.4528  -1.9193   5.6535   7.9257   1.6852   5.6245  -9.1236
-#>   -2.1876   5.5739   2.5077  -1.3249  -0.3368 -12.2279   5.1158   9.0283
-#>    7.9109   9.3029  -0.0488   7.3976 -11.4432   0.1573   8.6820  -6.8024
-#>    7.6055   6.7798   0.9633   0.9630  -8.1765  -2.1018  -4.6992   2.1717
-#>   -5.0130  11.0520 -15.4249   0.6652   4.8052  -0.2627   2.0799  -7.4576
-#>   -5.7443   7.9972 -15.6795  -6.0751 -12.8827   3.2815   1.8649   7.1333
-#>    0.3538  -9.3284  -6.3689  -3.5229 -12.5694  -2.9542   8.0311  22.9101
-#>    9.3625   2.6567  -5.3627  -4.1245 -15.9260  -8.2227   3.4513   5.8330
-#>   10.5383   6.4723   5.7762  13.8513 -11.9889  -0.1678  -6.6589 -16.1521
-#>  -17.6372  -6.7296  -8.8622   0.8981  -4.1522   7.4152   5.9546  -1.9361
-#>    5.4169  -4.5138  11.0641   5.9831 -10.9107   6.2039  -0.9973   4.2854
-#>   -0.9271   3.5063  -8.0455  -7.9462   5.0734  -0.4818   2.4464   3.7186
-#>   12.8333  -0.2911   6.5849   6.0337  -2.4569  12.1207  -9.7083   7.0773
-#>  -10.5021   3.0687  -7.7843  -4.2201  -5.2013 -10.5865   5.5016   2.3961
-#>   12.5794 -20.6448  16.9042   5.0654   7.4768  -1.5273   6.9260   6.6722
-#>    6.9695 -17.0422  -1.9053  -7.2440  -2.6287   7.0495   4.2520  -2.3256
+#> Columns 25 to 32  -6.8371  -4.1226  -5.8224  -0.6768  12.5131  -8.3509  -0.2055   2.6692
+#>   -0.2869   1.3097   3.2707  -4.5621   1.1508   2.0422  -0.3216  -0.8699
+#>    3.4153   4.8268   1.1675  10.1520  -5.3198  -3.5681  -4.6958  -1.2969
+#>  -11.8568   1.9811  -4.6707 -10.8200  -0.0186  -1.2309  -8.4277 -13.6789
+#>   -0.6888   1.2529   0.8965   3.8172  -3.7613   7.7163  -0.9515   4.4871
+#>   -3.4012   0.2477  -2.0684  -3.2691   1.0814   0.6780  11.7068   6.5404
+#>   -5.4002  -7.8514   6.4168   1.5765   4.2092 -13.0321   8.1352  -0.5110
+#>    9.9061   2.6390  -3.1091   3.0181  -6.4826   2.5061  -4.5573  -7.6747
+#>   -7.4247   4.2733  -8.9704   4.5667 -15.4277   8.4194 -21.0000  -3.9035
+#>    2.9435   3.9285   6.4788   0.6277   9.9094  -0.4325   5.6967   5.5039
+#>   -3.9571  -8.7044   2.3540  -8.3360   7.0798   3.1047  -5.2919 -25.0818
+#>   -1.9560   2.7290   0.5972   8.1528   4.1175  -0.5435  -2.2930  -1.2568
+#>    1.4238   9.6548  -1.3722   0.7341   1.6796   5.7211  -7.4429  -0.9193
+#>    7.4011  -2.0598  -6.6328  -5.7342   3.1807 -11.3711  -0.3847   9.0154
+#>    2.2146  -1.2954   2.2340   9.4409  -6.7533   0.9151  -1.1049   7.2711
+#>    8.8156  -1.4804  -3.7520   3.3352  -9.6580   2.1291  -1.4695   2.7972
+#>  -13.1061  -5.3580  -8.5591  -5.1088   7.5780   0.5580  -3.4692   4.7791
+#>    0.9782   4.6440   6.4239  -1.8737  -6.3170  -4.8075  10.8375 -13.9694
+#>    7.6675  -2.7296   3.0915  -8.7033  -1.4836   4.2377  -5.2127  -6.1850
+#>    7.3466  -9.5054   1.0619  -2.6130   8.8450   0.0129   4.5675   6.9152
+#>    4.6388   1.1716   1.9376  -2.0347  11.5259   3.6685  -2.1238  -2.1908
+#>   -8.3402   3.9436   2.4728   3.3376  -4.6346   3.6179  -4.3339   7.4139
+#>   -4.5564  -2.6069   2.5472   8.8655  -1.6108 -11.2277   5.7506  -8.5124
+#>   -3.1873  -0.3410  -3.9907  -2.0182   1.8068  -7.3776  -9.1523   1.6266
+#>    0.8815  -4.4806   5.5074  -7.5697   0.4252   1.0953  -2.0151  -5.7848
+#>    6.7691   0.2646   4.1325   3.6660   8.2494   0.2321   7.8379   7.5525
+#>   -6.2269  -1.8113 -10.2644  -0.6707  -3.3515   5.9845  -7.8346   7.7601
+#>    0.6047   3.6865  -0.8295   2.2470  -3.9284   4.3618   2.9738  -6.4380
+#>    4.6167  -3.6654  -0.5332   1.9339   5.6124  -0.4041  -3.8660   5.6782
+#>    1.8679  -0.1434  -7.8072   8.4092 -11.9295  -0.4093  -6.3832  -3.8197
+#>   13.2778   5.4347   0.0776  -1.3714   2.4193   4.3692  -4.3794   1.0340
+#>    7.8648   5.3887   0.7544  11.6970 -13.4516  14.0049 -10.4700  -5.6420
+#>    1.6491  -1.3838   5.0096   2.7583  -2.7833   5.9338 -18.7820  -9.9932
 #> 
-#> Columns 33 to 40 -10.0027   3.8379   1.0881  -5.4896  -3.4261  -2.0429   8.5668   0.6859
-#>   -0.2681   1.0662  -3.8098   3.2342   2.6800  -8.1446  -2.7712  -4.1984
-#>    4.7517  -2.0557  -7.7677   5.1592   0.5944   3.9591   4.3850  -1.2466
-#>    8.1936   3.0673  -0.3543 -10.5834   2.9359 -10.8625   0.6194  -2.0070
-#>   -5.1751  -2.5127  -1.9720  -6.5750  -0.1732   4.2047  13.0346  -2.6657
-#>   -1.8758   8.4967  -2.0558  -4.6847  -2.2359  -7.7241  -3.3445  15.3346
-#>    5.9761   1.3729  -8.9394  -4.0960  -4.1077  -6.0165   0.7958  -1.0748
-#>  -13.7243  -7.8930   2.7955   0.2883  -5.9294  -1.7375   5.3112  -3.4478
-#>   -0.9705   9.0413  -2.2212   3.6686  -5.7288  -4.2316   3.9254   0.0704
-#>    2.9807   0.6605  -7.1388  -3.8520   2.1888  -2.2726   9.6096  -3.6688
-#>   -3.6191  -4.2340   2.6247   6.8139  -7.8175  -4.5176  -1.0235  -5.8777
-#>   -4.2742   5.0907   1.7450  -3.6382  -3.0324   2.9528   0.7540  -0.4634
-#>   10.9585   0.0143  -7.0358   6.8255   4.3354   0.3694  -1.9219   3.4484
-#>    0.5214  -0.1948   7.1384  -7.7696   0.6348  11.3390  -1.7319  -4.2369
-#>   -2.6518   6.9048 -11.4995  -5.9130  11.9696   8.3919  -4.3782 -14.1445
-#>   10.4091  -6.7464  -4.8839  -2.3931   5.8914  -1.0612 -13.6791   2.6800
-#>    2.1091   2.4291 -11.2586   5.8175  -6.0045   7.4189  -3.7206  -0.2299
-#>   15.1141  -1.9943  -2.2590  -0.6403  10.9068   2.3399  -9.3898  -1.8449
-#>   -1.6461  -0.8309   0.3367   1.4051  -2.9572  -1.9485  -0.5922   7.7519
-#>    7.2909  -1.9546   0.1197   1.6492   1.1757  -0.4608  -0.5773   2.3481
-#>    8.1006   3.4007   7.7575  -1.3652  -6.8355  -8.0055  13.8854   5.6229
-#>   -5.3073  -2.1287   4.6341  -0.0629  -3.8079   5.9468  -6.5184  -4.4899
-#>    9.1216   4.1665   1.5098   2.4553  10.6162   3.4273  -9.7515  -5.4893
-#>    8.5937   6.0146 -10.0838   7.7629   5.9513  -9.6387  -0.2607   9.8624
-#>   -0.9794   4.6749  -1.4617  -2.0968  -2.9037   2.0378  -0.8534  -2.7718
-#>   -2.9497  -0.1574   1.7163  -5.7772  10.2611   3.0250  -5.9499   5.6355
-#>   13.6754   6.2193 -15.8523   2.5245  -7.9110  -0.2794   4.3846   2.2639
-#>    1.1008  -3.4523  -4.9767   2.7175  21.2093  10.1844 -13.1447   2.7406
-#>    1.5942   0.2254   2.8626  -4.0579  -6.1620   3.5378   9.9390  -9.9881
-#>   -1.0034  -6.4215   1.5955   3.2818   9.3604  -1.8906  -9.9140   1.6970
-#>    4.4405  -1.0841  -1.2114  -6.8427  -1.5594   4.5440  -5.3968  -2.4016
-#>   -4.8038   6.7719  -0.6516   1.4910   2.9071  -1.2205  -5.2272   5.3886
-#>   -3.9530   7.0034   4.7422  -4.3794   0.1748 -10.2672  -3.6337   1.0284
+#> Columns 33 to 40  -4.9647   3.2308  11.2127  -4.2686  -2.2796   1.5136  -7.9512  -9.7500
+#>   -0.8298  -3.5603  -8.6381   4.3740  -1.2116  -6.4308   9.1653   1.0100
+#>   -8.8526  10.1238   4.5236 -10.3172 -11.7608  -4.4520 -12.6157   2.4357
+#>    6.1329  -3.0763   2.7849   5.8316   7.1820  -1.9002  23.3693  -4.3429
+#>    0.4906   2.0583  -1.9251 -15.1282  -4.4896   7.8140 -16.8594  10.9248
+#>   -1.9579  -5.4513  10.3123   5.6427 -11.2760  10.6730   1.7441  -7.5491
+#>   -7.3830 -16.6631  -5.7639  14.9311   4.9644  -6.7296   1.6038   2.6538
+#>    3.0646  -5.3544  -0.7530 -10.1325   8.3229  -7.3668  -6.0994   5.1784
+#>    6.8978   4.3912  -7.3905  14.2001  -2.5608  -0.0105   8.5202  -9.8634
+#>    1.8981   3.0568  -1.1767   4.4078 -10.5444   6.4013  -2.7760  -5.0415
+#>   -1.9648   9.3269  -9.7062   1.6772  -2.3212  11.6579  -0.8811 -10.7980
+#>  -17.1476  -6.0371   0.8275   3.4982   3.1655  -8.2893  -9.4494   7.3165
+#>   -0.2917  -2.5819  12.3945   8.9153   1.4974   2.9821  -3.9309 -12.7116
+#>   14.6344   7.7295  -0.9487   6.4490  21.7108  -5.0010  -8.6624   1.4799
+#>    1.6928 -11.3204  16.9016  -4.0679   5.2997 -14.9579  -4.8888  -0.6290
+#>    7.6388  -7.4302   6.7221  -1.8618  -5.3975  -0.0817  -8.6780  14.2581
+#>   -4.4833  -6.4302  -1.7611  -3.8002   9.6617  -5.6859   8.8622  -5.4209
+#>   13.5970  -5.3826  -8.2613   5.1612  -0.6774  -5.5049   0.3484  -4.2407
+#>   -1.5025  -3.3588   6.3384   2.6384  -1.6093   5.5890  -2.2253   2.2145
+#>   -3.2322   1.0356   2.5133   6.9882  -3.1620   0.7647   8.5024  -9.6295
+#>   -9.3587  -7.0906  -0.4331   3.1136   0.5888  -6.8040  -7.3496  -5.6686
+#>  -16.0691  -4.9708  16.1615  -8.4935 -13.3348 -13.1005  -4.3912   8.8010
+#>   -4.8395  -7.2985 -10.4431   9.4019  15.2359   3.0031   0.7892  -1.3266
+#>    4.8531  -5.3962  -4.2206   4.6176   0.1133   3.1347  -2.3769  -5.9446
+#>    9.9675 -12.0881  -6.1321   0.1691  16.1158   8.3490   1.8235   0.4516
+#>    2.1709   1.9073  -6.8797   4.6197  -7.2654   2.7184  14.5499 -11.2745
+#>    8.1649  -6.7803  21.4585  -6.6413   4.9666   8.7409   0.6242 -11.9948
+#>   -3.8563  -9.1333   2.3507   4.1116  -8.7302  10.6971   3.8956   3.3786
+#>   -1.1030  -2.1147  -3.6249  -2.4057   6.1311  -2.7988   6.4180  -7.8030
+#>    7.4551   4.3960   4.9457   8.0116  12.7592   7.1989  -4.0122 -11.1286
+#>   11.2901   3.6341   5.1785  -9.3690   9.7895  -5.5296 -10.6360  -5.7740
+#>   -9.8512  -3.8732   8.5334   3.5523   0.8450   9.8966   3.3078   2.1560
+#>   -1.8370   3.1390   1.6548 -15.7944   5.7936  -2.7611   3.6734  -0.4723
 #> 
-#> Columns 41 to 48   3.9176   9.3826 -18.1409  -1.2317  18.4283  14.2380  10.9816  16.3214
-#>    9.3704   8.3192  -3.7702   9.3107  -6.9517  -1.9656  -2.1970  -5.9058
-#>   -6.1956   8.0480  -0.4568  -7.2946  -7.2395  10.2508  -1.6460 -17.5588
-#>   -5.8348   6.3836 -15.9138  -7.1003   0.7682  -8.1109  -4.9736   4.3648
-#>  -12.1081   7.9106  11.7615  -7.2715   7.0097   4.9865  -1.4555  -2.7906
-#>   -7.2788  -6.8688  -5.7403   0.8716   2.4997   2.0539   6.3284  -3.5148
-#>   -0.6817 -14.6984  -1.2534  14.0554   3.7611   2.5724  -6.4090   0.5131
-#>    3.0084   3.5823   1.3246   0.2096   5.1705  -0.3375  -4.4088   0.3239
-#>    8.4180  -2.8182  13.1569   7.0721  -9.1781   0.7615  12.5517 -14.7409
-#>   -5.2442   2.1872 -10.3611   3.4234  -7.4395  -6.3410  -0.9863   1.5954
-#>    7.6250   7.5944   9.0311   0.7588  -9.2115  10.0633  -7.0872  -6.7356
-#>    5.4534 -16.1096   5.8245   3.7962   5.2196  -3.4442  -2.0334  -0.8086
-#>   -9.7670  -8.5133  -0.4997   1.8853   1.8471  17.3971  -2.1560   6.0896
-#>   -2.7300   0.1150   7.3367   3.4939   2.0422   2.3700  22.4666   2.8843
-#>    1.3771   0.3651 -12.6147  20.4125   1.0130  10.7655  -0.1040  -6.2868
-#>    7.7744   7.6784 -10.9426  -6.5254  -3.0292  12.7293  -1.1339   1.9785
-#>   -7.1104  -0.2583  -0.5166  10.2442   5.2892   5.7679   2.6449   2.1772
-#>   10.5209 -10.9635 -16.7092  15.8249  -2.4129   7.9576   1.9455  -3.7210
-#>    7.9507   3.4032   6.5550  -0.7690  -1.6202   8.4765  -3.6645  -3.3324
-#>    7.6527   5.3433  -1.8237  17.1745  -9.4284   0.2114  11.2357 -10.2487
-#>    1.7741  -1.8712   2.9795  -0.9704  -2.9409  -6.4351   5.4212  -0.7826
-#>    1.7348   5.4718 -10.0168  -1.3006   4.4316   4.7486  -4.0691   0.6938
-#>    7.6392   6.3423  -4.4581  -9.5702  -1.7579  -6.8640  -5.6261  -9.5138
-#>   -4.7566 -14.3245  -9.4253   2.2101  -8.7745  -0.5797   0.4113   7.4549
-#>    0.7994   8.8246  -6.1234   5.6424   2.0019  -4.7824   1.0309  -0.9710
-#>    0.6113   9.3829  -9.3782   4.8075  13.1326  -1.6003  13.6170  15.4264
-#>  -13.4268  -3.9375  -4.0792  -2.3965   2.2949   1.7475  -2.0046   9.6053
-#>   -5.2425   1.8676   1.0152   6.7712   1.6528   6.5646   7.5009   6.0387
-#>    6.4326  -3.4127  -5.5756  13.0351   6.9856   3.1156   8.2280   1.4301
-#>    7.9989  -1.2362   1.1412   7.9949   3.8730   7.1355   7.6854   5.3798
-#>   10.1957 -12.0346  -5.5023  11.2652  13.5204 -10.4236  -2.8808   1.6140
-#>   -3.5676  -4.3917   1.2228  -6.8104   1.3814 -12.4657   8.9958  -3.9858
-#>   10.2427   0.1554   0.9914   7.9511  -2.0589 -10.3418   4.6433   1.2253
+#> Columns 41 to 48  13.1554   4.5368 -11.5878   5.8889  -0.8603  -4.7708  -2.2904   8.5790
+#>    1.0638 -11.4233   0.3845   8.7523   8.9705   5.4159   0.2301   1.5610
+#>    0.6623 -12.0311   0.0608  -0.6107   4.0495  -2.9505  -9.9194   3.3289
+#>  -18.5428   0.8324 -10.6181  10.4965  -7.9841  -2.9386   7.5027   6.5604
+#>    1.5604  -7.4559   5.5142  -2.7431  -1.0200   1.9060  -1.6006  12.4555
+#>    1.1579   6.6539   5.4687   6.2231   6.4771   7.4508  -2.4626  -5.4610
+#>    0.8283   3.2415  -4.4330  -0.6756  12.4004  13.7175 -19.5081 -10.4585
+#>   -3.2825   7.8557  -9.6537  -2.6479  -4.8104  -2.9254  -1.2627   2.6681
+#>   -7.4804   2.9199   6.7078  -0.3411   3.2901   3.1529  10.8884   3.2526
+#>    4.4639   1.5764   4.8269  -2.5805   0.3061  -3.0173   5.6379   1.1188
+#>    0.8612  -2.2516  -9.9114   1.1516 -13.8117  -2.7587   9.8810  -0.4223
+#>    3.9313   4.8661  -5.0012  -0.0035   7.4963  11.3205  -4.9284 -13.0219
+#>    2.6311   9.0599   7.2511  -2.9654  15.0330  -2.7267   8.7870   5.4603
+#>    2.6643   2.5921  -5.4289  -2.4073   2.9424   4.9401  14.6705   8.6155
+#>    4.0140   7.6933   5.0783   1.6056   6.1860   3.1892   4.9573 -10.5108
+#>    5.5388  -9.8183  -4.7116 -15.2184  -8.2728 -13.3160  -1.7169   3.7088
+#>   -0.1177   4.7125  -7.9725   2.1683  -3.0102   4.6499  10.7717  -2.0351
+#>    5.2081  -3.4989   5.9486   4.2025   3.3391  -0.7537  -2.6627  -9.4868
+#>    7.5867   0.7116   2.4756   1.1903   4.8731   0.0058 -12.6252  -2.8911
+#>   -5.0843   0.7840   1.9415  -3.1917   4.1606   8.4567  -0.1356   2.3008
+#>   -0.3319   6.3024  -6.5113  -2.4423   1.5237  -1.3574  -6.2694   1.5541
+#>    4.4886   6.0935  -7.2428  15.6083  10.4199  -5.8588 -14.6442  -5.4900
+#>   -0.3305  -5.6454   7.4324   2.3234  11.2828  21.6849  11.9538  -5.7894
+#>   -9.8699   9.4737   3.7661   2.9901   3.5069   1.6304 -18.5274   2.8883
+#>  -11.4057  -3.4616  -2.0560  -2.5238   6.0255  14.8701   7.9306  -0.4151
+#>   -1.1735  -6.2336  -4.4946  -2.2977  -5.0078   1.5601   8.7399   1.6781
+#>    2.0795  14.5577   0.0213  -2.3282  -1.4510  -0.9494  -0.6021  -5.8925
+#>    7.9549  -0.6925   3.3029   2.2754  -7.2076  -3.0433   0.4470  -5.6953
+#>   -7.6109   2.3223  -8.8471   8.0376  -7.0846   7.4322   0.8175  -3.9597
+#>    9.9533   9.2735  -1.7063   0.1567  14.3063   0.6450  10.3923   3.4195
+#>   -4.5897   0.4865   4.5298  -9.0790 -10.4982  -6.5221  11.7866   2.2506
+#>    0.1368  -8.7985 -11.9200   0.2101 -11.3728   1.7139   2.6160  -5.5503
+#>   -6.6139   6.6750  -2.4265   5.9767  -1.6100   2.0475  -5.2217  -8.7147
 #> 
 #> (6,.,.) = 
-#>  Columns 1 to 8   7.2243   8.3392  -1.3849  -3.9614  -2.4271 -21.3446  -6.4720  -8.3673
-#>    4.5975  -3.0668   0.3427 -11.5763   6.4074   0.9656   7.8276   6.9289
-#>   -1.3308  -6.9574 -10.1657  -4.7257   2.6630  -3.6961   4.3472   6.0477
-#>    5.5895  -2.4206   8.7348   7.3996  -7.2864  17.2307  -4.4007  -8.8312
-#>    8.1620  -1.6408  -7.1717  -5.5062   5.5247   1.3972  -9.0990   1.6231
-#>   11.3486   4.4702   4.3114   8.5954  -2.8606  -2.3586  -3.0956   3.8136
-#>    0.6174 -10.7279  10.3699  -0.2084  -7.2189   6.3811   7.4373  -5.8331
-#>    4.2271   7.9513  -2.5008  -5.1251  18.8871  -6.0456  -9.1673   8.8651
-#>   13.8916   2.3057  -4.5864   4.5505  -2.0082  -2.5087   0.9130  -5.7247
-#>   -0.8457  -5.6887  -6.4370  14.4166  -2.5539   6.7175   3.5127  -4.8348
-#>    4.2450  -0.7323  -4.0768 -10.9424  15.4770 -13.5984  -3.0378   3.8981
-#>   -3.0415   0.1284  -4.0245  14.9781 -12.0422   2.1056   3.5895   1.7076
-#>   -6.8926  -0.9696   8.6010   1.8853  -8.9117   8.2546   4.2719  -6.5448
-#>   -0.2702   9.9633  13.8592  -7.1001  -0.2188 -10.0001 -13.6396   5.4360
-#>    5.0542  -8.5357  -1.4633  13.0656   0.2974  -2.0189  16.9817  -8.3320
-#>   -3.1462   6.0385  -0.4890  -5.6040  -0.0464   2.3662  -5.6113   3.7265
-#>    2.3273  -0.7567   4.0286   0.0487   6.6288   7.2548  -5.5849  -1.8846
-#>    2.4761  -8.6424   9.6763  13.4918  -8.8691  -7.4611   6.6871   4.9654
-#>   -4.5020   8.7862  -9.5270  -3.2915  16.6043  -4.4464  -4.8521   2.0281
-#>    5.3709   9.0742  -0.1652   0.2802   5.4078 -10.7173   8.5004  -0.5599
-#>    1.3871   2.2028  -6.8016   7.5116   0.0626  -3.4792   4.5549   4.6654
-#>    4.8865   7.4633   2.3566  -2.2726   4.8016  -0.7082  -3.7235   6.7077
-#>   10.7146  -6.8378  -2.8024   9.2662   4.8256  -0.8213   4.1914  -3.8058
-#>   -7.5799   4.1754   8.7691  16.6743  -7.9259  -0.3989   8.3513 -11.5092
-#>   -0.1406  -7.8099 -12.4845  11.8588   6.0273  -7.4990   1.4426   0.4183
-#>    2.9798   5.2181  -9.0369  -0.1315   3.9919 -12.7542  -6.8516  -1.9525
-#>    0.9739   0.1558   9.1333   4.2886  -5.1501  17.3377  -2.2212  11.3604
-#>  -11.1918  -2.9488   7.4725  -1.0213   4.8964  -1.2393  -6.3039   0.7197
-#>    8.7943   7.6386  -3.0797  -1.4985  13.1746  -6.3380   5.8917  -0.8362
-#>   -0.2607  -8.8499   9.9483   1.3202   4.7826  -3.1972  -4.9586  -4.9227
-#>    7.2917  -8.7727   5.4313  17.7468   2.7473  -4.0644  -8.2797  -8.8895
-#>   -0.4174   3.1143  -0.2159   5.9918  -2.2373  -0.7149   4.2587  -1.6637
-#>    5.2812  -2.3961   0.0392   6.4498 -13.8805 -15.2444   6.3047  -6.9894
+#>  Columns 1 to 8   4.9643  11.6540  -2.9674  -8.4480  -5.2572   9.7754  -1.1589  -0.1752
+#>   -5.8853  -1.3620   2.0815   2.8105  -1.8144  -7.6909   7.0140   0.6261
+#>   -1.1412   6.5285   2.8428  -0.8144  -3.6057  -1.6444  -2.6238  -3.7695
+#>   -6.7091  -3.7541   0.8434  -0.9902  -0.8535  -4.1227  -1.9138   4.1991
+#>   12.4982  -3.3525 -11.3399   1.8386  -5.2578  -0.0574   3.7135  -1.1334
+#>   -6.8838   3.0344  -0.4220   1.3971   1.0182  -0.7255   2.8937   1.7662
+#>  -17.6761   2.9822   6.0978  -2.3869   0.3980   4.5247  -4.5784  -6.4718
+#>   10.9750  -7.9185  -1.5992   3.2146   8.4577  -1.5642  -2.9899   6.5457
+#>    1.4073 -17.0348 -20.3213   0.0156   4.8215  -1.3920   2.3412   2.1156
+#>    1.0515  -8.9364  12.1244   3.2727  -3.7608  -5.6263   2.1407  -4.9717
+#>   18.2835   1.9000  -2.8328  -7.1934   2.3920   9.4755   2.3911   8.1935
+#>   11.7145  -2.0491  -1.3463 -16.0241  -3.6619  11.3029   5.0877   3.8353
+#>   -8.5743   7.7737  -9.8040   2.4682  -7.3646   1.7419  -8.5698  -2.7935
+#>    2.3935 -13.4116   2.1091   7.2845   0.4879  -5.8124  -6.3251  -8.7806
+#>   -5.9028  -0.2727 -13.6060  -4.1157  -5.9458   2.6326  -4.3760   4.3805
+#>    7.9992   0.0014  -4.2887   8.9887  10.2583  -4.2636   1.1477   6.7995
+#>   -4.0028  -1.6544  11.6460 -16.1031  -3.0375   3.8146   1.6839   4.5914
+#>   -7.3443  -4.5123 -11.0934   3.7476   4.5815   6.2037  -4.5460  -2.3024
+#>    0.6555   1.5199  -4.2085   9.5304   6.5171  -6.9942   2.0877   1.3464
+#>  -13.1823  -3.1720   4.0616   2.5459  -2.5710  -3.8787  -8.6574   1.9016
+#>   -0.2266  -7.4780   5.9274   1.9775   0.4186   1.7564   2.7752  -7.7965
+#>   -6.9900  -0.4437   1.8363  -1.8641  -8.5694  -8.5238  -6.2230  -1.5640
+#>   -4.1086   1.6125  -4.2173 -11.7851  -0.2085  11.9441  -0.1282   2.3233
+#>    0.5848  -4.9375  -7.9527   0.5647  -4.1062   6.8245  -4.9902  -3.6350
+#>   -3.3431  -7.9958  -3.5205  11.3099   3.0815  -6.2839  -5.7426  -1.8254
+#>   -2.3275   1.2306   6.9384   2.6011  -5.6683   2.8506   4.1322  -6.2968
+#>   -6.3331   4.0115  -0.7671   8.3992  -6.1710   3.9180   5.6787  -6.4543
+#>   -8.9433   2.7003  -6.2055   6.7646  -1.8495   0.8824   2.5887   2.5353
+#>    7.1492  -3.1850   4.6098  -6.6905  -6.2035  -4.2005  -1.5706   1.1120
+#>    3.0364  -6.0301 -19.7268   4.7421   7.2392  15.4236  -0.8455  -1.1945
+#>    3.0844  -4.0971  -3.1498   9.3190  -0.7303  -3.3551   2.1204   0.4835
+#>    9.0847  -8.7505 -10.4133   0.7508  10.7469  -1.6424  -2.5937   9.1267
+#>    5.4801  -6.6073 -11.3375  -1.5280  -6.3908   7.1243   5.5544  -3.7556
 #> 
-#> Columns 9 to 16   1.3572  -9.2639  -3.6357   7.5815  16.4982  -5.5324   3.9752  -4.3839
-#>    7.2428  -7.1613   0.4689   3.8176  -2.4002   0.4925   1.8304   2.1115
-#>    0.6485  -3.9916   1.7729   0.7433  -0.5789  -1.0123  -3.7413   5.1510
-#>   -3.0748   6.1491   2.2909  -3.0659  12.8628  -2.2961  22.6958  -0.5127
-#>    6.3820  -2.0064   7.3224   7.8251   1.0473  -8.0397 -10.5067   0.9349
-#>  -11.4967   3.2232   8.5371  10.9801   9.2796  -4.1002  -3.6480   1.2015
-#>   -1.4213   2.9341  -0.9396  10.2263   0.6507  -0.9364   5.5842  -1.1230
-#>    4.6090  -6.3211   9.4724  12.5845  -1.7015   4.1189 -12.2793  -7.4774
-#>   -5.4928  -0.5904  -0.2987  -0.3799   9.6282   4.2732 -10.4704  13.7935
-#>    1.5482  -0.3901   3.1564  -7.1711   4.9637  -5.9584   1.0225  -0.7327
-#>    0.7841 -21.3481  19.5244   5.3517  -6.1240   3.0176  -2.3114   1.9763
-#>    8.1032   2.8423   0.5301   2.2284  -9.0271   3.6010  -9.2538  -4.2355
-#>   -1.7511   8.3882   0.8187   4.0027  -5.3239   4.3685   3.4672 -10.8329
-#>    0.5106   3.5901   1.4955   0.8682   0.7976  -5.2305  -5.9664  -6.8258
-#>   -9.1375   0.9898  16.0165  -7.1575   0.5436   1.2575 -14.7935  -1.9664
-#>    5.6937  -3.7379  -6.6352   3.8704   8.8477   6.6741  17.8161  -0.2798
-#>   -2.6651   4.0551   7.2325   4.8498  -4.1348 -10.7248   5.0212  -7.8985
-#>   -1.6667  12.8498   8.3556   4.0415  -9.2053   1.0740  12.3649  -3.1267
-#>    9.4538  -7.2539   0.2545  14.1017  -0.7963  14.3931 -10.0916   2.4066
-#>  -12.3760   6.1386   5.3391  -9.2521  -9.7553   8.1339  -3.7875 -10.5115
-#>   -3.7960   5.5098   4.1127   3.2031  -3.0166  -2.5891 -10.9013   5.1421
-#>    0.8645 -10.0569   1.1566   4.1279   9.3596  -3.6824   4.2446  -8.3089
-#>  -10.0649 -10.2554  -4.7482 -15.6579 -10.9623  -1.0678  -0.2935   6.8447
-#>   -6.6380   4.3850   0.8957 -11.3851   1.7734   9.1456  -6.6717  -4.7344
-#>   -4.5926   2.4124  11.3638 -13.4111  -0.5375  -0.5407   6.2940   4.6291
-#>  -12.7819   0.4595   0.4682   4.7299   6.6059  -2.4985  -4.2601  -3.3678
-#>   11.4470  -5.6345  10.5911   5.3314   7.6042  -2.4948   7.9147  -5.3031
-#>   -3.2883   6.0255  -3.3904  -6.2489   0.7011  -4.9232  -6.4242 -15.4514
-#>   -0.8515  -8.9004   8.9762   7.3365  -1.6781   1.1573  -1.3595   0.8473
-#>  -10.0676  -4.2501  -4.2951   0.9042  11.0537  -6.8604  -2.8014  -8.3547
-#>   -0.3443   1.6298   2.3734   5.8460  -2.7301  -1.4867  -2.6336   2.5605
-#>   -1.5731   5.2042  -6.0489  -3.9748  -3.5646   4.9206   4.5472  -0.7470
-#>   -4.9682  -0.6410  -2.7100 -11.2327   7.9666   0.3811   5.3322   9.1792
+#> Columns 9 to 16  12.6808   0.5330 -12.8937  -2.7859  15.5644   0.9624  -6.2626  11.9900
+#>   -0.1552   7.7801 -19.0119  -7.2924   1.6066  -5.5839  -7.1300   3.2822
+#>   11.9017   0.7113   6.2135  -4.5541   7.0030  -3.4481  -2.9240  -6.0002
+#>   -9.9037   2.1092 -10.0918  -7.9957  -1.3933   2.6607  -0.8607   7.0772
+#>   -1.2853   3.1144  -2.8884   7.5204   1.1099   6.9568  -1.2871  -1.0250
+#>    8.2587  -7.8240   2.3218  13.2488  -8.0419  -1.7403  -2.0949   3.9785
+#>   -1.3944  12.9996  -5.2578   7.0092 -14.0327   2.5392  -9.2376   1.3824
+#>  -11.2618  -4.2080  11.3066 -17.6130  12.5172  -6.8254  -0.0752  -4.1533
+#>   -0.3817 -10.8712 -22.6696  -7.9308  -7.5825  -2.3434  13.3477  -5.4850
+#>   -0.9359  -1.8684  13.2252   0.1293  -3.2058   3.6114  -1.6075  -4.8637
+#>    5.8733 -13.6607  -2.0309  -2.4843   3.5198  -0.6333   5.1845  -1.7883
+#>    3.3861  -3.3107   5.9968  -9.4100  -7.8720  -1.3470  -1.2180  -3.4692
+#>   14.5247 -13.5109   9.5149  -8.8031  -0.0814  -9.8632   5.8781   8.4470
+#>   -4.4439 -10.0945  -1.6390  -0.6935  14.8900   3.2884   1.3039  12.0052
+#>   -3.5714  -8.7955   7.3329  -0.1114   4.0742   1.3243  -1.1030  -3.6246
+#>   -0.4619  -1.9217  15.9153  -1.9339   5.3932 -15.1013  13.9770  -7.1978
+#>    8.7619   3.1970   4.5950  -0.9813   1.7180   0.1048  -7.3949   3.8046
+#>   -4.4874  -4.3989   2.0573   3.1280   1.9856  -6.4502  -9.7534  -1.9015
+#>    0.5677  -4.4264   3.5549  -8.7555   4.8288  -2.4239  -7.1515  -3.0211
+#>    4.8010  -0.3816  -1.5811   5.5144  -8.4569   0.1808   8.1338   6.8291
+#>    1.5628  12.1958 -12.9617 -10.5019  -2.1109   7.1425  -6.9045  -0.4025
+#>    3.4785   5.1717   4.8155   5.7336  -8.1549  -1.8475  -1.4936   6.4870
+#>    5.9224   4.6069   1.4809  -2.4848  -1.5496  -5.0209  -4.0053 -18.6442
+#>   -0.7273   5.0900  -9.5803   4.0261   3.5100   5.8257  -6.8150  -2.2200
+#>   -3.2454  18.3768   5.1196   1.3036  -3.4961   0.8341  -5.4575 -15.2099
+#>    3.1255   5.8000  -2.8170   8.2330  -0.8312  -1.1323  -1.2057  -2.6477
+#>    1.2513  -4.9404  -8.6827   0.3445   1.3802  -1.3472  -0.7938   4.0705
+#>    8.3097  -2.8209   3.6688   8.4533  -5.4572  -3.4359  -0.3630  -1.2266
+#>  -16.7214   7.5560  -0.8280   1.6409  -2.4671   5.9001   7.4502  -5.8646
+#>    8.3846 -18.6311  -4.9897  -1.7705   8.6169  -1.8708  -6.5290  -2.3889
+#>    4.4340  -6.3649   9.0433  -8.1999  14.2589 -11.0242  16.2992  -3.4696
+#>  -10.9789  -3.5818  11.3362  -2.9983  -9.1604   3.9324  10.5847 -12.7304
+#>  -17.8350  10.2712  -9.4794  -9.9219  -1.2685  10.4942  -6.4026  -5.0820
 #> 
-#> Columns 17 to 24  -1.9419  -5.7336  11.9942   4.7601  -1.1021   0.1526  -2.7318   0.4802
-#>    9.9439  -9.4554   1.3996   1.3670   7.8745   4.9322  10.9325  -4.1276
-#>    5.9729  -0.2948  -3.4857  -6.6527   4.3021  -4.2746   4.1884   0.5543
-#>   -3.9633   7.9015  -8.6306   5.5853   0.2484   8.5325  -5.4779  17.4002
-#>   -2.2753   4.9165  -1.4233  -4.3736  -0.2018   2.6888   0.5883   1.5900
-#>    2.7971  -0.4647   0.6217 -12.6645   0.1814  -2.6693  -2.5864   1.4766
-#>    0.1495   3.5149   0.8440  -7.1651   2.7019   5.8067   6.0512   3.3628
-#>    7.3606   0.4496   9.6615  -1.6973  -6.6609 -11.4058  -6.6486 -12.5350
-#>   -5.2156   0.2924  -5.0834 -16.2553   0.3100  -2.5564  11.9973   4.2131
-#>   -5.6752 -10.3026   4.2528   2.2497   4.1874  14.9477  -2.5128   1.4847
-#>   13.0678   5.6539  -7.4028   0.5487   4.6282   0.2611   9.8012  -4.0386
-#>    2.3555  -2.8775   2.5253  -0.3183  -9.4194   7.1053  -2.6442  -2.2717
-#>    3.3136   7.7977  -4.0650   3.8109   3.1865 -17.8396 -12.4813  -4.0486
-#>   -0.3119  -4.4636  -3.1033   1.1887 -11.4041   3.6654   6.1167   4.8608
-#>   19.5184  -7.2677   3.1874  -9.7602   3.3361  -7.0507  -3.6390   7.9522
-#>    0.9450   1.6529  -6.6908  18.1123   3.8180   5.7401   4.9581  -0.9846
-#>    1.0836  12.0571  -5.5106   1.3248   1.5558 -10.3360   0.0579  -2.6499
-#>    3.1612   5.1826  -1.1608   6.2212  -3.1358  -8.7089  -7.4634  -0.5527
-#>   -8.9989  -1.6155   7.4303   6.5009  10.9928  -2.6357   0.0780 -16.1420
-#>    2.0072   0.7219   0.9024  -0.4102   0.1720 -11.4900  -3.7621  -2.7232
-#>   -8.0794  -2.6456   1.0228  -7.7458  -3.6464   0.9997  -3.6155  -3.4252
-#>   14.5272  -0.7068   4.7087   4.9182  -4.6983   4.6151   1.5049   1.8046
-#>   -6.9474  -3.9977  -9.7340  -8.4386   5.1468   1.1116   0.7188   3.6614
-#>  -16.7842  -2.0017   0.0629   1.1708   4.9418 -14.1091  -8.3668  -3.3363
-#>    2.5515  -3.3029   1.2168   3.5226   9.0527   6.2613   1.6803   3.9490
-#>    5.5269   3.0326   5.7349   7.2353  -2.8270  -4.0605  -3.7755   0.9408
-#>   -6.9021  16.4827 -12.1811  -1.6658   1.7088  -5.2144  -4.3741  -6.3384
-#>   -2.3419   6.4386  -2.0155  13.3472   2.9459  -7.8724 -11.8473  -4.2770
-#>    0.5906   1.5462   4.4134  -0.2814   2.9043   1.3267   6.0800  -1.0883
-#>    1.9978  -4.2010   2.7048   2.1470  -0.1110  -1.9612 -11.0203 -14.5765
-#>   -8.3450  -2.3093   1.0679  -7.3374  -6.5944   4.6366   3.8195  -4.5595
-#>   -7.4569   0.4207  -2.8997  -6.5984  -4.0737 -18.4086  -7.7615  -3.0192
-#>   -2.1727 -12.4999   7.7563  -0.1128   6.9967  17.2992   7.5060   4.3193
+#> Columns 17 to 24   1.8320  -3.1759  -1.1398   2.5388 -13.5057   1.9528  13.1616   0.8494
+#>    1.8656  -3.4356  -4.0238   3.0397   0.7453  -6.3322  -1.2731  -6.4666
+#>    3.1645  -5.7271   0.7629   9.0021  -2.9395   5.8612  11.9598  -2.3970
+#>   -2.0722   6.3475  -3.3564  -8.6790   7.7533   5.1905  -1.1516   5.1052
+#>    4.1972 -10.7616   7.7671   5.5924 -14.5078   2.6350  -2.6565   5.7551
+#>   -3.9981  -1.9179   0.8381  11.2294   5.2864   1.7804   3.6303   9.0391
+#>   -2.8847 -11.2142  -5.0437   4.9487  10.5897  -9.5995  -3.9740   0.6665
+#>    6.6208   6.5008  -3.9683 -11.7661  -9.3548  -6.6910 -17.1924 -12.7470
+#>   -3.2187   8.7711  -0.0417  11.7885  12.8211   7.0743 -10.0382  12.7762
+#>    5.1791   6.2349  -0.0541  -3.5075  -0.3101  -9.7404   1.6222  -6.4921
+#>    2.5555   6.4526  -7.6652 -10.2250 -11.1258  10.9575   1.1775   4.6364
+#>   -6.6211  -4.1664  -4.2235  -2.0842  -5.1163  -2.5953   3.2280  -6.3467
+#>   -5.4063  -4.8193   6.9550  -0.4367  -9.9244 -11.2391  -6.3830   5.4532
+#>   11.0889   1.1156  -8.4720  -7.0509  -8.0857  -4.7256  -9.9408  -0.6579
+#>   -5.8587   1.5096   2.5751  11.6155  -1.6031   0.2888  10.7469  -0.6830
+#>    6.5458  -0.4497   1.7877  -4.0397   7.0951   4.1959  -1.2999   4.4580
+#>   -5.7290   1.6790   0.7839  -6.0438  10.6346   7.2770  26.1049  11.2897
+#>    4.2910  -8.8323  -1.8377   1.7808   6.2114  10.9089  -6.5466   3.4632
+#>    6.1936   5.0888   5.5520   3.3283  -9.9337  -0.4847   1.1930  -2.1552
+#>   -1.0579  -2.9818   7.2234   9.4281  13.3017  -1.9589  -5.1578   9.1888
+#>    8.7781   5.6805  -1.5421  -0.4016  -5.9968 -14.2629  -3.7544  -1.8125
+#>   -7.7402  -3.1605   7.6911  10.1392  -2.9578  -0.4831  12.3641   4.6766
+#>   -4.3519  -9.2597  -4.8442  -4.2942   9.0184  -1.5517   0.6835   2.6036
+#>   -5.0041   2.5134   5.5086  18.9455   6.1282  11.1234  -0.8727   3.1704
+#>    3.5416  -5.2895   1.0142   5.0341  14.8271   2.0903   2.9948  16.1239
+#>    8.5684   0.9961  -3.9033  -0.5856   1.7722  -2.4203 -11.7632  -4.0131
+#>   11.3016  -7.3752   4.6808  10.2017  -1.1311  -3.0729  -8.2118   7.7834
+#>    5.9561   7.4157   2.6822   2.1982 -14.1028   1.8127   1.8970  -0.1184
+#>   -0.2686   0.0259   2.5236  -2.2401  14.7103  -3.1769  -4.6914   4.0513
+#>   -0.1675  -0.4423  -3.4561   2.6460  -8.6589   1.3691  -8.9794   2.1208
+#>    0.7164  -0.6444   7.9152  -3.0760  -2.1215  -2.8115  -4.8829   6.1151
+#>    2.0674   5.5024   0.9901   0.4501 -13.4113  -2.2804  -4.7955   1.6406
+#>   -2.4607   9.0029  -4.8079   1.3073  -0.5409  13.5237  -1.8930  -7.9569
 #> 
-#> Columns 25 to 32   0.5489  -2.7851   1.2910  -2.2830  10.3267  13.5500   1.8178  -9.0620
-#>   -5.9758  -3.8377  -6.0791  -3.0669  -4.7702   5.1763  -6.1482   8.1660
-#>    2.9291  -6.6904  -2.7593  -3.2061  -6.3909   2.5089  -7.2814   2.2478
-#>   -4.3237  -2.3705   2.0448  -8.5145  -6.7395  -4.7917   1.4278  18.8616
-#>    0.9036  -4.9193  -1.8782   1.3285   2.3220   6.1395   1.8885  -2.8320
-#>   -2.3343  -8.1989   2.6961   9.1824   7.3328   4.9421   3.1049   0.7268
-#>   -4.9143   0.4837   5.9807   1.4539  -0.0714  -3.9909   7.5337   6.3653
-#>   -0.3442   6.1906   1.8804  -0.9160  -0.4821  -3.0456  -0.8775   3.1517
-#>   -1.5952  -0.0023  -7.2607   5.9104   0.8051   8.0313 -10.2251   6.7405
-#>   -2.6411  -3.0006   7.9548  -1.5612   3.7520  10.1731   2.7219  -0.3250
-#>   12.8345   4.2254  -5.2382   0.9249 -11.7240  -6.5759 -12.0975   1.4645
-#>   -4.2454  -0.7754  -3.2727   6.7287  -2.9156  -8.4225   7.2463  -9.1077
-#>   -1.6413 -11.3781   7.5926  10.0934   1.9571  -9.0014   1.8181 -13.9184
-#>   -2.2377  14.4022  -0.3523  -0.0334  -1.3645   8.2180   6.2597   6.9555
-#>   -2.1595 -12.7020   5.5225   8.1006  -5.1287  12.3235  -7.3240   2.6665
-#>   -7.5441  -2.7023  -7.9101  -6.7034   1.2142   0.8901   1.9602  -3.6886
-#>   -1.1957  -9.2353   8.6472   2.5590   9.5425  -0.2925  14.7062   1.1890
-#>  -15.5934  -2.7007  -6.9255   1.8178   5.3152  -5.9453  -8.6526   9.2370
-#>   -6.7564   4.7754  -0.7221  -1.1370   2.2326   2.2628  -1.2350  -9.8540
-#>    1.0357  -0.1774   3.2705   1.8051   2.4900   5.3541  -2.1648   2.5351
-#>   -0.0126   5.2413  -4.6247  -3.5731  -4.6246  -2.1948   9.3360  12.5130
-#>    1.9397   4.2349   2.4305  -1.1732  -1.8129  -3.7480  -8.5892   0.7909
-#>   -2.0414  -0.3399   2.2622   5.8912   5.0537   4.1629  -2.7187  -8.9195
-#>    6.0152  -4.7058  18.0248  13.2227  -1.2188   3.7254  -2.6734  -2.7519
-#>    1.6365  -4.4861  -5.3771   0.5365   2.0207   8.5161  -0.0189   4.6858
-#>    9.8376   0.5626   0.3699   2.3950   7.1727   8.5187   7.6857   6.0384
-#>   -8.6334  -9.1464   8.0587  -0.2189   7.0526   4.0767   7.4329   5.8209
-#>   -0.0165  -5.7914   6.4489   7.2829  13.7562  -5.0777   5.1799 -10.5214
-#>   -3.0783   7.1251 -10.0074  -2.4632  -1.2107  14.3788  -4.4379   4.4232
-#>   -1.1358  -1.6174   4.0920  16.6688  12.7050  -5.0852  -0.8548  -8.4431
-#>   -7.3369   3.8981   1.5434   2.7106   8.9853   0.2032   0.9945  11.2380
-#>    5.3462  -8.5643  -4.6187   9.0935  -6.7361  -5.9773   0.6195 -10.5092
-#>    2.4468  -1.5816  -0.6258   3.5935  -4.3062   0.5268 -13.2230   0.6924
+#> Columns 25 to 32  -0.7563  -3.1377  -1.0834  -2.9332 -10.1355  16.8718   6.0414  -2.7665
+#>   -4.9733   0.3464   0.5792   5.5910   4.2675  -8.5153   1.2330  -5.9161
+#>   -5.1655   3.6488  -3.8651   1.6267   5.8430  -1.6695  -3.6735  -8.8895
+#>   -0.8614  -1.8020  10.4417   9.3554  -2.0547  -1.2789 -10.6759   3.3694
+#>   -3.2737   1.7721 -10.0527   5.2873  -0.4413  -2.3700  11.6751   8.7873
+#>   -1.1394   4.7643   0.0122  -4.4051  -2.8833  -0.8338  -3.8994   1.0279
+#>   -2.0636   2.4231   6.0373  -3.8296  10.7801   1.3824   6.2070   4.4545
+#>   -6.8036  -2.2004   0.0728  -1.3673  -4.0747  -0.7697  -3.6788  -2.8313
+#>    0.5440  -9.7875  -0.6648  17.4862   6.6901  -4.3782  -3.8415 -10.0056
+#>    5.4451   4.1119  -5.7517  -1.1534  -2.4415   3.2575  -4.3561 -12.8561
+#>    6.2678  -7.6002  -1.2815  -5.6933   2.6289   8.6845 -12.2586 -23.6212
+#>   -5.9066   4.5378  -5.7446  -0.1118  -2.3540  10.3066  -4.9900  -8.2678
+#>   -4.3486   6.0296  -0.1091   0.5328  -9.1646  -2.5070   0.0259  -9.7795
+#>    1.3511   2.3896   1.1812  -4.2610  -5.9330   3.0627   6.1465   8.0010
+#>   -6.1905   2.7403   2.3075  -6.5115  15.6600  -1.0810  -7.4869  -6.4436
+#>    1.7964  -0.2026  -1.9205  -9.8444   1.3398  -7.1471   3.0225  -4.9096
+#>    5.1326  -8.3308   2.4599  -6.3199   9.2679  14.6197   0.8064  -6.3736
+#>    1.3317   4.6877   3.8298  -5.5421   7.2200   7.4253  -4.9861  -9.4068
+#>   -0.0105   4.7573  -0.2542  -3.4788  -5.9175  -6.1011  -0.9577  -8.1726
+#>   -4.0756  -0.0195   2.9053  -1.1690  -3.5253  -5.1791  -1.5896 -11.1682
+#>    3.5392  -5.4845  -1.2150   1.6893   1.1187  -2.5950   1.5323  -8.0660
+#>    2.6975  -0.0813  -1.0512   1.2911  -1.3961   9.6188  -0.1213  -0.6774
+#>   -8.0032   0.6810   3.2119  -6.4014   8.0465   2.0252  -0.1155   6.9570
+#>   -5.3503   3.8933  -0.4020   9.4015  -5.8290   4.2362   5.3367  -2.3122
+#>   -1.7339   0.7496  10.9024   1.3516  -4.2970 -16.5161   0.4227  13.8916
+#>    0.1649   0.2497   2.3354  -2.2941  -5.2923  -9.3202   1.1490  -5.3199
+#>   -7.1270   1.9761   9.3285  -4.1921  -6.7225 -11.0163   3.4185  -5.1099
+#>   -6.3438   4.2835  -1.2014  -1.1916   5.0112   1.1191  -2.2677  -2.5989
+#>   -3.4770   0.8742   1.2298  -0.8830   1.1060   3.1815   1.3849   1.8166
+#>    1.3781  -0.3195   1.2158  -8.5691  -4.7141   6.2978   6.7545 -14.0948
+#>   -1.9397  -9.4017   4.5247  -3.9402  -0.7005 -12.1142   6.0034   3.6635
+#>   -1.3631   7.6884  -2.5925  -0.7068   8.4824  -7.9555  -5.5553   4.6373
+#>    1.9530  -3.4722   2.5056  13.4879   2.9922   3.5060  -9.9254   1.3374
 #> 
-#> Columns 33 to 40  -7.6027  -2.2487   2.1900  11.9675  -3.3487  -9.6965  -2.9535  -0.7557
-#>   -3.2157   4.8165   4.5860  -0.2468  -2.9938  -6.0932  -0.5876  13.6491
-#>    4.5272  -2.9818  -2.8203   6.5853  -5.0184   1.7838   0.4984  -1.9432
-#>    1.1660  -3.9935  -1.8290   5.3999   6.6793   2.8560   4.8642   6.5097
-#>   10.5755   3.2100  -3.6359   0.4694  -0.8818 -11.4375  -4.1460 -10.3099
-#>   -5.6870   6.3213   8.4095   4.9459 -11.2726 -14.5157  -7.3464  -8.7629
-#>   -8.5790   5.5107   6.3126  -5.6498   6.9198  -8.4822  -7.7633  -6.6192
-#>    7.5044  13.3505   9.8939  -4.0529  -5.3569 -12.1263  -5.1392 -12.6179
-#>    0.2750  -8.4532  12.4254   1.4368   5.1401  -3.1507  -4.9469  -3.5927
-#>   -8.8588   1.4924  -2.4861 -10.4314   0.3319   5.8148  -4.7300   9.7827
-#>    5.0628   8.7692  -2.4614   2.7285   3.5396   0.4264   5.5486  -0.9388
-#>    7.9110  -0.0486   4.8019  -7.9210   0.9448   3.8109  -2.0076  -0.4768
-#>    4.4348  11.1488  -8.0820   5.5546  -1.5815   2.9275   6.0719   3.2779
-#>   -2.9327  -8.0244  -2.7404   2.2129  -4.0796  -3.0339   3.3700 -13.2439
-#>   11.6239  -6.7035  -7.9736   4.2794   3.0072  -5.7825 -10.7079   2.9405
-#>   -8.3416  -8.4063   0.1600  20.7802  -0.7024  -1.7364   3.3059   7.9594
-#>    3.2505   0.9716  -5.6477   3.5167  -1.7800  -9.7658  -6.5191  -3.1310
-#>   -7.3276  -1.4290   7.9197  -0.0182  -0.8833   1.8832   8.2552   4.7918
-#>    3.2498  -2.9161  12.6821   6.7317  -6.3797   7.1543   7.1523  -0.3207
-#>   -8.8900   7.2855  -0.7633   5.5332   4.4910   0.5948   4.8468  -6.2392
-#>   -0.6436   4.6097   9.0892 -13.1945   4.9483   1.6102  -2.2883  -5.4042
-#>    1.6717  -2.0507  -4.9608  12.4468  -1.3210   4.8892  -0.3961   2.6101
-#>   -0.7270 -17.2108  -5.1492   6.3074  11.6628   1.2956   4.2730  -0.8359
-#>   -5.0182  -5.8871   8.5032  -2.6112  -6.5407  16.0788  -3.7708   8.1343
-#>    3.7324  -8.7810  -2.2099   2.1963  -2.1305  11.4818  -1.9305   0.0411
-#>   -5.2839  -3.4533   1.1692   0.5344  -2.7929  -5.6060  -5.2001  -3.6439
-#>   -2.6511   4.7609  19.7984  -9.0056  -6.3521  -8.7703 -17.5731   3.9446
-#>   -9.1425   2.8850  -4.3467  -1.8818   3.7963   6.1533   3.1709   2.0593
-#>   -4.9947  -8.8462   1.5875   8.7470  -1.5148  -0.5422   3.0933  -0.3313
-#>  -10.4418  15.3400  -3.9069   1.0759   1.3927 -11.4530  -5.4137   1.2727
-#>   -9.3344 -12.7449  22.2623  -4.2399   2.1708  -1.2364  -6.7290  -2.2009
-#>    4.0125   6.1803  -5.5008   4.6972  -5.7827   2.0033   4.9381  -7.6543
-#>  -12.5544  -0.4399   9.3944  -5.7592   5.7587  11.4921   2.9829   7.2321
+#> Columns 33 to 40  -6.1832  -5.5194   5.9813   3.2443  11.8303  -2.3967   7.3920  -6.8471
+#>   -6.6810   4.6580 -14.9449  -6.4745   3.3112  -0.3352   4.9441   4.0487
+#>   -5.2918  -0.7715  -5.5605   2.8274   8.0645   8.4500  -3.4707   0.2175
+#>   -0.9468  -4.1640  -1.4569  -8.5820   1.2725   7.2586   8.7897   6.7605
+#>    5.1701   4.4470  -3.2844   5.3602  13.4440  -7.0952   0.3988  -8.6702
+#>   -6.1545  -1.0399  -6.9969  -7.6437   0.3062   7.8289   0.9772  -6.0135
+#>   -0.9278  -3.9188  -6.6503  -7.6383 -10.1762   1.5280 -16.5555   3.8182
+#>   -2.2838 -16.5922  -1.5669  -2.7178   0.2448   3.7886  -0.1248  20.3742
+#>    6.0117  12.0355   4.2730  12.5069 -11.8570  -5.2133  16.8669   8.0135
+#>    6.6300   8.3672  -6.1728   5.9324  -4.3560   1.3924   1.4100   2.8521
+#>    3.2527   0.4953   0.3846  -8.6821  12.1525  16.1244   1.2107   9.5278
+#>    6.4177 -14.2299  -8.8850   1.6650  -9.9103  -3.3309  -1.4360   6.3248
+#>   -0.0775  -4.1022 -11.8284   9.0366  10.0805   0.9275  -8.2746  23.4432
+#>    3.1072  11.8098   0.1838  -6.6031   3.0782  -4.0114   1.0860  15.9008
+#>    5.3532   3.2763   8.9540  -8.1582   1.8324   2.9592  -7.4635  -1.4551
+#>    0.1688  -4.6066   7.4812  13.9261 -12.0265  -7.4429   0.1429  12.6227
+#>    9.9611   5.8392   5.3770  -1.3641   3.0233  -3.5825  -8.9927  -3.0129
+#>   -6.4104   2.7867  -5.6676  -3.6038   8.3032  -4.6934 -14.7684  -4.8762
+#>   -7.3044   1.7220   3.8237  -6.7114   7.1139   1.3514   2.1974   4.4814
+#>   -5.4253   4.5850  -8.8358  -1.8651  -2.0532   8.8240   5.6605   2.2542
+#>  -13.1664  -1.7792  -7.4426   2.6703  -2.2109  -3.0612  12.6064  16.2590
+#>    4.8702  -1.3904   8.8560  -4.9722 -14.8709   5.0430   9.8667  -6.5478
+#>    0.4754  -4.9684  -2.3022   0.5134 -15.5100   4.9844 -14.2375  -5.6916
+#>   -6.6430  -2.4035  -5.0891  12.8923  12.1796 -10.2156  -6.2614   4.3246
+#>   -4.8086   8.8537   1.2531  -6.1469  -9.8700 -10.7666  -6.4972   0.1212
+#>   -5.4890   7.1199 -12.8535   1.5374  -0.2666   4.2801   3.3113  -2.0686
+#>  -13.5307   3.0859  -3.1014   2.7386   9.9933   9.1629  -3.5990   3.3388
+#>   10.5478   6.6070  -1.2144  -9.4319  -0.3269   4.4826   5.0256  -9.0302
+#>   -6.8853   3.6933   1.3049  -6.6004   2.2866   1.3105  -2.4859   1.5785
+#>   -5.1796   1.7918  -1.6358  17.3760   5.8859  -2.6087  -6.4701   2.9131
+#>    4.9040  -9.5469   9.3257   3.9669  -3.0699   5.8389  -5.0715   8.1503
+#>   11.1543  -2.5340   3.2666   7.3285  -3.8323  -6.3271   9.6846   5.1109
+#>    2.1329   2.8956   9.4863  -6.8131  -1.6180   2.1172   0.5781  -9.9176
 #> 
-#> Columns 41 to 48  -5.2301  -8.3919   0.4745  12.1680   8.2690   0.7700  -1.3986 -15.0949
-#>   -3.8473   5.5070   8.4038  13.3590   0.2387  -7.7313  -1.4964 -15.2668
-#>   -0.8133   0.6185   8.4404   1.8471  -7.6965   9.7787  11.1924   0.3181
-#>   13.5075   5.0517 -10.7942 -10.8533   0.4656  -0.7475   7.3318   0.0555
-#>   -9.7146  -4.7262   7.8232   2.9174  -0.7198  -1.2243   5.9117  -0.7655
-#>   -5.3252   9.9646  10.1692  13.3378   3.5901   1.3333  -4.9229  -6.5326
-#>    2.8730  -7.6642 -10.3643  -0.7128  11.7228   2.5994  -8.3758  -8.6394
-#>  -23.6110  -2.7063   6.4992  -3.1165  -1.9089 -10.5941  -7.0350  -4.2015
-#>   -7.3903  -3.7530  -0.4041  14.3706  10.5003   6.9389   4.9652   4.0909
-#>   13.5233  16.0055  10.8386   2.8506  -2.9455  -4.1969  -7.9186   6.8712
-#>  -12.5978   2.9476  19.6807   1.1032  -4.8529   2.4183  -1.7873 -10.5665
-#>    7.4521   5.0772  -0.3968  -5.8041   3.3329 -11.9797  -6.1260   3.4263
-#>    3.5739  -2.4945   1.2146 -18.5102   2.0377   0.5358   3.7605  -8.1046
-#>   -5.1213  -5.6798 -18.7280  18.6834   3.4769  10.0587   9.7840   1.1688
-#>   -5.1982   2.3021  -9.6020   1.8456  14.4900  -4.3919  -5.9182 -10.1780
-#>    1.4465  -3.4884   0.3757  15.2833  -4.7847  -6.8593   0.2916  -1.5028
-#>  -10.1953  -5.9133  -0.5997  -6.4112   6.6287   3.9975  -8.3894  -9.4420
-#>   11.0555  12.4552  -4.2484  -8.0617  -8.0408  -0.1720  -0.0028  -3.1412
-#>   -2.9399   4.6805  12.8330   6.3452  -5.2365  -4.5358  -8.0995   2.9361
-#>  -11.0020   5.1860  -9.4381   1.0695  -4.4030   5.9915 -11.9550   5.6597
-#>   -1.6070   4.5199   6.4365 -12.4429  -1.7472  -3.1261   4.8034   6.6773
-#>   -4.5037  -9.0047  -2.9427   4.7468  -3.2937   2.3801   5.7475   0.3027
-#>   10.0648  -1.4978  -4.0922   2.3945  -2.6983   4.7756  -5.0650  -9.1463
-#>    4.9168   2.3982  -0.2161 -12.9496   2.4841  20.3679  -3.3443   3.2126
-#>   -1.2465   0.5778   5.6310  -9.3660  -2.0670   3.2506  -6.5215  -5.0134
-#>  -13.8826  -3.2358  -5.5231  10.6802   1.2777   2.4405   2.5325  -0.5911
-#>   -3.6412  -2.0257  10.3329  -9.5415  15.9104  -2.6404  -5.0439 -11.1058
-#>    6.9971   2.9543   1.2786  -5.9788  -1.6136   3.2090   0.8348  -4.1627
-#>   -8.3324 -14.0545  -3.3994   3.9126   0.4258   4.7417  -1.5262  -6.5741
-#>   -3.9503  -8.7550  10.9331   7.1293  -0.4960  -5.8905  -4.1936  -7.0265
-#>    1.8810  -5.1962  -6.0232  -0.7553   3.0264  -6.1715 -10.1147  -6.3372
-#>   -4.4186  -1.0833  -7.4865  -0.2799  -8.6644   8.1144   3.3730   0.9093
-#>    5.1813  12.1565   5.8088   7.9778   2.0510  -3.9625  -5.2565  -4.9910
+#> Columns 41 to 48  -7.9284   4.9873   4.1779  -6.1162  -3.6498  -2.8251   4.2869   3.7481
+#>    7.4979  -2.6753  -5.0769   1.8825   2.2537  -3.3097   8.7565  -1.7117
+#>    5.5308  16.7922   0.2933  -6.3333  -1.1939  -1.7231  -1.7436  -1.6388
+#>    2.7287  -1.7296   3.9157   2.4734  -0.3337  -0.3143  -4.0392  -6.0396
+#>   -5.0149   1.1354  -5.4459  -6.2580   0.8626  12.8061   0.0037   6.0455
+#>  -14.2236  -5.8951  10.3078  -3.4013  -3.7706  -8.4288  -8.2334  -6.5647
+#>    1.3883  -9.6861  -8.7980  11.1817  -8.9138  -1.5163   4.6568   8.6941
+#>    6.2352   9.7211   2.6712   6.4424  -7.0919  -2.6066  -5.8110   1.1625
+#>   10.3115 -10.1433  -2.2922   5.6314   1.9124   5.2211  -0.6296  -9.4591
+#>    1.3935  -5.8105  14.4314  -8.8372   4.8992  -1.9700  -1.1853  -1.3094
+#>    9.3849   6.3331  -5.9613  -1.4827  -6.5452   3.6386  -5.1805  -9.5687
+#>  -11.9346  -1.2591   3.4297  -3.0498 -10.3658  -0.7680  -0.3425   7.4374
+#>    2.8123 -10.4788   0.6407   9.1444  -4.2235  -9.3844   6.6531  -1.0592
+#>    3.7813  -5.5850   0.4505   6.3826   2.1851  -7.7569  14.8819  11.0986
+#>   -9.2318 -14.0717   2.8687   6.7881 -23.6733  -2.2515  -8.3746  15.4845
+#>    3.3173  12.8945   1.9848   5.9713  -8.0577  14.2965   3.3777  -2.9870
+#>   -1.3728 -11.5388  -0.5859  -5.7856   6.0864   1.7767  -6.1268  11.7812
+#>    8.0477   3.2329  -3.9264   3.0232   1.9102 -11.9499   4.4760  -6.6128
+#>   -1.8114   7.0128  -1.0925  -2.4541   1.5321  -4.8009  -8.8199  -3.8976
+#>   12.8671 -10.3419   0.9053  -1.1208   3.1236  -2.3496   6.1610 -10.7058
+#>    3.6526   5.5662   6.3228   5.2560  -3.3483  -2.6746   5.1082   4.0885
+#>  -16.7589  -4.8014   5.0005 -11.4625 -12.0761  11.7755  -7.3408   3.9676
+#>   -2.7970  -3.5067  -9.6463   2.9909   3.1489  -9.3044   4.1732  -4.4003
+#>   -3.6463  -2.6350  -0.8322  15.4660  -7.5054  -4.4430  -8.8983  -7.1481
+#>    6.9156  -5.6635 -18.2293  -2.0972   8.0925   3.2215  -0.3246  -8.5379
+#>    6.6599  -0.1653   7.0766  -6.8543   7.7798  -6.0151  12.1312 -11.5232
+#>    4.2149  -1.2537   8.6473  -6.1466   2.5240  -3.1094  -2.6999  -1.8230
+#>   -6.6593   1.3472   2.6683  -6.7798  -3.1435   9.7068   1.5972  -1.4003
+#>   -2.5564  -0.9439   4.1342  -0.1172   5.3395  -4.2704  -2.6884  -5.3049
+#>   -1.3948  -2.7909  -0.5867   6.2574 -11.6177  -7.3170  -2.7714  -0.0627
+#>    0.8082   5.2609  -3.2268   9.1061  -1.3636  -1.6273   4.1140   1.9453
+#>   -0.5227   2.1425  13.7563   4.0745   3.7256  15.3566   8.3517   4.5250
+#>   -1.5137  -0.4835  -7.8734  -9.9395   3.9390   5.2584  -9.2616  -4.4245
 #> 
 #> (7,.,.) = 
-#>  Columns 1 to 8 -12.5637  -2.5921   7.1782   7.8186  12.1642  -5.9426 -14.8531   1.3177
-#>    2.1248  -5.9844  -4.4564  -4.5092   6.7187   4.8098  -7.4867  -1.2874
-#>    1.5000 -12.1292   8.3217  -5.3953  -9.2065  -5.9762  -2.2805   7.4073
-#>    0.2294 -16.4882   3.5766   2.2640  -5.6411  11.0628  -1.8238   1.7027
-#>    7.3974  -2.1059   0.7667  -1.1699 -13.5659   0.0552  -7.4127  -2.4198
-#>   -5.3633  -3.2434   9.4996   0.8949  -7.2516   3.3304  -2.1083   6.2119
-#>   -6.0371   0.9534   5.7248   0.9747   3.8753  -9.6830   1.2595   4.4478
-#>    4.3532   4.1490   1.4650   5.5049  -6.8201   0.3387   1.8417  -2.3344
-#>    5.4369  -3.8337 -12.7340   3.3148 -13.8978 -11.2822  -6.7416  -6.7818
-#>    6.7436  -0.0121   3.2164  -8.8355   2.4412   8.5234   0.2344 -11.6883
-#>    4.8147 -10.3121  -5.8911   7.6287  -3.2987  -9.7721   2.5174   7.5296
-#>    2.0386   4.1301  -1.2932   6.6238   0.4111  -3.3190   8.2287  -0.0189
-#>    0.6954   1.3338   5.9806   4.5208  14.4822  -2.7630  -2.8924  10.0675
-#>   -8.4399  -1.8747   4.0748  -3.2390  -3.1167  11.0646   0.7673   2.3934
-#>    6.4320  -4.3108   4.2115   5.2095  -3.3331  -4.7085   0.3097  10.0811
-#>    1.0512  -2.9661   2.0181   5.1407   3.9240   1.8381  -7.2879   3.1933
-#>    6.5491  -4.1914  -1.2071   4.5877   7.1293  -2.2568  -3.5221   0.0526
-#>    2.4886   2.6609   0.6132  -5.0607   8.6768   0.5913   3.3273   4.5410
-#>   -4.4869   4.9565  -0.4279   3.9000  -4.1421  -4.8384 -11.0614  -1.9895
-#>    4.3772  -0.2396   3.2004   3.2212   3.7800   5.5176  -7.6825  -2.1287
-#>   -9.9844   3.5585   3.2863  -5.5902  -4.6006   3.6855   8.6799   1.5601
-#>    1.8843   0.4628   1.8192   0.8578   2.7260  -5.9450   3.4451  -7.6032
-#>    6.0221  -3.7656  -3.4224  -1.3820   9.8885   2.1165   5.3021   7.6218
-#>    7.4587  -8.7724   4.6601   3.4141  12.8465   3.5597   5.6033   5.5195
-#>   -7.8602   4.5715   0.3401  -4.5892  -0.1992  -5.6559   6.1851   7.1525
-#>   -2.7145   0.3003   8.5194  -4.0813  -7.0842  -0.4283 -10.4691  -2.9705
-#>    1.7503  -1.6545   7.4607   6.6784  -1.7863  -1.5975   7.0052   3.1186
-#>    7.2097   4.8913   1.6908  -8.0299   7.3147  13.8606  -6.3935  -0.7843
-#>   -6.8344   8.7484   3.1693   3.3031   3.8666  -3.8317   4.2813   1.1445
-#>    5.0369   3.8190 -10.9977   3.7152  14.4262   3.8348 -17.4962  -0.2185
-#>   -2.1229  -1.1563   3.0345   1.8111  -3.2802 -11.0495   3.9942   0.6429
-#>    7.0119   3.9208  -0.6389  -1.0716   5.4505  14.2304  -1.4294   9.7966
-#>   -3.1686  -0.6396  -6.7988   0.9020   4.8284   8.9844   6.6045  -1.4290
+#>  Columns 1 to 6 -8.3951e+00 -2.8141e+00 -1.9402e+00  4.1384e+00  1.0136e+01  2.4281e+00
+#>   6.8307e+00  5.9611e+00 -2.6984e+00  1.1535e+00  6.4386e-01 -7.2545e+00
+#>  -3.2027e+00  3.9534e-01  5.7615e+00  1.7997e+00  9.1843e+00  7.1464e-01
+#>   6.6893e-02  6.7879e+00  3.1322e-01  5.5399e+00 -5.6604e+00  2.4444e-01
+#>   1.1373e+00 -5.3004e+00 -2.5642e+00  9.0171e+00  4.3727e+00  3.4009e+00
+#>  -5.0005e+00  5.9274e+00  6.4470e+00 -5.4805e+00 -5.0443e+00 -2.2729e+00
+#>  -7.1226e+00 -6.0422e+00  7.0493e+00 -7.1967e+00  7.0638e+00  1.3255e+01
+#>   2.2497e+00 -7.3121e-01  2.7929e+00  9.6507e+00  1.9365e+00  5.8614e+00
+#>   8.5673e+00  7.4503e+00 -3.3146e+00  9.1761e-01 -1.7096e+01 -8.8513e+00
+#>  -2.9284e-01 -4.4896e+00 -2.9050e+00 -8.0677e+00 -7.6166e+00 -5.5766e+00
+#>  -1.0739e+01  1.3708e+01 -1.3877e+01  3.0163e+00 -3.8852e+00 -4.8990e+00
+#>  -6.1831e+00 -3.2277e+00  1.4503e+00  1.3461e+01  1.0566e+00  4.1018e+00
+#>  -1.4416e+00 -6.2414e+00  1.0434e+00 -1.1244e+01 -1.7559e+00 -1.1035e+01
+#>   3.9959e+00 -1.0495e+01 -1.5808e+01 -1.0375e+01 -1.0629e+01  2.0521e+00
+#>  -3.5685e+00 -5.4997e+00  1.0150e+01 -8.0230e-01  6.7681e+00  5.7076e+00
+#>   1.3212e+00 -2.3734e+00  1.8750e+00  1.2222e+01  9.9166e-01 -7.7316e+00
+#>  -1.3964e+01  2.8276e+00  2.8477e+00 -5.8115e+00 -2.0284e-01 -3.3701e-01
+#>  -2.2911e+00 -8.8825e+00  1.6204e+01  3.5824e+00 -3.4929e+00 -2.7017e+00
+#>   2.4032e+00  3.4200e-01  3.1894e+00 -1.3613e+00  1.2333e+00 -1.0706e+01
+#>   7.3336e-01 -1.9942e+00  4.9378e+00 -1.3575e+01 -4.5079e+00 -9.7759e-01
+#>   4.8102e+00 -2.5514e+00 -3.8803e+00 -5.5959e+00 -1.7002e-01 -3.0349e+00
+#>  -1.5298e+01  9.8673e+00  1.3495e+01  8.0240e+00  6.9426e+00  4.0904e+00
+#>  -3.2232e+00 -1.3324e+01  3.6674e+00  3.2886e+00  1.9104e+00  5.8670e+00
+#>   8.5357e+00 -3.3991e+00  2.0941e+00  7.5265e+00 -8.3860e-01  5.0003e+00
+#>  -1.7020e+00 -6.0616e+00 -1.8175e+00  8.2393e+00 -3.9429e+00 -2.9742e+00
+#>   9.7033e+00 -1.0581e+01  3.0951e+00 -6.3763e+00  2.1652e+00 -4.9442e+00
+#>   3.4387e+00 -8.0203e+00  1.1168e+01 -9.4117e+00 -7.6029e-01  5.4250e+00
+#>  -3.9966e+00  3.7018e+00 -8.7184e-01 -2.3836e+00 -6.4874e+00  2.2617e+00
+#>   9.0030e+00 -6.0216e+00 -4.0307e+00 -8.2122e+00  4.1802e+00 -4.5851e-01
+#>  -5.5599e+00 -8.9511e+00 -1.0650e+00 -3.7465e+00 -4.4242e+00 -4.9428e+00
+#>   8.7741e+00 -3.1887e+00 -5.6206e+00 -7.5571e+00 -3.7323e+00  3.9213e+00
+#>  -3.6121e+00 -2.5497e+00 -7.9240e+00 -1.0469e+01 -1.3243e+01 -1.4155e+01
+#>   5.2936e-01  5.5162e+00  3.9372e+00  5.6735e+00  8.3649e-01  8.5373e+00
 #> 
-#> Columns 9 to 16   1.3615   6.7430  -9.0831   2.4756   3.7019   4.5959  -9.2389   2.4258
-#>   -6.5283   4.2182 -14.7588   6.0469 -13.5433   0.6464 -14.0126  -0.1585
-#>   -0.4165  -2.1957   9.4785  16.3252 -11.8770  -4.1678  -7.0924   3.0644
-#>    5.4771  11.0229  -6.5639 -17.6075  -7.7505  -4.6961   8.8753   6.9847
-#>   -4.9094   3.5675  -1.0285   6.8008  -0.9435  -2.4753  -5.8107 -11.1148
-#>   -1.3169   3.8941   6.4620   0.5569 -11.8652   1.5728 -10.2236  -9.3686
-#>    3.9423  -0.3688   5.2345  -9.8630  -3.5721  10.8983 -11.1943  -4.6832
-#>    0.7959  -1.5588  -8.7820   3.3694  -5.9139   6.2685   1.9696 -11.8555
-#>   -0.0112  -8.3372  13.6339   3.5996  -6.6341  -3.1870  -2.4487 -17.8097
-#>   -3.1110  -0.1339  -5.4582   3.0029   5.9676   1.6402   4.2996  -1.7442
-#>    3.0221  -7.6143   2.1927  12.5149 -17.3180  -4.4666  -6.2207  -5.8665
-#>   -1.7066  -5.6932  -2.6972  -9.1662  10.9101   0.1779   2.6959  -7.8873
-#>   -5.7650   2.6454   2.5285  -7.5337   1.6641   5.1793  -4.0750   9.8508
-#>    4.6218  -4.3656 -16.7795  -0.9792  -1.7471  -0.4700   0.4574   4.0469
-#>   -0.6444  -3.1186   1.3270  -7.1979 -10.8263   2.7043  -9.5467   2.8626
-#>    0.1377  -1.6119  -4.6476  -1.0249   4.4360 -10.2520  -0.8962   7.3968
-#>    5.6244  -4.7285   1.2343  -6.5620   0.1256   9.5666   1.6124  -5.8117
-#>    9.5864 -12.8068   6.7174  -0.9492 -11.1470   2.9382   2.7348   7.2478
-#>   -1.3516  -9.1671  -9.7949   4.4277   4.5211 -10.2511  -1.0271  -3.8912
-#>   -3.4992  -9.1626   2.4570  -2.6582  -6.1909  16.2682  -2.3738   2.5297
-#>   -4.0920  -8.7836  -0.3173   1.5961 -15.0735   2.7017   6.4111  -4.5299
-#>    0.8594  -4.6585   1.7853   3.0959   4.5689   1.6610   6.1223   1.7822
-#>   11.7365  -2.0738   6.6759  -0.0544  -4.5379  -2.9902   6.5058  -0.1362
-#>    1.2749   7.0817   3.4731  -4.2940  -1.4348  -4.4507   6.7489  -5.4719
-#>    4.1983 -13.3311  -3.0335   4.3130  -6.7434  -3.9381   7.5854   2.0121
-#>   -1.6503  -2.0314  -6.4046   6.1112   3.9059   6.7474   3.3489   7.1325
-#>    5.0405   4.1070 -10.8778  -5.6209  -0.6011 -14.6451  -2.2682  -9.9665
-#>   -0.4697  -4.2318 -14.3726  11.9567   7.8060   4.3527  10.3033   2.6359
-#>    4.1752 -10.0265  -8.4880  -1.4998  -6.4839   3.2508   4.9882   0.2567
-#>   -7.8847   0.6969   0.6589   9.8041   0.4428  12.1619  -8.5297  -7.3596
-#>   13.3403   1.4642  -7.9892  -7.5376   4.4967   7.2595   1.9894  -8.7621
-#>   -3.1823   8.1989  -1.4497   0.5435  -1.0498   1.2208   0.1718   4.7092
-#>   -3.5594  -1.3115  -3.3914   2.5498  -1.9613   1.6140  -5.5963  -1.0378
+#> Columns 7 to 12 -6.8527e-01 -6.6750e+00 -2.5422e+00  2.6952e+00 -1.0972e+01  2.0498e+00
+#>  -1.5139e+00  1.7229e+00  9.3764e-01 -6.3206e+00  6.7317e+00 -3.6483e-01
+#>  -2.4645e+00 -3.0016e+00 -2.7610e+00  1.4589e+00 -5.5854e-01  5.8668e+00
+#>  -1.9516e+00  9.1403e-01  2.0154e+00 -1.5233e+01 -6.8524e+00 -1.2154e+00
+#>   2.8007e+00 -9.3648e+00 -5.1805e+00 -3.4628e+00 -1.4907e+01  7.1098e+00
+#>  -2.7267e-01  6.1041e+00 -1.0484e+01 -3.1599e+00  7.6270e+00  7.5906e+00
+#>  -5.0598e+00  4.8635e+00 -1.6033e+00  9.4448e-01  8.7785e+00 -2.4621e+00
+#>   2.0608e+00 -1.4733e+01  5.3305e-02 -9.5760e+00  9.6377e-01  7.2839e+00
+#>  -3.5491e-01  2.4551e+01 -4.5958e+00 -1.3547e+00  6.3573e+00 -1.7185e+01
+#>   8.4866e+00  5.0311e+00  5.1737e+00  1.0702e+01  9.6019e+00 -3.6383e+00
+#>   3.4809e+00  4.5234e+00 -1.1526e+01 -8.0553e-01 -1.7719e+01 -1.2959e+01
+#>  -6.5285e+00 -8.9087e+00 -3.7754e+00 -1.3278e+00  4.3877e+00 -3.1284e+00
+#>   1.6558e-01 -8.8985e+00  2.9997e+00  5.4439e+00  1.1366e+01  6.5668e+00
+#>   7.6865e+00 -1.3078e+01  6.7320e+00 -4.4706e+00 -2.6305e+00  9.3138e+00
+#>   5.8655e+00 -6.2844e+00 -5.2472e+00  4.3737e+00 -4.8817e+00 -1.6727e+00
+#>  -9.8008e+00 -5.6359e+00 -6.3795e-01  5.9198e+00  8.7384e-01 -2.0822e+00
+#>  -4.0524e+00 -1.4944e+00  1.5081e-01 -4.3437e+00 -6.3435e+00 -7.3562e+00
+#>  -3.2516e-01 -2.4081e-01 -1.8803e+00 -3.3390e+00  8.2726e+00  4.6187e+00
+#>   9.4028e+00  2.6572e+00  1.1060e+01 -1.1542e+00 -6.4159e-01 -2.1363e-01
+#>  -7.8265e+00  1.1543e+01 -8.8785e-01 -1.0960e+01 -1.6193e+00  6.3225e+00
+#>   1.1244e+00 -4.2009e+00  9.6153e+00  6.5037e+00  8.6408e+00 -1.0582e+01
+#>  -7.4054e+00 -3.4970e-01  6.4795e+00  8.2708e+00 -3.5835e+00 -4.6855e+00
+#>  -6.9420e+00  3.8119e-01 -1.6974e+01 -7.0275e+00 -1.0874e-01  1.5721e+00
+#>   5.3262e+00  7.1328e-01 -3.1265e-02 -1.0126e+01  9.5178e-01  3.5983e+00
+#>  -5.6046e+00 -3.9303e+00 -5.9183e+00  7.1817e-01 -1.2804e+01  5.4781e+00
+#>  -9.3229e+00  1.5218e+00 -2.3454e+00 -2.5600e-01  9.8657e+00 -5.3319e-01
+#>   3.1509e+00 -3.6389e+00 -1.7747e+00 -9.9224e-04  1.2906e+00  6.6964e+00
+#>  -1.7609e+00  1.1773e+01 -3.5776e+00  3.8019e+00 -1.1530e+01  2.8797e+00
+#>   4.1346e+00  5.8012e-01  6.1191e+00 -8.9286e+00  1.2805e+00 -4.7190e+00
+#>  -1.0720e+00  8.4785e+00 -7.7949e+00 -1.6484e+00  1.0007e+01 -7.1135e-01
+#>   9.3990e+00 -7.7036e+00 -3.3507e+00 -2.6082e+00  2.5511e-01  6.0275e-01
+#>  -6.6124e+00  5.0587e+00 -1.8642e+00  1.0930e+01 -3.0234e+00 -3.4596e+00
+#>   6.0355e+00  1.4031e+00  5.5284e+00 -5.4199e-01 -8.0791e+00 -8.9905e+00
 #> 
-#> Columns 17 to 24  -6.1352   0.7855   5.2496 -10.7202   1.1006   6.4437   3.0041   2.3786
-#>  -11.1167  10.4239   0.2350   8.9435  -3.6779   4.8471   0.7140   3.8333
-#>    9.4847   0.8165  -3.8158   6.3221 -10.7197   0.8852  -5.6699   9.1540
-#>    4.0945  -3.8234  -0.7001  -1.1315   0.4381  -3.8137   4.0863   6.7160
-#>    1.1146  -8.9878   2.3985  -0.9147  -0.8046   2.3567 -10.0703  -2.1854
-#>    1.8469  -2.6686  -8.5182  -5.4251   6.5746  -7.8745   3.0007  -3.6743
-#>   -4.1822  -9.5143  -4.3121   7.2566   2.6041   0.5658  -2.4030   0.4245
-#>  -11.7281  -0.9243   5.2365  -2.4933   0.3209  -1.5823  -7.3054   0.5439
-#>    1.9351   5.7115 -14.8850   5.1515  -9.2994   0.5744 -10.7098   2.5380
-#>    3.1209  -1.7344   6.0623  -0.9722   1.9104  -4.2705  -4.6320   2.4032
-#>   12.0585   4.4151   4.1575  -0.8302  -9.3978   3.5050  -6.6041  20.1393
-#>    5.0416  -3.7321  -0.9123   1.5950   8.2849   1.2795   3.4207  -7.8130
-#>    1.9756  -6.4791   7.8072   2.6787   4.0306  -9.3902   5.1214   1.6635
-#>   -4.7427   9.7381 -14.8424  12.1369  -2.9461  -2.3381 -12.2591  -4.3829
-#>   -6.2556   1.9847  -1.4314   6.6051   2.5084  -5.3805  -4.3812   0.1995
-#>   -0.7258  -1.5201  -5.3685   5.4863   1.4409  -1.3454  -0.1501  -5.4024
-#>   -4.7067  -4.7743   2.3876   2.4734   2.3301  -0.7725  -2.1543  -1.3893
-#>    1.5617   4.9953 -10.5440  10.1063   7.0310  -3.7266  -2.5219  -3.8014
-#>   -3.4102  11.2320  -3.4485   3.0704  -6.8103   5.8674  -6.1492   3.8883
-#>   -8.3267   1.2774  -1.3571   1.9461   1.9410  -9.3266  -4.4550  -0.7253
-#>    8.4757  -2.4299  -3.2148   4.9527   4.3251   7.9487  -3.1187   0.4195
-#>   -1.0336  -5.3404   6.4247  -7.5989   1.9064  -6.2831   1.7634  -6.0175
-#>   10.4051 -10.9130   6.5344  -3.2940   1.8782  -6.1374   9.9976  -2.0857
-#>    1.6580   5.6035   1.0687   8.1476  -8.0305 -10.1498  -7.4877   0.9982
-#>    1.5491   5.0754   0.2085  -4.2722   6.8387  10.7773   5.8851  -0.3984
-#>   -4.5910   4.0407  -3.8389  -8.2975   5.9267   1.9849   8.1880  -9.6735
-#>    4.7822 -11.5824   0.7062   5.2624   8.5684  -6.3034  -4.5508   0.3868
-#>   -2.6230   9.7855   1.0649   7.0056  -4.0832  -7.7116  -5.7604   2.8494
-#>   -5.7510  -1.5148   1.2035   2.5692   2.1899  10.1006 -14.8982  -4.4679
-#>   -9.8924   1.5419  14.2471  -0.8770  -0.2646  -5.9197  -6.6279   6.6351
-#>   -6.1204  -2.9526  -3.7854  -0.1987   0.0084  -0.0103  -6.4451  -8.6840
-#>   -4.4767  -1.5587  -0.5513  -7.6515   9.3017  -6.6286   6.1258  -8.2799
-#>    2.5453  10.2133   1.3841  -2.8850   2.5809   1.0825   1.8629   9.2428
+#> Columns 13 to 18  5.9678e+00 -9.7474e+00  7.0909e+00 -1.1148e+01 -6.0623e-01  1.9961e+00
+#>   5.0613e+00 -1.6512e+00  1.4175e+00 -6.6100e+00 -6.8726e+00 -1.2160e+01
+#>  -3.7654e+00 -1.9567e+00  2.1503e+00  1.7443e+00 -2.3711e+00  3.1486e+00
+#>   2.0366e+00 -8.2696e+00 -1.3348e+00 -1.1307e+00  3.6387e+00  2.6933e+00
+#>  -2.4525e+00  7.7111e+00  6.7042e+00 -8.0989e+00  5.3495e-01 -3.4528e+00
+#>  -6.7053e+00 -6.0537e+00 -4.5964e+00  4.8832e+00 -4.2174e+00 -6.8970e+00
+#>   7.5400e+00 -1.0487e+01 -1.0757e+01  7.1635e+00 -2.6506e+00 -9.8790e+00
+#>  -1.2661e+01 -9.7000e-01  5.1582e+00 -4.5297e+00  4.7732e-01  7.3898e+00
+#>  -7.0311e-01  2.1044e+01 -2.3547e+00  7.8111e+00 -1.7919e+00 -3.6426e+00
+#>   3.4680e+00  4.1260e+00  2.2663e+00  7.7358e+00 -1.6395e+00  6.7834e-01
+#>  -2.2727e+00 -6.5998e-02  7.1943e-01  1.0914e-01 -2.6693e+00  1.7026e+01
+#>  -5.2759e+00  7.5612e+00 -1.1254e+01 -7.7761e+00 -1.5269e+01  6.3323e+00
+#>  -4.1168e+00 -5.3404e+00  1.0368e+01  4.1417e+00 -6.7626e+00  2.8983e+00
+#>   6.3700e+00  5.6963e+00  1.0627e+01 -3.0159e+00 -8.9321e+00 -9.8520e+00
+#>   1.0976e+01 -1.9774e+00  4.4388e+00 -6.9221e+00 -8.7519e+00  2.5778e+00
+#>  -1.3879e+01  7.2988e+00  6.7988e-01 -1.7957e+00  1.4793e+01  2.3950e+00
+#>   1.0804e+01 -1.8943e+00 -6.7267e+00  4.4527e+00 -6.4357e+00  1.0441e+01
+#>  -7.0792e-01 -7.7788e+00  1.0123e+01 -4.6441e+00  3.1377e+00  1.5371e+00
+#>  -7.0618e+00 -9.1846e+00  8.2692e-01 -4.3087e+00  1.3263e+01  3.5648e+00
+#>   1.4579e+00  3.5876e+00 -6.5496e+00  8.9657e+00  8.7569e+00 -1.5873e+01
+#>   3.7011e+00  1.2739e+00  3.5723e+00 -8.5728e+00 -4.6815e+00  4.1472e+00
+#>  -2.5111e+00 -3.9447e+00 -1.0414e+01  2.5352e+00  5.2065e+00  6.6207e+00
+#>   3.9797e+00 -3.9310e-01 -1.1669e+01 -2.9539e+00 -9.0991e+00 -4.8434e+00
+#>  -2.0420e+00 -8.9539e+00  4.0166e+00 -4.5039e+00  6.7315e+00  4.5821e+00
+#>   7.4207e+00 -5.7509e+00 -1.1452e+01 -1.2195e+01  1.0693e+01 -8.0132e+00
+#>   3.5018e+00  1.6697e+00 -3.0522e+00 -7.9866e+00 -6.6618e-01 -7.1744e+00
+#>  -5.5304e+00 -9.5552e+00  3.5623e+00 -2.9378e+00  4.5727e+00 -5.8968e+00
+#>  -8.9109e+00 -1.9113e+00 -3.4822e+00  1.2512e+00  1.4414e+01  8.4879e+00
+#>   4.1685e+00  3.5275e+00  2.0563e+00 -5.5765e+00 -3.3186e-03 -6.8468e+00
+#>  -6.9165e+00  2.2722e+00  5.2716e+00 -3.7791e+00 -8.1951e+00  3.2392e+00
+#>  -9.2950e-01  1.1593e+01  5.8807e+00  5.0382e+00 -5.1739e+00 -6.4292e+00
+#>  -9.6982e+00  1.0057e+01  3.2115e-01 -6.2153e+00  1.0875e+01  1.0603e+01
+#>  -7.7052e-01  4.3582e+00 -6.7134e-01 -9.0197e+00  7.6164e+00  7.8076e+00
 #> 
-#> Columns 25 to 32   2.0304   6.7152  -4.7547  12.5145  -4.5016  -4.0179  -1.2330   9.1559
-#>   14.2909   1.4769   0.6636   8.6500  -2.1221   1.7183  -5.6604   9.8726
-#>   -2.7097   4.5264   4.3187  -7.1046  -0.9723   5.9788  -5.5353  -1.4749
-#>   -8.6412   7.7463  -5.2986  16.9666  14.3188  21.4422   3.3956   1.1343
-#>   -7.0317   6.6929  -6.0369   0.6207  -3.9455   1.8005  -0.5599   1.8508
-#>    2.0361   2.7921  -4.3200  -2.3661   2.9078   1.1786   5.4417  -8.1194
-#>   -7.8485  -8.0762   3.2615  -5.6470   0.3354   5.4427  -2.8058  -4.3386
-#>    3.7953   3.5336  -4.9533  -5.2637 -10.4107 -12.4107   2.0646   9.5287
-#>   -2.7825  -4.6040   9.5309  -2.1843   5.5711   2.9852  -1.9312  -6.5354
-#>   -3.6551  -9.9503  -5.3903   3.4260   1.4111  -0.6740   2.9563  -2.8675
-#>   -1.4813   3.6377   2.2588   2.0900   5.0809   5.0369   5.1234   9.0396
-#>    5.2403  -6.4151   6.7793  -3.0877 -12.6484  -1.7808   1.7761  -0.5235
-#>    1.3175   5.3689  -0.2347  -7.3404  -6.2206  -0.3964  -2.9847 -12.7738
-#>    0.5948  -2.7842  -9.6191   8.1294   0.9181   9.2779  -5.9096   8.0563
-#>   -0.3378   2.3956   4.6235  -0.7978  -5.5830  -0.4862  -0.2478  -1.7941
-#>    0.0686  10.2917  -0.9817   5.5907   3.5988  -1.3564   2.4055  -0.9436
-#>    2.0567   6.2534  -2.2078  -6.1647   0.5606  -2.7191   3.2266 -11.3656
-#>    4.2932  -0.4873   8.3800  -2.3044   1.8511   8.4027   6.6752 -16.2451
-#>   -2.3357  -2.6602   7.1631   0.3165   3.9131  -7.8905  -3.5363  -4.4079
-#>    5.4912  -7.6143  -2.0331  -2.5894   3.9076 -18.9411   3.6695 -13.5628
-#>    1.0256   0.8843  -1.2594  -5.4993   1.2796   5.1276  -2.8727   1.2982
-#>    0.8742   6.3731   5.7563   0.4166  -2.4657  -6.1710   5.1675   7.3948
-#>    1.8751  -3.1597   6.6459   5.1926   1.8573 -12.9828 -10.6121  -5.7677
-#>   -3.8644 -11.0444  -4.1877  -8.5450   1.3069  -0.6498  -4.9872 -11.6447
-#>   -1.1720   6.4177   5.2251   4.5926   2.8544   5.3088   4.0496   0.8798
-#>    8.5778   2.0765  -3.0492   2.6605   5.9763  -1.0507  12.9023   2.8845
-#>   -2.5537   6.7471  -2.3166   0.1698   7.3152   4.9079  -5.6434 -18.8992
-#>   -2.4438  -2.8801 -10.5118   4.0220  -6.5804   0.2695  -3.0997  -9.9757
-#>   -1.8960   0.7844   5.0251   5.0129   3.5039  -1.9246  -2.0302  11.9824
-#>    3.2717   6.6071  -7.0335  -4.5295 -23.8735  -9.4977   0.9193   2.7063
-#>   -2.8423 -12.5636   8.4244  -2.9791  -3.4124   3.0636   1.3172  -1.1011
-#>    9.9325   0.5829  -6.6425  -2.1746  -5.1121  -9.9372  -3.6588  -1.7340
-#>    8.5402  -6.0357  -0.9178   7.4435   2.9151   8.6923   3.0402   3.1449
+#> Columns 19 to 24  1.1778e+01  2.0303e+00 -1.7353e+01  1.0082e+00  4.7677e+00  4.2631e+00
+#>  -1.0307e+00  1.6196e+01 -2.0418e+00 -1.0223e+01  1.6997e+00  7.1068e+00
+#>   7.2591e+00  3.5400e+00 -9.1838e-01  2.0646e+00  4.0366e+00 -6.4190e-01
+#>  -6.5149e+00 -8.8414e-01 -4.7157e+00  3.4367e-01  2.8840e+00  1.1695e+01
+#>   9.9104e+00 -2.4610e+00 -2.5285e-01  2.5838e+00 -2.4302e+00 -1.3316e+01
+#>   5.6723e+00  4.1739e+00  1.2386e+01  9.6780e+00  9.1150e+00  1.1069e+01
+#>   7.3253e+00  5.6702e+00  6.9528e+00 -9.8993e+00 -6.4625e+00 -3.9740e+00
+#>  -1.2725e+00  7.3350e+00  7.4261e+00  3.6785e+00 -5.1651e+00  4.0601e+00
+#>  -1.1368e+01  4.8053e+00 -9.5350e+00 -2.2408e+01 -6.8687e+00  6.5463e+00
+#>  -2.6451e+00 -7.2305e+00  1.5720e+00  6.9827e+00  1.7672e+00 -2.5382e-01
+#>   1.2897e+01  4.4085e+00 -1.2364e+01  3.8108e+00  1.1252e+01  9.4853e-01
+#>   7.0377e+00  5.0779e+00 -3.5618e+00 -2.5290e+00 -5.6112e+00  3.3708e+00
+#>  -1.3505e+00 -2.9384e+00 -1.5031e+01  2.5628e+00 -2.9015e+00  1.3181e+01
+#>  -1.6910e+01 -1.5070e+01 -1.4999e+01  5.2842e+00 -3.4203e+00  7.3843e+00
+#>   3.2007e+00 -1.3544e+01 -7.9596e+00  1.5601e+00 -8.5167e-01  7.3819e+00
+#>  -5.0569e+00 -1.6224e+00  2.3452e+00  3.4707e+00  1.5098e+00 -5.0575e+00
+#>   7.4184e-01 -3.1959e+00  1.3687e+00 -6.6558e+00 -2.4546e+00  7.8908e-01
+#>   1.0274e+00 -2.0857e+00 -1.5564e+00  1.5786e+01 -4.8150e+00 -3.8841e+00
+#>   6.8518e+00  8.3873e+00 -1.3750e+00  1.6364e+00  7.8108e+00  4.5983e+00
+#>  -1.8995e+00  3.0577e+00  5.1708e+00  7.0246e+00  6.0169e+00  7.6654e+00
+#>   9.4888e+00  8.9839e+00 -1.8928e+00 -2.1697e+01  2.0800e+00  4.9818e+00
+#>   1.0982e+01  1.7532e+00 -1.2756e+00 -4.3803e+00 -1.3254e-01 -2.1600e+00
+#>   1.3998e+00  7.1849e+00  5.7207e+00  8.3973e-01 -8.0522e+00 -7.9664e+00
+#>   1.1255e+01  3.7207e+00 -6.4622e-01  3.3669e+00 -4.1269e+00  2.9135e+00
+#>  -6.4567e-01  6.5039e+00  9.7643e+00  8.0798e+00 -5.7328e+00 -3.1476e+00
+#>  -3.4270e+00  3.8212e+00  4.5256e+00 -6.6063e-01  8.2981e+00  2.4167e+00
+#>   5.6869e+00  1.5453e+00  3.2006e+00  1.0211e+01  1.0770e+01  6.4138e+00
+#>   1.0142e+01 -5.2491e+00 -6.3918e+00  2.9710e+00  1.9272e+00 -6.7866e-01
+#>  -4.5972e+00  5.7021e+00  7.1677e+00 -4.3380e+00 -3.1425e+00 -3.9138e+00
+#>  -4.2965e+00 -6.1071e+00 -1.3520e+01 -4.8378e-01  9.3025e+00  9.0143e+00
+#>  -8.2621e+00  1.7109e+00 -2.1493e+00 -1.5014e+00 -2.9105e+00  1.1343e+00
+#>  -8.7325e+00 -1.2430e+01 -1.0334e+01 -1.4823e+01  4.4890e+00 -3.1203e+00
+#>   8.8041e+00 -3.0359e+00 -4.3546e+00  1.2777e+00 -1.8532e+00 -8.5947e+00
 #> 
-#> Columns 33 to 40 -17.0318 -20.0478 -11.5813  -0.8116   0.6021  -9.7061  -5.2541  -7.5142
-#>  -10.3421  -3.6284  -8.4016  -2.5618   5.3978  -9.7860  10.5355  -5.2481
-#>   -4.6916   1.1132   5.8259   4.2710  -0.0016   8.1836   5.5236 -10.1070
-#>    7.3221   6.5633  -3.7985   3.9131  10.9585 -17.2966  -5.5431  11.2191
-#>  -11.6022  -1.6023  -4.5938  -1.9300  -0.4376  -7.2064  -7.8986  -0.3702
-#>  -14.3030   2.5388   1.2509  -2.7564 -10.5961  -3.5264 -12.7500 -22.1331
-#>   12.6453   7.9274  -8.0327  -1.6566   5.7491  -5.4175  -3.5723  -1.2638
-#>   -8.9598  -3.8361   5.2146  -4.4693  -8.4153  -8.1980   4.2895  -4.7867
-#>   -3.0841   1.6341  -0.0632   4.4309   3.3442   1.7426  11.5374 -17.0239
-#>    3.0663  11.0731  -2.8260  -5.2820   5.2010   4.3655   0.5538   1.9090
-#>   -3.2084   5.2607  -0.3004   3.4364   2.2088   1.5744   6.6938  -7.3395
-#>   -6.4610   5.9028  -1.2363  -2.9217   0.4373   1.8325  -3.0021   3.0616
-#>    2.2177  -4.2643   7.7422  -2.1623  -5.7365   8.1062 -10.1076   5.0764
-#>    0.9173   8.6032   1.4039   4.1027   3.0920 -22.4057   7.6579   4.9080
-#>    0.8167 -10.3278   1.2432  -2.0476   5.4842 -10.7095   2.1405  -3.7050
-#>    1.7803 -22.8740  -1.0902   2.1489   9.6227  -4.6850  -5.7256  11.1705
-#>   14.4816  -9.7424   3.4600   3.2909 -16.3311  -5.0685  -5.5932   9.4876
-#>   -5.1677   2.5401   5.2312  -6.5760   3.9057  -9.6523   2.2021   3.4456
-#>   -6.3658  -2.7545  17.0195  -9.7690   1.8630   9.1328   3.1055   3.9391
-#>    3.1821  -9.0758  10.9002  -3.9246 -12.8605   1.5466  11.6374   0.1441
-#>   -3.7770  18.8824   4.3626  -2.8148  -4.7249   4.2569   8.2437  -7.0431
-#>    3.5508  -6.7707   0.8483  13.8608  -2.7573  -3.5297   1.8513   2.4026
-#>    7.9127   9.2728  -9.9353   3.2328   3.6214   7.5038   0.4667   9.2028
-#>    8.3492   6.9488   7.7495  -9.1828  -5.8187  11.7171  -7.3384  -3.0282
-#>   -5.8212   9.4377   3.9020   0.9103  -9.7031   4.4426  17.3763   0.3510
-#>   -3.7819 -11.6569   7.6439   2.5470  -7.9286  -3.5027   0.1709   7.4708
-#>    4.4982  15.0765  -2.6611  -7.6918  -1.7578  -0.8912  -7.4240   2.7192
-#>    5.5335   7.5726   8.4664  -5.9477  -3.5248  11.7820   0.5398  13.5853
-#>   -5.4325  -2.7990   3.2807   8.9660  -0.7183 -13.0419  13.5544   2.4505
-#>    0.3033  -9.9993 -11.8875   8.8428  -9.5778   5.9503   3.4623 -12.5212
-#>   -3.6057   8.8081  -1.8937   2.5456  -5.3832  -5.6471   1.6094   4.5927
-#>  -10.4760   0.6477   3.8404   2.4465  -5.8038   7.6152  -6.2678  -5.6440
-#>   -9.8051  10.2489  -6.0445  -3.1732   1.9759   6.9409   6.3362 -10.0141
+#> Columns 25 to 30  4.4787e+00 -9.3065e-01 -1.7664e+01 -2.0360e+00 -5.1864e+00  6.4398e+00
+#>  -4.8408e+00  6.0288e-01  1.0376e+01 -8.4517e-01 -1.2970e+00  1.5763e+00
+#>  -3.2578e+00 -3.0883e-01 -6.7075e+00  4.2618e+00  9.8819e+00  3.7765e+00
+#>   6.2440e+00  3.6400e+00  1.8097e+00  1.8065e+00  2.3145e+00  3.4393e+00
+#>   3.8853e+00 -2.0669e+00 -8.9789e-01  2.2191e+00 -1.1850e+01  2.7285e+00
+#>  -2.5323e+00 -4.2356e+00 -3.8099e+00 -4.0471e+00  8.5190e+00  1.1156e+01
+#>  -7.6452e-01  5.5126e+00 -6.9312e-01 -1.9476e+00  6.5130e+00  1.4541e+00
+#>   1.2041e+00 -4.7862e+00 -8.5009e+00  1.4784e+01 -2.8520e+00  2.3623e+00
+#>   7.9495e+00 -9.9608e+00  8.4147e+00  1.2598e+01  3.6789e+00 -4.3962e+00
+#>  -4.4529e+00 -3.3200e-01  5.6498e+00 -8.8855e+00 -5.5853e-01 -2.5021e-01
+#>  -1.0037e+01 -4.7682e+00 -8.7609e+00 -6.5646e+00 -1.2336e+01 -7.2553e+00
+#>   3.2602e+00  8.5436e+00 -2.7624e+00 -1.2816e+01 -4.3327e+00 -1.2763e+00
+#>   1.1232e+00 -1.1936e+00 -1.1497e+01  5.6471e+00  1.0081e+01 -7.3018e+00
+#>   7.0586e-01  1.8574e+00 -8.6821e+00 -1.9661e-01 -7.3537e+00 -4.4018e+00
+#>   1.0473e+01  5.9140e+00 -6.1157e+00  1.4409e+01 -5.4832e+00  2.4922e+00
+#>   5.1351e+00 -3.7543e+00 -3.7731e+00  1.1105e+01 -1.1417e+00 -3.9379e-01
+#>   4.8027e+00 -1.0496e+01  1.6196e-01 -1.3399e+01 -1.2744e+00  4.3051e+00
+#>   1.8298e+00  3.9455e+00 -4.9313e+00  9.3004e+00  1.2386e+00  8.9960e+00
+#>  -4.7810e+00  3.1256e+00 -1.9344e+00 -3.4038e+00 -6.6274e+00  1.4678e+01
+#>   1.1509e+01 -4.3661e+00  1.2026e+00 -3.1667e+00  4.0481e+00  7.6234e+00
+#>  -6.3613e-01  7.2668e+00 -4.9612e+00 -5.3380e+00 -5.9953e+00 -5.6242e+00
+#>   3.7431e+00  5.4591e+00  6.8118e+00 -1.9543e+00  5.9023e+00  6.3021e+00
+#>  -6.0931e-02  3.4162e+00 -8.6933e+00 -9.5165e+00  3.5879e+00  1.5674e+00
+#>   1.5797e+00  1.3125e+01 -1.2312e+01  7.6845e+00 -9.6787e-01  1.5916e+01
+#>   4.9078e+00  4.9698e+00  6.7853e+00  2.3861e+00 -1.7449e+01  8.7595e+00
+#>   1.7868e+00  4.2192e+00  3.7081e+00  1.8986e-01 -4.2665e+00  1.5277e+00
+#>   5.9242e+00 -8.3228e+00 -5.6850e+00  1.0597e+01 -2.9350e+00  1.1962e+01
+#>   1.3618e+00 -1.8300e+00 -1.6569e+00 -7.2417e+00 -3.9782e+00  9.8272e+00
+#>   5.0099e+00  5.2188e+00  7.7963e+00  3.9949e+00 -8.8119e+00 -1.3700e+00
+#>  -8.3802e-01 -6.9503e+00 -1.6966e+01  3.3190e+00  2.3536e-01  4.5285e+00
+#>   1.6660e+00 -4.0373e+00 -5.4824e-01  8.1201e+00  9.1323e-01 -6.5349e+00
+#>   6.4955e-01 -3.9583e+00  3.6544e+00  4.9247e+00 -1.5390e+01  3.1637e+00
+#>  -2.0783e+00  1.0263e+01  8.6899e+00  7.7819e+00 -4.2334e+00  4.9994e+00
 #> 
-#> Columns 41 to 48  -0.5757  -1.3542   7.1357  -2.2870  -2.8012   3.0657   5.0447 -15.8321
-#>    0.1535   0.3076  17.1299   8.4095  -2.6762   5.0797   1.7882  -4.3992
-#>    0.4592  -3.5133  -1.8708   0.8405   1.9575  -8.9528   7.9915  -4.7535
-#>   -3.2901  -2.6491  -4.4144   9.6573 -10.8269  10.2468  -8.4412  -1.5340
-#>    0.9673  10.0159   4.7422   9.9742  -0.5255  -8.9552   2.3655  -1.5989
-#>    1.6231  -6.4483  10.5318  -4.9875   1.7577   9.2817  -2.5071  -4.0078
-#>    1.4573  -3.2758  -1.6678   5.4910  12.7946   5.9670  -6.0930  -4.4879
-#>   -5.1614  -4.0308   0.7992  -6.0837  -0.3915  -9.4512  -1.2862  -0.5319
-#>    0.4310  -0.9557  -6.4016   2.8146   0.5966  -2.8999   1.7562   5.5143
-#>   -5.1265   4.3655   4.9410  14.8872  -2.8013  -9.8312   7.9825  -4.1148
-#>    1.8815   5.4926  -7.3356   2.1816   3.0059   3.2300   6.5676  -2.0128
-#>    1.1780   8.0908 -12.3752   1.3859   6.4601  -2.0588  -7.2472  -5.7050
-#>    5.9115  -0.6530   0.9260  -8.3563  -0.4082   0.7834  -5.9661  -0.5926
-#>   11.0495  11.7859   3.0577  -4.4598  -4.8840  22.0590  12.1955   4.7068
-#>   20.0985  -4.0410   4.3649  10.0583   6.1918  -1.7880  -1.9070  -6.3624
-#>   -5.8225   0.6477   0.3729  -0.1409   1.5204   7.7822   0.8015  -3.2746
-#>   -2.1589   0.5456  11.5298  11.6881  -2.7155  -5.6491   0.4327   7.7103
-#>    1.0508  -0.0593   8.5939 -11.2689  -8.9750   2.3534  -7.5014   0.4162
-#>   -2.2612   3.0069   6.5653  -7.6077  13.5167  -8.2681   7.5790   3.1596
-#>    5.2002  -9.1811  12.2834 -23.1594  -3.5273  -1.1709  12.7870   4.4330
-#>   -0.0276   3.1669  -2.0981  -4.0901   4.8303  -8.5770   1.6487   8.6810
-#>    7.7900  -6.3951 -12.2464   2.8105  11.5417  14.2515  -2.9096   2.5551
-#>    1.0313 -10.4730 -10.2800 -12.7194   5.4654  -2.9958   4.4049 -11.4623
-#>   -4.4022   3.6837   2.7533  -4.6619  -4.6065  -3.2298  21.5926   3.3128
-#>    5.3238   3.2650   1.3261  13.1070  -3.3967  -4.7740  -5.1533   3.8692
-#>    1.0264  -4.9872   7.8718  -5.9630   3.3917  -7.7953  -2.6791   2.2528
-#>   -7.6961   2.1876   3.5483  16.0663 -10.7377  17.4609  -0.0595   0.9353
-#>   -3.0517   3.1029  19.1180  -5.9878 -10.0726   0.6224   3.0131   4.7465
-#>    9.6907   7.8143   1.0314  12.2337  17.0781   7.3258   8.1910   2.9945
-#>   -3.3594  -1.7302   7.7383   5.9785  -6.6533  -7.8406  -8.4122  -0.4406
-#>   -2.0650  -9.0354  -5.8994   0.9212   3.4177 -13.0390 -11.4290 -11.9110
-#>   -2.7836 -11.7873  -3.5815  -5.7332 -11.7829 -13.6638   8.5906  -4.0360
-#>    5.6294  -4.0216   2.0273   4.5414  -5.5946   3.0184   3.4674  -2.7942
+#> Columns 31 to 36 -6.2444e+00  7.1131e-01 -1.4371e+00 -1.2998e+00 -1.4302e+00  4.2263e-01
+#>   3.9920e+00  3.3123e+00 -1.0602e+01 -8.0142e+00  6.2564e+00  2.8434e+00
+#>   1.7837e+00  5.2434e+00 -2.8463e-01 -2.8071e+00  1.8934e+00 -2.3110e+00
+#>  -6.5888e+00  1.2508e-01 -1.4155e+00 -4.6978e-01  2.1861e-01  7.5095e+00
+#>   6.8007e+00 -1.5046e+00 -6.1754e+00  4.5389e+00  7.0344e+00 -4.3467e+00
+#>   9.0569e-01 -3.4367e+00 -3.2700e-01  6.2864e+00  7.2274e-01  1.6950e+01
+#>  -5.0609e+00  4.0758e+00 -2.7577e+00 -2.0745e-01 -7.6169e+00 -2.4548e+00
+#>  -1.1708e+01  8.7182e+00 -5.7260e-01  1.0934e+00  7.7361e+00  1.3312e+00
+#>   1.1148e+01  1.1147e+00  8.2015e-01 -6.9233e+00 -5.7323e+00 -2.5504e+00
+#>   3.9960e+00 -1.5409e+00  2.2143e+00  4.3206e+00  1.9112e+00  1.4457e+00
+#>   7.5939e+00  4.7057e+00  7.9776e+00  8.1253e+00 -9.5355e+00  7.5308e-01
+#>   6.5773e+00 -2.1522e+00  1.3564e+00  7.1834e+00  2.6551e-01 -4.0239e+00
+#>   1.2855e+00  2.5885e+00  1.2113e+01 -5.2584e+00  2.6398e+00 -3.7213e+00
+#>  -7.6153e+00  4.0957e+00 -8.6184e+00 -7.9122e+00 -5.0349e+00 -8.7189e+00
+#>  -8.2374e+00  1.4646e+01  3.1650e+00 -6.6946e+00 -3.0146e+00 -1.6389e+00
+#>  -2.8947e+00  3.4498e+00 -1.5436e+00 -1.0615e+00  2.4674e+00 -4.9741e+00
+#>   1.4443e+00 -3.8404e-01  4.1229e+00 -3.5863e+00 -1.1883e+01  4.5949e+00
+#>  -8.0185e+00 -4.4125e+00  6.3695e+00  5.6233e+00 -6.2819e-01  2.6117e+00
+#>   5.9543e+00  3.3249e+00  2.6931e+00  1.4289e+00  3.7362e+00  5.3448e-01
+#>  -5.1573e+00  8.1121e+00  1.0221e+00 -2.4819e-01 -4.6568e+00  8.5596e+00
+#>  -2.2423e+00  3.8916e+00  1.3687e+00  1.6066e+00 -2.5599e+00 -6.8182e+00
+#>   4.2081e+00  4.8813e+00 -5.4527e+00  1.7301e+00 -9.5751e-01 -6.8657e+00
+#>   7.3364e+00  4.9294e+00  9.4030e+00  1.2489e+00 -6.0483e+00  4.7255e+00
+#>  -5.5124e+00  3.6873e+00 -2.6349e+00  3.2796e+00  2.6952e+00  3.8045e+00
+#>   3.3623e-01  1.3803e+01  5.5073e-01 -3.7316e+00 -9.7750e+00  7.7576e+00
+#>   2.6992e+00 -3.1129e+00 -8.2849e+00  4.5016e+00  5.2788e-01  1.0252e+01
+#>  -1.2969e+01  4.4722e+00 -7.5532e+00  8.1663e+00  6.2965e+00  1.1526e+01
+#>   1.1833e+01  1.8134e+00 -3.7881e+00  3.1859e+00  1.8822e+00  3.6497e+00
+#>  -3.2139e+00  1.1189e+00 -3.3406e+00  1.9521e+00 -3.6090e+00 -1.6607e+00
+#>   5.7041e+00  3.0485e+00  3.8008e+00  3.1866e+00  1.5065e+00  1.7498e+00
+#>  -4.0508e+00  2.9718e+00  1.2703e+00 -5.9761e+00 -3.8426e-02 -1.6752e+00
+#>   4.4547e-01  5.9566e+00 -1.0320e+00 -5.8180e-01 -4.0027e+00  5.1696e-01
+#>  -1.9607e+00  5.9724e+00 -3.7509e+00  6.8757e+00  3.2013e+00 -5.5150e+00
+#> 
+#> Columns 37 to 42 -6.4819e+00  9.0894e+00 -7.8052e+00 -3.7613e+00  1.2816e+01  2.1493e+00
+#>   1.2992e+00 -3.9447e+00  3.9124e+00  5.9837e+00 -4.9299e+00 -5.7494e+00
+#>  -4.2350e+00  5.8432e+00 -2.7184e+00 -9.1419e-01  8.4152e+00 -2.3614e+00
+#>   1.4124e+01  8.9136e+00  5.3466e-01 -1.3477e+00 -4.4206e+00 -9.8608e+00
+#>   8.3651e-01  7.7631e+00 -8.7169e+00  1.1039e+00 -9.6138e-01  6.6820e+00
+#>   8.6026e+00 -1.0605e+01  3.6970e-01  4.2069e+00 -2.0269e-01  5.4052e+00
+#>  -5.2573e+00  1.4911e+00  9.9503e-01 -3.4705e+00 -1.9677e+00  8.3456e-01
+#>   1.4981e+00  1.1222e+01 -4.0447e+00 -6.1750e+00  1.3435e+01 -4.3735e+00
+#>   4.9577e+00 -7.6459e+00  1.1665e+01  2.9231e+00 -8.0358e+00 -1.6056e+01
+#>  -7.7867e+00 -1.0212e+01  5.6575e+00 -1.1990e+01  4.8936e+00  1.1337e+00
+#>   7.9012e-01  5.6039e+00 -1.4291e-01 -4.8736e+00  1.1421e+01  4.9406e+00
+#>  -9.1965e+00  7.6237e+00  3.1152e+00  6.2133e-01  1.2733e+01 -5.3896e+00
+#>   4.9871e+00 -5.2846e+00 -2.7870e+00 -1.3440e+01  3.1454e+00  5.3618e+00
+#>  -6.3547e+00  2.7333e+00 -1.3748e+00 -1.8790e+01 -1.3703e+01 -1.8334e+00
+#>  -3.5498e+00  1.4475e+01 -6.4595e+00 -8.0995e+00  7.7304e+00 -2.0251e+00
+#>   1.1512e+00  5.1444e+00  1.3033e+01  1.1946e+00  1.0004e+01 -2.5338e+00
+#>  -3.1957e+00  4.4541e+00 -2.5417e+00 -2.4648e+00  1.3660e+01  4.8155e+00
+#>  -4.0732e+00 -4.5985e+00  2.8042e+00 -2.7015e+00  4.3687e+00  2.9475e+00
+#>  -7.9163e-01 -2.3531e+00  2.4937e+00  7.7356e+00 -1.3997e-01 -1.5672e+00
+#>   6.6296e+00  8.4515e+00  2.2220e+00 -1.7747e+01 -1.0495e+00  1.1736e+01
+#>  -1.3087e+01 -4.4062e+00  8.5306e+00 -3.4212e+00 -5.2333e+00  7.4359e+00
+#>  -1.4756e+00  1.2122e+01 -1.4518e+00  3.6727e+00  7.2365e+00 -5.3126e+00
+#>   2.8248e+00 -1.4126e+00  1.9080e+00 -1.7101e+00  7.1504e+00 -6.1774e-01
+#>   1.3368e+00  1.2437e+00 -1.8364e+00  1.5488e-01  7.3460e-02  2.2573e+00
+#>   7.9123e+00  1.2507e+00  5.1374e+00 -1.8863e+00 -7.5174e-01 -1.9342e+00
+#>   5.6390e+00 -1.0565e+01  1.2107e+01 -5.1673e+00 -4.0482e+00  7.2103e+00
+#>   5.0608e+00 -1.0691e+00  3.8633e+00 -1.1259e+01  3.7769e+00  5.3623e+00
+#>   1.6280e+00 -4.3256e-01  3.8420e+00  2.7479e+00  5.3882e+00 -5.7460e+00
+#>   1.6793e+00 -4.8205e+00  8.6883e-02 -5.4335e+00 -4.3271e+00  2.5010e+00
+#>  -6.4518e-01 -7.1352e+00  2.1878e+00 -8.7050e+00  5.3164e+00  4.5168e+00
+#>   8.7106e-01 -4.2204e+00 -2.2179e+00 -8.3934e+00  4.2304e-01 -5.3060e+00
+#>  -5.8044e+00  2.0624e+00  3.9457e+00 -1.6974e+00  1.7185e+00 -1.8120e+01
+#>  -1.8008e+00  7.9389e+00 -2.0292e+00  9.9911e+00 -4.1638e+00 -9.4970e+00
+#> 
+#> Columns 43 to 48  8.4844e+00 -1.0993e+01  1.8227e+00  4.6265e+00  8.9364e+00  5.3606e+00
+#>  -2.1073e-01 -4.0747e+00 -7.3497e+00 -9.5296e+00  2.0769e+00  4.8151e-01
+#>   5.1941e-01 -7.9306e+00 -5.7831e-02  2.7548e+00  1.1401e+00  1.6086e-01
+#>  -4.2443e+00 -4.9963e+00 -1.8685e+01 -1.0191e+01 -3.2068e+00  3.8290e+00
+#>   4.5830e+00  2.9600e+00  2.3919e+00  2.4379e+00  2.1759e+01  5.7938e+00
+#>  -3.6696e+00 -9.7537e+00 -1.6006e+01  1.6218e+01  9.8195e+00  5.1813e+00
+#>   4.2435e+00  1.1194e+01  3.7096e+00 -1.1631e+01 -1.0264e+01 -1.0055e+00
+#>   1.0176e+01 -1.1411e+00 -5.0666e+00 -5.7718e+00 -1.2300e+01 -5.1066e+00
+#>  -9.5501e+00 -1.6391e+01 -1.4565e+00  4.7620e+00 -1.5637e+00  2.0570e+00
+#>  -8.3540e+00  8.5855e-01  2.4663e+00  2.7962e+00 -1.3909e+00  2.4639e+00
+#>  -3.8378e-01  2.8382e+00  4.2586e+00 -7.7955e+00 -1.3444e+01  1.4908e+00
+#>  -3.2816e+00  1.4829e+00 -9.8738e+00  2.7608e-01 -1.1751e+01  1.8662e+00
+#>  -1.6035e+01 -1.1692e+01 -1.5651e+01 -1.1390e+00 -3.4574e-01 -4.6043e+00
+#>  -1.1310e+01  4.9410e+00 -9.6355e+00 -6.4729e+00 -1.0246e+01  2.7205e+00
+#>   1.1236e+00 -1.0729e+01  1.0165e+01 -7.4414e+00  7.2387e+00  6.5626e+00
+#>   7.2329e+00 -1.1892e+00  5.7062e+00 -2.4368e-01 -5.6294e+00 -9.3834e+00
+#>  -3.2415e+00  4.3069e+00  3.3141e+00  3.5591e+00  4.4030e+00 -1.5876e+00
+#>  -2.2057e+00 -5.5644e+00  4.4626e+00 -2.2278e+00  5.4963e-01  3.4175e+00
+#>   6.0468e+00  2.5824e+00 -4.5323e-01  1.0158e+00  2.1676e+00 -3.2730e+00
+#>  -3.4195e+00 -5.4786e+00 -9.8319e+00  1.1339e+00 -2.3785e+00  7.5867e-01
+#>  -1.6855e+00  4.9282e+00 -2.3620e+00 -3.9824e-01 -8.8625e+00 -6.2472e+00
+#>   6.7280e+00 -1.2828e+00 -2.5061e+00 -8.0683e+00  3.2559e+00  1.0335e+01
+#>   3.1020e-01  3.3637e+00  1.4163e-01 -6.4928e-01 -1.0285e+01  2.7480e+00
+#>   9.8778e+00 -4.2855e+00  7.4078e-01  9.6354e+00  8.0766e+00  7.8449e+00
+#>  -3.0624e+00  1.1307e+01  4.1798e+00 -1.0048e+01 -1.5100e+01  1.7636e+00
+#>  -6.1965e+00 -5.9411e+00 -4.2883e+00  2.8164e+00 -4.2836e-02  1.2683e-02
+#>  -5.2290e+00 -1.1419e+01 -2.5172e+00  9.8837e+00  8.6055e+00 -2.7170e+00
+#>   9.8374e+00  4.8829e+00  5.9017e+00  1.3813e+00  1.0165e+01  3.8461e-01
+#>  -4.0441e-01  2.4763e+00  1.3952e+00 -7.5240e-01 -6.3666e+00  2.2227e+00
+#>  -8.6997e+00 -2.0698e+01  2.4564e+00  1.0931e+01  7.2985e+00  7.0016e+00
+#>   3.6372e+00 -4.2753e+00  9.1222e-02  1.1007e+01  6.9259e+00 -6.6803e+00
+#>  -4.3660e+00 -3.8195e-01  7.0135e+00 -2.6839e+00 -7.3719e+00 -5.8731e+00
+#>   2.5388e+00  2.1216e+00  1.3341e+01 -3.4585e+00 -7.8873e+00  1.6598e+00
 #> 
 #> (8,.,.) = 
-#>  Columns 1 to 6  1.6767e+01  7.3242e+00  2.2688e+00 -1.1885e+01 -4.0734e+00  3.3318e+00
-#>   1.1166e+01  1.7645e+01 -6.4430e+00 -4.1363e+00 -2.6064e+00 -1.7742e+00
-#>  -5.5551e+00  8.0560e-01 -4.0773e-01  6.0480e+00  2.0295e+01 -3.6027e+00
-#>  -1.4078e+00 -2.3412e+01  1.2144e+01  5.2885e+00  7.3681e+00  2.1041e+00
-#>   2.5697e+00 -6.0522e+00  1.5430e+00 -1.5490e+00  4.9428e+00  7.4056e+00
-#>  -1.9167e+00 -6.7456e+00  7.2300e+00  1.8173e+00  1.1700e+01  4.0283e+00
-#>  -4.1001e+00 -1.3626e+01  7.4635e+00 -2.5361e+00 -1.2605e+01  4.6264e+00
-#>  -3.6862e+00 -8.2448e+00 -1.7109e+00 -8.2455e+00 -6.6692e+00 -1.0588e+00
-#>  -1.0147e+01 -5.4059e+00 -9.8364e+00  1.0415e+01  1.1184e+00  1.0907e+01
-#>   6.4281e+00  6.9009e+00  1.4842e+01 -2.3334e+00 -2.5984e+00 -9.1516e+00
-#>   6.5785e+00 -3.3714e+00 -1.7497e+01  4.7739e+00  7.6406e+00  1.7642e+01
-#>  -5.3044e+00  2.2508e+00 -1.0266e+01  4.9423e+00 -1.1276e+01  5.0438e+00
-#>   5.4556e+00  4.8300e+00 -1.0827e+01 -8.4740e+00  2.4237e+00  2.5224e-02
-#>  -1.2140e+01  4.3315e+00  1.0711e+01  1.6573e+01  7.7428e-01 -8.4217e+00
-#>   4.2287e+00 -1.9173e+00 -5.8150e+00 -7.3568e+00  1.4070e+01 -4.5091e+00
-#>   5.3989e+00  6.2967e+00  2.2752e+00 -7.8721e+00  3.6903e+00 -4.5436e+00
-#>  -3.9085e+00 -6.5628e+00  6.9691e+00 -1.7535e+01  2.0640e+00  2.8361e+00
-#>  -1.1818e+00 -1.7437e+00 -7.5470e+00  1.0356e+00  3.6821e+00 -9.2207e+00
-#>   4.5794e+00 -5.4332e-01 -1.0079e+01  1.0920e+00 -7.0514e+00 -6.2585e+00
-#>  -1.0217e+01  7.5117e+00 -1.0014e+01 -1.2400e+01  1.1899e+01 -6.6089e+00
-#>  -2.2282e+01 -5.4358e+00  1.4162e+00  1.1134e+01  3.8861e+00 -6.9433e+00
-#>  -4.4862e+00 -4.4780e+00  3.9681e+00 -8.0532e+00 -1.5911e+00 -2.8791e+00
-#>   3.1209e+00  2.8594e+00  4.0225e+00 -2.8797e+00  1.2730e+01 -3.6122e+00
-#>   8.4232e+00 -6.2612e+00  1.2269e+01  5.9408e+00  1.1985e+01 -5.5113e+00
-#>  -4.0361e+00 -4.6405e+00 -1.3339e+01  6.2889e+00  9.8516e+00 -7.8917e+00
-#>  -6.0944e+00  5.6074e-01  6.4615e+00 -2.0297e+01  4.6597e+00 -1.1273e+01
-#>  -8.1253e+00 -1.5362e+01  1.7911e+01  1.8621e+00  5.5949e-01  2.8031e+00
-#>   2.0541e+01  2.3189e+00  6.7424e+00 -4.7597e+00 -6.9405e+00 -1.1465e+01
-#>  -1.7620e+01  1.1792e+01 -7.7298e+00  5.4394e+00 -2.1601e+00 -4.3825e+00
-#>   1.9074e+01  1.3653e+01 -1.3568e+01 -1.2175e+01 -1.7947e+01  1.0558e+01
-#>  -1.0617e+01 -1.4844e+01 -1.3629e+00 -3.0530e+00 -4.5964e+00 -8.6009e-01
-#>  -6.2399e+00  1.8821e+01 -1.3541e+01  6.2537e+00  3.8780e+00  2.3052e+00
-#>   1.0811e+01  8.7568e+00 -6.4851e+00  1.1809e+01 -4.8284e+00  7.9604e+00
+#>  Columns 1 to 6 -2.1991e-01  1.6673e+01 -3.4127e+00 -1.0752e+00  4.6394e+00 -1.1888e+01
+#>   1.8849e+00  3.4665e+00  1.0323e+01  1.4550e+01 -1.1867e+00 -5.2968e+00
+#>  -2.2804e+00  7.7377e+00  4.6782e+00  2.2482e+00  1.8199e+00  8.1519e+00
+#>  -5.7545e+00  1.5397e+00 -1.7574e+00 -5.6203e+00 -6.7317e-01 -1.5588e+01
+#>   9.1743e+00  1.5010e+01  6.8509e+00  1.1166e+01 -4.2284e+00  4.9119e+00
+#>   5.7229e+00 -5.1175e+00 -9.0236e+00  5.2948e+00 -6.2189e+00  8.7238e+00
+#>  -5.9541e-01  2.5129e+00  1.7511e+00 -7.8496e+00  1.3394e+00  2.0330e+00
+#>  -3.7668e+00  7.1366e-01 -3.9535e+00 -1.3764e+01  1.6906e+01 -5.6384e+00
+#>  -5.5460e+00 -4.9393e+00  2.0917e+01  1.6005e+01 -2.2261e+00 -1.0742e+00
+#>  -9.8901e+00 -2.7049e-01 -1.5903e+00 -2.6724e+00  1.6916e+00  6.9718e+00
+#>   9.0495e+00  6.0712e+00  1.5977e-01  4.7806e+00  4.8632e+00 -1.1852e+01
+#>  -1.1590e+00  8.4845e+00  1.4225e+01 -8.9999e+00  8.8118e+00 -1.1974e+01
+#>  -9.8565e+00 -1.9384e+01  1.1098e+01 -3.7382e+00  3.4371e+00  1.3571e+01
+#>  -1.5114e+01 -5.4141e+00 -3.7146e-01 -1.1213e+01 -1.2086e-01 -1.0811e+00
+#>  -3.7298e+00  1.7604e-01  8.6379e+00 -4.3931e+00  1.6817e+01 -4.1764e+00
+#>  -1.0573e+01 -1.9006e+01 -8.9126e+00 -3.1380e+00 -1.1939e-01  1.0582e+01
+#>  -1.0518e+00  8.2879e+00  1.0026e+01  5.0245e+00  1.1725e+01 -1.4497e+01
+#>   8.8632e+00 -1.3124e+00 -4.7036e+00  8.3910e+00  1.0005e+01  3.0517e+00
+#>  -5.9235e+00 -7.5394e+00 -7.5574e+00  1.8704e+00 -2.7083e+00  2.5931e+00
+#>  -2.4205e+01 -6.4595e+00  8.2175e+00  3.9585e+00 -6.7921e+00  1.4578e+01
+#>  -1.1433e+00 -1.5799e+00  9.5144e+00  4.8407e+00  5.2871e+00 -6.1489e+00
+#>  -2.5805e-01  8.9328e+00  1.7978e-01 -5.2197e+00  7.0472e+00 -2.5667e+00
+#>   6.3651e+00  5.0874e+00  6.1812e+00 -4.6365e-01 -4.4905e+00  5.4756e+00
+#>   6.1972e+00  3.8184e+00  3.5411e+00 -1.1327e+01  4.2856e+00  2.0127e+00
+#>  -1.9146e+01 -1.0122e+00  2.7957e+00  1.4016e+00 -1.2378e+01  2.9539e+00
+#>  -4.9825e+00 -1.1890e+01 -5.8867e+00  2.0944e+00 -7.1109e+00  5.0450e+00
+#>  -7.1506e+00 -4.4769e+00 -3.5039e+00 -7.2694e+00 -9.4921e+00  3.4874e+00
+#>  -2.1425e-01 -8.6799e-01 -1.4497e+01 -5.6408e-01 -1.3404e+01  9.1972e-01
+#>   4.8577e+00 -3.8483e+00  6.9185e+00 -1.1345e+00  6.2568e+00  2.4765e+00
+#>  -3.0364e+00 -1.3705e+01  1.0795e+00 -2.4379e-01  9.6982e+00  1.8064e+00
+#>   3.8231e+00 -8.2187e+00  1.3277e+01  2.2467e+00 -2.2437e-01  5.5701e+00
+#>  -1.3268e+01 -6.1761e+00 -9.3416e-02 -1.0753e+00 -1.5743e+00 -5.4825e+00
+#>   9.7482e+00  1.4574e+01  2.8919e-01 -7.1233e+00  2.7676e+00 -1.8234e+01
 #> 
-#> Columns 7 to 12  8.9290e+00  1.3536e+01  1.6096e+01 -7.1693e+00  1.2483e+01  1.1455e+01
-#>   5.2199e+00  5.6739e+00  1.1925e+01  6.1331e+00  3.9429e+00 -4.3705e-02
-#>  -5.7627e+00 -6.5053e+00 -3.2291e+00  8.3001e+00 -7.4037e+00 -3.3170e+00
-#>  -8.6931e+00 -1.4302e+01 -5.0089e+00 -8.2270e+00 -6.2948e+00  7.2968e+00
-#>  -3.5095e+00 -5.2325e+00  7.1503e+00  1.1552e+01 -9.3108e-01 -7.8535e-01
-#>   9.4466e+00  4.1475e+00  6.0636e+00  9.1754e+00  6.2806e+00  2.5567e+00
-#>  -1.3670e+00 -1.1705e+01  4.5845e-02 -9.9646e+00  9.0778e+00  1.5874e+01
-#>  -8.5052e-01 -9.3265e+00  7.1684e+00  1.2459e+01 -7.0863e+00 -1.5340e+00
-#>  -1.0967e+00 -6.8202e+00 -4.1483e+00  4.5109e-01  7.2468e+00 -3.0846e+00
-#>   1.8900e+00 -2.9275e+00  1.2501e+01 -2.6666e+00 -1.0941e+01  7.5141e+00
-#>   1.2399e+00 -6.7585e+00 -3.3682e+00  9.8080e+00 -3.6451e+00 -5.7484e-01
-#>   2.3451e+00  3.7670e+00 -4.2033e-01 -5.0846e+00  4.7556e+00 -1.1006e+00
-#>   8.3667e+00  3.6975e+00 -1.0243e+01 -6.9477e+00  1.0840e+00  8.2389e-01
-#>  -1.3075e+01  3.0193e+00  2.7116e+00 -1.9629e+00  5.1973e+00 -4.0322e+00
-#>   6.1567e+00 -2.2938e+00  9.9950e+00 -3.3615e+00  8.2121e+00 -7.6035e-01
-#>  -2.1567e+00  4.1321e+00  2.0549e+00 -5.1987e-01  7.5985e-01 -2.0032e+00
-#>  -2.2345e+00  3.1456e-01  7.0497e+00  3.9596e+00  4.0817e+00 -7.9889e-01
-#>   1.2495e+01 -6.3476e+00 -5.9824e-01 -5.4388e+00 -8.6041e+00 -4.8455e+00
-#>   1.6624e+01 -1.1884e+01 -2.9734e+00  1.5225e+01 -7.3970e+00  5.7698e+00
-#>   9.8040e+00  4.3454e+00  7.0386e+00  5.5264e+00 -8.3666e+00  3.2071e-01
-#>  -2.4268e+00 -1.5725e+01  9.1409e+00 -4.2469e+00 -2.6802e+00  3.1987e+00
-#>  -6.8343e+00  8.6813e+00 -9.9641e+00  1.4666e-01  6.9162e-01 -8.6080e+00
-#>   9.1200e+00  6.2205e+00  4.8410e+00 -2.9351e+00  4.7084e+00 -1.6139e+00
-#>   1.8724e+01  3.2264e+00 -6.2778e+00  6.9400e+00 -2.6209e+00  9.7244e+00
-#>  -2.7610e+00 -5.3795e+00  1.8431e+00 -2.5759e+00  4.9165e+00 -7.0577e+00
-#>  -3.9320e+00 -4.4469e+00  1.7978e+00 -2.6139e+00 -2.1787e+00 -2.3098e+00
-#>  -9.8916e+00 -1.7884e+01  6.5494e+00  1.5247e+00  9.8584e+00  4.3776e+00
-#>   1.6337e+01 -9.0102e-01 -2.9395e-01  1.1285e+00 -1.3418e+01 -2.7005e+00
-#>  -5.5151e+00  4.3336e+00 -3.2838e+00  1.0588e+00  6.9050e+00 -2.5221e+00
-#>   1.2281e+01  2.1200e+01  1.0232e+01 -9.2227e+00  3.8279e+00  3.2877e+00
-#>   6.6581e+00 -8.2416e+00 -1.6229e+00 -5.3015e+00  4.4514e+00  2.3358e+00
-#>  -4.5829e+00  1.2662e+01 -1.1380e+01  2.7443e+00  4.3965e+00 -4.6810e+00
-#>   1.2336e+01  1.2162e+01  8.9652e-01 -1.9086e+00  1.8242e+00 -3.1326e-01
+#> Columns 7 to 12  6.0727e-01 -1.7450e+01  1.1066e+01 -1.9776e+01 -9.8348e+00  1.0180e+01
+#>   2.9319e+00  2.8190e+00  3.7577e+00  7.5215e+00  2.1050e+00 -9.6115e+00
+#>  -1.2627e+00  2.1427e+00  5.3874e+00  3.1262e+00  5.5355e+00  4.2578e+00
+#>   6.3264e+00 -7.9869e-02  3.2069e+00  6.9090e+00 -1.5014e+00 -3.4749e+00
+#>  -7.2077e+00 -1.1029e+01 -1.0292e+00 -6.8186e+00  6.8771e+00  2.7332e+00
+#>   1.2670e+00  4.8276e+00 -5.7310e+00 -1.3312e+00  3.3062e+00 -4.3557e-01
+#>   2.1689e+00  1.1304e+01  2.6170e+00  1.7815e+01 -3.4710e+00 -1.7491e+00
+#>  -3.0858e+00 -2.5723e+00 -2.0404e+00  7.3582e+00 -1.0074e+00 -6.5688e+00
+#>   7.2729e+00  9.6421e+00  5.5380e+00  1.1065e+01  9.7760e+00  8.9167e+00
+#>  -5.9246e+00 -3.4873e+00 -1.1045e+01  9.4299e-01  5.3879e+00 -2.0889e+00
+#>   4.6444e+00 -1.8008e+00 -1.3506e+01 -7.0103e-02 -4.6622e+00 -1.5777e+00
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+#>  -1.1641e+01  8.5029e+00  1.3203e+00  1.4275e+01 -1.2416e+00  1.5976e+00
+#>  -5.9534e+00 -3.2740e-01 -3.0885e+00  1.0430e+01  9.7626e-01 -3.2039e+00
+#>   9.0722e+00 -5.2142e+00  4.9964e+00  3.0354e-01  5.1905e+00  1.5381e+01
+#>  -3.9549e+00  4.7760e+00 -2.9696e+00 -1.2167e+00 -1.2935e+01  2.0562e+00
+#>   7.0679e+00 -1.2820e+01  6.6629e+00  5.4050e+00  1.1801e+00  4.4521e+00
+#>   9.7868e-01  8.8204e+00 -4.0238e+00  7.7560e+00  3.2546e+00  1.4237e+01
+#>   1.0233e+01  3.4770e+00 -3.6457e+00 -1.6441e+00  6.4198e+00  2.2741e+00
+#>   3.6677e+00 -6.1098e+00 -5.5732e+00 -7.4927e+00  5.7719e+00  5.5598e+00
+#>  -1.0086e+00  1.4438e+00 -5.7456e+00  3.3578e+00 -1.4560e+01 -1.2021e+01
+#>   1.4861e+01  6.8248e+00  6.8424e+00 -9.8675e+00  7.4436e+00  1.0655e+01
+#>  -8.8432e+00  1.5606e+01  9.1526e+00  2.3035e+01 -1.4971e+00  7.7885e+00
+#>   9.3633e+00  1.5908e-01 -1.1322e+00  4.9238e+00  1.3105e+00  7.8418e+00
+#>  -3.7211e+00  1.3144e+01  9.0097e+00  4.4444e+00 -5.7108e+00  4.3818e+00
+#>  -1.1863e+01 -2.7300e+00 -9.1255e+00 -6.2644e+00 -1.3065e+01 -1.0623e+01
+#>  -8.3062e+00 -9.0573e-01 -7.3139e+00 -4.5755e+00 -8.2685e+00  3.2447e-01
+#>   1.5757e+00  3.8333e+00 -4.7134e+00 -6.4036e+00  5.3045e+00  3.4003e+00
+#>  -1.6621e+00 -4.7173e+00 -4.3790e+00 -3.9702e+00 -1.6218e+00 -2.1331e-02
+#>  -3.9922e+00  6.5052e+00 -6.6319e+00  1.3835e+01  3.8747e+00  1.1379e+01
+#>  -9.1570e+00  9.7005e-04 -9.3493e+00  2.5065e+00 -8.8862e+00 -5.2180e+00
+#>   2.7796e+00  2.4641e+00 -1.2143e-01 -1.6348e+00  7.7353e+00 -7.5575e+00
+#>   8.0157e+00  8.2147e-01  2.1710e+00 -3.7390e+00  2.7682e+00  1.3253e+00
 #> 
-#> Columns 13 to 18 -6.5864e+00 -1.0712e+01  1.9184e+00 -7.9737e+00  8.0341e-01 -6.6428e+00
-#>   3.9817e+00 -4.6648e+00 -2.4271e+00  1.6329e+00  3.4401e+00  5.3616e+00
-#>   2.2465e+00 -5.6628e+00  4.1712e-01  2.4878e+00  8.9113e+00  1.2079e+01
-#>  -1.0932e-01 -1.2413e+00 -1.4202e-02  1.0870e+00 -3.4432e+00  1.7965e+01
-#>  -1.1225e+00 -3.2036e+00  7.4699e+00 -1.8162e+00  3.0432e-01 -2.7530e+00
-#>   6.3094e+00  1.5723e+00 -4.2568e+00 -1.4982e+00  1.3841e+00 -1.3459e+01
-#>  -1.0469e+00 -3.9008e+00 -1.3422e+01  1.2605e+01 -2.4243e-01 -6.4081e+00
-#>   4.0135e+00  5.0065e+00  7.7655e+00 -3.5427e+00 -7.3873e+00  1.6300e+00
-#>   5.7995e+00  1.1139e+01 -6.9868e+00  3.4718e+00  3.2083e+00  8.6517e+00
-#>   3.8380e-01 -4.6602e+00 -5.0067e-01 -4.6188e-01 -1.0016e+01  7.1506e+00
-#>   8.1005e+00 -8.0245e+00  1.1189e-01  2.7982e+00  1.0693e+01  2.4759e+00
-#>  -4.8095e+00  4.7866e+00 -5.4752e+00 -2.1411e+00  4.1932e+00 -1.1467e+01
-#>   4.3658e-01 -5.7922e+00 -3.6600e+00  4.0172e+00 -4.1100e+00 -3.1343e+00
-#>  -6.9230e+00  1.6722e+01  1.2365e+01 -1.2803e+01  4.0601e+00 -9.9373e+00
-#>  -5.9905e+00 -4.2004e+00 -3.8107e+00  1.3444e+01 -6.3351e+00  1.8679e+00
-#>  -7.2217e+00  1.9600e+00  1.8366e+00 -9.0836e-01  6.3407e+00  2.5531e+00
-#>   5.2419e+00 -1.9273e+00  2.6189e+00 -1.6239e+00 -5.9923e+00 -8.3155e+00
-#>  -5.4557e+00  1.4535e+01 -1.5117e+01  7.2507e+00 -4.4490e+00 -1.6060e-01
-#>   1.4703e+00  9.8438e-01  4.8113e+00 -8.3850e+00  8.4060e+00  7.6364e+00
-#>   2.7511e+00  1.2935e+01  1.8571e+00 -2.6687e+00 -1.7088e+01  4.8235e+00
-#>  -3.5270e+00  7.8432e+00 -3.4736e+00  2.9558e+00 -4.3210e+00  8.6346e+00
-#>   7.3180e+00  4.5550e+00  2.7375e+00  8.4454e-01  3.2313e+00 -8.0284e+00
-#>   3.5681e+00  4.2156e+00 -1.3016e+01 -2.6686e+00  7.8514e+00 -1.1472e+01
-#>   4.8989e+00 -3.6281e+00 -3.1882e+00 -9.6628e-01 -1.1654e+01  3.9920e+00
-#>  -6.1137e+00 -1.6611e+00  7.9596e-01 -2.5879e+00  3.8247e+00  5.4991e+00
-#>  -3.2835e+00  2.8457e+00 -4.2449e+00  5.1501e+00 -8.8733e+00 -4.2499e+00
-#>   2.7270e+00  3.2537e+00 -7.4448e+00 -3.9524e+00  6.0253e+00 -2.9487e+00
-#>   8.9148e+00  1.3280e+00 -4.6664e+00 -1.0784e+01  3.8554e+00 -8.9095e-01
-#>  -5.5961e+00  6.6477e+00  8.5598e+00  2.1061e+00  6.4038e-01  5.2681e+00
-#>   1.1707e+01 -1.2155e+01 -5.7153e+00  2.8509e+00 -1.4552e+00 -1.5719e+01
-#>   2.7364e+00  8.6112e+00 -1.3925e+01 -7.8262e-02 -3.3054e+00 -1.2692e+01
-#>   1.5652e+00 -1.6843e+00  1.4623e+01 -5.7382e+00  1.3702e+01 -6.5341e+00
-#>   8.5536e+00 -2.3798e+00 -1.0280e+01  6.6825e-01  7.5417e-01  4.4648e+00
+#> Columns 13 to 18 -7.6920e+00  5.9269e-01 -7.1614e+00  9.5002e+00  7.1864e+00 -7.5563e+00
+#>  -6.5451e+00  8.7649e-01 -5.2288e-01  1.6775e+00 -1.5374e+00 -5.7273e+00
+#>  -3.5930e+00 -1.9869e+00  7.3439e+00 -5.8190e+00 -2.2919e+00 -8.5922e+00
+#>   5.2737e+00  6.9455e+00  3.0809e+00  4.3869e+00  4.8120e+00 -6.1846e-01
+#>  -1.2605e+00 -4.5768e+00 -1.2679e+00  5.4245e+00  7.8463e+00  8.2616e+00
+#>  -1.1359e+00 -1.6792e+00 -1.2544e+00 -3.9346e+00 -6.1791e+00 -6.8598e-01
+#>  -4.9028e+00 -1.9557e+00 -8.6282e-01 -9.4554e+00 -1.6487e+00  1.8983e+00
+#>   6.5064e+00  4.5290e+00 -4.7162e-01 -9.3650e+00  7.5711e+00 -8.2074e+00
+#>   1.4114e+01 -4.6716e+00  1.4927e+01  1.0862e+01  6.3469e+00  4.1091e+00
+#>  -1.7531e+00 -2.6965e-02 -3.8979e+00  2.8491e+00 -3.5397e+00  5.6824e-01
+#>   3.3010e+00  8.1866e+00 -1.6086e+01 -6.2636e+00  9.4287e+00 -1.3996e+01
+#>   6.8234e+00 -6.6048e+00  5.9744e+00 -5.3237e+00  8.4941e+00 -5.3328e+00
+#>   2.9628e+00  3.5036e+00 -3.8744e+00 -4.8421e+00  9.9429e+00 -7.7878e+00
+#>  -1.6670e+00  1.6263e+00 -1.5387e+00  2.1850e-01  9.7224e+00 -1.5103e+00
+#>  -2.4517e+00  4.4724e+00  6.1326e+00  1.2219e+00  3.6750e+00  4.7848e+00
+#>   7.1773e+00 -6.3502e+00  1.8285e+00  5.2765e+00 -5.7786e+00 -3.5509e+00
+#>  -4.9454e+00 -8.0056e+00  1.7885e+00  1.4500e+00  1.7754e+00  1.0025e+00
+#>   5.7514e-01  1.3720e+01 -6.2601e+00  1.3260e+01 -5.1964e+00 -4.8863e+00
+#>  -2.3515e+00  2.5392e+00 -6.5431e+00 -3.7797e+00 -3.4916e+00 -1.2545e+01
+#>  -6.3052e+00  2.8412e+00  4.0891e+00 -9.2261e+00 -7.8222e+00  4.0850e+00
+#>   4.6204e+00 -1.3443e+01  9.2306e-01 -1.3092e+00  2.0783e+00 -6.4244e+00
+#>   5.3291e+00 -7.3519e+00  7.4421e+00 -6.1952e+00 -3.4651e+00  1.2389e-01
+#>  -5.1909e+00 -4.0824e+00 -8.4516e-01 -3.5375e+00  4.0704e+00  4.3225e+00
+#>   1.1315e+01 -2.6833e+00  6.0377e-01  1.4464e+00  5.2066e+00 -5.5932e+00
+#>  -7.0935e+00 -8.3806e+00 -5.8547e+00 -3.6996e+00 -1.3486e+01  2.7401e+00
+#>  -7.4829e+00 -5.2384e+00 -4.1449e+00  2.7311e-01  8.5946e-01 -1.8546e+00
+#>   2.6979e+00 -2.4109e+00  9.1093e+00 -3.7012e+00 -4.6450e+00  7.0998e+00
+#>  -1.7188e+01 -5.8254e-01 -2.5691e+00  8.2737e+00 -4.1726e+00  2.9384e-01
+#>   7.8109e+00 -2.2146e+00 -2.4523e+00 -3.4486e+00  7.5098e+00 -3.4225e+00
+#>   8.9175e+00 -3.4010e+00 -1.0550e+00  5.2823e+00  1.1399e+01  7.6961e-01
+#>  -1.6160e+00  4.5074e-01  3.5103e-01 -1.4586e+00  8.9113e+00 -1.4385e+00
+#>  -7.9514e+00 -7.7930e+00  7.1304e+00  4.4759e+00  9.9903e+00  3.4564e+00
+#>   1.4941e+01  2.3468e+00  1.0423e+01  3.4320e+00 -1.6993e+00  5.8884e+00
 #> 
-#> Columns 19 to 24 -4.4853e+00 -2.1131e+00  2.3472e+00  1.5614e+00 -9.1763e+00  1.3149e+00
-#>   3.3663e+00 -1.1476e+00 -4.6271e+00 -6.8828e+00 -2.7274e+00  8.1809e+00
-#>  -6.0512e+00 -1.9255e+00 -1.2470e+00  2.3085e+00  5.1944e+00  2.7115e+00
-#>   6.8703e+00 -9.9798e-01  5.7421e+00  1.2826e+01  1.3569e+01 -1.3485e+01
-#>  -7.8852e+00 -5.3608e+00 -1.2194e+01 -2.2309e+00  7.4194e-01  3.8362e+00
-#>  -7.9683e+00 -3.7411e+00  9.5716e-01  7.7865e+00 -1.2211e+00  1.3266e+01
-#>  -8.6393e+00  1.0575e+01  1.8087e+01 -6.3996e+00 -2.0487e+00 -2.4887e+00
-#>  -1.4954e+01 -2.8493e+00 -1.0947e-01  9.6807e+00  9.7672e+00 -4.4043e+00
-#>  -1.3268e+01  2.1509e+00 -1.2294e+00  1.0422e-01  6.8833e+00  1.1067e+01
-#>  -1.1917e+01 -5.0383e+00  6.8034e+00 -1.3399e+01  3.1128e+00 -1.1073e+00
-#>   1.5490e+00  1.5129e+01 -5.0927e+00  4.9878e+00  1.4499e+01 -2.5065e-01
-#>  -2.5972e+00  7.2299e+00  7.2963e+00 -1.6446e+00 -9.7672e+00  2.2963e-01
-#>   3.6946e-01 -1.1741e+00 -5.4392e+00 -3.6667e+00  2.6146e+00 -4.2971e+00
-#>   6.6354e+00  7.4895e+00 -1.1072e+01  6.1214e+00 -2.4847e+01 -1.9569e+00
-#>  -5.5859e+00  3.3497e-01 -5.0915e+00 -7.4545e+00 -1.7294e+00  6.5300e+00
-#>   6.9209e+00 -2.9821e+00 -1.0543e+01  1.0908e+01  7.0799e+00 -7.8464e-01
-#>  -4.7602e+00 -1.8840e+00 -5.3847e+00 -5.1367e-01 -6.6393e+00  1.1736e+00
-#>  -3.5408e-01  6.9321e+00  4.6526e+00 -3.6509e+00 -1.8282e-01 -8.6614e+00
-#>  -1.3014e+01  9.7575e-01 -3.6201e+00  4.9557e+00  3.5110e+00 -1.1991e+00
-#>   4.2591e-01 -7.2595e+00 -8.4041e+00 -6.0745e+00  5.0202e+00  8.3158e+00
-#>  -1.0734e+01  6.3550e+00  1.2172e+01 -6.8847e+00 -7.6938e+00 -1.1426e+01
-#>   1.0593e+00 -4.3771e+00  1.9166e+00  7.3604e+00  6.9675e+00 -2.2241e+00
-#>   9.0315e+00  2.5758e+00 -1.6054e+00  3.1866e+00 -3.8649e+00  2.2475e+00
-#>  -4.5584e-01 -8.9639e+00  2.4443e+00 -1.3255e+01 -7.6367e+00 -1.4364e+00
-#>   6.0904e+00  5.5175e+00  2.9946e+00 -1.7121e+00 -1.1451e+01 -2.2756e+00
-#>   4.5679e+00 -2.0458e+01  8.9792e-01  8.5564e+00 -6.2259e+00 -2.7014e+00
-#>  -4.5265e+00  3.2387e+00 -6.7524e-01 -6.3879e+00  4.6612e+00 -7.4260e+00
-#>   4.2265e+00 -9.4775e+00 -1.7111e+01 -4.3356e+00 -1.8778e+01 -6.7874e+00
-#>  -2.2547e+00  6.5539e+00  2.1854e-01 -1.2272e+01 -5.7822e+00 -6.0883e+00
-#>  -8.5049e+00 -2.1965e+00 -5.3985e+00  9.2189e-02 -3.4820e+00  1.0115e+01
-#>  -8.2111e+00  7.4293e+00  2.7259e+01  7.2645e+00  1.7943e-01 -9.3449e+00
-#>   7.5274e+00 -5.9399e+00 -7.6304e+00  1.6460e+01 -9.6886e+00  8.5740e+00
-#>   1.2634e+01  7.2209e+00  9.3985e+00 -1.0274e+01 -6.0598e+00  9.2750e+00
+#> Columns 19 to 24  6.1759e+00  6.0554e+00 -1.0185e+01 -6.2366e+00  2.9177e+00  4.6337e+00
+#>  -5.8615e+00  8.1667e+00  1.2196e+01 -7.4644e+00 -8.1270e+00  1.8900e+00
+#>  -5.7314e+00  2.4860e+00  6.7279e+00 -1.8599e+00  3.7760e+00  4.9084e+00
+#>   5.9388e+00  1.0217e+01  2.7069e+00  6.4934e+00 -1.2533e+00 -6.9669e+00
+#>   5.6870e+00  2.5023e+00 -8.7799e+00 -9.3814e+00  1.6580e+00 -5.7190e+00
+#>   9.6274e-01  2.9697e+00 -3.8694e+00 -1.7460e+00 -2.8062e+00  1.5483e+01
+#>  -7.7445e+00  1.3139e+00  1.0859e+01 -2.5925e+00 -1.6224e+01  1.0072e+01
+#>   2.9867e-01 -1.3264e+01  3.5334e+00  6.3471e-01  6.7853e+00 -1.3984e+01
+#>  -3.7439e+00 -2.4743e+00  7.8707e+00  4.2741e-01 -1.3062e+00 -1.0236e+01
+#>  -2.7701e+00 -6.6087e+00 -3.5980e-01  2.6709e+00  3.5123e+00  8.5269e+00
+#>  -6.3297e+00 -2.0583e+01 -1.1579e+01 -2.4796e+00  6.0775e+00 -4.1803e+00
+#>   7.4053e+00  2.3480e+00  1.9527e+00 -8.5983e+00  1.2637e+00 -4.6078e+00
+#>   1.1062e+00  1.2743e+01 -5.0619e+00 -3.7008e+00  9.8096e+00  8.0512e+00
+#>  -1.9407e+00  1.2887e-01  9.4372e+00  8.3466e+00 -1.0280e+01 -7.1358e+00
+#>  -6.1644e+00  1.1022e+01  9.2906e+00 -1.9282e+00  7.3731e-02  2.8973e+00
+#>  -1.2167e-01 -7.1812e+00  5.8666e+00  5.1820e-01  1.1867e+01 -1.2023e+01
+#>  -5.0495e+00 -3.6320e+00  2.3960e-01 -9.4786e-01 -8.2408e-01  1.7076e-01
+#>  -8.9994e-01 -1.2141e+01  1.5057e+00  5.0502e+00 -1.0551e+01  9.8551e+00
+#>  -2.9791e+00 -7.1137e+00 -7.4300e+00  2.0750e+00  6.2254e+00  8.0560e+00
+#>  -4.0976e+00 -4.5619e-01  5.6149e+00 -1.4378e+00 -5.2066e+00  1.0559e+01
+#>  -1.6755e+01 -1.0811e+01  1.6114e+00 -4.3966e+00 -3.3135e+00  5.0921e+00
+#>   4.0110e+00  2.1547e+01 -4.0566e+00 -2.4677e-01  1.3307e+01  2.9139e+00
+#>  -5.4219e+00 -7.3401e+00  5.8030e+00 -3.1524e+00 -1.2266e+01 -4.1048e+00
+#>   7.2911e+00 -6.4379e+00 -1.7985e+00 -5.6837e+00 -1.0565e+00  7.3205e+00
+#>  -6.8436e+00  5.7475e+00  1.0584e+01  5.5899e+00 -8.1106e+00  5.2447e+00
+#>   1.8906e+00  7.2292e+00  5.0902e+00  4.5608e+00 -9.9641e+00  1.2505e+01
+#>   4.5123e-01  1.0429e+01 -8.5217e+00 -8.4860e-01 -4.6473e+00  1.2811e+01
+#>  -1.5758e+00  1.1252e+00 -1.6221e+01  6.2742e-02  1.3340e+00  2.5629e+00
+#>   3.6046e+00 -2.0595e+00  2.5240e+00  8.4628e+00 -8.3138e+00 -1.2419e+00
+#>  -6.2531e+00 -9.2732e+00 -7.5239e+00 -2.8955e+00 -1.8137e+00  3.0279e+00
+#>   1.8885e+00 -3.4502e+00  3.5810e-01  1.3649e-01  5.4780e+00 -1.2280e+01
+#>   2.6698e+00 -1.1913e+00 -6.9305e+00 -3.4033e+00  8.0761e+00 -8.2204e+00
+#>   4.0017e+00 -7.5190e-02 -6.1816e+00  1.2372e+01  9.1658e-01 -8.7322e+00
 #> 
-#> Columns 25 to 30 -1.6500e+00  1.2556e+01  4.7886e+00 -1.2065e+01 -1.1266e+01  1.6267e+01
-#>   5.6686e+00  7.5015e+00  4.9051e+00 -5.6231e+00 -8.5556e+00 -8.8199e+00
-#>   2.3384e+00  1.2451e+00 -3.1599e+00  1.2265e+01 -4.1203e+00 -5.0749e+00
-#>  -1.7114e+00 -7.9214e+00  2.9079e+00  3.2128e+00  2.6000e-01 -1.0404e+01
-#>   6.9109e+00  8.9490e+00 -8.1327e+00  3.3842e+00  9.4082e-01 -1.2268e+01
-#>   1.9725e+01  7.5589e+00 -3.3212e+00 -6.2901e+00  4.8042e+00  9.6396e+00
-#>   1.6098e-01  6.2835e+00 -4.2581e+00 -9.5596e+00  5.3666e-01  4.8876e+00
-#>   1.1118e+00  7.3037e+00 -8.2213e+00  1.0765e+00 -4.7982e+00 -1.8013e+01
-#>   2.7118e+00  1.0105e+01  4.1488e-01  5.4600e+00  2.4152e+00 -6.6327e+00
-#>   8.3795e-01  4.6922e+00 -2.7764e-01 -3.3877e+00  5.3466e+00  7.5152e+00
-#>   8.2482e+00  1.9565e+00 -1.5874e+01  1.2110e+01 -7.5095e+00 -4.2220e+00
-#>   1.7925e+00  4.7533e+00  1.5763e+00 -9.5619e+00  1.4166e+01  8.8671e+00
-#>   1.2803e+00 -7.2786e+00  1.9553e-01  3.9502e+00  8.1943e+00  3.4421e+00
-#>   1.4177e+00 -8.2810e+00  1.8800e+00  4.0896e+00  4.0136e-01  3.8002e+00
-#>   8.3683e+00 -7.2149e+00  2.0337e+00 -7.7635e+00 -1.4227e+00  1.4717e+01
-#>   7.8810e+00 -4.5467e+00  7.2520e+00 -5.8690e+00 -7.0170e+00  1.0698e+01
-#>   6.4822e+00 -3.0455e+00 -5.8516e-01  3.7871e+00 -2.0598e+01  2.5791e+00
-#>   1.1569e+01 -1.0959e+01  4.0629e+00  1.4087e+00  5.1808e+00  1.4298e+01
-#>   9.5239e-01  8.9995e+00 -3.8282e+00 -5.4321e+00 -2.1114e+00 -5.2939e+00
-#>   6.0747e+00 -5.8771e+00  2.1364e+00  3.0143e+00 -1.2609e+01  8.1266e+00
-#>  -6.9970e+00  4.0875e+00 -4.6289e+00  4.5900e+00  5.8148e+00  2.1470e+00
-#>   5.9624e+00 -8.3459e+00 -3.9486e-01  1.1296e+00 -7.7978e+00 -8.5532e+00
-#>   2.5669e-01 -9.6446e+00 -2.1524e+00  8.1044e+00 -6.5395e+00  9.2771e+00
-#>  -1.6091e+00  8.4890e-01  7.0265e+00 -3.2210e+00  3.4743e+00  8.7699e+00
-#>  -4.3547e+00 -1.0256e+01 -5.4367e-01 -1.8768e+00 -9.9176e+00  1.3847e+01
-#>  -3.5261e+00 -7.5636e+00 -2.1715e-01 -7.0807e+00 -1.4738e+01  1.4545e+01
-#>   7.3968e+00  7.2117e+00 -1.4983e+01 -3.1700e-01  4.2418e+00 -3.1306e+00
-#>   1.5941e+00 -2.3137e+00  4.9425e+00  1.1920e+01 -6.7446e+00  1.3191e+00
-#>  -1.9429e+00  8.0296e-01 -7.1434e+00 -6.7915e+00 -5.0054e+00  6.8658e+00
-#>  -8.3416e-01  6.5902e+00  2.2657e+00  4.3535e+00 -6.2116e+00  9.4883e+00
-#>  -4.5389e+00  8.2557e+00 -5.1779e+00 -2.0092e+00  2.3989e+00  1.1704e+01
-#>  -1.7732e+00  7.6986e-01  6.3287e+00  7.3798e+00  2.2958e+00  2.1280e+00
-#>  -2.2058e+00  7.9456e+00  2.4047e+00 -5.4327e+00  4.2458e+00  8.6109e+00
+#> Columns 25 to 30 -3.0744e+00 -3.2717e+00 -3.7389e+00  3.1911e+00 -1.0997e+01 -1.8438e+00
+#>  -7.5858e-01  4.3105e+00  5.5301e+00  2.1326e+00 -2.1913e+00  1.0136e+00
+#>   1.0817e+01  1.5704e+00  4.6649e+00  1.0951e+01 -2.3652e+00 -1.5745e-01
+#>  -1.5238e+00  1.0873e+01 -2.3438e+00 -4.2410e+00  5.2481e+00 -1.8236e+00
+#>  -3.6706e+00 -7.9073e+00  9.2750e+00  1.3319e+01 -1.0781e+00  2.4713e+00
+#>   5.7494e+00  6.4843e+00  5.4720e+00  5.0992e+00  1.2212e+00 -4.7350e+00
+#>  -2.2603e+00 -6.5329e+00 -1.3011e+01 -3.4549e+00  1.3042e+01  3.6660e-01
+#>   3.2602e+00 -5.0753e+00  4.9790e+00  1.4474e+01  4.2875e-01 -1.0536e+01
+#>   2.7088e+00 -1.6873e+01 -1.4222e+00 -9.5343e+00 -1.4356e+01  7.0343e+00
+#>  -3.0289e+00  1.2548e+01 -5.3476e-01  9.6326e+00  1.8405e+00 -1.2127e+00
+#>  -3.6812e+00  1.0437e+01  8.0682e+00 -1.6652e+01 -1.0142e+01 -1.5206e+01
+#>   4.3219e+00 -1.0197e+01 -1.3338e+01 -2.9317e+00 -1.8587e+00 -9.4293e+00
+#>  -2.9066e+00  3.7189e+00 -3.3114e+00  1.9270e-01  6.3411e+00  1.2446e+00
+#>  -1.5592e+01 -8.3166e+00 -8.0093e+00 -1.7355e-02  1.3073e+01 -1.2499e+00
+#>  -4.8396e+00 -8.6553e+00 -2.1501e+01  1.0832e+00 -1.5631e+00  3.5211e+00
+#>   6.4349e+00 -1.4709e-01  1.0039e+01  4.1366e+00 -5.1818e+00  5.3779e+00
+#>  -2.8191e+00  1.0056e+01 -8.1596e+00 -7.9815e+00 -8.8640e+00  1.4352e+00
+#>  -1.3145e+01  5.0618e+00 -4.3751e+00 -6.5379e-01 -8.6885e-01 -7.8195e+00
+#>   4.0526e-02  7.5483e+00 -1.9344e+00 -1.4227e-01 -1.0023e+01  5.8452e-01
+#>   1.3192e+01  1.3451e+01  6.0892e+00  3.4051e+00  8.8799e+00  4.9666e+00
+#>   4.2960e+00  5.7212e+00  6.3871e+00  9.6408e+00 -4.0683e+00 -1.0980e+01
+#>   3.8653e+00 -9.0519e-01  2.7193e+00  3.2622e+00 -2.5227e-01  4.5618e+00
+#>   3.0555e+00 -1.0348e+01 -6.2554e+00 -8.3331e+00  8.2203e+00  2.1232e+00
+#>   1.0208e+01 -5.9385e+00 -1.3444e+01  4.4038e+00  1.4671e+00 -8.2144e+00
+#>   1.1789e+01  1.3811e+01  6.1038e+00 -7.4244e+00  1.3696e+01  9.6627e+00
+#>   6.3077e+00  1.0908e+01  1.8206e+01  8.7864e+00  5.6093e+00  4.2826e+00
+#>   4.2985e+00  7.8137e-01  7.8540e-02  9.3323e+00  3.5540e+00 -5.1729e-01
+#>  -1.6727e+00  6.6090e+00  4.7937e+00  5.9470e+00  3.9930e+00  3.1163e+00
+#>   9.4688e+00 -1.6961e+00  1.0365e+00 -1.3247e+01  7.5337e+00 -3.6556e+00
+#>  -1.6152e+01 -1.8936e+01 -7.3162e+00 -3.1980e+00 -1.3776e+01 -2.8769e+00
+#>   3.5247e+00 -3.0341e+00  1.0915e+01 -9.8410e-01  6.7528e+00  7.6098e+00
+#>  -1.1487e+00 -1.0516e+01  5.7968e+00  6.2888e-01  9.3936e+00  1.2855e+01
+#>   8.2081e+00 -1.0463e+00 -1.2325e+01  5.4360e-02 -2.9514e+00 -2.2365e+00
 #> 
-#> Columns 31 to 36  1.6968e+01  1.4056e+00 -1.3579e+01 -9.4824e+00 -3.1901e+00 -1.5056e+01
-#>   3.3381e+00  3.8086e+00 -1.0171e+01 -8.9115e+00  6.1601e+00 -1.3511e+01
-#>  -8.7886e+00  1.1156e+00  2.0683e+00  7.6585e+00  1.0455e+01  9.0514e-02
-#>   2.2720e+00  1.0692e+01  5.2476e+00  1.5036e+01  5.3032e+00  5.4630e+00
-#>   1.5361e+00 -1.8013e+00 -2.5648e+00  3.2145e+00  4.1681e+00  3.9148e+00
-#>   1.1932e+01 -1.6998e+00  6.7076e-01  8.8148e-01 -6.3605e+00  1.7223e-02
-#>  -1.9262e+00 -9.2899e+00 -9.5948e-01  1.8220e+00 -3.3391e+00  5.1185e+00
-#>   6.4869e+00 -4.8870e-01 -1.2153e+00  5.8586e-01  5.8405e+00 -5.5425e+00
-#>  -1.3434e+01 -2.8273e+00 -3.8836e+00  3.6068e+00  4.8271e-01  6.4796e+00
-#>  -2.6460e+00  2.7995e+00 -9.0357e+00  1.2773e+01 -2.1468e-01  2.2131e+00
-#>  -8.1002e-02  6.3334e+00 -3.2733e+00  1.2021e+01  5.1330e+00 -1.6836e-01
-#>   3.5073e+00 -8.2699e+00  1.6375e+00 -3.3127e+00 -4.3346e+00 -1.8677e+00
-#>   7.9125e-01 -9.4631e+00 -2.6451e+00 -9.7767e+00  2.7838e+00 -1.1135e+00
-#>  -8.0633e+00 -3.0523e-01  1.0451e+01  3.4521e+00 -4.9708e+00 -1.2935e+01
-#>  -1.3916e+00 -2.5956e+00 -1.1536e+01 -1.3008e-01  1.2098e+01 -7.4245e+00
-#>  -8.8167e+00 -4.2268e+00 -4.3778e+00 -3.3864e+00  2.3205e+00  2.8307e+00
-#>   1.5622e+01  5.6263e+00 -1.1112e+00 -1.5210e+01 -3.7397e+00  2.1831e+00
-#>  -6.3162e+00 -6.0929e+00  2.0127e+00 -5.9087e+00 -2.6422e+00  1.6015e+00
-#>  -7.7263e+00 -1.6963e+01 -1.6966e+00 -5.4852e+00  5.0603e+00 -1.1660e+01
-#>   8.5882e-01 -1.3065e+00 -3.0651e+00 -1.0387e+01  7.6108e+00  1.0662e-02
-#>   3.0485e+00  1.5695e+00  2.5101e+00  1.7252e+00  3.2778e-01 -2.9300e+00
-#>  -8.3939e+00  5.5164e+00  1.6709e+00  3.5003e-01 -1.2074e+01  2.2491e+00
-#>   8.1846e+00  6.9727e+00  8.1906e+00  4.9120e+00 -1.6916e+00  1.5517e+01
-#>  -5.5969e+00  2.9195e+00 -3.6840e-01  1.7274e+01  4.2599e+00  1.2123e+01
-#>   4.5651e+00  8.8605e+00 -5.9988e+00 -4.6366e+00 -3.1383e+00 -1.1470e+00
-#>   7.4988e+00  4.8204e+00 -4.8057e+00 -1.4195e+01 -1.6288e+00 -7.9563e+00
-#>  -7.8994e-02 -4.8934e+00 -7.4796e+00 -3.6926e+00 -4.7582e+00  4.3932e+00
-#>   5.4703e+00 -2.7433e+00  1.5770e+00 -2.6086e+00  5.7119e+00 -1.0186e+01
-#>  -8.3719e+00 -4.3971e+00 -1.1563e+01 -5.9293e+00 -6.1670e+00 -9.7025e-01
-#>   1.2016e+01  1.7604e+00 -1.4021e+01 -8.7414e+00  3.9784e+00 -7.5222e+00
-#>   4.3311e+00 -1.9517e+00  7.4661e+00  6.4736e+00  7.3379e+00  3.1902e+00
-#>   1.4190e+00 -3.5703e+00  2.5072e+00 -2.0089e+00  7.2083e+00  3.6831e-01
-#>   1.4973e+00  8.0480e+00 -4.3088e+00  5.0519e+00 -1.2431e-02 -3.9246e+00
+#> Columns 31 to 36 -2.4160e+00 -9.8490e+00  9.2617e-01  1.2981e+01  6.3057e+00 -8.5699e+00
+#>   7.7927e+00  3.1773e+00 -4.7796e+00 -3.2451e+00  2.4790e+00 -7.4513e-01
+#>   1.8965e+00 -1.4839e+00 -2.0183e+00 -1.1404e+00 -5.8512e+00 -4.9910e+00
+#>   9.4973e+00 -3.0604e+00 -7.5236e+00 -1.0549e+01 -6.0912e+00  3.4864e+00
+#>   8.7613e-01 -1.2540e+01  1.5420e+01  1.0203e+01 -7.3026e+00  5.7082e+00
+#>   2.7574e+00  8.5051e-01 -3.7750e-02 -5.1999e+00 -1.4755e+00 -1.2914e+01
+#>  -8.6040e-01  3.3290e+00  5.2815e+00  6.3405e+00  5.5871e+00 -4.1847e+00
+#>  -3.1350e+00 -1.1125e+01  1.3637e+01 -5.1070e+00 -6.0118e+00  1.7395e+01
+#>   1.5454e+01  7.0023e+00 -1.2538e+00 -1.2083e+01 -1.0761e+01 -2.5431e+00
+#>  -1.9039e+00  3.4253e+00  2.7404e+00 -1.4629e+00 -4.5087e+00  4.0576e+00
+#>   3.8297e+00  4.5924e+00 -3.6834e+00  7.7188e+00  9.2847e+00  1.2748e+01
+#>   2.7658e-01 -4.9807e+00  6.5602e+00  5.4798e+00  1.4785e+00  1.8425e+00
+#>   3.7173e+00  3.8542e+00  6.7257e-01  3.5744e+00 -5.4453e+00 -9.5923e+00
+#>   5.0061e+00 -2.5951e+00 -3.4896e+00  1.0720e+01 -7.5133e+00  3.1578e+00
+#>  -5.6386e+00  1.0525e+00  2.2377e+00  3.7038e-01 -4.9494e+00 -1.8860e+00
+#>  -1.9891e-02 -8.7955e+00  2.1543e+00 -4.7553e+00 -9.9719e+00  5.4613e+00
+#>  -4.1496e+00  1.3958e+01 -1.6059e+00  5.4896e+00  6.5098e+00  4.0166e+00
+#>  -8.3209e+00 -1.8790e+00  1.2269e+01 -3.8065e+00 -4.5256e+00  7.0439e-01
+#>   9.8887e+00 -2.9514e+00  4.1527e+00 -2.8896e+00  8.8576e+00 -8.5063e-02
+#>  -4.8624e+00 -3.8806e-01 -9.4698e-01  2.0949e+00  2.2369e-01 -1.1511e+01
+#>   6.9678e+00  7.0676e+00 -7.7105e+00  8.2408e-01  3.9933e+00  2.4516e+00
+#>   8.0568e-01  7.3904e-01  1.1489e+00  9.2044e-03 -2.7105e+00 -1.0492e+01
+#>   1.3313e+00  1.3976e+01  7.7165e+00  6.0428e+00  5.6637e+00 -3.3999e+00
+#>   5.5349e+00 -1.3613e+01  1.0381e+01  2.5384e+00  1.4881e-01 -6.0892e+00
+#>   1.1582e+01  8.4071e+00 -9.1990e+00  8.6955e-01  4.5253e+00  4.6655e+00
+#>   4.6368e+00 -1.1878e+00 -9.3051e+00 -4.8539e+00  1.7904e+00 -3.5288e+00
+#>  -5.9411e+00 -6.2883e+00  1.3240e+00 -4.7931e-01 -5.6301e-01 -2.9917e+00
+#>   1.2742e+01 -2.5881e+00  6.3592e+00 -1.7294e+00  3.9995e+00 -3.3552e-01
+#>   3.3428e-01 -3.5750e+00 -2.8391e+00  2.4259e+00 -2.8570e-01  5.7347e+00
+#>  -1.0743e-01  2.9691e+00  1.5777e+01  5.7520e+00 -8.2096e-01 -8.0071e-01
+#>  -5.8091e+00 -2.8520e+00 -3.7087e+00 -7.4047e+00 -3.3277e+00  2.3684e+00
+#>   1.8535e+01 -1.7203e-01  4.4053e+00 -8.0303e+00 -8.4598e+00  5.1239e+00
+#>  -3.7432e+00  3.9723e+00  1.1141e+00 -1.3880e+01  1.4985e+01 -1.1681e-01
 #> 
-#> Columns 37 to 42  1.0349e+01  3.5146e+00  4.7248e+00  2.4224e+00  3.1631e+00 -1.8932e+00
-#>   3.7355e+00 -4.9438e+00  5.1820e-01 -1.3734e+01  2.2520e+00  1.2284e+01
-#>  -7.2834e+00 -2.9175e-03 -9.4973e+00  3.5105e+00 -2.9854e+00 -7.4132e+00
-#>   4.6324e+00 -4.4956e+00 -2.1906e+00  6.3653e+00 -1.3877e+01  1.4574e+00
-#>  -6.3226e+00  5.5992e+00 -1.6028e+00  1.2614e+01  2.3026e+00  2.4687e+00
-#>   5.3798e+00 -5.2953e+00  5.1357e+00 -2.8384e+00  8.5415e+00 -4.4999e-01
-#>   1.3274e+01 -5.1165e+00 -9.4891e-01  5.1584e+00 -1.4090e+01 -2.0401e+00
-#>  -1.0071e+01  8.7172e+00  2.4762e-01  4.5268e-01  9.5065e+00  6.5990e+00
-#>   3.8933e+00 -1.4519e+01 -4.2085e+00 -5.6620e+00  2.6159e-02  2.7152e-02
-#>   1.9220e+00  8.1747e+00  5.4029e+00  8.3436e+00  7.5888e+00 -6.9745e+00
-#>  -8.2549e+00  7.4162e-01 -6.1332e+00 -2.8622e+00  3.1681e+00  1.2724e+01
-#>   8.1403e+00  3.7808e+00  2.9839e+00  3.2436e+00 -2.0750e+00 -6.6230e+00
-#>  -3.8046e+00  1.1949e+01  2.0603e+00  1.3661e+01 -4.4081e+00 -8.8752e+00
-#>   4.0630e-02 -9.7636e+00  1.0656e+01 -1.0516e+01  6.4355e+00  2.4332e+01
-#>   4.1552e+00 -4.4734e+00  9.5094e+00 -8.9332e-04  1.4007e+00 -6.9024e+00
-#>  -7.1951e+00 -9.5963e+00  6.6720e+00 -7.8115e+00 -3.9879e+00  1.7070e+01
-#>  -1.1219e+01  3.4550e+00  6.2057e+00  1.7176e+01 -3.6684e+00  9.2366e+00
-#>   1.0725e+01 -4.2982e+00  1.2791e+01  5.9354e+00 -6.8996e+00  1.2754e+00
-#>   3.3122e+00 -5.9823e+00 -1.6210e+01  5.5249e+00  7.5663e+00  1.0667e+01
-#>  -7.7834e-01  2.7156e+00  1.1732e+01 -7.9248e+00  1.3731e+01 -7.1842e-01
-#>   8.4354e+00 -2.6473e+00 -7.0378e+00  9.5294e+00  1.0219e+01  4.3076e+00
-#>  -1.6865e+01 -1.0685e+01  3.3568e+00 -8.7478e+00  5.4412e+00  4.8094e+00
-#>   4.0752e+00  1.9527e+00  2.2571e+00 -3.4768e-01 -2.9087e+00 -8.7077e+00
-#>  -1.1830e+00  1.9568e+01 -5.2073e+00  6.3466e+00  5.4534e+00 -1.6413e+01
-#>   5.9593e+00 -8.8745e+00  2.7595e-01  2.5411e+00  8.2347e+00  4.6738e+00
-#>  -5.1192e+00  5.9188e-01  9.3946e+00 -6.3051e+00  1.1390e+01  6.2909e+00
-#>   2.7971e+00 -7.5224e+00 -3.4047e+00  1.0831e+01 -1.3286e+01  1.6648e+01
-#>   4.9520e-01  2.1406e+01  3.6402e+00  8.1908e+00  2.2226e+00  1.0036e+00
-#>  -5.8694e+00 -1.9102e+01 -1.5645e+00  2.6963e+00  1.2637e+01  1.9963e+01
-#>  -7.9123e+00  1.4164e+01  1.4852e+01 -6.8379e+00  7.8168e+00 -2.5681e+00
-#>   1.5386e+01  6.3521e+00  8.1042e+00  6.7304e+00 -1.3655e+01 -8.0183e+00
-#>  -5.5019e+00  2.0131e+00  5.0412e+00 -7.0756e+00  5.0653e+00 -1.2269e+01
-#>   1.1495e+01  4.0187e+00  4.1938e+00 -1.1167e+01  1.0863e+01 -9.0092e+00
+#> Columns 37 to 42  5.9127e+00  3.5513e+00  3.4210e+00 -3.0361e+00 -3.9979e+00 -3.2414e+00
+#>  -8.3478e+00  3.9888e+00 -6.8009e+00  2.9108e+00 -3.9676e+00 -1.3084e+01
+#>   6.1419e+00  3.3308e+00  4.2453e+00  2.0735e+00 -3.0325e-01  4.0093e-02
+#>  -7.2299e+00  9.5466e+00  4.7664e+00 -1.4321e+00 -9.0915e+00 -3.8821e-01
+#>   1.6036e+00 -2.7499e+00  1.3207e+01  3.6614e+00  1.2311e+01 -1.5622e+01
+#>  -3.8523e+00 -4.6020e+00  2.6399e+00 -7.8059e+00  1.6730e+00 -4.3755e+00
+#>   9.1574e-01 -8.5406e-01 -6.4620e+00  3.0858e+00  3.9684e+00 -3.4153e+00
+#>  -8.5680e+00  7.8068e+00  5.7354e-01 -4.5577e+00  1.0946e+01  1.0115e-01
+#>  -4.4545e-02 -5.7618e+00 -1.7288e-01 -1.3676e+01 -1.1941e+01  1.0912e+01
+#>  -7.1148e+00 -9.3002e+00  3.5723e+00 -4.7340e+00  9.5595e+00  2.9633e+00
+#>  -4.9323e+00 -1.1229e+01  1.9060e+01 -2.2314e+01 -1.0375e+01  1.2683e+01
+#>  -5.2458e+00  2.5543e+00  2.5698e-01 -8.3591e+00 -2.1949e+00 -4.7709e+00
+#>  -8.6382e+00 -6.1757e+00 -7.5267e+00  8.6819e+00 -6.2349e+00  1.6083e+01
+#>  -3.9643e+00 -6.1261e+00 -5.2405e+00 -1.3970e+00  1.3901e+01 -1.4500e+00
+#>  -3.7972e+00 -4.2547e+00 -1.5126e+01 -2.8267e+00  4.7399e+00  6.9800e+00
+#>   7.8953e+00  6.9430e+00 -5.5523e+00  1.2434e+00  3.8093e+00  2.5516e+00
+#>  -1.2301e+01  9.0655e+00  4.8731e+00  7.8256e-01 -7.1606e+00  4.4650e+00
+#>  -1.0572e+01  1.8517e+00 -3.2508e+00 -1.7122e+01  6.7357e+00  1.9725e+00
+#>  -1.7413e+01 -2.0952e+00 -6.9225e+00 -8.7225e+00  6.2306e+00 -3.5343e+00
+#>   3.6866e+00 -2.4677e+01 -4.2415e+00  1.8065e+00  3.0915e-01  9.5712e+00
+#>   1.7296e+00  2.8315e+00 -1.0357e+01 -2.9803e+00 -2.7833e+00 -6.4746e+00
+#>   2.3642e+00 -3.4611e+00 -6.5354e+00  1.8402e+01 -2.0546e+00 -9.8110e+00
+#>   9.7047e-02  2.5412e+00  8.1969e+00 -2.3119e+00 -8.7385e+00  6.1981e+00
+#>  -1.7581e+00  7.0399e+00 -8.3292e-02 -1.6233e+01  9.5128e+00  4.0066e+00
+#>  -2.5750e+00 -2.7078e+00  8.2693e+00 -6.4337e+00  1.0517e+01  1.2219e+00
+#>   1.1326e+01 -6.1008e+00  5.5458e+00  1.7954e+00 -5.8911e+00  5.3163e+00
+#>  -5.4871e+00 -5.5184e+00 -5.6121e+00 -3.8479e+00  4.5289e+00  1.8836e+00
+#>   3.5214e+00 -8.7408e+00  9.1136e+00 -6.3647e+00 -2.6298e+00  4.8887e+00
+#>  -2.4187e+00 -2.5903e+00  8.9128e-01  6.8829e-01  2.1995e+00  7.2179e+00
+#>  -8.5783e+00 -9.0089e+00 -9.2762e+00 -1.1818e+01 -9.8745e+00  8.3556e+00
+#>   4.3238e+00  9.6471e+00 -1.9621e+00  1.2542e+01 -7.3664e+00  9.5723e+00
+#>   4.4038e+00 -2.9037e+00  1.1862e+01 -6.4084e+00  1.6031e+01  6.6063e+00
+#>   4.8039e-01  2.2151e+00 -2.4990e+00 -6.7493e+00 -3.7793e+00  3.6258e+00
 #> 
-#> Columns 43 to 48  1.9086e+00 -6.0084e+00  1.1663e+01  1.6831e+01  1.2646e+01  1.4408e-01
-#>   7.7910e+00  9.5390e-01  6.2202e-01  8.2527e+00  6.3771e-01 -2.7207e+00
-#>   2.8097e+00 -5.4443e-02  8.8826e+00 -1.0211e+01  5.2685e+00  5.2125e-01
-#>   8.4363e+00  5.9659e+00 -2.7217e+00 -1.3296e+01 -5.1549e+00  1.4063e+01
-#>   1.0081e+01  3.2227e+00  9.6486e+00 -7.0275e+00 -4.2905e+00  8.8995e+00
-#>   8.6326e+00  1.1378e+01 -2.3089e+00  8.4529e+00  1.1244e+00 -9.1948e-01
-#>   3.7497e+00  8.2288e+00  9.7651e+00 -3.2653e+00  1.7484e+00  6.9823e+00
-#>   1.0540e+00 -2.4206e-01 -8.1490e-01 -2.3298e+01 -1.6047e+01  4.2955e+00
-#>   6.5837e+00  1.1039e+01  1.2911e+01  9.7721e+00 -1.1140e-01 -7.1750e+00
-#>  -8.2676e+00 -1.0484e+01  1.3251e+00 -2.4151e+00  3.7316e+00  2.1665e+01
-#>  -2.0550e+00  8.4096e+00  1.5844e+01 -1.5410e+01 -4.6701e+00  1.7298e+00
-#>  -8.3883e+00 -8.4074e-01 -7.2652e+00  3.0749e+00 -5.4976e+00 -7.0820e+00
-#>  -3.3652e+00 -1.8007e+00 -3.4501e-01 -2.0708e-01 -5.1626e+00  2.1304e-01
-#>   1.6354e+01  9.7717e+00 -1.5131e+01  8.6530e+00  1.5403e+01 -6.7722e+00
-#>   1.2625e+01  1.7819e+01  3.2093e+00 -5.8925e+00  6.5707e+00  8.1253e+00
-#>   2.4240e+00  5.0578e+00 -1.0251e+01  6.3943e+00  1.5771e+01 -2.9332e+00
-#>   2.9671e+00  4.1355e+00  5.8757e-01 -1.5622e+01  3.2887e+00  6.6223e+00
-#>  -1.5192e+00  5.2934e+00 -1.2964e+01 -7.3480e+00  2.9503e+00 -6.8738e+00
-#>   4.9112e+00  4.4184e+00  1.4162e+01 -6.7740e+00  2.7057e+00  2.2440e-01
-#>  -1.0507e+00 -2.7629e-01 -1.4210e+01 -5.9002e+00  3.1342e+00 -2.1610e+00
-#>   2.3724e+00 -6.5675e+00 -3.6146e+00 -1.4949e+01 -1.0355e+01 -3.2275e+00
-#>  -3.8806e+00  4.0648e+00 -6.2838e-01  6.4692e-01 -3.7762e+00 -4.5512e+00
-#>   5.0239e-01 -1.0756e+01 -2.6947e-01  1.3397e+00  1.3567e+01  3.3119e+00
-#>  -4.1779e+00 -3.8258e+00  9.0232e+00 -2.8485e+00  9.2501e+00  1.4849e+01
-#>  -2.0827e+00  1.7546e+00 -3.7182e-01  4.5059e+00  1.2492e+00 -8.6025e+00
-#>   8.0779e+00  3.7081e+00 -7.3670e+00  4.0668e+00  6.6366e+00 -3.3706e+00
-#>   1.1349e+01  7.9921e+00  4.4069e+00 -5.2089e+00 -6.7050e+00  1.2475e+01
-#>  -1.4296e+00 -8.9067e+00  7.0266e+00 -2.1112e+00  3.0178e+00  1.9257e+01
-#>   1.1055e+01  1.1767e+01  5.6965e+00 -2.8091e+00  7.3366e+00 -1.0011e+01
-#>  -1.2181e+01 -1.1801e+01  1.3236e+01  1.6488e+01 -6.9282e+00  6.8039e+00
-#>   2.4913e+00 -7.4843e+00  3.3452e+00 -8.6046e+00  3.4812e+00  5.3174e+00
-#>  -3.3165e+00  1.2759e+00 -1.6281e+01  4.1924e+00 -3.1811e+00 -1.9584e+01
-#>  -1.4903e+00 -5.9860e+00  6.4547e+00  2.1014e+01 -4.2636e+00  6.3224e-01
+#> Columns 43 to 48  6.3546e+00  5.5814e+00 -4.1345e+00 -2.1214e+00  1.7819e-02 -9.3293e+00
+#>   1.5115e+00  1.1845e+00 -1.1078e+01 -3.0541e+00 -1.8373e+00 -5.1780e+00
+#>   4.6270e+00  4.3330e+00 -1.9397e+00 -3.9596e+00  8.7563e-01 -3.5595e+00
+#>  -4.3821e+00 -1.0747e+01 -1.0921e+01  6.2560e+00 -8.3863e-01  6.7045e+00
+#>   6.0324e+00  5.4766e+00  6.1607e+00  6.2573e+00 -1.1745e+01 -5.8074e+00
+#>  -8.7181e-01  7.0205e+00  2.7019e+00  4.2208e-01  1.3686e+00 -4.8149e+00
+#>  -1.8445e+00 -1.2732e+00  8.9257e+00 -1.3513e+01  1.6947e+00  4.7902e+00
+#>  -1.4727e+01 -6.4389e+00 -6.7826e+00  4.7253e+00 -1.6116e+01  1.5001e+01
+#>  -2.7950e+00 -5.0996e+00 -1.4505e+01  3.0346e+00 -4.9890e+00 -6.1698e+00
+#>  -2.2781e-01  2.3249e+00  3.5943e+00  6.8096e+00  3.8828e+00 -3.9986e+00
+#>  -7.1669e+00 -6.5423e+00 -5.9308e+00  2.9696e+00  4.4083e+00 -9.7395e+00
+#>  -1.3168e+01 -3.5437e+00 -7.0148e+00 -8.5682e+00 -4.7069e+00  1.1038e+01
+#>  -1.1073e+01 -3.4101e+00 -6.7015e-01  2.9419e+00 -1.2253e+01  1.3861e+01
+#>  -2.5928e+00  7.5227e-02  5.7482e+00  1.3727e+01 -1.1598e+01 -7.1034e+00
+#>  -1.1612e+01 -7.7452e+00  8.0805e+00 -9.9243e+00 -8.6924e+00  5.9893e+00
+#>  -1.3262e+00  2.4119e+00 -3.1468e+00  7.0294e+00 -7.4336e+00  1.2526e+01
+#>   1.1899e+01 -4.6263e-02  1.0874e+01 -1.6575e+01  1.3480e+00 -1.0570e+01
+#>  -7.2232e+00 -1.1131e-01  7.9359e+00  7.3654e+00 -1.5571e+01  8.5639e+00
+#>   3.2576e+00  2.9600e+00 -5.3451e+00  1.8798e+00  7.2577e+00  4.4541e+00
+#>   6.3007e+00  1.4342e+01  4.6795e+00  1.4109e+00  6.0204e+00 -5.9023e+00
+#>   4.8023e+00 -2.3711e+00 -2.9629e+00 -1.2690e+01  4.0853e+00 -2.9595e+00
+#>   4.1702e+00  3.0889e+00 -6.3344e+00 -5.7244e+00  1.3824e+01  1.0056e+01
+#>  -1.0141e+01 -6.1246e-01 -1.1567e+00 -4.7049e+00 -1.3935e+01  9.1054e-01
+#>   7.9943e+00  1.3407e+01  1.6255e+00  4.1522e+00 -5.3960e+00  4.7836e-01
+#>   2.2200e+00  1.1039e+01 -3.0500e-01  4.4403e+00  2.0097e+00  1.1695e+01
+#>  -2.2005e+00  1.8139e+00  1.7448e+00  4.1344e+00  5.5313e+00 -3.9247e+00
+#>  -1.8229e+00  3.6532e+00  2.8835e+00  3.0477e+00 -8.1991e+00  1.0751e+01
+#>   3.6532e-02  3.6095e+00 -9.7047e-01  5.4361e+00  1.5120e+00  5.3995e+00
+#>   3.1780e+00  8.9854e+00  7.1141e+00  3.9608e+00  1.4050e+00  3.4557e+00
+#>  -1.2866e+01 -3.5154e+00  4.4069e+00  2.0944e+00 -1.4698e+01 -5.0193e+00
+#>  -3.6509e-01  5.1551e+00  8.4945e+00  7.6052e+00 -8.5984e+00  5.1943e+00
+#>  -1.8555e+01  6.3359e+00 -1.5282e+00 -4.0118e+00  5.0232e+00  1.1312e+01
+#>  -6.1000e+00 -1.2387e+00 -8.2431e+00  8.6410e-01  9.8649e+00  1.3506e+01
 #> 
 #> (9,.,.) = 
-#>  Columns 1 to 8   4.0794   6.0684  -0.8311   4.8778  -2.0680  -5.0483  -8.1212  -0.4670
-#>   -5.0321  -1.8980  -7.3105   3.9990   2.5946   3.8788  -5.8263  -0.4804
-#>   12.8792 -12.9890   1.0354   0.2266   4.5250  -7.3148   5.9497   3.1359
-#>   -3.6702   1.7862   3.8205  -1.1715   9.8759  -2.4723  -4.8305 -20.5428
-#>   13.3035  -1.2442  -2.8034   4.6820  -5.0879   6.6643   3.9401   2.0031
-#>    3.3032   2.2903  10.9000   9.5233  -8.0382 -17.9027  10.9481   1.0601
-#>    8.1374   2.9132   6.7715   6.9283  -7.8365   0.8160  10.2724 -10.3356
-#>    5.3852  -3.9204  -0.3595  -1.8751  -3.5394  20.6540   9.7091  -1.9489
-#>    0.8717   1.9024  -7.1412   5.2893   2.1951 -23.9145   9.6487   2.1870
-#>   -2.4710  -3.1546  11.8893  -3.1098   5.0098  -6.4714   4.5560  -6.5721
-#>    8.2237  -3.3640  -6.0437  10.2538  -8.5034   9.4943   4.6656   0.5343
-#>    4.6898   4.6695  -6.3377   6.2074  -2.6848   8.3498   1.1032   0.8084
-#>    5.9736  -3.9981  -0.2361  -0.2370 -13.3947   8.2142   0.5673  -6.0443
-#>   -5.6291  18.3545  -9.2522 -10.8182   7.8836 -18.0514 -10.5459   1.1597
-#>    4.2296  -7.4623   6.4381   4.7830  -4.2380  -5.9684  14.1344 -19.2029
-#>   -7.1332  18.8846   5.4187  -9.2719  -5.0517  -2.2815   2.1851   4.0934
-#>   -5.2267  -8.4886  -2.4892   5.5292   4.2910   1.0350  -4.9162  -0.9101
-#>   -8.1712   0.3072   7.1822  -4.1487   4.3976  -9.8743  14.2932 -14.2236
-#>   -3.3688   1.2148   2.7096 -11.7475 -12.5490  -3.2319  -0.9382  15.8213
-#>  -11.5855  -5.4447  11.0557   3.4124   1.1715 -10.6065   6.2076   2.0333
-#>    1.6404  -4.7255   1.6720   5.3021   6.5244 -17.9380   3.5248   4.6255
-#>    4.3144  -9.9478  -5.6320 -11.0713 -11.2514   7.6034   9.0796  -7.7695
-#>  -10.1595   0.9970  14.5487  12.4250  -3.0845  -3.2217   0.8475   4.6780
-#>    5.8065  -2.8322  14.9710  -7.3477   0.8346 -11.5608   1.4898   0.7894
-#>   -7.8699  -5.5137  -6.0862   3.4121  15.1010 -22.8861   3.0404  13.9179
-#>   -6.6843  -4.1122   4.2866  -9.9555   7.2913 -10.2866  -9.7519   2.7445
-#>    1.7172   1.6126  -2.0818   3.9591  -8.4740  -5.7567  -0.8639  -3.9426
-#>   -4.2496   6.4542  -0.2463   1.8102   5.1814  -6.9287  -7.0213   9.4895
-#>    6.3897   0.4685  -0.1945  -5.5021  -6.2104  -7.8718   0.0966   6.0887
-#>   -3.4026  10.1926  -6.2556   8.6156  -7.1564   3.1795   2.7905  -0.4113
-#>    4.7720   1.4452  15.7820  -2.6927   7.7603   8.4853  -2.7105  -5.2556
-#>   -4.3637  11.7902  -7.3200   3.3080  12.9666   8.0143 -15.2166  15.6200
-#>   -2.3635   1.5295   2.4287  10.6374  10.6979  -5.8618   2.1516  -5.7713
+#>  Columns 1 to 8  -8.1832   0.1362  -0.4915  -1.0407  -7.3703  -1.7879   5.7507  -5.8673
+#>    2.5422  -5.5568  -1.7756  11.5901   5.1868  -3.3447  -4.9699   4.5047
+#>    4.9694  -0.7470  -3.1076   0.1330   3.4620   5.4725  -1.0366 -11.4798
+#>   15.0149  -3.8052  -1.1027   1.5937  -9.0624  -1.3845  -2.5101   1.0986
+#>  -10.2025  -4.1056   6.7674  -4.0452  -4.5385   1.0042  -0.6998 -14.1617
+#>    5.8363  -2.8939  15.5251  -6.4314   0.3172  -7.2951   7.1723  -4.0533
+#>   18.3843  -3.0947   2.6987  -8.5305   5.4004  -4.0875   1.3148  20.6922
+#>   14.1377   8.8000  -3.4150  -0.7623  -1.8689  11.0341  -5.1328   7.1987
+#>  -13.8579 -15.4919   4.2459   7.4631  -2.6184   5.5489  -7.0029 -12.7808
+#>   -4.8014  -0.8354   8.5274  -1.4477   5.5920   3.5952  -2.3063  -2.6907
+#>   -5.3179   6.0025  -1.3447   4.3491   9.0115  -0.0241   3.5481  -5.7069
+#>   12.1946  -1.5472   3.3849  -4.4272  -5.5386  -0.7148  -4.7987  -4.0082
+#>    5.1601   4.7451  -4.8614   4.9870  -3.3710   0.6032   1.7901   2.4665
+#>   -3.7900  -3.7001  -2.2769  -4.1085  -0.5727  -9.7645  -4.4055   9.1107
+#>    4.1666   5.6311  -5.0046   0.7492  -5.3866   1.3544  -9.2811  -5.3068
+#>    7.3567   3.6154  -4.6574   3.1771   1.0163   6.2261  11.2779  -3.6957
+#>  -11.2783   7.8325  -4.5468  -0.2824   2.1565  -3.8123  -9.9824   5.4123
+#>   18.8053  -3.7631 -10.6808  -8.9003   7.5764  -1.1539  -0.8487  -3.6032
+#>    4.1850   7.3493   3.6412  13.9481   4.9575   2.6773   5.1736   2.7721
+#>    4.7698 -11.3023  12.4770   5.3364   0.9790  -1.0861  -2.8539  -3.8610
+#>    3.7222  -5.4386  -1.4738   9.0638   7.7817   6.4650   0.5258   5.3134
+#>    2.4380   3.2938   4.0400   3.5199 -11.8223   3.3059   2.5778  -9.8781
+#>   17.8766   6.6763   3.9912  -8.6001   8.9255  -4.0468   0.9085  13.7909
+#>   15.8659 -16.5383   8.3743  -2.0670  -6.8738   2.8211  -6.6428   0.1441
+#>   19.6792  -1.6840   6.4083   4.1757  10.2453  -9.0501   0.9174  -2.7111
+#>    3.2741  -0.5703   8.9895   1.2622   3.8401  -0.8652  -3.2099   0.5349
+#>    4.4368  -4.2240  11.9494  -0.2721  -3.0057   5.3236  -3.0448  -3.8302
+#>    4.0558   0.2912  12.0330   5.8881   1.2482  -2.2835  15.3772  -7.7132
+#>   -4.0999  -0.4149  -4.5999  -0.0200  -6.6630   0.6751 -12.8473   3.6422
+#>    0.1158   3.0992   2.3752  -3.0391   3.6136   5.9456  -0.8301   1.3793
+#>  -14.9808   3.0920 -13.9783   5.8663  -7.4728   3.7850  -5.3515  -4.2583
+#>   -2.8341  -0.4232  -1.5558   4.1194  -4.2162   4.6438   3.8111  -0.7015
+#>    8.0191  -4.1810   1.1353   0.2684  -6.2055   9.3124 -17.1230  -9.4973
 #> 
-#> Columns 9 to 16   6.4279 -16.6403 -18.3629 -17.5751   1.5928 -11.3314   2.3082  -3.3119
-#>   15.1420  -7.8180   5.0243  -2.5117  -0.7384  -3.4678  -2.5084   9.2290
-#>    1.9041   1.7503   2.8069  -6.1292   7.4328   6.1056   9.0484  -3.3383
-#>   16.0871  16.7116   4.0324  -2.9360   9.1257   2.7541   5.0165  -3.1155
-#>   -3.6804  -5.7240  -2.9193  -5.5015   4.6540   2.6300  -0.3867  -5.0567
-#>  -18.2437 -23.9267  -1.5425   4.6601  -2.0346  -0.8490  -5.6068  -3.2220
-#>    7.2447   6.9831   0.4325  -7.8024   1.7635  -8.6065  10.7907   4.7603
-#>   -2.1499 -18.3106  -1.5499   2.3885   4.2410   0.3205  -0.0706 -15.5370
-#>   -7.2642  -3.3160   2.4902   0.7529  -2.6600   1.8324  -4.9594   9.6799
-#>    3.5323   7.4025   8.2952  -5.4834  -2.4099  -5.4144   3.5544  -4.9415
-#>   -6.3884  -3.4431   7.9970   2.7729   3.4578  -0.4114  11.7179   7.9719
-#>  -11.1124  10.7049   2.7164   6.0928 -10.8485  -2.9538  -8.0745   7.4084
-#>   -6.2716   0.8655  -0.5051  -2.3025   2.4459  -2.4969  -2.2801 -12.8532
-#>    1.7287   0.0174  -0.5900   8.8881   4.1497  -3.9259 -13.2147  15.8763
-#>   11.9867  -6.9489  -1.4523  -4.2875  -0.1344  -3.6711   9.0379  -4.5052
-#>    9.7145  -2.6730 -15.3691  -7.4209   1.0313  10.1746   6.8281   8.4267
-#>   -2.2905  -2.6880 -12.2839  -2.8750   7.6246  -3.2068  -3.9528  -8.3951
-#>   -1.7096  -6.3767   1.9717   6.9931  10.4830  -0.2552  -2.3697  -7.0752
-#>   -3.4325 -10.2179  -3.4009  -4.4899  -3.8635   8.8770   2.8008   4.5130
-#>    2.5901 -18.7449  -0.3828   1.1593  -3.4166 -10.1580 -14.9132 -10.8550
-#>   -5.1128  -0.5076  15.3124   0.3820   4.5298   0.6550  -2.3290 -13.3959
-#>   -0.8302   0.2536  -4.9642   4.4154  -5.0953  -4.1481  10.3788   2.9103
-#>    3.0757   5.9847  -0.6975   1.0189  -6.6328  -8.9430  -5.0165   5.8381
-#>  -12.5129   3.4130   7.9631  -1.8649  -8.0697  -7.1588 -13.7465 -16.1740
-#>   -4.3849  -3.6834   1.4595   4.1363   1.2030   4.3309   7.0658   4.2559
-#>    6.0773 -19.6393 -14.0629  -2.3869   4.5976   7.4737   5.9223  -6.7138
-#>   -8.0946   5.0019  -0.4931   0.1409   8.9070  -0.9026  -1.5408   2.8584
-#>   -3.2197  -7.2077   0.3732  -1.0563   8.2695  -7.9381  -2.0098 -11.3553
-#>    3.1388  -3.3002  -3.0741   1.2747   3.5834   5.7041   4.2506  11.6135
-#>   -6.2947 -18.5550  -7.1581  -9.2618  -2.6979 -14.5040   7.1882  -7.9180
-#>    3.8713  -1.1188  -5.3525  -4.0106   3.4619  -9.9489   0.9650   0.0564
-#>   -7.3784   7.5384  -2.5166   9.6918  -0.9690  16.9486 -23.4199   2.4217
-#>   -2.1453   2.9490  10.9724  -0.0240  -6.9801 -10.6743   1.9304   8.1391
+#> Columns 9 to 16  -7.7459   4.7234  -2.1911 -10.3581  -2.2793   4.8364   7.2442  -0.5625
+#>  -10.0197  -3.6309  -5.1790   6.8189  -5.0406 -10.3516   3.8497  -0.9915
+#>   -8.6123   2.8491   5.1664   2.8275   1.8867  -7.2216 -10.5986  -5.6497
+#>   -8.7524  -7.0032  -1.2434  -2.0073  -1.4083   3.9759   3.0168  11.4859
+#>    6.1296  -1.4526 -11.5173  -7.5842 -10.2113  -0.3445   6.7264  -3.3998
+#>   -7.1876 -11.4746  13.3634   8.6027  11.5729   5.9233  -6.0313  -8.2863
+#>    1.7004  -5.0417   8.5269   8.5074   6.8426  -3.0775  -1.9318  -0.6201
+#>    1.6841   3.9921 -12.5881   6.9904  -7.7008   7.8078   5.5877  10.4576
+#>   -4.0274  18.1002  -2.9453  -6.1221 -15.1447 -13.1688 -10.2129  15.9692
+#>   10.6322  -4.1466   0.6274  -6.8538  -2.7226   2.5010  -1.6684  -9.3393
+#>   -0.2958   8.0991  -0.7922   4.3306   5.3981   8.5330   2.5476   0.6380
+#>   -9.0524   1.1968  -3.4523  -0.3024  -1.2216  14.8349  -4.5176  -5.2358
+#>   -6.3026   8.5910  -6.5132 -11.6790 -12.0950   6.6668   5.7587  -1.5212
+#>   13.2454  -7.2372 -15.5973 -17.9738  -8.1724   3.7138  15.1896   2.7070
+#>    0.0089  -0.8720 -11.7476  -2.9856   1.3286   6.0723   2.0188  -2.2052
+#>   -1.5125  11.5158  -6.9612  14.3976  -4.8587   1.2791  -3.4618  -2.5441
+#>    3.6691  -4.3201   7.5167  -6.8287  12.8890   0.2217  -5.9513  -6.8837
+#>   -6.8306  -3.9219  14.5606   6.5739  10.0210   0.3813  -5.7225 -11.4863
+#>    7.2751   7.9474   4.8315  13.9806   3.6597   6.1145   2.9858  -3.3464
+#>   -1.0838  -0.9017   4.5285  -6.6221  -9.5912 -11.7182   1.9417  -5.7042
+#>   -3.5688   3.4659   1.4189  -9.5359  -4.7904  -8.4741   5.6314   5.3858
+#>   -8.2044   4.3410   6.5003   0.7294  -4.0890  10.9251  -3.4258  -1.7692
+#>   -5.3508 -12.6048   1.1534   9.6404  16.7233   1.8397  -5.7716  -8.0276
+#>   -5.8340  14.3254   7.6403  11.0916  -8.6014  -3.7149  -6.6651   0.4798
+#>    6.9197  -5.6689   6.9883   6.9236   5.3596 -14.9410   0.2660   1.1348
+#>   -8.4866 -12.5675   0.6859  -8.4340  -1.3663 -12.2784   1.8450   1.8364
+#>    6.2671   0.1223  12.7108  -9.3529  -3.5482  -0.5294   8.0272   8.0868
+#>   11.7264   4.9685   2.8389   0.8228   0.8212   9.9968  -3.0364  -0.4173
+#>   -6.6905  -1.4530   0.1888  -1.7171  -3.2134 -11.5802  -3.9344  -5.0706
+#>    0.4806   6.2594  -3.0297  -1.6364   6.2587  12.8124   6.4464  -0.1632
+#>   -3.2312   5.1486  -8.9598  -3.0052 -21.4659  -8.1963  -6.5604   2.8753
+#>    8.1991   2.8298 -19.9156  -5.5093   1.9804  -5.0241  -1.7925  -5.1321
+#>    0.6226   6.3552   4.6889   0.0354  -4.2593   1.1301  -8.1101  12.5086
 #> 
-#> Columns 17 to 24   8.0659  -9.8109 -17.5197  -1.4956  -2.7980  -1.3839  10.8934  -9.2477
-#>    2.9348   3.9345  -6.3472   6.7811  -6.7502   3.8585   5.7719   7.3738
-#>    3.7759  -1.2448   4.2845  -4.3191   0.2600   4.1004   2.6863   4.5444
-#>   12.4635  13.6476   4.2648  -9.2781  -1.6858  -4.4844   4.1397   5.1457
-#>   -0.3290  -9.6221  -1.0781   1.6044  -0.5301 -11.1221   5.6580  -4.9634
-#>    6.2739  -5.5969  -4.1184  -7.9925   0.6532  -1.1652   3.4576  -5.9513
-#>   10.6276   1.0927  -9.2528   2.5044   9.9256  -5.4391   0.4126  -1.3181
-#>   -0.9920  -2.8386   0.3108  -4.3165   5.5252   7.3908  -0.4162 -14.1043
-#>    2.3134  -7.4856  -0.0544  -5.2347  -0.9355   6.6240  -1.3276  10.7852
-#>    7.8179  -9.1380  -1.4468   6.2373   6.9862   1.4667 -12.8261  -0.8780
-#>    0.6620   5.7587  11.7828  -1.8801  -7.8894  11.5090   4.9039  -5.0160
-#>   -4.0376   1.7842  -7.6461   6.9464   5.3759  -3.3801  -7.9744   1.3897
-#>   -0.2717   4.3383   5.1764 -12.3860  -2.6778  -4.4454   6.4609   3.0602
-#>   -7.5645   9.5570  -7.4381  -4.3807  -1.6359 -14.0753  -0.6845  -5.7670
-#>   12.7688  -8.1250   1.3557  -5.4335  -5.8085   3.7587   3.5603   2.1621
-#>  -11.5333   2.3040  12.5750  -4.4823  -8.4828   2.9692   9.9874  12.1453
-#>    5.8875  -0.1194  -0.8944 -12.4041   1.4352  -8.2784  10.2505  -6.2482
-#>  -14.2126   7.0171  12.9963  -7.4035  -9.4354  -5.3856  -5.4378  14.4636
-#>   -8.6178 -14.3717  10.9290  -1.1101   0.5204  10.4756   1.0523   1.6210
-#>    4.2888  -2.1153  -2.1217  -6.3444  -6.8318  11.7709  -3.7033  10.7405
-#>    8.0226  -4.4725  -2.1581  -2.4154   4.2568  -2.2097 -19.8357   5.1823
-#>    0.3821   3.0292   3.1383   7.3895  -5.0592   9.0426   5.7879 -11.8164
-#>    2.0660  -5.6077   8.9119   0.3280  -2.1825   0.6266  14.0744   2.4195
-#>    1.0469  10.6938   8.7573 -25.5889  -0.2029   9.9365 -13.8058   5.6670
-#>    3.1036  -6.7063   3.8607   2.4476  -3.6309   7.1585   4.8015   1.0726
-#>   -4.9733  -1.1730  -4.1544  -5.9858  -3.5929   4.7898  -5.2797  -4.2409
-#>    7.0606  -1.9421   2.2402  -9.2652   0.2939  -3.5939   5.4470   1.3376
-#>  -12.6290   8.0336  13.5466 -14.6799  -0.1997  -6.8844   6.4218  -7.7101
-#>    4.3238  -7.4270   2.7846  10.7714 -11.9544   8.1263  -7.6938   9.7714
-#>   -9.7766  -5.1577   1.0592  -3.9683  -7.2028   4.2519   9.6452 -16.5199
-#>   -3.3280  -8.5588  -3.2580   1.6575   0.7049   3.9927  -6.2243  -0.4779
-#>  -10.1726  15.6080  -0.0723  -8.2501   4.1061  -1.6644  -0.3377   4.0142
-#>    5.6474   8.4324  -2.6393   4.7854  -1.1411  11.3926  -4.7050   0.1635
+#> Columns 17 to 24   4.2059  -5.5520  -3.0468   1.0175  -1.3807   4.9594  10.9674  -3.8942
+#>    7.4939  -0.8236  -9.6058  -1.5212  -0.3222  -0.3243   5.8993  -1.2364
+#>    4.9936  -2.1095   2.8616   1.7382   5.2608   2.1405   2.0487  -9.2167
+#>    3.1877   8.1751  -1.2342  11.0327   3.2423   8.2091   0.5770  16.0137
+#>   -0.6983  -8.1531  -3.3467  -4.4953   5.9530  -7.3115  -1.2206  -5.6281
+#>   -1.8305  10.6471  -4.0879  -4.4938 -10.7697   5.4321   0.4087  -6.4028
+#>   -7.3518  15.0429  -3.3821  -5.3942   5.4415  -0.4463  12.8811 -10.3852
+#>    2.4327   8.3312   2.4391   9.8791   9.1811   5.7551  -7.1100  -0.9688
+#>    9.9638  -4.2095  -3.8897 -14.0447  -5.1093  -6.3825   3.2285  11.5336
+#>   -1.5851 -10.9154   1.2062  -5.7882  -8.2579 -14.6081 -11.9815   4.9466
+#>   -5.6415  -1.8218   6.0796   4.8861   1.7285   0.2765  -1.6939   8.0356
+#>    6.3597  -2.0178   1.5816  -1.7511  -3.6893   7.0550   3.7846   7.6283
+#>   -0.9841  -3.6696 -12.9781  -2.2069 -14.8485  -3.4331 -19.7907   0.7851
+#>  -14.2028  -2.5504  -9.0771  -4.2099  -3.0954   8.3411  -5.1700  12.2364
+#>   -3.8958   5.7019  -6.0307  -3.9535  -3.2254  -4.6280 -12.5457  -8.7411
+#>    8.2590   5.4200   4.5460   8.4914  12.2911   7.1513   4.7144  -7.6375
+#>   -1.4695  -0.5575   4.0604   0.3423  -1.9028  -6.8345  -0.1294  14.7587
+#>  -11.1111   8.5032  -2.8651   8.1954   7.2075  -0.8381  -9.3413  -6.8504
+#>    4.9361   0.4057  -2.0924  -0.5368  -9.7859  -1.0365  -3.2669  -8.5951
+#>    0.1877   2.9919  -0.2260  -2.0668  -7.8431 -10.3760  -3.0541 -10.7712
+#>    1.1386  -2.3280  -7.5244  -6.3388  -0.0155  -8.5991   4.8890   8.2987
+#>    9.2263   4.3248  -0.2449   5.7908  -6.4480   6.5837  -2.9764 -13.5566
+#>   -6.2898   8.9691   2.7937  -9.9365  -7.4681  -2.1230   8.0409   3.3539
+#>   -0.5466   9.6928   5.8119   4.5839   2.9515  -7.6144   1.0381 -14.2992
+#>   -3.0932  13.5023   3.1045   2.4480  -5.2515  -7.1009   8.6616   5.2182
+#>   -1.4976   5.5373  -6.7405  -1.2780   0.2723  -5.2043   0.9465   8.5725
+#>   -5.3337  10.3196  -8.6103  10.2320  -3.7096   2.1194  -1.5716   1.5836
+#>    4.1773  -2.2314 -10.3426  -1.2117 -13.9390  -1.1418   2.0845   0.0757
+#>   -6.8736  13.4079  -2.3401   5.9668   1.2662  -4.9621  -7.1224   3.5072
+#>  -11.1563  -1.2891 -15.4368  -3.5740 -10.9554  -3.9699  -5.3479  -7.8923
+#>   -2.9025  -1.1671  -8.8319  -3.4025  -4.6392   4.4500  -6.6146   9.7060
+#>   -4.2532  -2.2575 -17.1578   3.2153  -2.4606  -7.2451   3.6524   7.7684
+#>    3.4458  -9.6891   9.8224  12.1008  -4.8138   2.0533   0.1830   7.4203
 #> 
-#> Columns 25 to 32  11.1852  -7.6783  -4.4760  -6.2867 -14.4768  -5.5915  -2.3897  -1.7277
-#>    2.5738  -1.7489  -0.8135 -12.1182  11.3483   8.8064  -6.4352   5.5023
-#>   -0.9505  -1.6135  -5.0113   9.0222  -3.6225   3.2098   3.7084  -5.9199
-#>    5.4133 -16.0761   7.3412  19.7539   8.0536  -4.3415   2.0151  -6.6417
-#>    0.5821  -9.7743  -8.9166  -2.1462   4.3378   1.4944  13.7540   5.7123
-#>  -11.6263 -14.4128 -12.4400  -6.0476 -11.2459   6.1059   2.8093   9.7881
-#>    6.1340  -1.5156   4.7161 -12.7458   0.4976   8.1295   8.6126   3.0070
-#>   -2.7693  10.9625   2.0651 -19.3172   4.9871   7.9184  13.0141  19.7211
-#>  -11.5707   3.4166 -15.3739  -4.2086   0.3017  -7.8660   1.3321  -8.0142
-#>    5.0140  -4.8983  -7.2128   0.9540   4.8724  -0.1939  -1.9584 -15.3688
-#>   -1.4485   5.1045  -0.0854 -14.6064   9.1149   1.7403   4.2503  -2.0895
-#>   -6.7345   9.3218  -1.2156  -7.1773   1.4140   7.4115   5.7588   8.5236
-#>   -4.1262   0.9882  15.1218  -0.9274  -7.9207   3.1481  -1.6564   4.9407
-#>   -6.0797   2.7882   4.1580  12.1277   6.2366  -2.8015 -11.5175  -3.9804
-#>    0.5365  -2.5556  -2.7976  -9.2249   1.2070  12.6141   5.7339  -0.6995
-#>    1.4168 -14.0447   8.2472   6.8786  -4.0087  -5.6162   0.5010  -4.9696
-#>    3.9318  -7.7873   3.0492  -5.4148  -9.5561   0.0113  14.3477  -3.5788
-#>   -6.2673  -0.4624  16.4651   0.4618   2.4340   7.2395  -5.6198   8.3328
-#>   -7.6682   4.2533   7.8766   0.0981 -10.9411  -9.1038  -4.9945  -0.3450
-#>   -7.6848   0.6633  -4.0730  -7.5607  -8.0175  -0.1730  -4.9055  11.3131
-#>   -5.7167   9.9912  -1.9191   2.7759   4.4893   1.2290   4.7103   8.0413
-#>   -1.6533   8.7521   9.9300   4.1369 -11.7643  -1.3706   4.2014   0.8237
-#>   -2.6175  -8.3520  -3.8934   0.8523 -12.1993  -6.4672  -4.6347  -5.2350
-#>   -6.7551  -3.6316   1.6006   1.2255 -10.7569  -3.4983  -4.6958 -18.2304
-#>   -2.2933  -0.8002   5.0204   9.0503  -7.8023  -1.4203   0.3685  -6.3405
-#>    0.9046   1.7930   4.5703   3.4607  -6.1397  -7.0052  -1.4754  -3.1954
-#>   -2.8187  -9.5606  12.8859  -9.7074   3.5686  12.6171   9.1333  -6.4353
-#>    2.7439  -2.4240   8.5999   3.5995  -3.5231  -9.2750 -11.4203  -4.5213
-#>   -4.9503   6.8071   8.7293   4.0040  -5.3628   4.6692   2.8335  -0.7443
-#>    0.4532   3.8679 -10.4293 -14.7597  -8.6314  -3.1424   3.6973  -1.7702
-#>   -5.7853   3.6905  -8.3404 -10.1098  -4.8931   7.1072   4.4414  -5.0839
-#>   -1.0830   0.6989  -2.6288   9.4472 -13.4725   5.8905  -2.6344   6.1472
-#>    1.6652   5.0443 -13.9105  -2.0468   3.3163  -0.7483 -12.5587  -4.1535
+#> Columns 25 to 32   1.0887 -11.0118   4.4736   2.2329  -3.0081   1.9307  -5.0697   2.9423
+#>    2.1104   8.6393   1.6732   1.1195   1.5393   0.0249   0.8813 -12.0733
+#>   -4.2103   6.2671   4.4297  -3.1274   6.8547   1.7689  -6.7992  -3.9158
+#>    0.5626  -4.8139  -0.3864   9.4383  -5.1803  18.1212   2.3827  -2.1123
+#>    2.4489  -3.3559   0.2965  -2.3292  -4.8591  -2.9052   0.5543   0.8507
+#>   -3.5223  -0.8679   6.5141  -4.8131   0.0583  10.5951   3.3942   1.0627
+#>  -12.1853   7.0096  -5.6897   3.4268 -10.6364  -5.2994   1.9010   3.2238
+#>  -11.1066  -5.0477   2.3751  -0.6185   4.4833   0.6942  -4.7928   5.6108
+#>    8.4516  11.1090  -5.9245  10.3845   7.9494   2.8893   5.7679  -8.9791
+#>    8.8283   0.8126  -1.9831   3.9480  -2.8942  -1.8668   1.9301  -2.5843
+#>    8.4339 -12.7653  10.8994  15.5482   2.7457  -7.3268   0.6044   1.2060
+#>   -1.8305   2.2700  -2.4256  -0.6081   5.7981  -0.4782  -8.6941  -0.5383
+#>    3.3600  14.2027   9.7570  -2.8196  -3.7621   9.5351   3.1167  -6.0473
+#>   -0.4774   6.0749   4.8148  11.0053   4.0574   1.9971   0.7213  -2.9641
+#>    2.4432   5.2264  -2.4432  -4.9660   4.1106  -1.9219  -0.2957   7.2579
+#>    4.5602   1.1611   3.5060  -4.1962   9.4393  -5.2955  -2.0844   1.5498
+#>    3.1192   5.8630  -0.1532   5.3690 -10.0420   4.4278   5.0017   5.3303
+#>  -13.6625  12.8651  11.4828   6.1397  -8.0348   5.1076   4.9746   5.4097
+#>   -7.9518  -7.3636   2.9843  -9.4875   1.4911   0.8673   4.1667   3.9759
+#>    0.9254   7.8925  -1.2477   2.6830  -4.7077 -11.0350   2.6056  -4.0730
+#>    3.8865   8.0677   0.3313   4.9630   7.8809  -6.8292  -8.3703  -9.9351
+#>    3.7381  -8.4871  -4.8211  -5.0227  -9.6585  -1.0686  -0.0386  -4.4425
+#>  -10.0503  -1.6145  -4.6398  -4.0048   4.0530   4.5118   4.4244  11.3235
+#>  -15.1534  -0.6262  -4.1878  -6.9952  -2.9259   2.9190  -0.0752  11.6472
+#>    5.0678  -7.3431  -3.0300  -2.3666 -10.2400  -2.1587  -1.8177  -0.3579
+#>   -0.1289   1.2054  -4.4791   5.3275   3.4119 -12.6049  -5.6836  -1.6711
+#>   -8.5504   0.8343   1.2572   1.5737  -4.6236   6.7879  -3.3966   2.5857
+#>    2.7257 -16.1171   1.6263  -7.9978  -4.6713   3.5073  -4.5688   1.2174
+#>   -5.2920   2.6312  -4.6101   3.1988  -2.4407  -6.9844  -2.6392   1.0342
+#>  -10.9614   3.5416   3.4600   2.8762   2.6182   4.4955  12.4071  19.2520
+#>    4.7675   2.4569   3.9275  -7.7440  11.9608   0.2377  -5.3626 -17.5080
+#>   18.0704  12.2137   4.5459  -5.4836  10.2440  -2.5025   1.7040   5.0205
+#>   -1.1751 -12.4466  -5.6714  -3.4590   0.4078 -10.4913 -11.7068   3.2886
 #> 
-#> Columns 33 to 40   1.2980   5.7922  -2.6835   6.7159   9.6417  -2.6298  -0.8528  -0.4202
-#>    1.8438  -2.5133  -0.9831   2.2937   6.0663   8.0411   4.4082   2.6379
-#>   -8.1019  -1.3295   2.5620  -4.6356  -2.6643   3.4993   0.4289  -7.3983
-#>   -3.0134  -4.4428  -1.4359  -5.3858   8.7989 -13.9669  -7.9427   3.1195
-#>   -4.4803   4.7607  -6.0444  -0.9186  -1.2217   1.9578   2.5686  -0.9102
-#>   -8.7065   4.3657   1.5683   5.5544 -17.4600  12.2941   4.0490  -1.7500
-#>    0.0132   2.6190  -0.4216 -12.0082  -9.7049  -6.1584  -1.5848  -0.1581
-#>    9.8927  20.7229   7.2420   2.7999 -11.8450   1.7785   2.4026  -7.7475
-#>   -7.6714  -5.8588  -2.8094   3.0173 -20.9604  -2.8228   4.7490  -0.5274
-#>   -3.7243  -3.8045  -7.7921 -10.8642   5.1522  -0.0962   2.9651   9.7325
-#>   -6.3715  -0.2433   0.0752  -9.4436  -4.0806   9.0486   9.3798  -6.6853
-#>    8.6038   4.7407   2.6414   5.2493   1.0450  -1.2455  11.3315  -5.4282
-#>   -1.3117  11.8059  14.3912   6.1844   3.2220   5.4814  -6.3132  -1.5302
-#>   -3.6532 -17.5908  -3.5290  -6.2984  -1.7378  -7.9013   6.8136  -2.7963
-#>   -9.3930   3.2737  -2.1988   6.9773   2.0967  -8.0507  -9.9822  -4.4279
-#>    3.3786  -8.5124   6.4465   1.9150  10.1108 -13.1152  -0.7935   9.3200
-#>   10.4707  -8.0685   8.6091  -2.0858   4.2249  -9.6019  -2.6548  -5.5713
-#>    3.5279  -1.3915  16.7588   7.1058   5.7903   2.1495  -3.0769   8.0010
-#>   -7.8178   7.4678  -1.4509   2.5190  -5.2619  -2.2746  16.0377   1.3202
-#>    1.2576   4.2236  11.5194  13.0194  -3.2023   9.1482 -13.7077   4.5081
-#>   -5.5141   7.7185  -0.4163   2.6531  -5.2403  10.6327  -4.4554  -0.7468
-#>    2.4275  -5.7975   1.7842  -2.6893  -2.7460  -4.6191   4.1462  -3.3226
-#>   -8.3906   6.9027  -3.9422   9.1921   0.6376   2.4150 -10.8794   6.3257
-#>    2.2617  -1.5724  -2.1324   1.4894   0.7507   3.9992  10.0400   4.0461
-#>   -3.0378  -7.1001  -6.8504   1.8413   0.2720  -9.0168  -8.3117  -3.9334
-#>   -2.4101 -10.7373   4.4570  -0.2030  -5.8664  -1.8609 -17.1171   4.6217
-#>    9.4701  -5.9071   7.8036  -3.2998  -8.9704   4.3793   0.5720  -6.6505
-#>    0.3020  -6.4357  11.4205 -10.2687   5.2483  -2.8666   7.2009   6.5887
-#>   -7.9362  -6.2417 -10.4147  10.7933  -5.1977 -10.6599  -2.4203   3.5938
-#>   -0.5701   5.9710   9.2179  -2.2129  -3.2973   0.7501   3.1773   6.7176
-#>    8.1722   2.4580   8.0178  -3.3873 -16.7557 -11.7991  -1.3586   4.7263
-#>    8.6734  11.5497   1.4913  17.1166  -5.3510   6.3995  -3.3012  -0.8676
-#>   -1.1865  -5.8330  -5.4534  -3.7517  -4.7330  14.3180   7.2902   7.8606
+#> Columns 33 to 40   9.0291   9.0648  -4.7912   0.8361  13.4520  -0.9470   6.0231   8.4682
+#>   -5.5403   1.3308   0.0535   1.0446 -10.3582  -0.5971   3.5746  -0.6527
+#>   -2.0753  -0.8903   1.6490  -0.8700   1.7476  -1.5503   5.8583   2.7897
+#>   10.6961   8.0565   3.9005   9.8578   5.3039   7.0780   2.4352   8.1389
+#>    4.8794   9.1367  -1.3914   8.5088   2.4580  -0.0621   9.0786 -13.3420
+#>   -2.1885  -7.0007  -8.8945   0.6700   4.8290   3.8721 -11.3740 -11.3452
+#>   -1.1829  -1.9312  -7.4180  -8.6648  -5.4902   2.2234  -7.1657  -3.5329
+#>    7.1127  -0.9872   1.5894   4.6443   5.7469   0.2491   2.3115  -1.9354
+#>   -5.3910  -6.0073  21.1120  10.7926  -8.2970   0.3224  16.0895   2.7767
+#>  -16.0522  -4.2267  -5.5280  -6.0465  -8.7839  10.4525 -10.5019   3.0818
+#>    7.5702   8.8413   2.9530   8.4895  12.8108  10.4018   1.6808   4.3083
+#>    8.0420  -5.5155   3.2963 -10.0506  -5.8500   6.9933  -4.2845   6.4808
+#>    6.7079 -12.9063  -9.3036  -1.6147   0.6322   6.3550  -8.5571  -0.9226
+#>    1.3621   0.3922  -3.9505   6.2516  -3.3053   9.8737  13.5528   5.7667
+#>   -0.3757   0.7022   8.6452 -13.9733   4.3118 -10.8438  -1.8795  -4.4047
+#>   -7.5107  -9.4712   7.2232   3.2643  -2.5581  -9.4539   3.0422   7.1533
+#>    0.3523   7.1380   5.0800  -1.5233   6.1655   2.6686  -4.8108  12.0540
+#>   -4.0293 -11.2158  -8.3625   8.2846  -4.3129   0.5222 -16.2305   2.1044
+#>   -1.6736   1.8621  -6.5074  -3.5824   1.0331   0.1216  -5.3052  -4.4795
+#>  -13.8506   6.9267   3.1798 -10.5809  -7.9187   8.3874   7.4720  -4.1051
+#>  -12.7617  -8.2905   6.1391  -0.9022  -9.8472  -4.6668   7.4433   6.1648
+#>    0.9251  -1.8448  -3.1751 -11.2548   5.0614  -7.0791  -0.8570  -7.6254
+#>    9.2979   3.2845   0.2584  -5.9465   3.8216   5.0401 -12.9721  -1.2510
+#>   15.6567  -4.8082   5.8797   5.8092  -0.9878   3.6345   4.4043  -3.2140
+#>   -0.4622  10.6627  -2.3295  -9.9826 -10.2325  -7.2459   0.4033   2.2700
+#>  -11.3552  -1.3845  -1.7160  -5.5424 -11.6904   0.8719   1.9306  -5.1464
+#>    6.1921   5.0279  -3.1814   1.1111   2.1819   4.8440  10.7639  -9.0983
+#>   -0.5592  -0.6208  -2.1767   2.3614   1.5910  -0.5328  -3.2849  -4.1854
+#>   -2.6891   6.2450   1.9662   2.5096  -5.1621  -1.9323   8.2095  -5.4009
+#>    8.1952 -15.6097   5.9874   0.3915   2.0798   9.8226  -2.2809  -7.5705
+#>   -0.0894  -1.9000   0.2765   9.2129  -0.9447  -9.9310   6.3105   3.1701
+#>  -13.3567  -5.2193   9.2523  -4.4614   6.6329  -5.9363   8.1410  -3.6325
+#>    8.4824   3.9591  11.5334  -3.9089   2.0096 -11.1190   6.3440   4.1236
 #> 
-#> Columns 41 to 48   3.5615  -9.7688  -4.3698   9.4824  -3.1192   4.5284  11.1475   6.8623
-#>    8.3879   2.4612   9.4136  10.2137   1.5750   1.5142  -2.0387  -4.3419
-#>    4.8508   4.3983   0.7569  -4.0457   1.3259   0.2923   1.1547   3.2599
-#>   -1.4479  -3.5318  -3.8466  -4.2446  -7.6283 -11.9392 -19.9979  -4.2009
-#>    9.4132   5.7710   8.7189   9.7178  -1.7340   6.4248  -1.3654  -2.6776
-#>    0.6586  -1.7411   6.1335  -8.5081   9.3015   9.7500  -1.5191  -4.5925
-#>   -5.9769   2.0140  -7.3969  -2.6265   6.8943  -4.0132  -5.8919   6.3048
-#>   -6.8142  -5.2937   3.0421   5.4276  -3.1331   9.1253   3.9011  -1.3756
-#>   -8.5611  -0.3279   6.4001 -12.0204   3.4183   7.4415  -9.0594  11.2156
-#>    8.1157   4.5898  -1.0780   2.5033  -2.1577  -6.4207 -14.1143  -8.2264
-#>    2.4492  -0.1197   4.2152   5.1966 -10.2710   2.1175  -0.4986  -3.3390
-#>   -2.0919   2.5979   8.4456  -1.3720  -3.2895   6.1981   3.1747  -0.2003
-#>   -3.3364   1.5890  -2.6446  -4.0857   8.2205 -18.2212   8.3387  -1.3977
-#>    8.6574   4.8976   4.3133  11.0241  -9.1563  10.2224  -9.8328  -7.7376
-#>    6.9685   6.5083  11.3045 -10.6788   1.1892   1.8784  -3.1766  -4.8822
-#>    8.6182  -6.2387 -18.0660   4.8077   2.5121 -12.4155   0.6158  -1.9639
-#>   -5.5851  11.7859  -0.7654  -3.9968   4.2055  -6.8894   9.0475  -2.4525
-#>   -3.0200  -1.1177   3.6660  -7.0122   2.4538  -0.5365 -10.3393   6.7757
-#>    0.0465  -9.8708  -4.2814   0.0838   4.1645 -10.5060  -0.7702   3.3377
-#>   -0.3392  -3.5162   2.9724 -11.0832   6.6663   0.7159  -1.9853   2.1120
-#>   -4.8323   0.7137  10.9749  -1.8166   4.4660   6.5846 -15.5988  -1.3574
-#>   -4.1564  -3.3339  -5.3380  -8.1182 -10.7575   1.1331   7.1809  -7.1301
-#>    2.8081  -9.6512   2.0413  -3.0798  -5.8924   0.5218  -2.0901   9.6971
-#>   -4.0738   6.1353   3.4338  -5.3469   2.7293 -11.0841   0.2694  -5.0426
-#>   -2.1924  -1.7987  11.2064  -9.8901 -15.7909   6.7003  -0.3568  -1.9345
-#>    0.7802  -2.9752  -9.0987  -4.5661   3.1597   0.8458   0.2071  -4.5137
-#>   -3.6960  -1.9876  -0.2447   3.1732   6.7256  -8.5313  -4.9369 -13.9011
-#>    3.2326  14.2881   3.1216   2.2416   2.1360  -6.8162   0.7646  -7.6339
-#>    4.7298  -4.4067   9.1713   5.2442  -4.9678   4.6076  -9.5215  -4.5575
-#>  -14.0215   3.5428  -1.2730  -1.0921   6.3475   2.0168   9.2348   5.8859
-#>  -18.8806 -10.0248  -4.5760  -8.5119  -1.5314  -2.2611  -1.2832   9.1316
-#>    3.2393  -5.1773  -0.6072   5.6596   3.7721  -0.3780   9.8461   4.7959
-#>    4.3407   7.1963   6.0763  -4.1961  -0.9477  11.8882  -6.1514  -1.7034
+#> Columns 41 to 48   5.0410  -8.9959   3.6210   2.3558   4.2792   9.0765  -0.2901  -8.2545
+#>   -0.2864  -0.9212   0.5931  10.9194  -2.4218  -0.4575   5.8690   1.6568
+#>    5.0047 -10.1728   0.8103  -1.6926  -2.3361  -3.8185   1.9042  10.5510
+#>   -3.0268  10.0630  -1.2243  -0.4988 -14.4268  -7.6654  11.6947  -5.8465
+#>    5.3218   4.6032   2.2118   8.5098  11.5692   6.5156  -5.8169  -9.4412
+#>    3.7137   9.5904  -2.6944  -3.6659  13.0890  -1.9711  -7.6408  11.7283
+#>   -4.1888  -2.9035   8.6672   3.3554  -2.0120   4.0365  -3.9517  14.0364
+#>   -9.1515   6.8065   2.0609  -6.2702  -8.2293   1.2109  21.0128   6.6906
+#>    0.3517  -8.4302 -17.9337   2.4910  -4.2998  -0.7051  -6.1338  -8.6990
+#>   -8.0499   5.7869  -3.4726  -5.7918   3.7812   1.0511  -4.8952  -7.1046
+#>   11.4852  11.1430   6.8332  -9.5315   3.1408  12.5682  16.7221  -7.8358
+#>    6.6932   7.0197   6.2696  -4.2915   6.3723   6.6846   0.4017   4.4911
+#>   -1.0270  -5.7155   4.1574  -5.4587 -14.7176   6.8744  11.3282  -3.0413
+#>   -7.4301 -13.1757  -7.2101 -10.7080  -0.5443   0.6558   0.6875 -15.6206
+#>  -12.2979 -13.3088   8.0980   4.7962   0.9168  -1.0282  -1.9946  12.9978
+#>   -0.2283   0.5386  -9.6258  -1.7718  -4.5427  -7.0732  -0.4332  -1.1528
+#>    3.6472   0.0716   3.0132  12.9852   7.1906  -6.3459  -7.9011  -2.5922
+#>    1.9467   0.1932   0.2445  -2.1829  -9.1354   7.1187  -1.3450   6.5238
+#>    2.5183   1.3257  -4.5913  -2.2107   3.0072  -4.8577   2.1004   9.1451
+#>  -16.4159  -8.5217   5.0396  -1.4263   1.5453   1.7852  -1.6999  -2.6252
+#>    9.0679   0.6447  -3.7581  -0.2467   5.2097   3.9798   7.8734  -1.7358
+#>   -7.2657  -0.9888  -4.7308  -4.8582  13.1597   1.8404  -1.4779   7.1479
+#>   11.2440   5.4012   6.5676  11.6884  -6.2825  -3.8195  -7.5253   8.1161
+#>   -2.4993 -11.7202   4.9578  -7.6192  -1.8207   5.4769  -6.5508  -3.3858
+#>    4.2418   1.5527   3.6143   6.1662  -7.3733  -8.1268   0.6360  -8.6289
+#>    3.3160   0.1369   3.3930   4.6690  -6.5854   2.5423  -3.6526 -10.4141
+#>   -4.6225  -1.1011  -4.3853   1.1562  -7.5589   5.6440   0.0104  -9.5033
+#>   -0.4008   6.5317   4.6031   6.1779   4.5459   3.6929  -8.5979   3.1530
+#>    2.4049  -5.8995  -5.0820   2.2342  -3.6889   2.4698  -0.3644 -11.7565
+#>    5.5751  -1.0420  -2.6784  -8.4789   1.6883   6.2200  -8.2652  -1.7530
+#>    9.4669  -5.5824 -10.3935   6.8019  -9.0175   1.6938   4.5165  -5.6580
+#>   13.7564  12.0536   7.8328  -8.3668  -4.7477  -7.2689  -9.9702 -12.1428
+#>   -6.0093   3.6453  -0.5948  -7.6315  -0.8672   7.7942   5.7707  -7.6996
 #> 
 #> (10,.,.) = 
-#>  Columns 1 to 6 -1.1510e+00  1.4708e+01  6.1299e+00  1.9380e+00  9.1910e+00 -5.4459e+00
-#>  -2.4515e+00  5.8284e+00 -7.1605e+00  4.7084e+00  3.7136e+00 -9.5652e+00
-#>  -1.7493e+00 -9.4196e+00 -3.2808e+00  1.5286e+00  7.8173e+00  8.9133e+00
-#>  -4.3640e+00  9.2855e-01 -6.1887e+00 -6.1604e-03 -1.9447e+00  1.2969e+01
-#>   9.0246e-02  5.5193e+00 -2.7066e-01 -3.4072e+00 -2.3908e-01 -1.2357e+00
-#>   1.1252e+01  7.1942e+00  1.1412e+01  4.9808e+00 -3.6412e+00  1.6060e+00
-#>  -3.5517e+00 -1.7384e+00  2.6949e+00 -2.0860e+00  1.4382e+00 -6.8337e+00
-#>   3.3280e+00  2.3369e+00 -1.0039e+01 -3.3540e+00  3.2409e+00 -1.2759e+01
-#>   4.4777e+00 -3.3609e+00  4.2431e+00  1.2564e+01 -7.6268e+00  1.8690e+01
-#>   3.0412e+00  1.4121e+00 -6.2280e+00 -6.6548e+00 -1.7658e+00 -5.1015e+00
-#>   1.5029e+00  2.9603e+00 -3.1640e+00  6.9582e-01  8.4434e+00  2.5271e+00
-#>  -4.1522e+00  4.7775e+00 -1.0516e+01 -9.1064e-01 -6.6537e+00 -3.6508e+00
-#>  -8.0951e+00  6.3684e+00 -9.9464e-01 -8.3192e+00  1.2819e+01 -1.2725e+00
-#>   1.7781e+01  1.4571e+01 -5.4962e-01  1.0911e+01 -3.1689e+00  1.7386e+01
-#>  -2.2865e+00  6.4819e+00 -9.7681e+00 -2.3027e-01  6.4727e+00 -5.5001e+00
-#>  -8.3685e+00  1.9537e+00  5.4966e+00 -1.0910e+01 -1.1238e+00  1.0691e+01
-#>   7.1031e+00 -2.3167e+00  1.8460e+01 -3.7902e+00  1.3384e+01 -7.9244e+00
-#>  -2.3056e+00 -3.9997e+00 -2.2651e-01  7.2948e+00 -2.4566e+00  8.5297e+00
-#>   8.0650e-01  3.1507e+00  8.2394e-01  5.3150e+00 -2.3854e+00  4.6375e+00
-#>   8.0931e+00  4.0334e+00  5.6126e+00 -3.0156e+00 -3.7886e+00 -9.7867e+00
-#>  -4.6833e+00  6.6924e-01  5.7609e+00  7.9856e+00 -1.3209e+01 -1.2543e+00
-#>   5.6184e+00 -7.3573e+00 -3.9674e+00 -4.5558e+00  1.1325e+01  1.1856e+01
-#>   2.3098e+00 -7.6610e+00  1.2973e+00 -2.5097e+00 -1.1555e+01 -1.6619e+00
-#>   1.0846e+01  1.2510e+01  5.4761e+00 -3.8703e+00  1.8016e+00 -1.6538e+00
-#>  -1.0300e+00 -5.6389e+00  7.5530e+00  9.5494e+00 -9.2211e+00 -1.8173e+00
-#>   2.5764e+00 -6.1685e-01  1.0452e+01 -3.8054e+00  7.0047e+00  2.2879e-01
-#>   9.5835e+00  3.4535e+00  2.5474e+00 -5.9788e+00  1.5455e+00 -1.1756e+00
-#>   1.0323e+01  8.5548e-02  1.0385e+00 -6.7566e+00  1.1697e+01  4.6183e+00
-#>  -4.9143e+00  9.3366e+00  2.1455e+00  6.2903e+00 -6.1693e+00  1.2083e+01
-#>  -1.3754e+00  3.9394e+00  1.0684e+01 -7.6846e+00  1.5168e+01 -4.2258e+00
-#>   1.2749e+00 -4.5121e+00  2.4132e-01  1.1152e+00 -1.5815e+00  5.2832e-01
-#>  -9.0462e+00  6.7138e+00 -3.1081e+00 -5.1703e+00 -2.0812e+00  6.7342e+00
-#>   4.6960e+00  9.0685e+00 -6.0166e-01  5.2299e+00 -2.3888e+00 -3.4944e+00
-#> 
-#> Columns 7 to 12 -1.0082e+01 -1.0771e+01 -8.9546e+00  1.8042e+00 -2.7159e+00 -1.7762e+00
-#>  -8.4417e+00 -7.9267e+00 -9.4689e+00 -2.6069e+00 -2.0608e+00  1.0649e+00
-#>   1.4768e+00  6.8986e+00 -2.8778e-01 -5.5315e-01  8.9970e-01 -4.4995e+00
-#>   9.3775e+00 -7.7333e+00  1.3670e+01  4.7990e+00 -2.5009e+00 -9.3834e+00
-#>   3.6530e+00 -1.1222e+01 -5.3500e+00 -5.1788e+00  5.5077e+00 -5.6291e-02
-#>  -1.0494e+01 -3.6301e+00 -1.0994e+01 -2.9919e-01  3.4897e+00  5.0841e+00
-#>   6.2714e-01 -4.2035e+00  1.6428e+00  7.9124e-01 -9.2246e+00  3.1176e+00
-#>  -9.9808e+00 -8.9762e+00  5.5213e-01 -3.8203e+00 -4.1071e+00  4.6112e+00
-#>   3.8380e+00  5.4504e+00 -4.7822e+00 -9.8952e+00  4.3101e+00  1.2299e+01
-#>   3.6037e+00  7.2293e+00 -1.0695e+01 -2.0063e+00  6.5496e+00 -2.8960e+00
-#>  -1.3454e+00 -3.6698e-01  9.2473e-01  7.9984e+00  2.8898e+00  9.4804e+00
-#>  -1.0162e+01  4.9699e+00  3.4657e+00 -1.2196e+00  5.4783e-01 -4.5905e+00
-#>  -4.7108e+00 -2.1457e+00  5.7715e+00  9.0599e+00 -1.3146e+01 -1.2753e+01
-#>   1.1410e+01  4.6495e+00  7.7839e+00 -9.2500e-01  1.3030e+01  3.1535e+00
-#>  -7.5110e+00  1.3772e+01 -2.4349e-01  3.3336e+00 -1.5929e+01  6.0472e-01
-#>   8.5152e+00 -7.2888e+00 -2.2775e+00  3.6564e+00  7.3078e+00 -2.1732e+00
-#>  -8.1812e+00 -5.6365e+00  2.7685e+00 -5.5288e+00 -8.5448e+00 -1.0250e+01
-#>   1.2640e+00  1.5171e+00  2.8921e+00  1.3248e+01 -1.1732e+01 -2.6864e+00
-#>   3.8779e+00 -6.2568e+00 -1.8679e+00  3.7103e-01  5.4897e+00  4.7297e+00
-#>  -1.1899e+01  7.1531e+00 -4.0514e+00  4.0651e+00 -6.1371e+00  9.5976e+00
-#>  -2.0377e+00  7.9734e+00  3.2218e+00  2.6228e-01  1.4351e+00  7.6534e+00
-#>  -1.9397e+00  1.1279e+01  4.0064e+00  7.8378e+00 -1.6255e+00 -2.5241e+00
-#>  -8.9815e+00  1.8569e+00 -8.1455e+00  1.0342e+01 -2.6133e+00  1.6513e+00
-#>  -4.7943e+00  4.7982e+00  3.4861e+00  2.0369e+00  2.9492e+00 -1.2867e+01
-#>  -1.0601e+01  1.4021e+01  2.1461e+00 -2.4602e-01 -7.3911e+00  3.3181e+00
-#>  -3.6000e+00  1.0415e+00 -5.2782e-01 -5.1445e+00  2.0022e-01  1.7768e+00
-#>  -4.7952e+00 -6.2317e+00  1.9980e+00  2.2601e+00 -2.6737e+00 -8.5609e+00
-#>   8.5771e+00 -2.4351e+00 -1.1265e+00 -2.6971e+00  5.2554e+00 -1.7823e+01
-#>   6.7204e+00  1.4255e+01  6.1903e+00  5.5310e+00 -7.5880e+00  9.5411e+00
-#>  -7.2577e+00 -6.7162e+00 -9.2917e+00 -2.7819e+00  2.8843e+00 -7.8432e+00
-#>  -3.5886e+00  1.1184e+00  2.5095e+00 -3.2145e+00 -5.5742e-01 -2.6120e+00
-#>  -1.0957e+01  2.7352e+00 -3.9247e+00 -1.8870e+00  9.8008e-01 -1.1580e+00
-#>  -4.1271e+00  4.0719e+00 -1.3393e+01 -4.4279e-01  6.8259e+00  8.2849e+00
+#>  Columns 1 to 8   0.5967   8.9061  -3.1299  -7.8840   0.3534 -13.0330  -3.8970  11.3081
+#>    3.7395   5.4733   4.7818   0.1871  -4.5540   4.7952  -1.7746  -3.1318
+#>   -5.6015  -0.1544   2.0885  -1.7926   3.2126   0.9821 -10.2387  -0.6123
+#>   -6.0875  -6.2407  -3.3544   5.1424  -3.3596  -1.1933  11.7744   5.2448
+#>    4.0958   4.7821  -4.8804  -6.7308  -9.5122   2.2718   1.8152   1.3776
+#>   -2.6449   0.7836  -6.0213 -10.6665  -4.9997   7.1032   6.6242  -3.3453
+#>   -3.5176  10.3968   5.4079  -9.2823  16.6938   5.0424  -8.1388  -8.3120
+#>    4.4481  -4.6667  -6.1982   5.0855   8.4598   0.4721  -4.2269  -5.6700
+#>    9.0840 -10.8703   9.1757   4.4316 -25.6014  14.8390   1.2594  18.1604
+#>   -4.1434   0.2375   1.4550   5.8239  -7.9385  -1.3985  -3.4671  -2.3989
+#>  -13.7737  -0.7641  -4.8603   6.7941   7.3874 -11.3247  -0.3040   3.3053
+#>    4.6027   2.2413  -4.7244  -6.3537   1.2002   2.4862  -0.0701   7.7824
+#>   11.8750   4.3681   4.4364  -2.3271   6.1403 -12.6188  -9.9999   3.2493
+#>    3.5347   9.2051  -1.1649   3.9263  19.1030  -6.0443   1.2443  -3.0241
+#>   14.7534   2.4395  -1.2814  -6.4214  10.4381 -21.0720   1.3778   9.6047
+#>   -2.2916 -19.9770  -5.5558  10.4334   5.0819   5.1359  -1.5383   2.8387
+#>   -3.8058   0.3947  -4.4943   0.8695  -5.1525 -14.0862   2.4184   2.7103
+#>    8.2592   6.3667 -13.5373  -5.4544  12.4179  -0.0283  -2.8308 -10.8751
+#>    1.3312   2.1701   1.9028  -4.5062   1.2966   2.2194   4.3314  -7.5127
+#>  -10.6727   2.8034  15.8894  11.9669 -12.2913 -10.0215  -7.8647  -4.2430
+#>    0.4096  -3.4861  -1.3180   2.8427   9.9121  -7.2794 -13.5559  -2.7689
+#>   -3.9413  -5.7351   1.7413  -3.3382  -3.8379 -10.9658  -2.6630  11.9106
+#>    9.9347  10.1389   0.0226 -11.3068   3.4018  15.2355   0.1433   1.0414
+#>    5.6263  11.1606   2.1327 -10.9379   2.4913  -3.4527 -12.6513   0.5640
+#>   -6.5287   2.1751  12.7916  -1.3484   0.8640   3.0026  -1.8041  -3.4488
+#>   -8.5781  -1.3665   8.6437   8.0611  -5.7120   1.5846   1.3035   0.4388
+#>    0.2692  -1.2841   7.3197 -10.4970  -3.8196   0.9985  -1.8457   2.9985
+#>   -5.2647 -11.2524  -7.7536   3.1290  -3.0876   5.2501  10.8070   7.6927
+#>   -1.6310   9.4374  14.7801  10.3291  -7.6170  -7.8053  -6.4189 -11.4885
+#>   19.7077   8.9745  -0.7391 -10.9278   9.4445   5.7570   7.7951  14.4237
+#>    5.3998  -4.8437   2.6966   1.2622   4.2669   3.3835  -3.7844  -6.4461
+#>   -3.1946 -12.8971  -4.9457  13.2197  -2.3462  11.8761   8.9372   6.7757
+#>   -0.2066  -8.3420  10.6258  -2.2407   0.5455  -2.1206  -3.1131   9.0821
 #> 
-#> Columns 13 to 18  1.4518e+00 -1.7907e+00 -7.9384e+00  6.5315e+00  4.2660e+00  1.4122e+01
-#>  -4.7786e+00  9.9377e+00  7.1004e+00  9.3690e+00 -2.6187e+00  1.4162e+01
-#>   9.8217e+00 -1.7407e+00  8.8441e+00  7.0788e+00 -1.1299e+01 -7.9481e+00
-#>   9.8827e+00  5.8448e+00 -9.9542e+00 -7.7639e+00 -4.0326e+00 -3.8740e+00
-#>  -9.0378e-02 -2.6884e+00  4.5369e+00  7.4611e+00 -1.6236e+00  8.2237e-01
-#>   2.6702e+00 -8.4511e+00  7.7322e+00  6.1408e+00 -5.8271e+00  1.1371e+01
-#>   2.4814e+00  1.9165e+00 -1.1149e+00 -7.0363e+00  2.5774e+00  1.0552e+01
-#>  -1.3126e+01  9.4007e-01  1.2221e+01  1.2636e+01 -7.5024e+00 -1.8134e+00
-#>  -3.1079e+00  3.2881e+00  5.4640e+00  8.8803e+00  1.3867e+00 -1.4285e+01
-#>  -4.6069e+00 -7.2583e+00 -8.5716e-01 -6.4116e+00  6.0483e+00  1.4786e+00
-#>  -2.1766e+00  1.1177e+01  7.7207e+00  7.4819e+00 -9.5557e+00 -1.5603e+01
-#>  -3.5557e+00  1.5440e+00  3.4989e-01  1.0391e+00 -1.3528e+00  9.3870e+00
-#>  -3.6253e+00 -1.6029e-02 -8.2342e+00  6.4836e+00  2.6881e+00  2.9792e+00
-#>   5.0866e+00 -1.5473e+01 -9.9891e+00 -7.4947e+00 -1.3624e+00  7.0891e+00
-#>  -8.1447e+00  5.6452e+00  1.6197e+00  1.3334e+01 -1.1119e-01  3.0060e+00
-#>   2.4880e+00 -2.5340e+00 -8.1627e+00  7.4808e-01  1.6019e+00  4.5472e+00
-#>   1.5419e+00  5.1208e+00 -3.9342e+00  5.4785e+00  7.8769e-01  5.2958e+00
-#>  -1.0982e+01 -2.1442e+00  3.8397e+00 -6.2287e+00 -5.1945e+00  4.4689e+00
-#>  -8.0164e+00 -6.6248e+00 -3.7490e+00  1.5171e+01  4.9799e+00 -3.7437e+00
-#>  -1.1349e+01 -2.8744e+00 -2.7050e+00  4.0806e+00  1.3722e+00  4.1536e+00
-#>  -7.2407e-01 -9.3004e+00 -3.5112e+00 -5.6065e+00 -6.6253e+00  5.4250e+00
-#>  -4.0743e+00 -4.7070e+00  1.2838e+01  2.6905e+00 -8.5392e+00 -8.7264e+00
-#>  -1.7624e+00 -5.0954e+00  6.2427e+00 -6.9174e+00  4.3744e+00 -4.5048e+00
-#>   3.0713e+00 -1.1409e+01 -9.2123e+00 -3.3423e+00  1.1981e+01 -1.6001e+01
-#>   3.4765e+00 -2.1769e+00 -2.9399e+00 -4.0751e-01  6.6048e+00 -9.9558e-01
-#>   5.0735e+00 -8.0494e+00 -5.2977e+00 -2.9626e+00 -6.8171e-01  3.6940e+00
-#>  -6.0202e+00 -5.2359e+00 -9.8874e+00  1.0407e+01  7.2523e+00  6.6818e+00
-#>  -4.5084e+00 -8.7041e+00 -1.4744e+01  1.1044e+01  6.7042e+00 -7.7103e+00
-#>  -5.2094e+00 -9.2715e+00 -2.5132e+00 -2.0430e+00  3.9644e+00  1.8019e+00
-#>  -1.0330e+01  1.0737e+01  6.5686e-01  7.8642e+00  9.6778e+00  8.8608e+00
-#>  -2.2792e+00 -2.1896e+00  1.7922e+00 -8.7522e+00 -3.9561e-01  1.1902e+01
-#>   1.1399e+01  4.3614e+00 -3.1763e+00 -3.0516e+00  9.3060e-01 -6.1095e-01
-#>   2.6698e+00  8.5572e+00  9.3059e-01 -8.6888e+00  1.2132e+00  2.0137e+00
+#> Columns 9 to 16  12.7184  -5.6334   7.6452  14.8108 -10.3884 -10.6418   7.3686   3.8239
+#>   -0.0840  -3.4907   2.3260   3.1506  -1.7152  -4.5557  -4.8369  -3.9408
+#>   -7.2580  -0.9143   7.4439  -2.6752   9.8491  -3.4175  -0.6750   7.0462
+#>    6.4653  -3.4637  -8.7393  11.3686  -6.0135   3.1535   1.3512   5.3406
+#>   -2.4577   4.0158  12.4024   0.9398   6.4745   4.8406  -6.1452  14.1451
+#>  -17.5642 -17.8735   6.4547   6.2830   9.7406   1.4131  -2.1599  -1.9808
+#>    9.6575   6.9286  -4.0918  -6.6685  11.1647  -1.9471  -1.7669 -11.0983
+#>    4.8917   9.8644 -18.0006  18.3614   0.4248  10.6514  -4.2588   6.4091
+#>    1.5368   7.1106  -0.0565 -11.4301 -15.7930   1.1931  -8.7124  -7.0264
+#>  -12.7397 -11.5190   1.1062  -9.4952   8.2471   5.2444  -2.4862   5.4913
+#>   13.5010  11.9210   2.5241   5.7832  -8.6986   6.7588   4.6441  26.7168
+#>    6.4930  10.7462  -3.5446   8.4761  -0.1220  -1.3715 -10.6841   7.6107
+#>    1.1948  -0.5737 -14.9850   9.1370   7.8642 -16.4949  12.9352  -4.4521
+#>    2.3075  -8.9138 -16.7157  -6.4073  -2.3865  12.6094   6.3325   1.7814
+#>   15.0262  -2.5743 -18.1020  -2.8585  -6.8411  -4.7078  -0.4035 -12.8958
+#>    0.5476   4.2362  -5.4529  -0.1188 -11.6455   5.6475   1.0045  -7.7516
+#>   14.5935   2.3331   6.2971  -0.2265  -6.4906   4.2403  -0.5130  10.8227
+#>   -0.5713  -2.5741  -7.0101  -2.7351   3.6928  -9.9242   2.6234  -2.9524
+#>    2.8933  -8.8181  -5.1029   1.0112  -4.7493  -6.1398  -1.5642  13.0257
+#>  -10.0909  -7.3498  -3.4673 -12.0644   5.6308   5.1258  -3.5983  -1.8730
+#>   10.3024   2.6471   1.5830   0.0541  -1.8486  -6.8148  -9.4120   3.1598
+#>   -6.7805 -14.3555  -1.7404  11.5228  -2.2756  -3.9881 -10.4924  -7.8470
+#>    6.7334  21.2917  -4.4236  -4.5718  17.6852   2.0614  11.6169  -2.8394
+#>    3.1136   1.5566  -4.4270   3.0665  -5.2457  -2.5503   1.2777  -0.2787
+#>    7.4344   1.5674 -12.1284  -7.9893   7.1850  10.1423   4.3577  -2.5188
+#>  -11.7506  -6.5480   2.8386  -1.3697   5.8747  -0.7517   2.4682  -8.0993
+#>   -6.0596  -7.6772  -1.9051   3.0569   0.1072  -1.4377  -4.3270   0.6160
+#>   -5.7295  -6.1819  11.3433   3.7970   2.1330   4.5662   2.4839   2.5734
+#>   -2.6202   4.8083  -7.7703   5.7833 -10.3329   6.8201  -1.2716  -3.6942
+#>    2.8626   5.2851  -8.5269  -7.2407  -2.9539 -11.7345   9.8633   2.6877
+#>    1.6020   7.5345  -5.3247  11.1320  -3.9768   3.8956   6.8449  -6.2985
+#>   -1.3377  12.3795  -5.5648   3.3444 -11.1755   8.6887   2.6475   4.8080
+#>    8.7424   7.3310  -4.0678   4.0101  -9.6832   1.9098  -5.8224  -1.9697
 #> 
-#> Columns 19 to 24  3.1242e+00 -1.7359e+00 -3.3670e+00  9.2639e+00  1.1055e+01 -2.3429e+00
-#>  -4.2909e+00  3.3208e+00 -1.5062e+01  4.3947e+00  5.0361e+00 -2.6086e+00
-#>   5.6307e+00 -4.7571e+00 -1.1382e-01 -3.3869e+00 -6.9188e+00  7.4758e+00
-#>   4.4813e+00  5.7593e+00  8.7943e+00 -1.1875e+01 -6.0524e+00  8.9488e+00
-#>   1.0235e+01  8.4402e+00  1.4450e+01  2.7812e+00  7.2263e-02 -3.3179e+00
-#>   1.5148e+01  9.6237e+00  6.9552e+00 -3.8001e-01 -1.0859e+01  6.4538e+00
-#>   5.2313e+00  7.1842e+00 -9.8373e+00 -5.4476e+00  9.7978e+00  2.4108e+00
-#>   1.5244e+01  1.4151e+01  3.2298e+00 -7.6963e+00 -4.4103e+00 -2.9889e+00
-#>   1.0298e+01  6.3490e+00 -4.4758e+00  8.8059e+00 -1.7732e+01 -2.1141e+00
-#>   3.7406e+00 -8.9840e+00  2.5201e+00 -5.0936e+00  6.2817e+00  5.8291e+00
-#>   9.6266e+00  1.9386e+00 -5.5450e+00  7.9442e+00 -6.1569e+00  3.7012e+00
-#>   3.7923e-01  1.5367e+00  4.1575e+00 -6.0499e+00  5.9302e+00  2.2745e+00
-#>  -1.0050e+01 -2.2731e+00  8.6788e+00 -9.9291e+00 -5.6922e-01 -1.3604e+00
-#>  -6.5292e+00  1.3730e+01 -1.0047e+01  9.6298e+00 -9.2012e+00 -4.4376e+00
-#>   4.0541e+00  3.1760e+00  1.2248e+01 -2.8897e+00  3.2389e-01 -1.1149e+01
-#>   2.2017e+00  4.8893e+00  4.7247e+00  6.1307e-01 -1.6959e+00 -1.9882e+00
-#>   1.0252e+01 -2.2491e+00  2.3286e+00 -4.3153e+00  1.2832e+00 -3.1327e+00
-#>  -2.3985e+00 -3.4597e-01  2.4154e+00 -4.7692e+00 -3.3074e+00 -7.4276e-01
-#>  -3.8533e+00  5.3527e+00 -8.8127e+00 -1.4880e+01  4.0541e+00 -3.2262e+00
-#>   1.3978e+00  2.5510e+00  2.0188e+01  9.2224e+00 -1.1162e+01 -6.1515e+00
-#>   4.1505e+00 -6.7055e+00 -4.2155e+00  1.4835e-01  1.5798e+00  9.3494e+00
-#>   1.1808e+01  6.8497e-01  2.3234e+00 -1.0927e+01 -1.1371e+01  6.6349e+00
-#>   4.8906e+00 -5.8453e+00  1.0335e+01  3.2712e-01  6.6653e+00  2.5518e-01
-#>   4.0984e+00 -1.0536e+01  1.5835e+01  2.6016e+00  1.6003e+00  3.5174e+00
-#>  -3.7511e+00 -1.2930e+01 -8.8864e+00  8.9835e+00 -1.7069e+00  2.0311e+00
-#>   8.3025e-01 -3.5143e+00 -1.0223e+00 -5.9961e+00 -4.2259e+00 -4.4451e+00
-#>   1.5221e+01  2.0810e-02 -1.1063e+01 -1.3123e+01 -2.5652e+00  1.4035e+00
-#>  -1.1284e+01  7.8458e+00  5.7174e+00 -6.0471e+00 -6.5509e+00 -1.2211e+01
-#>   3.0079e-02 -2.7623e+00 -5.8705e+00  1.0009e+01 -4.2271e+00 -1.3138e+00
-#>  -6.4721e+00  6.9665e+00 -7.0353e+00  3.5454e+00 -5.5335e-02 -7.7748e+00
-#>   1.4640e+01  1.3125e+01  1.0948e+00 -1.2123e+01  8.9720e+00  4.7290e-01
-#>  -1.4898e+01  4.9410e+00  2.4164e+00  1.2534e+01 -9.3768e-02  1.6201e+00
-#>  -5.7218e+00 -4.0255e+00 -2.5160e+00  1.2270e+01  2.6487e+00  4.5178e+00
+#> Columns 17 to 24   9.9252  -6.0601  14.5614  -4.6357  -9.7399  -3.4691  -3.6213   4.3343
+#>   -1.1188   9.0092  -7.7302   5.4782   0.7325 -14.4709  -6.1643  -1.5871
+#>    0.1377  -6.8225  -9.1232  -2.2449   5.5945   3.1723   0.5147  -0.6275
+#>  -10.6650  -3.2306  11.6264  -8.4183  -5.8531   1.1509  -3.7451  -2.3484
+#>   -2.6869  13.4504  -7.0607  -3.8833  -4.8293  -5.4682  -7.0744  -4.7783
+#>   11.5476  11.2247  -4.3399  -4.6338   8.4566   2.7376   8.3324  -3.0561
+#>   -7.7542   4.6611   1.0957   6.0585  10.8559  -1.2190  -0.9405  14.1794
+#>   -7.9773   5.7414   0.5181   1.5246   6.6802   7.2207  -2.9101   2.9138
+#>   -4.5942  12.4311 -11.4073 -12.1179   0.8398 -18.3194 -21.1181 -12.0491
+#>    4.8846  -5.1675 -10.9318   3.3900 -11.0907   4.5585   1.5767  -2.1348
+#>    3.8656  -7.2082  16.0581  -4.6179 -15.4396   1.4072  17.3777  -1.4314
+#>    8.9796   7.3150  -0.5738  -1.7750  -0.5341  15.3414   4.0000  -9.2475
+#>    4.2670  -5.9049  -3.6708  -6.0051 -12.1615  -2.7186   9.6878  -6.5743
+#>   -9.5218  -7.5280   3.2344  -1.6397 -16.0515 -18.9206  -6.6279  -9.2495
+#>   10.8967   2.1110   3.9062 -17.6723  -3.1092   5.1993  10.1052  -5.6322
+#>   -7.5139   5.9768   0.3199  10.9290   4.6202   8.0122   3.5969   2.8177
+#>    0.2765  -6.1538   7.9534  -3.8241  -8.8093   9.6636   3.2367   0.6593
+#>   -1.1557   8.1527  -0.5829  -8.9356   2.5215  12.6012   2.9697  -2.5946
+#>    5.5167   0.6529  -9.0097   6.1313  -0.3132   7.0266   7.4054   2.5653
+#>   13.3081   3.3736   3.5746  -1.6584  -9.7668  -7.1930  -1.1739  13.8205
+#>    7.5616   4.2413 -11.7830   5.7772  -7.7152 -15.0252  -6.6403   3.4414
+#>   12.1636 -16.4812   6.1080   1.4087   1.1822   7.9212  16.6163   7.1504
+#>   -6.7957   2.9469   4.6523  -2.6631   4.4369   9.3618   2.6648  -4.6262
+#>   -2.5317  16.4987  -9.9909  -1.9704  11.5051  -2.7488  -0.9009   2.9463
+#>   -8.2265  -3.5278   3.9527   2.4009  10.1392   4.0727   9.8097   8.1686
+#>    4.0022   2.4145  -0.9292   6.1996  -1.2054  -6.8335  -5.9702   7.3219
+#>   11.5061   3.1860  -6.2495  -5.5714   2.9508  -7.2449  -2.2884   3.9701
+#>   10.0300  -4.2100   6.6654  -8.6852   2.7246   7.7847   2.6797  -1.3201
+#>   -4.3981   7.1251   2.5460  -2.6470  -1.9696  -1.4992   1.2303   3.8541
+#>   20.3686   2.7208  -3.7131  -3.9702  -0.4787 -17.2879   1.5020 -12.7779
+#>   -2.2966   3.4540   1.9025  -1.8796  -0.6216  -1.9349 -11.4880  -5.6661
+#>    1.8981  -5.0815 -16.0781 -13.4546   5.5494  -3.1190  -3.0422  -7.8317
+#>    8.1814 -14.1720   1.1857  -6.0221   8.1155   8.0420  -2.3536   0.0388
 #> 
-#> Columns 25 to 30 -2.2760e+00 -7.3144e+00  4.8292e+00 -4.9657e+00 -7.1232e+00  1.3142e+00
-#>   6.5929e+00  3.1490e+00  6.9204e+00  6.4151e+00 -6.3404e-02 -3.7897e+00
-#>  -4.0338e+00 -4.5316e-01 -7.0003e+00  3.3699e-01  6.8378e-01 -4.5510e+00
-#>   1.6622e+01  5.6350e+00  3.6938e+00 -3.4149e+00  6.7602e+00 -6.1815e+00
-#>  -4.3909e+00  1.5651e+00  2.4429e+00 -3.0016e+00  9.1529e-01 -2.4233e+00
-#>   2.3989e+00  5.4087e+00 -7.5278e+00 -1.5690e-01 -2.3424e+01 -7.2567e+00
-#>   3.1272e-01 -1.2439e+01  6.5578e+00  9.9514e-01  1.0721e+01 -1.0280e+01
-#>   5.9367e+00  4.2118e+00  7.7408e+00  8.6852e+00  1.1312e+01  3.4991e+00
-#>   9.7336e+00 -4.2098e+00 -1.2592e+01  7.2535e-01 -5.9812e+00 -1.1667e+01
-#>  -8.3478e+00 -2.7242e+00  1.3632e+00 -1.0446e+01  1.7912e-01 -3.8909e+00
-#>   6.3733e+00  6.0159e+00  3.1218e+00  2.2932e+00  3.8410e-01 -8.7694e+00
-#>  -8.2073e+00 -1.6457e-01  3.4766e+00 -4.1029e+00  7.8382e+00  4.4347e+00
-#>  -1.7276e+00  4.3395e+00 -5.2719e+00  9.4849e-01  2.7905e+00  1.4499e+00
-#>  -1.5380e+00 -2.5573e+00 -2.8523e+00 -3.6523e+00 -5.3597e+00 -2.7917e+00
-#>   9.0720e-01  1.2394e+00 -3.1198e+00  4.7940e+00  5.8422e+00 -1.1933e+01
-#>  -6.5024e+00 -4.4094e+00  1.1358e+01 -5.3117e+00 -6.8802e+00 -6.3841e-01
-#>   6.5537e+00 -4.6925e+00  2.1657e+00  9.1326e+00 -2.0540e+00 -9.5667e+00
-#>   7.2024e+00  1.1624e+00  6.0365e+00  2.2904e+00  6.0530e+00 -1.9205e+01
-#>   1.4611e+00 -4.2919e+00 -1.4080e-01  2.7746e+00  2.0329e+00  2.0843e+00
-#>   5.4795e+00 -8.7125e-01  2.6906e+00  3.5602e+00 -3.3491e+00  1.1599e+00
-#>  -2.3461e+00  6.5558e+00  8.0490e-01 -1.0127e+00  7.3854e+00  5.4343e+00
-#>  -2.3029e+00  2.5260e+00 -3.6641e+00  2.6716e+00  3.3921e+00  3.6307e+00
-#>  -8.0277e+00 -4.0514e-03 -1.1109e+01 -2.7329e-01 -5.4768e+00 -2.4345e+00
-#>  -4.2815e-01  5.5739e-01 -1.0275e+01 -1.1990e+00 -3.5567e+00  2.1626e+00
-#>  -2.6098e+00  1.0137e+01 -6.7354e+00  6.6041e+00  8.3207e-01  4.4087e+00
-#>   2.3618e+00  1.0020e+00  7.4331e+00  1.3256e+00 -7.2381e+00  6.9058e-01
-#>   4.2137e+00  4.6010e+00 -3.9047e-01  1.5557e+00 -3.4771e+00 -6.7903e+00
-#>   3.8030e+00 -4.4897e+00 -2.7895e-01  8.4346e+00 -2.8834e+00 -7.8370e+00
-#>  -7.1090e+00  5.3218e+00 -8.2733e-01 -5.1612e-01  8.7156e+00 -4.2369e+00
-#>  -8.5179e+00 -1.3605e+00 -1.1377e+00  1.4068e+00 -8.1943e+00 -1.7865e+00
-#>  -5.5196e+00 -7.5730e+00 -5.7586e-01  3.5259e+00  4.4455e+00 -1.3109e+01
-#>  -3.4827e+00  8.6397e+00 -1.4932e+01  4.6374e+00 -9.3930e+00  1.1858e+01
-#>   5.6901e+00  2.3284e+00 -1.0366e+00  6.8662e-01 -5.5608e+00 -2.5195e+00
+#> Columns 25 to 32 -13.0714  -0.0187   9.9873   0.0273  -1.5594  -8.8974   1.9009  -0.2988
+#>   11.1452  -3.4016  -1.0266   5.2595  -1.3008  -9.7955   1.9404   6.3022
+#>    4.5345   6.6938  -1.1893  -5.0986  -7.8672   0.3647   9.1410   2.2182
+#>   -0.1823   3.1071   0.5731  -3.8624   4.6680  -3.8057  -1.7355   5.4212
+#>   -3.7469   1.0013   0.6827   5.9371   6.4182  -3.6334   0.0980  -8.1209
+#>   -6.9428   8.4255  -5.2805  -4.1397   2.5125   6.7380   0.0117   2.8179
+#>    3.2635   5.5990   0.2279  10.8135 -13.2858   2.0835 -12.2490   8.7211
+#>    9.7543   2.1740  -7.9529  -8.4028  13.3070  -0.6142  -6.8777   3.5090
+#>    2.0193  -2.8902  -0.1670  -4.3193  -8.3546  -7.4165   9.1943  -0.2285
+#>   -6.0421  -4.7265  -3.4241  -2.6472  -1.3033  -1.3679  -0.6396   2.3762
+#>   -9.2454   2.1403   7.1917   4.1617   9.3208  -1.4319  -0.3995  -3.2467
+#>    1.4912   1.0969   8.8175  -4.7641  -2.3168  -1.2784  -0.9874   2.8688
+#>   -1.6398 -19.2599  -4.5990  -7.5227  -9.5624  -3.0111  -1.9332  -0.4576
+#>    2.2556  -8.7141  -1.2503  -2.5759  -2.0213 -12.4116 -13.0938  -1.2698
+#>   -1.8907  -6.3740   2.7677 -11.0381 -14.6966   1.2929  -2.7640   1.9016
+#>    7.2026  -5.4447   1.2816   0.6494  10.8643   4.5731  11.8345   4.1345
+#>  -10.0139  -4.1466  17.8547  10.1177  -0.6593  -3.2823  -2.8638  -1.1804
+#>    5.6003   9.5377 -12.8658   8.2076  -5.6384  -4.2426  -3.7383   3.3327
+#>    0.8414  -2.0776  -9.0444  -8.1033   8.3204   1.2580   1.1945  -0.6222
+#>  -13.1733  -1.8308   2.2248  -1.5546 -17.0267   3.8451  -7.9885  -3.7998
+#>   12.2557  -8.9716   0.5187   6.3482  -6.0040  -8.8644  -1.6706  14.2456
+#>   -5.2001  -3.9760   4.0510 -15.6646   2.8702   7.1827   8.8502   5.0523
+#>    0.2147  14.3354   2.8928   7.9836 -13.9572   0.6832  -4.1280   2.5752
+#>    7.6655   2.1213  -0.3125  -1.6562 -13.0862  -4.2538  -8.4234  -0.8730
+#>    8.7337   8.2235   1.0279  -0.9177  -3.9001   8.1327   0.9260  -3.5470
+#>    1.6943   0.7340  -8.3707   9.8251  -4.3107   2.7295  -1.9003   6.3897
+#>   -0.9235  -0.2456  -8.6805  -2.3808  -8.1465   3.7409   2.0240   1.7404
+#>  -11.2546   7.3871  -2.0856  -0.5858   7.3479   3.9408   9.0609  -4.7659
+#>    0.8641   1.7741   1.0623   1.4678  -4.4862  -9.6961 -15.5146  -1.2309
+#>   -1.9795 -10.4593  -4.6745   5.4269  -5.7725 -12.6849  -7.0410   0.6236
+#>   -0.4239  -0.0613  -4.8713   1.7130   0.9216  -3.7975  -0.5672  -4.8570
+#>   -3.0661  -1.4783   0.3980   0.0231   5.6443   0.4720  -1.4982   4.9216
+#>    4.9952   8.3241   2.3390  -4.7183  -5.2696   2.5039   3.4909  -2.3067
 #> 
-#> Columns 31 to 36  2.0549e+00 -4.3663e+00  4.6274e+00 -2.7770e+00  4.0951e+00 -3.0836e-01
-#>   1.6512e+01 -5.8965e-01  4.0134e+00  1.1879e+01  1.4031e+01  3.2302e+00
-#>  -2.9421e+00  6.2940e+00  5.0424e+00  3.0158e+00  4.4634e+00 -2.1569e+00
-#>  -1.1351e+01 -3.4089e-01  5.8826e+00  4.3915e+00 -7.7045e+00  7.1392e-02
-#>  -6.5426e+00 -3.9611e+00 -1.5092e-01  4.2079e-01  5.9726e-01  2.1377e+00
-#>  -1.3936e+01  3.3846e+00  5.6410e+00  1.8708e+00 -8.9681e+00 -4.8510e+00
-#>   3.8352e+00  7.1991e-01 -8.4910e-01 -1.2214e+01 -3.6696e+00  9.2911e+00
-#>   1.4515e+01 -2.6906e+00 -7.7254e+00  4.5797e+00  1.1218e+00 -2.0977e+00
-#>  -4.4800e+00  2.6482e+00  6.9065e+00  3.1507e+00  2.0082e+00 -5.3859e+00
-#>  -1.4128e+01  1.3816e-01  2.9477e+00 -1.9106e+00 -3.6697e+00 -2.2761e+00
-#>   5.4178e+00 -5.8320e+00 -5.7231e+00 -1.5806e+00  1.2152e+01 -1.0140e+01
-#>   1.9179e+00 -2.3483e+00  5.8366e-01 -6.8551e+00 -3.2098e+00  8.6950e+00
-#>   3.8399e+00  9.9482e+00  1.5313e+00  6.8653e-02  4.2482e+00  3.6826e+00
-#>  -2.5470e+00 -1.4484e+01 -9.0480e+00 -4.8119e+00 -1.6745e+01 -4.4600e-01
-#>  -5.5114e+00  1.3359e+00 -1.6710e+00 -1.4465e+00  8.3191e+00 -9.3249e+00
-#>   6.0060e-02  9.4988e+00  6.0412e+00  8.8024e-01  6.9324e+00  1.2031e+01
-#>  -5.6851e+00  2.5318e-01 -9.3610e+00  9.1362e-04  9.4072e-01 -4.5883e+00
-#>   1.7162e-01  9.5146e+00  5.0873e+00  7.1316e+00  3.6845e+00  7.4398e+00
-#>   2.5172e+00  7.7000e+00  1.1956e+01  4.7511e+00 -2.8945e+00  1.5428e+01
-#>   5.9333e+00  7.9293e-01 -7.8763e-01  9.9137e+00 -6.2309e+00 -1.2783e+01
-#>  -4.6147e+00 -1.4190e+01  2.4994e+00 -2.6133e+00 -7.3028e+00  5.2317e+00
-#>   1.6406e+00  9.9635e+00 -6.1104e+00 -1.0154e+01  9.4588e+00 -2.3062e+00
-#>  -8.0695e+00  1.3592e+01  1.2976e+00 -1.0431e+01 -1.4089e+00 -6.0822e+00
-#>  -1.1015e+01  1.5045e+01  1.5913e+00  2.8774e+00 -3.9416e+00 -1.8998e+01
-#>  -1.2227e+01 -7.4663e+00 -8.8610e-01 -7.8272e-01  4.3430e+00 -3.1237e+00
-#>  -1.0315e+01 -1.3596e+01 -8.0018e+00 -2.9016e+00  6.0070e-01 -3.3632e+00
-#>  -3.8378e+00  9.5447e+00  5.4928e+00 -1.2036e+00  5.7343e+00  7.3284e+00
-#>  -1.5390e+01 -8.8993e+00  5.3606e-01  6.2705e+00 -1.0778e+01  2.5031e+00
-#>  -4.9752e+00 -3.1720e+00 -2.2769e+00 -7.5106e+00  8.8023e+00  3.0209e+00
-#>   8.2876e+00 -5.6669e+00 -6.2541e+00 -5.9230e+00  7.6724e+00 -6.0533e-01
-#>  -9.2757e-01 -2.7814e+00 -4.3140e+00 -1.3984e+01 -4.1415e+00  1.2586e+00
-#>  -4.3848e-01  1.1996e+01  3.2413e+00  6.3810e+00 -1.3565e+00 -5.3411e+00
-#>  -1.7962e+00 -6.0509e+00  3.4097e+00  2.9417e+00  1.9023e+00 -1.4072e+01
+#> Columns 33 to 40  -9.8567   7.6175  -1.8756  -8.4411  10.6488  -8.1493   6.0528  10.1028
+#>   -0.9945  -0.7705  -4.7930   2.1427  -0.1435  -2.7452  -4.7158  -2.5213
+#>    0.6202   3.9948  -5.6909  -6.4643   0.3004   0.9035   7.0773   3.1519
+#>   10.1589   2.7722   1.8978  -4.3193   3.9828  10.7195   3.5876   1.9366
+#>   -4.4238   1.0247   0.5953  -1.6051   0.0304 -11.7576  13.4233  -4.1562
+#>   -3.1859  -5.7735  19.4141   4.7993  -0.6175  -8.4062 -16.4626   3.1498
+#>    6.9534   5.0277  -2.9447  -0.0850  -2.2145   6.2498 -15.7354   1.9657
+#>   13.0054   4.9416  -3.7534  -0.1095  -3.8238   2.8011   0.8824  -4.2184
+#>   -0.0062 -17.3774   1.0912   5.1841  -2.2457   2.5393  13.0165 -10.6976
+#>   -9.7258  -3.5219   6.1499 -12.4502  -1.0758  -9.1970  -2.8117  -3.9221
+#>    2.8484   5.5359  -3.9454 -10.4596   3.0916  -3.4259  -2.1721  -0.8166
+#>   -1.3490   1.1040   3.8767  -3.0998 -12.2260  -2.0939   1.1307   2.4352
+#>   11.8101  -0.2954  -7.6868  -4.6325  -3.1428  10.8741 -10.9336  16.3077
+#>    4.8506 -13.5014  -2.7887   6.8250  15.0096   9.8246  -8.1045   8.1444
+#>  -10.8244  -3.0767   1.9881   6.4509   0.7621   5.1647  -8.5194  13.2671
+#>    6.9365  -3.5365   3.8543  12.9086  -2.8667   1.2013   9.6631   6.2177
+#>   -9.3565  15.8733   0.6735  -0.6567   7.3761  -2.2755   3.4360  -2.6279
+#>    0.3020  -1.3872  -2.5643  -7.3403   3.0948  -4.3499  -6.3204  -3.7817
+#>   -4.7272   6.5900   5.1556  -0.6818  -1.1616   3.2967  -1.8406   2.3409
+#>   -4.6962  -5.2557   5.7527  -0.5302  -8.6238  -0.9163   0.3526   0.5857
+#>   -4.2859  -4.8647  -4.0140  -6.6039  -9.6733  -6.1373  -8.1921   3.4339
+#>   -8.1660   2.6921  11.1481  -5.1359  -4.8097   0.5925   6.0313   9.1418
+#>   11.4851   2.3422   2.0015   6.1188   2.0525   9.4460 -10.7253  -8.3846
+#>    0.3583  -7.9371  -0.4494   5.2844   0.9358   3.5774  -1.1877   0.7038
+#>   11.8828  -4.3449  12.6311   1.8897   1.4277   8.2822   3.1796  -4.4574
+#>   -7.6745  -4.8668  13.4500  -5.0439  -1.6162  -8.7502  -4.2821   9.2119
+#>   -0.5675  -2.4104  14.9981  -5.2886   3.6040  -0.5608  -3.1493   5.8028
+#>   -1.7977   1.2788  15.8534  -9.6430   1.1484 -11.0741   4.0682   3.3635
+#>  -10.4483  -9.8690   5.2062  -6.3103   1.8113  -7.4166  -2.6534   2.8004
+#>    0.8338  -3.5295   4.4940   5.8538   6.6255   9.1051  -1.6787   3.8386
+#>    0.6720  -7.0088 -12.6543   6.1760  -7.6490   5.6031  -6.8800   2.8252
+#>    3.3358  -1.0577   8.0275 -11.5627   2.7493  -5.3755  16.5066  -0.5246
+#>   -1.5817   0.4956  -9.7349  -7.9308  -9.1433   6.4206   7.8914  -5.4136
 #> 
-#> Columns 37 to 42 -6.2390e+00  7.4627e+00  1.5723e+00 -2.5190e+00  4.9774e-01  3.5490e+00
-#>  -1.8892e+00  9.1216e+00  7.8881e+00 -6.1767e+00  4.9554e+00  8.8803e-01
-#>  -7.0721e+00  4.0161e+00 -1.0343e+00  1.2696e+01 -6.3369e+00 -2.1948e-01
-#>   1.3684e+01 -9.2964e+00 -8.0246e+00  1.0011e+00 -2.0440e+01 -2.1537e+01
-#>  -6.7623e+00  6.8869e+00 -2.3903e+00  5.0027e+00 -1.3824e+00  7.4134e+00
-#>   1.7012e+00  2.9455e+00  6.5387e+00 -2.4028e+00 -3.4351e+00 -9.1958e+00
-#>   3.7916e+00 -8.9537e+00 -7.2207e+00 -4.9574e+00  8.1806e-01  1.7337e+00
-#>  -3.7733e+00  1.3423e+01  1.3992e+01  1.0672e+00 -1.1168e+00 -5.3264e-01
-#>  -2.9634e+00  4.7297e+00 -5.1181e-01  2.1821e+01  2.2174e+00  2.6987e+00
-#>   8.9396e+00 -7.7852e+00 -8.7303e+00 -2.0843e+00 -5.4726e+00 -3.1127e+00
-#>  -1.3309e+01  1.7285e+01 -3.7094e+00 -1.1378e+00  3.7256e+00 -7.7327e+00
-#>   4.6996e+00 -1.4067e-01 -3.8312e+00 -6.2441e+00  7.1122e-01  2.4115e-01
-#>  -8.7191e+00 -2.7296e+00 -3.7690e+00 -6.6840e+00  2.8123e+00 -9.0216e+00
-#>  -7.8271e+00 -1.4785e+00  1.2006e+00 -3.0301e+00 -3.6380e+00  2.2138e+01
-#>  -1.8020e-02 -7.3946e+00 -1.8668e+01  4.9143e+00  8.0434e+00 -7.5144e+00
-#>   2.9670e-01 -6.9659e+00  6.4073e+00  3.5793e+00 -4.7447e+00 -6.0572e+00
-#>  -7.2437e+00 -8.0995e-01 -1.1089e+01 -2.2674e+00 -4.6293e+00  4.8363e+00
-#>   7.0735e+00 -7.6543e+00  3.0502e-01 -4.8940e+00 -2.1813e+00 -1.7265e+01
-#>  -2.1917e+00  9.3046e-01  4.0582e+00  7.7997e+00  2.3249e+00  2.7131e+00
-#>   6.4485e+00 -1.8380e-01 -2.9445e-01  2.0924e+00  7.4262e+00 -8.2395e+00
-#>  -2.2302e+00  1.7512e+00 -3.4890e+00 -2.6745e+00  3.4653e-01  3.6789e+00
-#>   8.9544e+00 -6.1406e-01  9.0091e+00 -2.5289e+00 -6.6100e+00 -9.2875e+00
-#>  -8.6285e+00 -7.6416e+00 -1.2472e+01 -3.9108e+00  1.2114e+01 -3.0555e+00
-#>  -3.7552e+00 -1.0725e+01 -1.0359e+01  1.1363e+01 -4.7689e+00 -6.3023e+00
-#>  -4.3483e+00 -2.4968e+00 -1.6712e+01 -2.3206e+00  9.0312e+00 -4.3267e+00
-#>   1.7978e+00  1.7474e-01  4.3791e+00 -5.9808e+00  4.6256e+00  7.4821e+00
-#>  -3.0482e+00 -7.1479e+00  1.4285e+00 -4.9415e+00 -5.8376e+00 -5.3080e+00
-#>  -1.7139e+01 -5.7809e+00 -8.0277e+00 -5.4555e+00 -2.2411e+00  1.5101e+00
-#>  -1.6970e+00 -5.6225e+00 -1.0386e+01 -4.1191e+00 -4.6791e-01  1.1010e+00
-#>  -2.1864e+01  1.1266e+00  1.2700e+00 -7.2086e-01  9.0356e+00  1.3241e+01
-#>   1.5384e+00 -1.5492e+01 -2.5981e+00 -2.3097e+00 -7.5876e+00  1.6444e+00
-#>  -9.8640e+00  7.9875e+00  7.5978e+00 -5.3028e+00  7.6840e+00  9.5088e-01
-#>   4.4981e+00  2.9509e+00 -3.7114e+00 -5.6748e+00 -2.7754e+00 -9.2440e+00
-#> 
-#> Columns 43 to 48  7.5252e+00 -4.7240e+00  2.8171e+00  3.8666e+00  1.0257e+01  1.6760e-01
-#>   1.4654e+01 -1.1176e+01  6.4938e+00 -4.7155e+00  1.6589e+01  1.9941e+00
-#>   2.4068e+00  3.9986e+00  2.0345e+00 -5.9613e+00  1.1134e-01  1.2031e+01
-#>  -4.1788e+00 -2.1450e+00 -1.4720e+01  1.2275e+01 -1.3673e+01  8.7555e-01
-#>   6.7612e+00  1.4690e+01 -7.0012e+00 -4.7893e+00  3.9739e+00  7.7816e+00
-#>   5.9188e+00  3.5156e+00 -8.0060e+00 -4.2263e+00  1.0632e+01  4.4382e+00
-#>  -8.1016e-01  3.7534e+00  4.6450e-01 -2.3587e+00 -3.2603e+00  9.0285e+00
-#>   1.1181e+01  6.6565e+00 -1.0311e+00 -1.6246e-01  2.7070e+00  7.7686e-01
-#>   2.6272e+00  2.6926e+00 -2.1784e+00 -3.2185e+00 -2.2183e+00  1.5972e+00
-#>  -5.3338e+00 -1.2750e+01 -4.8989e+00  2.2367e+00 -7.1979e+00 -5.7989e+00
-#>  -8.5229e+00 -2.5259e-01  1.8121e+00 -1.6339e+01  1.4385e+01  3.1480e+00
-#>  -5.5123e+00 -2.9095e+00  9.6932e+00 -7.3890e+00 -2.1089e+00 -5.4664e+00
-#>   5.6571e-01  2.2835e+00  2.0445e+00 -3.1459e+00 -1.8582e+00  1.9847e+00
-#>   1.1227e+01 -3.7863e+00  2.6117e+00 -2.0254e-01  2.9419e+00  3.3584e+00
-#>   1.8669e+01 -2.5709e+00 -7.3639e+00 -1.0516e+01  3.2457e+00  1.2949e+01
-#>  -1.4009e+01 -1.4490e+01  1.3553e+00  2.0864e+00 -3.2283e-01  1.0447e+01
-#>   6.4845e+00  1.0992e+01 -8.9764e+00 -4.2840e+00 -6.8353e+00  2.0669e+01
-#>  -4.7237e+00 -1.5023e+01  5.2116e+00  2.4891e+00 -5.8575e+00  7.5567e+00
-#>  -1.1441e+01 -7.2402e+00  1.1403e+01  3.5053e+00 -9.5773e+00  8.1672e-01
-#>   1.3615e+01 -6.2740e+00 -7.0639e+00  3.1193e+00 -4.7688e+00 -3.8634e+00
-#>   5.3512e+00  2.4257e+00  2.7007e+00 -7.2868e-01 -1.0831e+00 -1.0120e+01
-#>  -1.9115e+00 -3.5195e+00  7.7107e-01 -1.0501e+01 -9.2194e+00 -2.2617e+00
-#>  -1.2850e+01 -8.3718e+00 -2.8517e+00 -3.1152e+00  1.5854e+00 -4.3248e+00
-#>  -7.8824e+00  1.0903e+01  1.5360e-01  4.4607e+00 -6.1502e+00 -5.7983e+00
-#>  -4.9865e+00 -7.0381e+00  3.4789e+00 -6.8242e+00  6.5550e+00  1.0764e+00
-#>   1.3635e+01  1.7434e+00 -1.0626e+01  2.4001e+00  1.0004e+00  4.2354e+00
-#>   5.1739e+00  1.3836e+01 -7.8074e+00 -1.8637e+00  9.1099e+00  3.1453e+00
-#>  -8.1692e+00  6.1423e-01 -4.5621e-01  1.4270e+00 -1.0939e+01  1.0901e+01
-#>   5.8934e+00 -7.4564e+00  4.9315e+00 -8.1992e+00 -4.8292e-01 -2.0732e+00
-#>  -4.3536e+00  1.4776e+00  5.8203e+00 -8.0869e+00  6.1614e+00  9.3853e+00
-#>  -1.3184e+01 -1.1708e+01 -2.4239e+00 -1.0928e+00 -7.8648e+00  7.4512e+00
-#>   3.7807e+00  4.5977e+00  1.4227e+01  4.8748e-01  5.5077e+00 -3.9448e+00
-#>  -1.1252e+01 -1.0866e+01  3.1634e+00  4.5651e+00  1.2367e+01 -1.0874e+01
+#> Columns 41 to 48  -8.1875   8.5302  10.6741  -4.4169   5.6607  -3.9774 -15.0424  -4.5615
+#>    5.8743  -4.6295   8.2095  -7.0607   2.1486   6.9981   9.3540  -1.5149
+#>   -3.6533   6.1376  -0.4473 -10.2394  -2.5592   4.9061  -0.0012  14.1799
+#>    3.1752   2.6807  -6.0404   5.3171  -2.9454   2.4533   2.5071  -3.0980
+#>   -0.3000  -1.1908   3.2271  -8.3158   5.7898  -0.3585   1.7213   0.7952
+#>   -4.8271   6.2385  -2.0346   7.9903  -7.1636   7.2280  -9.0562  -7.4104
+#>    3.2648  -8.7601   6.2029 -11.8512 -13.2813  11.5996  10.7559   2.0016
+#>    0.6342   4.8021  -5.3250   2.9570  -2.0764  10.3792  12.6361   7.9569
+#>   11.1874  -6.3212  10.1186   2.2684  -1.2053 -16.2543  -0.4046  -4.7055
+#>   -2.0598  -5.7966  -0.0920  -2.3383  -0.2680  -1.0659   2.0296   3.0285
+#>   -2.9306  -5.7754   2.7468  11.0579  -0.0750 -12.9258 -10.6918 -17.1543
+#>    3.9201  12.7043   7.3187  -2.8003  -6.3074  -0.3992  -2.8109   4.0858
+#>  -17.7045   3.9539  -9.2181  -1.6945   0.3697   9.2919  -5.8179   8.4449
+#>   -3.6705   3.6850  -0.4694  -0.4929 -20.8587   8.9568  -6.7975   1.9337
+#>    0.5909   3.7837  11.8904 -10.8828  -4.3004  -8.3260 -17.8307   7.4843
+#>    6.9089  -1.5590   1.6277   3.2958  10.1334   0.6770   2.4188   4.8049
+#>   -4.1183 -12.6335  14.2806  -1.3766  -1.6701 -13.4123 -14.5154  -0.8717
+#>   -3.5142  -2.7452   4.8765  -6.7347  -3.3675   8.8471 -17.2389   1.0827
+#>   -8.4277  -2.8826  -5.3520   7.4995  -4.1673  -0.2969  -6.6840  -3.0668
+#>   -3.6709  -9.5021   7.6367   2.4663  -5.0088 -12.1178  -3.3172  10.5893
+#>    4.2535  -4.5195   3.8522   4.4381   0.0149  10.4340  -4.3466 -13.1328
+#>   -3.5570  13.2433  -7.2976   0.5737   8.0422   0.1279   3.7678   4.0912
+#>    0.6259 -10.8859   3.5943  -9.6395 -11.6260   8.6014   4.0807   0.4150
+#>   -5.6781   0.0669   1.6359  -6.9778 -10.9852  -0.5076  -8.5315  -4.4731
+#>   -3.2479 -15.9125  -0.9775   0.9561 -12.3057   3.4267   6.1202   7.7863
+#>    8.0905  -2.5483  -1.8191   2.7612  -1.0365  12.4728   4.0729   4.6249
+#>   -5.0980  -2.8091  -0.0682   4.9691 -12.2233   9.7946  -9.6531   5.6263
+#>   -7.1407   4.0348 -12.3803  -0.7324   8.3051   2.3644  -3.1617   5.3572
+#>   -2.0959  -0.8399  -2.7117   8.7299 -14.4823  -8.4834  -3.7089  -7.1273
+#>   -4.4759   1.5071  -0.0862  -1.5696 -11.0392  -0.3375 -21.2326  -4.0626
+#>   -1.9187   0.8044   0.2210   6.2557  -3.0297  -1.9610   0.5112   1.5936
+#>    4.0483  -0.3336 -21.5891  -2.8182   5.2686  -3.8660   5.1294   6.1881
+#>    4.3781   2.6013  -7.9393  10.4154  -5.9459  -6.2546   6.0568   0.0836
 #> 
 #> (11,.,.) = 
-#>  Columns 1 to 8  -2.0044   4.3699  -2.4127   4.2359   4.1969  -0.2678   3.8926   7.5892
-#>    6.1867   0.1656  -4.8839  -1.8466  -4.4712  -5.9437 -10.9038   4.0003
-#>    8.4201  -8.1247  -1.8980  -4.0356  -6.8123   4.3460   5.1257  -9.9883
-#>    0.9669   8.8183   8.4483  -4.3425   0.9247   6.5621  12.3047   2.4185
-#>    6.7167  -2.4476  -1.9954  -3.5758  -6.7449  -8.1649  -5.5353  -1.2549
-#>   -2.4296   2.2598  14.8066   4.2901  -8.1575  -0.0060 -10.8620  15.7208
-#>   -3.4516 -10.6639   5.1614  -7.7433   2.2696   2.9458   3.3822   0.3909
-#>    6.6280 -11.6794   0.7623   7.4987   3.8300   1.4233 -13.8746 -11.0928
-#>   11.9090 -16.2859  -2.0074  -5.6302  -6.8406  -7.0040  -0.6425  -3.5537
-#>   -8.4572   1.7583  -2.4892   1.3537   4.7256  -3.4651   3.8739   0.7713
-#>    9.7891 -11.9684  -0.2066  15.0137  -7.3798  -1.7238  11.3389 -16.5909
-#>   -2.2045   8.7826   3.2816   0.7466  -5.2149  -2.3559  -3.3869   1.5777
-#>   -2.5540   7.1872  11.5563  -2.1551  -0.1032  12.4840   6.1519   3.8442
-#>    1.0610  13.9169  -5.9530  -3.8892  -4.4112  -5.2429   9.1333   9.5078
-#>    5.8089  -4.8836  15.1818  -1.9421 -10.6217   6.9401  -3.8909  -1.3724
-#>   -7.8908   5.5054  -8.3785  -8.3396  -3.5264   1.3435  18.2884   6.9249
-#>    6.6423  -7.1030   1.7219   7.1863   7.9274  -0.1598  -4.0641  -9.6481
-#>    5.8048   2.5447  14.5549   6.4658  -1.0197   6.5001   7.7019  -2.9925
-#>   -2.3873 -14.1483  -8.2676   0.0137   7.7786   6.5895   6.4276  -6.6812
-#>   -8.9539 -10.5986   8.0475   3.8106  -2.6754   0.6577  -6.8355   4.2199
-#>    9.1807  -1.5445  11.4027   3.9223   3.0354  -2.9551 -15.0646   0.2942
-#>   -1.2010   3.2711 -18.3424  -2.1797  -5.7391  11.5237  13.7566  -7.5049
-#>   -1.2460  -2.2535   1.8274  10.3330   4.5104  -2.5318   4.1912  -5.8933
-#>   -2.9833  -2.7955  13.6504   4.9941   5.8426   3.9711  12.0044   3.4093
-#>    4.5080   0.0392  -7.4748  10.0702   2.1613   6.0438  -0.6968 -12.2627
-#>   -5.8455  -4.4407   5.9451  -1.1177   7.1119   4.5139  -9.7403   2.9613
-#>    5.2632   2.9664   3.9723   2.8500   3.8921   4.1397   4.4269  -3.3525
-#>   -4.6684   2.3957   6.1306  16.3635  10.9679  -0.5797  -0.5639  -2.7507
-#>    9.3602  -2.3128 -11.8529   3.9773  -1.8450   2.9816  14.0366  -6.2402
-#>   -6.2591  -1.8565  -0.4631  14.7339   2.1109  -2.1565 -12.1677   6.6745
-#>   -1.3769 -11.6554   5.5780   5.5344  12.0577   7.3156  -3.7152  -3.5623
-#>   -7.9856  10.3130  -1.8823   2.1432  -0.4400  -4.6726  -6.2517   1.1903
-#>   -0.9517  -2.9278   1.0489   7.3569   0.5211   0.1772  -0.7285  13.3707
+#>  Columns 1 to 8  -3.1056   7.1014  -1.7520  -5.2867  -2.6633  -6.5338   5.3263 -15.7428
+#>    0.4713  -3.7744   2.3758  11.3051  -0.9939   5.6276   5.1841   7.1862
+#>   -2.2767   7.0200  -0.0728   2.2325  -3.2260   7.8706   3.6194   4.8128
+#>    5.3578   0.0121   5.4959   7.4852   7.2253   3.3149  -6.7105  -8.1140
+#>    2.7616   0.8186  -2.4662  -0.6929  -9.9915   0.4618  -1.7305  -9.5296
+#>   -5.8322   7.4398   8.4307   2.1667  -1.6221  -4.0324  -2.7911  -8.0866
+#>   -5.8750  -6.1321   8.3655  -6.7013   1.1581   9.8018   3.4853  12.6581
+#>   -9.6312  16.3625 -14.0993  -1.0879   6.6407   6.3807  -9.1717   2.6844
+#>    2.7172   4.4788   1.5058   2.4096  -1.5733  10.3485  -0.8834   2.2941
+#>    7.0686  -8.5086   2.7823   6.2788  -1.9113   6.3283  -3.2110   6.8253
+#>    1.9269   4.3827   5.4367   5.6315  -4.0407  -2.6768  -4.7427   3.3134
+#>   -4.0706  12.0355   1.4931   1.4809   1.0108  13.7341   0.7601   5.6928
+#>   -7.4500  13.5813   5.3255   0.8900  16.8893  20.4150  -8.8246   0.1538
+#>    3.7011   3.4544   2.9602   9.5964  -2.5339  17.3166  -3.7979  -6.1747
+#>  -10.3421   6.0965  -6.9043  -6.9519   6.8128  10.0486  -2.5503   5.5157
+#>   -1.1109   7.4610 -18.2971  -4.9955   5.7766  -4.3632  -3.2210   0.2086
+#>    7.9126 -12.5112  13.7044  -6.3423  -0.1060   3.5485  -7.7820  -2.0850
+#>    4.6018  -6.4806   6.5563  -7.7512  -0.2436  11.1964  -7.4478  12.9881
+#>   -3.8200   2.3284 -11.0287   7.9292  -3.1138   2.1129   3.8329   7.1461
+#>   -0.2347 -13.2690  10.6711   2.7179   6.8641  -3.2205  11.2996  -5.7390
+#>    1.3354  -0.1358  -7.1959   3.9022   1.3122   5.8613   4.3540  16.2754
+#>   -5.4127   7.2560 -15.3921   4.6897   2.4843   2.6917  19.3003  -7.2927
+#>   -9.1813  -1.6702  24.5728  -6.3445  -0.2350  -2.5025   2.0758   2.5542
+#>  -11.9177  10.7019 -10.7787  -4.1487 -13.7692   8.1852  -6.5000   6.6651
+#>   -1.7072  -8.3519   8.5500   3.7614  -0.6362  -3.5548  16.1865   3.6186
+#>    3.0541  -6.4272   1.8470  -0.4228   2.4739  -5.1297   2.7983   0.3182
+#>   -2.3400   0.9296   2.0187   1.1432  -5.7968  -2.0452  -5.9075 -11.6250
+#>   -0.0323  -0.5476  -1.7421  -1.9274   2.6061  -1.9536   5.5033  -9.6515
+#>   -6.3654   1.6957 -10.5462   3.6596  -0.6410 -10.5334  11.2728   3.3740
+#>    1.4619   9.2676  -2.7505   3.3931   1.0666   7.7865 -15.5857   4.3630
+#>   -4.1392  11.9711   1.6731  -5.9038   1.9859  -3.8609   2.0448  -8.1301
+#>   -0.7937  13.8198 -18.9691  10.3603  24.2892 -14.3045   2.7017   2.4326
+#>    0.7147  -0.5399 -12.1657   8.7173  -8.7910  -9.3547  10.9389   9.0797
 #> 
-#> Columns 9 to 16   6.3823   3.0219  -2.8446  -4.6026  -3.7400  -4.1116  -7.1448  -7.5928
-#>   -3.3294  -4.4999  -5.0383  -3.9010  -2.0349  -6.5806  -0.7017   0.5485
-#>    0.2619   0.9938  -6.3134  -3.0559   3.3365   1.7051  -0.3802   2.8995
-#>   -8.0614  -0.8129   0.3033  20.1874   1.7606   0.4109   5.9526  10.6269
-#>   -4.0922  -5.5850 -13.6079  -5.6689 -11.0693  -8.8210  -3.5062  -6.0664
-#>    5.8566   2.6684  -5.3008  -9.9224   0.8673 -11.0890   7.2668   5.1669
-#>   -3.4305   4.8934 -13.3550   1.1970  -0.1186   4.9312   0.2421   3.2356
-#>   -0.8893  -1.7562  -4.0922  -1.5024 -24.6047   1.4579  13.1380  -5.0338
-#>    0.1682   2.2131   1.6275  -7.8510   7.6663  -6.3077 -14.3870  22.1853
-#>    5.7356  -5.8148   2.0537  10.2324   2.7517  10.0743  -1.6487  -6.4380
-#>   12.8964  -4.1159  -4.4931   2.6539   3.5004   1.7731  12.6290  -4.6256
-#>    5.3116  -3.7887   4.4995 -14.8593  -4.2049   7.0560  -3.3320  -7.5902
-#>   -4.0621   6.1609  -4.2993  -0.3039  -8.4894  -1.1391  14.2142 -10.8272
-#>   -0.8747  -6.0277   1.8341 -16.5662   9.2631   9.6649   0.5672  -3.7222
-#>    6.7275 -13.1337  -7.5456 -12.3858  -4.7681   9.5557  -5.8113  -4.4115
-#>   -1.1570  -7.7482  -1.9757   2.1171  -0.6067  -3.9330   0.0538   3.5047
-#>    1.0995   1.2456   0.8121  -7.7823  -8.1074  -3.2819   6.8381  -3.0345
-#>    2.5230   1.4343  -1.5845   4.0496  -7.0159  -7.2508  -0.9545   9.0573
-#>   -9.3421  -1.2291   5.5133   4.4957  -4.7450   1.2507  -0.0840   3.7238
-#>    5.9721   0.4973  -0.8971   1.9492 -11.6507  -6.1942  -0.0588   3.0740
-#>    0.6651   8.9529   1.2361  -3.9383  -4.2929   6.1735  -1.6646   4.5894
-#>    9.7561  -8.0153   2.2952   1.5529   0.2461  -0.9306   7.2547  -2.9878
-#>    9.1697   4.8139  13.1205   0.1607  11.1355  -3.9690  -2.9050   9.5772
-#>   13.4256   7.8929   5.7852  -1.8729  -4.2819  12.1169   4.5292  -1.7870
-#>    7.7253  -4.2477  13.1721  -6.5561  10.9189   1.2953   1.1537  -0.6233
-#>   -8.4916  -0.1692   3.4664   7.2991  -1.1835   3.4548  -1.7278   0.9493
-#>   -2.8162  -4.2595  -1.5781  -6.4060   2.9334  -7.0955  12.3795   2.8014
-#>  -10.3906   6.7465   3.1686  -0.3980  -2.3234  -0.3691   9.6803 -14.8288
-#>    4.0592 -10.3295  -3.2436  -3.0166   2.3703  -2.3240  -4.1811   8.5929
-#>   -1.2070  10.0707  -0.4461  -7.8677 -11.1716  -5.1939  14.7600 -11.8722
-#>   -5.8241   4.7446   7.1372  -0.0313  -4.6634   1.8151  -5.5978   7.6861
-#>   -3.1263   5.6811  13.5624 -14.0331   6.8948  -7.2957   8.0425   1.4875
-#>    6.3600   5.2668   0.5438  -0.3248  15.2348   4.9118  -5.3705   0.7440
+#> Columns 9 to 16  -7.4168  11.0759   1.1003   6.0576  -9.2876  -8.4411  -3.5381   9.7936
+#>   -5.1028   8.0608   3.7483   2.6942   3.2878   1.6583   2.9900   2.8672
+#>    0.7359   6.1439  -0.2405  -0.4419  -3.0154  -1.7064  -8.4121   1.4606
+#>    3.8276  -2.2371  -3.4983   1.0718   4.5416 -16.9983  -3.7466   2.8815
+#>   -3.8746  11.3641   7.9679  -4.7086  -4.7910   1.2206  -3.7255   2.6025
+#>    2.8650  -4.5575  -4.8067 -10.0891  -5.6783  11.3451   8.9307 -22.3631
+#>   -7.2190  -2.3230  -2.5787   9.3403   8.6515  -2.5033   5.2275   4.6794
+#>   15.8816   3.1307   7.7790  12.0086  -5.4551 -15.1441   3.1261   9.8844
+#>    7.3713  12.6069 -12.5193  14.9959   9.2254   5.9878  -1.8536  -2.9194
+#>   -2.3693   1.9804  -1.8490  -2.7110  -1.7631  18.8069  -2.5947  -7.2365
+#>   11.1356   7.7725   2.9667   9.3270  -2.9968  -0.2738  -3.1082  10.0524
+#>   -0.4463   7.3109   4.3322  -0.8646  -2.2492   2.0766  -3.0842  -5.9032
+#>    6.6210   3.5389   1.9736   9.0659   2.1851 -11.8417   0.1917   2.0172
+#>   -1.2623  -3.3925  11.4168   2.3249  -4.8050  -9.3300   5.1964  13.0922
+#>    3.8708   5.5887   5.2564   5.0277 -11.3091   5.1434  -1.6130   3.2994
+#>   -2.0875  -8.1905  -3.9017   0.8841 -12.7772  -1.0022   6.7764  -5.8629
+#>   -3.3820  -2.0941  -4.3281 -14.3973   5.4035   2.8416  -8.3787  -3.1533
+#>    8.8659   9.1797   1.2592  -4.6483 -12.7040  10.8555  11.3138  -2.1920
+#>   -3.7947  -4.4181   9.3954   0.4649   7.3404   5.9480   3.3579  -2.8093
+#>   -6.8092   7.5523  -1.2000   6.2166   5.8215   9.4886   1.9996  -9.5033
+#>    0.3237   6.0587  12.7698   7.3901  16.7772  -2.5261   4.7448   1.9454
+#>   -2.9198  -1.9480   1.3946  -4.9714  -0.1512   6.7602  -4.6601 -12.6422
+#>    0.8079  -1.1571  -5.7779   3.2545  -2.1266   0.3567  -1.2619  -5.9750
+#>   -5.4052   7.6162  -9.3153  12.9787  -1.5731  -3.2355   8.6699   1.3725
+#>   -5.8511 -18.1395  -8.7816  -1.3586   4.7561   2.3052  -4.5350  -5.8648
+#>   -4.7533  -2.0937  -1.9807  -3.3829  -1.8970   4.3063   4.9850  -6.0642
+#>    1.5990  -5.0245   3.8857  -2.0606  -4.4603  -0.3299   4.1605  -9.2712
+#>   -0.4386  -5.2443  -1.9523   5.1703   2.6962  16.3616  -5.8466  -4.4763
+#>    7.1911  -7.5282  14.3286  -1.2862   7.9797   0.7726   8.6860  -4.6079
+#>    5.9871   6.4725   1.2052   7.9906 -16.6894   3.8827  18.7941  -7.3567
+#>   11.7971   1.0183   1.1445   5.7499   7.6029 -11.9659  -3.6406   1.5794
+#>    7.0029  -8.7005   6.3045   5.2256  -1.0162   7.7319  -1.4217  -4.6253
+#>    8.2087  -0.9443   1.8711   3.2622   2.5441   0.5841  -3.8732   1.5598
 #> 
-#> Columns 17 to 24   0.5236   6.8693  -0.4034  -9.7509  -4.5863  18.9446  12.2359  -1.6541
-#>   -2.9956   4.0989 -10.3061   1.1723  -1.5345   7.7239  14.5010  -0.1832
-#>   13.2186   0.7272  -8.6150   3.3184  16.5073   1.1210   1.6489  -2.2074
-#>   -3.4138 -16.0043  17.6044   1.4957 -10.2243  -4.1157  -0.2742   5.9374
-#>   -4.2704   0.2206  -4.0499   8.9308   1.4830  -1.8442  10.6688  -4.1980
-#>   -0.2917 -13.9340   6.4268  12.3239   8.9711   7.4519   7.6545   1.0119
-#>   -2.5605  -1.7786   5.2483 -10.9202  -9.1474  -3.8889  -4.1318  -4.5254
-#>  -10.1291  -4.8287 -14.1620  -6.3614 -11.1534  -8.4878  12.9382  -1.4245
-#>   16.6430  -8.2832  -4.1078  14.2384  13.0596   1.6659  -0.0978   6.2452
-#>   -9.2022   6.4704  15.6910  -2.6631   0.6269   1.7004  -1.7806   0.7099
-#>    3.6032   7.8300 -24.2912  12.1176   9.7138  -0.3804  15.4161 -10.8723
-#>  -10.0752   9.4278   2.1156  -8.8559  -2.5048   6.6876  -4.4726   5.2917
-#>    4.8320  10.4034   0.9745 -12.8283   1.6226  -0.0745  -9.1379  -3.6713
-#>   -5.8681  14.4700  12.2456   1.0197  -7.4787   0.7518   7.1122   3.2161
-#>   13.5318   1.5475  -6.2782 -10.4522   3.0410   8.6182  -2.5363  -7.1108
-#>   -4.6763   0.1332   9.1238  -3.5555  -2.0220  -1.4984   7.1836   8.1871
-#>    1.5535 -12.9286   2.1660  -0.5209  -7.5615  -4.8321  -1.2277  -3.6797
-#>    0.9089 -10.6601  10.4946  -1.6091   6.9751  -8.0629  -4.4556   2.6121
-#>    6.1206   4.4565  -4.0688  -2.7858   5.5975  -1.0423  -3.8477   0.0219
-#>   -2.9188  -6.3728   8.4461  -3.2103  -7.5373   5.3769  -0.4173  -6.6054
-#>    0.5667 -12.7742   2.3528   1.9002   1.5721 -12.6775   0.5521  -3.3713
-#>   -0.7820   4.8531  -1.1454  -4.0201   8.4258   3.1378   5.1369   4.2380
-#>   14.7232  -0.8633  -7.0142   3.2225  -2.5383  -9.4545  -2.2383  -4.1793
-#>    4.2805   1.1091   9.2722   3.3933  -5.3543   0.2697  -3.0848   8.4100
-#>   10.8122  -9.1854 -14.0378   1.1658   8.9828  -7.5437 -14.8283  -1.8276
-#>    0.2647  -4.6193   5.6080  -3.8388  -7.6890   3.8130  -2.9010  -7.1018
-#>   11.1065  -1.7242   2.5281  -2.9828  -4.6451 -11.2156  -1.1177  10.7715
-#>    4.6313   5.9269   6.4877  -5.0038  -7.0321  -7.7048   0.2439  -3.5283
-#>   -3.2314   3.8181  -0.7659   0.2947   3.5189  -0.3271  -1.7176  -9.7372
-#>   -7.1887  16.1351  -6.9254 -10.8912  -5.4141   3.3091  14.0301  -5.9519
-#>   -1.1507  -7.1501   2.9040 -11.2942  -8.9464 -11.5441  -7.8685   8.8295
-#>  -11.5279  10.8920  -2.8373   8.6184 -10.8648   6.1828  -5.2552   8.0270
-#>   -6.4354  -0.0202   2.1212  10.6847   1.9121  10.6659   5.9482   3.1849
+#> Columns 17 to 24   3.0514  -1.7865  -2.6677 -13.7430  -4.6591   0.2537  -1.3093 -12.1497
+#>   -3.6335   3.2847   0.2427   4.6065  -3.5308  -5.7190  14.7120   3.3639
+#>   10.5363  -0.8013   7.5259   2.2988   5.0467  -5.5617  -8.1138   2.5266
+#>  -13.9912  -3.8696   2.9843  -2.2994  -2.6520   1.3427   1.4171  -5.3636
+#>   -8.2402   5.0633  -0.3580   1.5580 -20.1335 -14.2977  -5.0227  -1.5824
+#>   -5.3244  17.7564 -11.2515  -5.4180   6.8312  17.0223  -1.9325  -6.5925
+#>   -6.7305   1.7371  -0.5783   6.5701   3.2058   1.1611   9.8399  20.0164
+#>   -6.8536   4.8541  11.9151   1.9440  -1.5002   0.7527   5.2115  -0.6330
+#>   -2.0804  11.0721   5.3992  -3.6796  -5.1200   2.0026   6.9874 -17.9426
+#>    7.2199   6.7778  -0.8521   1.0804   1.8044   6.0031  -2.9563  -0.3860
+#>   -6.6783   1.3658   0.8428   7.0860   6.9103   1.2721  -2.1449  -9.8141
+#>    1.8483  -5.3921   9.4492  -1.6840   1.2464   3.8513  -0.4600 -12.6121
+#>    8.2959   1.5687   9.5056   2.2983  -2.0890   4.1798  10.1808  -4.0232
+#>   -0.7005   3.6303   0.8530  -2.4843 -10.8943 -13.2525  11.2986  14.8797
+#>    1.8221 -13.6809   4.9042  -2.2119   4.3699  -5.6280  -8.7255  -5.4939
+#>    2.5839  -4.6317  -5.0155  11.8297  -3.2862  10.7527   4.1126   0.1293
+#>   12.8436 -15.2097  -0.2136 -11.3170  -3.1281  -0.3734  -1.6421  -1.0410
+#>  -16.1127  12.7949  -2.7832  14.0396  -1.4200   5.4328  -2.1606  -4.6819
+#>   -4.2130   8.1218  -0.1096   7.9438   9.6171  13.0847  -0.8271  -1.9071
+#>   -5.3947   4.8697   7.9126   9.0326  -1.9985   7.4682   3.2385   7.7796
+#>    7.7056   9.0513  -0.6917  -0.0706   6.3404   7.3673  13.3844   2.8799
+#>    8.9054  -3.3671   6.1026  -3.1914  16.4169  10.8542  -6.9700 -15.3057
+#>   -0.5790  -7.5054  -2.7670   3.4779 -10.5296  -1.6088   1.4685  11.3029
+#>  -22.5003  21.0097   4.5520  -7.8701  -3.9777  -4.1955   6.4590  -7.7822
+#>  -13.9366  -6.3445   0.4268  12.9550  -8.9029   7.3922   9.0724  22.1141
+#>   -6.6576   6.8693 -14.1005   1.7424  -4.5490   3.0694   7.3529   8.7343
+#>   -7.2280  10.5721  -8.0667  -2.8534  -6.6502  13.8471  -3.8542   0.9561
+#>   -1.7408   5.3414  -7.1313   2.6918  -0.6835  16.9315  -4.9303  -2.7553
+#>   -6.0870   4.1576  -2.2776   4.7928   1.5814  -7.6875  -0.2116   4.9794
+#>   -2.8338  12.3172  -4.5041  -8.0548  -3.9249  -0.1538   6.8541 -18.6167
+#>    7.8449   1.0817   8.0695   1.4604 -14.7621  -3.2426  -2.7435   3.1915
+#>   -4.2422   0.4061  -5.7550   2.7467  -0.0334   2.0099  -1.6942  -0.1959
+#>   -8.5752  -2.8829   8.5170  -6.1580  11.2926   2.9038 -16.6995  -8.4883
 #> 
-#> Columns 25 to 32  -5.0729  -4.2956   2.7086   6.4407  15.8604  -0.3588   3.0230  -8.7129
-#>   -7.9630  -0.9580   5.0442  -2.5717   1.9893   5.3521   6.3859   5.3545
-#>    2.0941   1.9097 -12.0480  -7.8120  -1.1532  11.4927  -8.4720   0.4449
-#>    4.2617  -6.9544  -8.1263  -3.9371  -0.5503  -4.5696  19.1867   7.3988
-#>    5.5247   0.5356   0.7781   1.2835   1.0388  -1.3812   2.8845   9.4449
-#>   -0.6169  10.7332  -4.6661   3.8535  -1.2422  -5.3036   2.2425   9.5974
-#>    4.5023  -8.9653  -4.7451   6.3774  -3.6699  -1.5664   1.6872   4.6048
-#>    3.5291   2.9527   8.3790  -1.0565  -9.1058   1.3912  -4.0224  12.2775
-#>    6.6163  11.7969  -0.0560  -6.3300  -6.5183  -7.4860  -8.6395   9.1613
-#>   -4.0831   0.6745  -8.1844   3.2280  -3.4047  -1.9166   0.7233   4.1844
-#>    6.8935   3.1620  -0.2730  -8.7033  -3.1915   8.9787  -0.6695   3.8883
-#>   -4.3835  -1.0841   5.7311   1.6674   2.7202 -10.2040   6.5731  -3.4295
-#>    6.2861  -4.3858   0.8793   5.7060  -0.1589   6.7821  -7.4484  -0.8987
-#>  -11.4815  -1.8563   8.6766  -5.0828  -0.2160 -10.1367  16.3202 -15.2852
-#>   -0.9740   4.4800   4.1621  11.5320   4.9337   2.3787 -13.8811  13.0770
-#>   -2.3275  -6.4719  -5.6679  -0.2408   3.0308   2.7526   8.1827  -7.3714
-#>   -7.6305  -1.1428   8.5583   6.8418  -1.1626   6.2628   2.1209   7.9571
-#>    1.3825  -4.8033  -1.8967   3.3390  -5.9439   1.8516  11.8852   4.2061
-#>    6.7047  -1.5728   1.9421  -6.5667  -3.1609   2.4729   0.5374   4.6061
-#>    3.9707  13.9025  12.1335   6.5156  -2.7861   0.3097  -8.4294   4.0610
-#>    1.8200  10.6741  -6.9567  -3.4479   6.3126   3.0632  -9.6933   5.1884
-#>   -6.5620   1.9818  -3.6081  -0.1819 -10.5116  -3.7730   3.1047  -5.6763
-#>   11.4688   8.5353   3.8160  13.8754   5.2576  -0.4056  -7.2265  -6.5845
-#>    6.4180  -1.8389  -0.7055   8.5180  -8.2785   7.0835 -19.0100  15.5229
-#>   -6.8713   7.3240  -1.0499  -1.4583  10.3664   5.5127  -1.9181  -5.3814
-#>  -10.7972   5.2820  -1.3866   2.9625   1.5788  10.2621  -6.9272   0.7044
-#>   -9.1831  -3.3115   1.0362  -3.3940  -4.2613   5.9859   9.9480   3.7198
-#>   -8.1137   3.6621   8.2821   1.7979  -1.2668   1.4004   4.5593   1.2181
-#>    1.0208  -0.7449   2.9976   5.4707   2.6559  -3.3447   1.3942  -0.3096
-#>   -4.3125  10.7800   7.2531   9.5339   0.3425  -1.7535  -7.0209 -12.0271
-#>    1.3050   2.2791   6.5436   9.1550 -10.5545  -6.5303   4.5502   9.5093
-#>    8.7469  -4.9613   2.1701   1.9083   9.6481  -6.2135  -2.7381 -14.1108
-#>   -4.4900   5.6005  -2.7029  -1.0956   2.2024  -1.2472   7.6779   1.9417
+#> Columns 25 to 32   4.5461  -1.7874   6.9406  -6.1462   0.8465  -3.4333  -0.1287  -1.2245
+#>  -10.6198 -10.2090   6.8449   9.1826  -8.7153   0.3717  11.9924   2.0144
+#>   -0.2221  -3.0370  -1.6119  -5.1413   0.4188   2.3011  -1.1918   3.7638
+#>    0.4165  -3.6168  -3.3551   9.3613   3.7573   0.0487  14.0333   3.4898
+#>   -7.1865   2.1603  -0.6283   3.7277  -2.4244   5.4873  -4.1391   0.3937
+#>  -14.6706   6.0137   4.3979  -6.1696   4.8594   9.4999  -3.8605 -12.1960
+#>  -10.8756   3.3342  -2.3423  13.3649  -8.9117   2.1469  -1.4568  -2.2630
+#>   -1.9746  -2.0341  -1.2929  -4.8138  13.0213   0.6383   7.1013   1.6213
+#>   -0.9603 -12.5348   4.4349  -3.1326  -6.6191   3.7959   8.8567  18.0121
+#>    8.1876   1.0164  -3.2779  -4.2590  -4.0202  -0.4069  -5.3847 -10.4643
+#>    1.8676  -4.2744  -1.2372 -12.6805  16.6786  -0.3825   1.8035  -1.3214
+#>   -0.8577   1.7369   1.8565  -9.1353  10.9804   3.5655  -1.0358   2.5880
+#>   -8.1819   4.4556   4.6743  -3.0216  -2.0720   8.6054 -10.6734  -0.2614
+#>    5.4568  10.4090   0.3215  -1.2129   6.4001  -2.3890   8.2217   0.7159
+#>    2.0578  -3.1233  -6.9508  -2.6483   4.2655  10.3881  16.7621  -1.3654
+#>    5.0270   2.8696  -3.2183  -6.5008   0.4007  -7.3746  -4.1468   1.1954
+#>    8.9372 -10.4825  -1.0310   6.3987  -4.7489   1.2082  -4.1153  -4.4258
+#>   -1.3007   9.6133 -13.8906   9.1863  -5.9332   5.5027  14.8598   0.9152
+#>   -8.4679   5.6421  -2.6120  -5.4634  -1.2663  -3.8618   4.9288   0.1378
+#>   -4.5856  -9.3817   2.7296   2.4823  -7.1148  14.9971   2.7313  -3.3222
+#>   -4.9916   8.9191  -5.7191   4.3404  -9.1205  -8.4813   4.2785  -1.1341
+#>    5.8225  -0.9654  -0.7333 -10.1825  11.3850   0.4820   1.1737   2.0676
+#>   -8.7471  -4.1528  -4.5781   5.6589  -1.0593   4.3626  -8.3686   3.9424
+#>   -8.3945   7.3017   0.5110  -0.9259  -0.0898   1.5115  13.4798  -0.3394
+#>    0.0846  -2.1283  -3.7992  12.5121   5.7055  -9.6746  -0.0973  -8.4925
+#>   -1.9461   0.7274  -1.2698  13.6316  -8.7979   0.6258  -5.7439 -10.4442
+#>   -8.3608   7.2671  -2.9568   4.3619  -8.1883  11.5379   0.0437 -15.2504
+#>    4.9201  -3.8489   1.7777  -1.0836  -1.1132  -3.6810  -5.9686  -1.6751
+#>    4.2006   4.4060  -4.5429   8.2122   3.8339   1.5789   7.6177   4.1010
+#>   -6.1299   6.6651  -0.9906  -6.2403  -5.7599   3.3714   3.9567  -3.8084
+#>   -3.2045   5.2026   4.9811  -5.1504   4.5886   2.0285 -10.9803   3.6086
+#>   14.0256   1.5228   9.4775  -4.2767   0.6166  -8.6033  -3.7829  -5.1891
+#>    8.1207   0.6480  -6.2109  -0.8614   8.4112  -2.7397  10.1298   4.2593
 #> 
-#> Columns 33 to 40  -0.0019   0.2802  11.2920  -9.1013  -5.6047  -0.0040   5.0107   6.8514
-#>    6.9784  -4.2512  -0.7175  -4.0739   8.1430  -6.1468   2.5069   8.5195
-#>    1.0547  -0.3367  -7.9803  -4.1151  -1.3741  11.1988   1.6343  -2.4667
-#>   -8.8407  -7.3048   0.8817   1.6348  19.7482  -2.2385 -11.9681  -0.4096
-#>   14.3348   8.9444   6.1513  -3.8608  -4.9776   0.7029   4.0575   0.0101
-#>   -1.0902 -10.9092  -6.6897  -5.8111   5.5139   9.2579   5.1977   9.8372
-#>    0.8237  -4.5022  10.2385  12.2472  -1.3605   5.4792  -4.8483  -9.0908
-#>   12.6671   3.5102   6.4837   5.3618   6.4916 -10.6185  -7.3910   6.6741
-#>    1.3334  -0.3487  -7.4179   1.1611  -6.2333   0.7965   3.6717   0.0761
-#>   -6.9966  -4.8677   1.2862  -3.7916   0.1066 -16.2524   6.9257   9.2883
-#>   14.2929 -15.8576   0.3450  -4.3398   3.7223  11.3818   3.6238  -7.7689
-#>    0.7186   3.9697  -1.0347   8.9312  -9.0946  -4.7356   6.0722  -1.6076
-#>   -6.7104  -2.8066  -1.2940  -9.2257   6.4690  11.5947  -4.0898  -7.5930
-#>    9.8058  13.1059  -4.8653  12.2930 -12.9543  -8.7284   2.0773  -2.7334
-#>   10.1541  -2.2709  -0.0826 -13.8402  13.5996  10.3662  -8.8221  -7.8720
-#>    3.1338   3.4794  -8.2790  -1.3139   8.1712   5.2529   0.6040  -7.0523
-#>   -2.3543   9.7280  -3.3076   1.4279   0.8343  -0.5399  -0.3940  -7.1317
-#>    1.3395  -0.0886 -14.3154  -3.6316   4.7655  11.4265  -6.9424  -0.0195
-#>   -7.2402   0.8065  -6.1732   5.3272 -10.5435   0.5907   7.7513   6.7939
-#>    0.6357   3.2251  -4.7789  -8.3201   4.2994  -3.6608 -10.4161  14.7643
-#>    1.0850   5.2056  -1.1094   9.1666  -9.4409  -8.2777   0.9586   8.9043
-#>    9.5923  -4.4406   0.3631  -4.2734   7.5759   5.4691  -4.3900  -6.4848
-#>   -4.8792 -13.1105  -9.9396  -6.0821  -4.2209  12.5849  -2.5196  -7.9711
-#>  -23.4039  -9.2183  -8.1744  -8.4877   4.3403  -1.6354   1.4089   2.3033
-#>    4.3382   4.4030   1.3752  -6.4615  -2.0714   0.8318   4.8986   2.2077
-#>    2.5029  10.4185   1.9057  -7.3488   5.1091  -2.3736  -5.1177   8.5054
-#>  -11.5271   1.1920  -1.4756  -4.4427   8.2370   5.6577   4.2550  -7.8401
-#>    3.8904   4.7149 -14.7242  -5.9000 -12.1250  -3.5753   9.7209   3.8768
-#>   11.7669   3.3826   7.0856   2.0755  -1.0698   3.9380  -8.9544  -1.4195
-#>   18.2892 -11.3806   0.7879  -2.0373  -8.2313  -9.0982  10.7094   0.3240
-#>   -4.2813   1.9703  -0.1238  16.0385  -7.7890 -12.1289 -10.8846   4.2699
-#>   -8.0051  -3.0747   4.6354   1.3537   6.1540  -0.7435  -3.5279  -8.5859
-#>   -1.3912 -15.9602   1.5367  -5.4228  -1.3131  -4.3742   7.9185   9.7122
+#> Columns 33 to 40   9.2424   5.3477  -3.3071  -8.9944   3.3546  -6.3851  16.6293  -8.3728
+#>    0.5228  -5.2786  -3.1655  10.0735  -4.0833  -6.4511  -3.5643  11.1066
+#>   -6.8805  -6.3628   0.5653  -7.7253  -2.9464  -0.7135  -1.6304  -0.9286
+#>   -4.5952  -5.4674  -6.6472   2.5320   4.3021 -12.3713  -3.1266   3.0358
+#>    3.6776   5.2333   4.1299   1.3047   2.6016  -4.1254   3.0028 -12.5456
+#>  -16.5943 -16.6593  -5.0624   5.2969  -5.4644  -0.2972   4.1406  12.2008
+#>   12.8467  13.4065  -1.4311  -7.0946  -4.9756  11.1617  -8.7335   7.9459
+#>   -2.1967  15.2606   1.1168  -2.2982  14.1499  -6.3651   0.1412  -5.4841
+#>    3.8924  -7.0436  -3.8106  15.5210  -5.5157  -0.7474   0.7868   6.6903
+#>   -4.6871  -8.0278   4.5998  10.6388  -5.5684   5.9090   0.2474  11.6656
+#>   -1.9133   7.0300  17.2314  -3.8331  -4.5409   5.6244   8.5037  -6.6785
+#>   -1.5067   1.8693   6.3062  -4.7347   0.0889   3.0335  -3.5098  -0.5445
+#>  -13.4363   5.1465 -11.8876  -8.6249   2.7343   7.8906   4.2040  11.1498
+#>    2.5616  16.8276 -12.9997  -3.1459  -0.2504  -0.3913  -1.4329   3.8785
+#>    1.2087 -10.3873 -18.7725 -10.7496  10.2618   1.0601 -11.6176   1.2907
+#>    6.6043  -4.2279  -4.2960  -6.6874  11.4313   2.0871  -1.8794 -10.9946
+#>    8.9732 -17.4081   3.0446  -6.6103  -9.8251   3.8942  -8.6507   2.8292
+#>   -5.3151  10.3033 -21.6493   4.1621  -4.6132   2.6497  -9.7478  -8.4951
+#>   -7.8956  -1.2738   3.8441  -2.3730   4.7064   3.7897   4.9676   6.5962
+#>   -7.6967 -14.8281  -8.7461  -2.9554  -6.8432  -2.1544   0.5735  12.7185
+#>    9.7002   5.3942   5.0005   3.4487   0.1106   4.1346   3.3380  13.2634
+#>   14.0664 -20.6510  -4.8552  -4.3232   4.7416  -4.5424  -2.4989   1.3249
+#>   -6.9674  11.2402  -1.6994 -10.3309  -2.8903   4.0565  -6.2305  -4.8603
+#>   -2.9008  10.0424 -18.4889  -8.2101  -2.9165  -9.3681  -0.6713   0.0844
+#>   -7.2360  -2.5705 -11.9855 -13.4294   1.0608  -5.7634  -6.5146  -6.0410
+#>   -9.9475  -2.0874  -6.5069  14.7216 -12.8900  -3.6905   6.6518   3.1321
+#>   -8.3534  -3.0493  -4.1314   7.4558  -6.2577   0.7444  12.5636   9.9604
+#>   -0.3961  -2.7503  12.8154  18.9382   2.4836  -0.2249  14.2536   0.8238
+#>    1.2639   6.7575 -12.8442  -0.0376  -8.2375 -12.7026  -0.5518  -7.0981
+#>    0.4576   7.1450  -7.4989  -0.1666  -6.9255  12.3310   5.8064   5.1844
+#>   -7.9315   7.3573   1.4095  -1.3109   7.9046  -4.1940   0.4275   1.4821
+#>    0.9128   1.9522   5.4874   9.3033   6.4007   2.2500   1.5040 -12.2080
+#>    7.1595   8.6311  11.8110   0.6751   2.7331  -8.2961   2.1748  -9.6275
 #> 
-#> Columns 41 to 48  11.9842  -0.5593  -1.7805  -0.0418   2.9324   4.4272  10.7479  -5.3032
-#>    6.4636  -7.0260  -2.1067  -2.6697  -0.7586  -0.1127  -1.9880   4.2114
-#>   -7.8123   4.6202  -1.8848   2.7081   7.1877 -24.3626   2.2723   5.8324
-#>   -3.6324   2.1598   1.4967   2.0073   2.5986   1.4709   8.2080   8.4126
-#>   -3.0173  -2.4682   1.8973  -0.4220  11.2470  -8.9623  -0.4299   1.1831
-#>  -11.3260   0.8946 -11.9878   8.5653 -13.7869   2.4784  -3.6501   3.3985
-#>    0.7377   3.5998   1.3043  -4.0607   3.9259   5.5577  13.7190  -4.1788
-#>    3.7997  -7.6852  -2.1255   2.1282  16.8586  -0.1670  -0.5216  -5.6847
-#>    0.0417   0.4327   3.9841   3.7029  -3.3788  -1.5266  -3.1260   9.1985
-#>   -7.9677   3.4836   2.0614  -9.5991  -4.0841   5.0656  12.3368  -3.2452
-#>    6.2654  -4.9748   4.1019  -9.4819  30.9802 -20.2084   1.4996  -1.2812
-#>   -2.4100  -1.8367  -0.7233   1.9043  -4.4144   7.0083  -4.5828  -3.8193
-#>   -1.2719   7.6519 -12.3358  -1.2329   2.1559  -0.8138  -0.6092   0.2082
-#>    1.6161   6.0475   0.1815  -5.6616  -2.7878  -1.0785  -2.3120   0.9993
-#>    5.1240  -1.1110  -8.6762   8.2783  -3.1611  -8.0870   3.4626   2.6431
-#>    0.5291   3.3617   0.6551  -8.7125  11.2056   5.7662   5.2713  -2.7741
-#>   11.6409  -2.7260  -4.5710  -3.0022   7.6567  -9.3038  -0.0798   5.8057
-#>   -5.5851   3.2143  -1.6160  -3.7551  -2.0959   1.3541   5.3000  -3.7737
-#>   -3.2166  -2.7181   7.8093  -9.5603   8.7075   7.0108   5.1225 -11.7553
-#>    6.5461  -7.2921  -1.2451   7.5864   6.0977  -1.2823   4.6137  -0.9353
-#>  -13.1320  10.1831   2.5122   6.5948  -8.3315  -9.3659  -1.7092   1.8071
-#>    1.1201  -0.9914  -2.0848   4.5972   2.9202  -1.4931  -2.1520   3.0542
-#>    7.4057   5.9809  -4.3698   1.5064  -2.8317  -2.2117   2.1553  -0.2234
-#>    3.6103   7.1969 -12.8206  -7.6817  -0.5296  -3.9748   5.1967  -1.6486
-#>   -5.1564   6.3437   4.0816  13.9357 -10.6232  -6.3037  -5.1245   4.8427
-#>   -0.6854   1.5132   3.3787   9.7651  -1.6342   3.9888   1.7152  -3.4456
-#>   -2.9518  -1.5337  -3.2749  -7.5476  -2.8363   0.7673  -4.6810   4.3637
-#>    3.4508   5.3307   0.0274 -18.9641  10.7559   0.6545  -2.6214   2.7683
-#>   -0.3699  -2.1346   5.4093  -2.6155  -1.9389  -6.1243   9.4638  -2.6488
-#>    4.5731   9.7016  -3.4944 -14.8443   8.1975   8.4388  -7.5679  -0.7393
-#>    1.9754   2.5775   7.9926   3.1289   0.2847  18.3038  10.5872  -6.6662
-#>   -1.1725   5.3218 -12.1124   0.4054 -11.2314   9.3301  -8.0784   0.4656
-#>    5.2721   1.0929   3.0305   0.9061  -9.7753  12.5669   2.3650   6.1578
+#> Columns 41 to 48  -3.1392   6.3236   0.7073   1.5120  -1.8391  -5.7788   4.0804  -8.3241
+#>    2.5106  -6.4301 -11.1320   3.6476  -0.7345   0.6073   2.4868  -1.4135
+#>   -3.1169  -2.2953  -1.0181  -6.0643  -4.7081  -3.0841   7.2758   4.2352
+#>   -6.0995  -1.8265  -8.3836 -10.2064  -4.8300   6.6835   6.6905   1.2455
+#>    1.8673 -12.9154   6.3378  -2.2437   2.5361 -11.8875  10.1140   0.9790
+#>   -0.1967  -6.7477   7.1395  -2.1479   7.5633  -7.3922 -13.5113  -1.1924
+#>    3.2614  -3.0319  -0.5722   2.5267  -7.7651  -0.8806  -9.4327   8.8522
+#>   -3.0833   1.5224  -0.3071  -1.7744   2.4594  -3.0523   5.2288   0.7627
+#>    7.5373 -12.6389 -15.4380 -19.1049   2.9447   4.7457  10.5029 -20.2439
+#>   -0.6883   3.6972  10.2625   7.4831  12.7269   0.2402  -2.7910   0.3031
+#>    5.8815   4.4874   1.6697  -2.4667  -2.5506  -6.5303   5.3810  -5.0018
+#>   -1.9833   1.0399  -1.3247  -2.0429  -9.2600   3.3040   2.0330  -5.5974
+#>  -14.7912  11.8963   1.3233   3.1297  -2.8905   6.8713  -0.6342   4.2857
+#>    4.1237   2.4904  -2.5805  10.9582  -2.2437  -3.8681   2.7818 -10.3735
+#>   -4.5889  -2.3082  -4.3875  -8.3914 -11.7653  14.0941  -7.1736  -0.7640
+#>    5.8828   3.9000   1.2275  -4.7927   1.9082  -2.4038   4.7111  -1.7195
+#>   -2.2195   4.5441  -2.2650  -5.1624 -14.8150   3.0868  -3.3167   0.7638
+#>    6.2045   0.6251  -6.5964  -0.9374 -15.1725  -9.3577   2.6756  -1.7007
+#>    4.1191   2.1594   6.4082   6.6128   8.0440  -6.0048 -17.7271   5.2561
+#>   10.8162 -15.8923  -1.5520  -2.8257   1.6919   0.5887  -0.5257  -1.1731
+#>   -0.3497   8.7414  -2.5578   7.8543   6.5660   3.1135   5.4449   4.9379
+#>  -12.8605  -2.5898   1.3534  -3.8071  -6.5426  -5.0482 -10.5402   8.2086
+#>    5.5201   1.8604  -4.7878  -1.7672  -8.1637   3.5020   7.9703 -11.6711
+#>   -1.9367  -7.4007  -6.7922  -8.3268  -3.9128  -3.5023   4.7860  -6.6200
+#>   15.3472  -6.4593  -7.5412  -2.4014   8.0834   5.2980  13.1981 -14.9130
+#>   -0.4958   2.8085  -1.0653   8.3809  19.6020   6.4079   5.1017  -5.3585
+#>   -5.0959  -3.7581   0.4790   3.6858  13.8312  -7.0175   3.4018  -0.0525
+#>   11.7728  -6.3824  14.8093   3.0395  11.0097  -2.1108 -10.7993  -8.3392
+#>   -4.4606   5.5990 -12.7177  -3.5385   0.6170  -5.5415   9.0312  -2.2260
+#>    0.9364  10.8148  -0.4495   9.6610   0.5725  -8.4637  -8.2022 -12.4599
+#>   -2.4467   2.0941  -3.6358   1.4687   0.7493   5.3189  10.9268   1.3595
+#>    6.6599  -0.7239   3.0088  -4.1595  18.0101   2.9459   6.7269  -3.7759
+#>   -3.1599  -0.8773 -10.0258  -3.6227  -1.7192   0.7841   0.9650   7.5942
 #> 
 #> (12,.,.) = 
-#>  Columns 1 to 8   4.5001  -1.2295  -1.8164   0.6520   3.2763   5.5859   3.9245  -4.6731
-#>   -3.8160   2.2018   5.1452   2.9829   7.7092   2.6458  -3.2690   1.8272
-#>   10.2061   5.9445  -5.4832   5.7572 -13.4972  -9.2555  -4.7143   1.7513
-#>   -7.6837   8.4554  -6.2225  -7.9215 -15.1581   4.2338  -4.3397  -8.5616
-#>   -1.4925   0.9310   0.1745   7.8605 -10.1225  -9.7224 -10.6454  -5.1328
-#>    5.1166  -5.5563  -3.4195  -7.5142  -6.0150   3.4729 -12.7218  -4.4432
-#>   -0.6968   1.9179  -1.0473 -12.1094   1.7510  -3.5513 -10.3687   3.9037
-#>   -8.8777  -8.4637   9.1328   7.5304   2.5435  -0.7738  -4.5382   7.4405
-#>    3.4902  -4.5657  -6.3298  -8.0692   6.9725  -9.0220  -4.4722  -9.1183
-#>    4.3632   7.8274   7.3080   1.6116  -3.2386  -2.5663  -2.4155   2.2608
-#>   -6.8906  -7.7736  -3.5131   5.5850  -7.2101  -7.0828  -3.4648  -1.2636
-#>   -4.9135   7.8486   6.0787   9.6393   6.3337   2.7853  -2.4782  -2.6175
-#>   12.2056  11.0097  -5.6749   6.6335  -0.6023   5.6280  -3.9017  10.6491
-#>   -3.4025 -15.7746 -15.6751 -16.0670   1.3937  -4.1239  -5.8367  -6.8215
-#>   12.1459   3.2775   7.3165  -6.2589  -5.5058  -3.6283  -6.4149  -9.3527
-#>   -1.2078  -0.0522   3.6285   0.1165  -5.0967   3.7390  -1.1201  -9.5000
-#>   -3.5308   2.7058  -3.9890   3.4945  -5.7438  -0.8554  -8.4195   2.9762
-#>    6.6982   3.4786   8.0461  -9.3455  -1.4272   6.8840  -7.2722   1.4466
-#>   -4.4001  -0.1649   2.9268   8.8747  -1.2902   5.5666   2.5303   0.6259
-#>    8.3606  -7.1928  13.2341  -0.3941  18.6431   6.5971  -1.3029   4.8651
-#>    1.9562   5.0652   1.6687   8.4165  -2.4939   3.4391  -3.4970   2.5919
-#>   -6.0691 -18.4595  -1.0337   6.6505  -1.8230  -0.6532  -1.8843  -6.8945
-#>    8.5778   1.2562  13.4635   5.7836   6.2390   6.4734  13.7121  -1.7506
-#>   16.3378  12.4865  -5.7148  -9.5783   1.0756   6.7436  11.4067  -5.2211
-#>    3.4916   0.0112   0.4477   5.7018  -9.1030  -0.5422   2.6076  -5.5469
-#>   -4.8885  -7.4376   2.1646 -15.9880  -2.2590   1.2272   1.8984  -7.8221
-#>   -8.0032  14.9478 -12.9263   1.4746 -10.2535   2.0050  -9.9589   1.8762
-#>    6.5848  19.4712  -4.4186   1.4986  -9.8512   7.1145  -0.2163  10.5178
-#>    0.1889 -11.4429  -1.6276  -2.4710  -0.2683  -0.3900  -5.8692 -18.0839
-#>    9.9172   7.0181   7.4458   3.8973   1.2154  -1.8324  -1.7741  17.6313
-#>   -3.3293  10.3854   7.7157  -2.8775  -0.8746   0.2266  -1.4422  -2.2013
-#>   13.3809  -2.5503   0.5669  -8.6428  12.1443   6.1498   5.0442  -0.9521
-#>    0.7790  -5.2123   4.2130  -6.1849  10.4151   0.1508   3.2000  -3.6462
+#>  Columns 1 to 8  -4.7494  11.6914   6.2275  -2.5189  -9.1071   1.7811   8.3844  -4.6822
+#>    7.3511  -1.7579  -5.5751   3.7172  -2.1976  -2.1622  -1.7208   1.7950
+#>   -4.0640   5.4961  -5.4718  -9.3183   5.4233   0.9490  -5.1374  -2.9941
+#>   -3.8344  -3.7589   1.6410  -0.5586  -0.2092  -5.3019  -0.4378   3.5170
+#>    0.2908   3.2402   5.5140   0.5267   1.3939  -9.7284   2.4027  -6.8657
+#>    6.4532  -9.8105   4.2592  -3.6302 -12.7820  -1.5951   3.8619  -2.7962
+#>    5.3662  -3.8730 -14.0795  -1.0919   8.4617  -4.6664  -2.9468   0.8340
+#>   -3.8975  -4.4485  -9.7173  -6.4734  -4.1037 -13.0983  -0.2139   7.3276
+#>    5.7682  -6.4684   9.7062  14.8237  16.2986  -2.7680  16.0024  -4.0138
+#>    3.4810  -4.3589   9.3481   1.0873  -2.2977   0.8925  -6.1126   2.3789
+#>    1.4736  -3.9429   2.4021   8.4457  -5.0132   9.4390   5.1010   4.0382
+#>    0.5604   2.8878  -1.6742  -5.9348  -8.8134  -5.6354   0.0670   1.6170
+#>   -7.7194   0.8488  11.2531   3.9743  -4.1372 -13.6285   4.5616  12.9156
+#>    0.9943   8.7256   4.8928  10.7819  -2.6758   1.5081  -6.8332   1.9871
+#>   -3.2482  17.7955  10.7015   2.5543  -0.5682  -9.8350  -3.6823  -4.3511
+#>   -7.0937  -8.6378  -0.5751  -2.0821  -2.4636  11.8386   8.7885   2.4834
+#>   -6.0151  12.8042   9.7828   4.2838  -5.9085  12.9451   2.2958  -4.7117
+#>    5.5618  -4.4812   0.2074 -10.1383  11.1834  -7.6623   1.9025   4.2811
+#>   -6.7463  -8.3927   2.1827  -0.8423  -6.8870   5.1483   9.9108   6.1397
+#>    4.0850   3.3250  -0.9212   2.1076  11.6495  -3.4480  -6.3162  -4.4809
+#>   -6.7212   1.8606  -0.1036   7.6388  -1.1124   2.6191   7.7316   9.8316
+#>  -13.5180  -7.1545  -1.3133   2.3878  -3.2021   8.1715   5.6784  -1.3819
+#>   12.5770   3.9169  -2.9238 -13.0430   1.9886 -11.6656  -8.8397   0.6372
+#>    2.6831  -0.3313  -7.7377   4.8823  10.0838 -15.3990   2.4189  -9.2835
+#>    0.8459  -3.4268 -11.0981  -4.7562   2.3583   1.1973  -2.7463  -0.1310
+#>    6.0014  -5.1915  -0.9598  -8.1805   0.9224 -12.0693  -3.3295  -2.6039
+#>   -1.9592   0.8019   1.9063 -14.8500   2.9979 -12.3182  10.9789  -0.3586
+#>    2.7076  -9.6546  10.1328  -7.4256  -0.0473  -0.6829   2.6728   6.6606
+#>   -3.8981   4.1951  -2.3174   4.1387   5.6621  -5.3716  -3.5363 -13.7948
+#>    8.4676   3.2628   8.0927   3.1231  -5.4016 -13.5442   6.5253   5.4612
+#>   -5.4619   5.2149   9.5666  -3.4838  -2.8675  -0.5318  -2.1331   4.0017
+#>   -2.9222   1.2974   7.2555   3.4485   3.1459  -7.7185  12.1555   0.9251
+#>   -4.2126   4.7496  -9.8861  -9.8656  14.1192  -4.2276   4.6448  -7.6168
 #> 
-#> Columns 9 to 16  -4.0860   6.4213  -2.5722  -2.3741   4.1568  -5.8059   1.7523  -1.2796
-#>   -3.1006  -3.4590 -16.6207  -8.0393  -1.1310  -3.5510   4.8503  -2.9037
-#>    3.0810  -3.6914  -6.7095   2.1614  -2.9226 -10.6996  15.6299   6.2988
-#>  -11.1525 -16.5291   9.1431   5.3459   3.2410 -10.4744  -6.6682   1.0263
-#>    1.0383   1.9176  -2.6346  -2.2827  -3.5935  -3.7414   1.7668  10.5311
-#>   -0.7062  -1.7054   0.5477  -2.0877  -7.5597  -2.5377   1.7576  -6.5614
-#>   -8.4782  12.9652  11.9687  -3.9546   9.3618   9.1582 -10.6152  -4.3985
-#>    2.9258   5.9505   1.6781  -2.0285  -6.0790   2.2242   6.5264   6.0665
-#>   12.6039  -2.1076   4.9537 -11.3343   2.3227   1.4610  -2.6695  13.5633
-#>    6.5875 -11.4847   6.2436   2.8999   0.8432  -0.0396   0.9859  -1.0960
-#>   -2.4116   4.0544 -15.8423  -0.0828   0.6131  -5.3123  -0.8512   3.7520
-#>    2.1860  -3.6086  -0.5867  -3.6484   0.5793  10.9506  -6.7126  -4.9702
-#>  -13.6129   5.2558  -3.1411   4.7320   0.3424   7.0105   4.9324  -6.7802
-#>    4.0216   6.1902   5.0901  10.2227  -0.1422 -11.2877  -6.1075   3.0605
-#>    5.2406  -1.4246   3.9030  -2.4046   0.8196  -8.4962   4.4973   6.3254
-#>   -7.6489   6.6798  -7.4386   3.0880  -0.5156  -8.3741   3.2873   6.4475
-#>   -3.4557  14.1308  -2.1692  -4.5955   7.5381   3.6648  -0.5949  12.6182
-#>   -8.7020  -7.9468  -3.3009  -5.2928  -1.0027   7.5290   9.5723  -9.5589
-#>   -1.3027   5.1076   8.0577   0.2842 -11.1052   4.8339   6.8218  -0.0609
-#>    2.5851   0.7047   6.0073   9.2696   5.6514 -10.3622  10.6630   1.7014
-#>    0.3271  -7.3651   4.2371   0.7115  -2.7260   4.1920   1.4280 -10.3795
-#>    3.9296   8.8945  -0.0389  -0.4414  -3.4227  -8.3822  15.2850  20.2510
-#>    5.6511   4.8389   2.8384  -2.2936   2.6809   3.3876  -9.1269  -3.5801
-#>    8.9159 -13.7750  14.9805  15.4946   2.3645   4.7642 -14.0470 -10.4856
-#>    8.8515  -4.1713  -1.1083   5.2987  -3.5451   1.7682   8.2087  -5.3819
-#>    4.3616   9.0161   6.3545   2.6342  -3.4428  -7.2009   6.3231  10.2266
-#>   -1.8823   7.2042   4.8943  -3.7700  -0.2878   4.5386 -11.0202   4.4571
-#>  -10.8001   2.1424  10.2672   8.6709  -4.4365   3.3502  -5.4543  -4.8962
-#>    6.6005  10.0152   1.3169   2.4047  -1.9600 -12.1608  11.7453   8.2295
-#>    0.8924   8.1780  -7.9939  -4.9859   5.4768   8.9285   5.1992  -9.6600
-#>    9.9078   7.4913  11.5323  -4.9031   8.4436  12.0946  -1.5957  -8.7517
-#>    2.2561  -3.1106  -7.4184   4.5571  -2.6696   2.9912  -1.4644 -10.2755
-#>    6.9493 -20.7305  -2.5021   0.0686   7.6799   0.9227  -8.6592 -16.3700
+#> Columns 9 to 16   1.1053   3.5929   3.0596   8.0787  -3.8845   1.6366   6.9312  -3.5006
+#>    6.6065  -0.7426  -8.6614  -0.8896  -1.1389  -4.3423  -2.0040  -4.8005
+#>    5.8483  -3.6555  -8.0874   0.7958  -3.8505  -1.4514  -5.1474  -1.1738
+#>  -16.2258   4.3450   5.8417   6.3028   4.4347   9.6735   9.1234  -0.5046
+#>    8.5183  -0.5802  -0.7787   5.9788 -12.6460   0.7465 -13.3365  -3.4691
+#>   -2.7279  -0.3538 -13.0109  -1.0451   3.0467  12.7585   3.3072  12.5092
+#>    6.4983   9.7885  -4.6848  -6.7536  10.7469  -6.1712   0.0495 -11.3812
+#>   -4.7200  -0.9540   0.8806   9.6815  -3.0431   8.7748   4.5873  -8.6234
+#>   -0.4974  11.3381  -3.9772  -3.7338  -3.8782  -3.2250 -18.5084  -7.4382
+#>    4.1628  -3.9405  -8.6841  -6.2948  -3.6343  -7.3230   0.3029   1.8037
+#>  -12.4989   2.2490  13.7228  10.4315   1.7481   6.1588  15.8546   8.0660
+#>   -3.2981  -0.5289   9.9215  10.8434  -1.1319  -0.6649  -4.8565  -4.5528
+#>   -2.8793  -5.5543 -11.6374   1.7953  -8.5499  -0.5389  -5.6380  13.0501
+#>    5.3041   6.5157   2.8312  -0.8540  -9.9015   0.3593  -7.3609   1.7051
+#>    2.2576  11.1360  -5.2332  -7.1754   0.3668 -13.2184  -8.1626 -12.7167
+#>   -4.5291  -5.2401   6.8047   2.6375  -3.2612   9.4454   8.7669   3.9668
+#>    2.7251  -1.5767   1.8422  -9.4165  12.5574 -10.6839  13.6745   6.8854
+#>    1.3396   7.8490   0.4546  -6.4737  -4.5701   2.1387 -16.8663 -12.9710
+#>   -4.6366  -7.5210  -7.7491   4.3944 -11.0267   6.6839  11.0491   3.2236
+#>    8.8870   5.1582  -9.1483 -21.4267  -3.1668  -2.6182  10.5676  -2.0491
+#>    4.5671  -0.6424  -0.2153  -0.7346  -2.4687 -11.5683   5.5841   0.1110
+#>   -9.0368  -7.6692  -4.8518   4.1419  -3.9568  -3.0074  -3.5510  -0.1042
+#>    1.9658   1.7985   3.5889 -11.9282   8.5851   3.2788  -2.9930  -6.4279
+#>    5.5135  15.1663 -12.9999   2.7351 -12.1934   2.2852  -5.7878 -13.8305
+#>    1.4929  -2.5567  -7.4677 -10.8425   4.2928   6.5082  11.4283   6.1736
+#>    2.0748   2.6545  -4.9670  -5.5344   3.1958  -0.7656   9.6221  10.2205
+#>    2.7918   8.2074 -15.2004   0.7522   2.1662   8.3776   0.4561   7.9721
+#>   -4.3121   2.1842  -7.0578   6.0894  -5.7502   6.8616   1.7681  14.7573
+#>   -1.8653  14.6449   0.8759  -5.7035  -1.0147  -4.5710  -0.3629  -9.3235
+#>    5.2758   8.0527  -1.0049   0.5178 -10.0698   6.3923  -8.6552  -4.0547
+#>   -7.1736   3.9217   7.2714  -5.0306   2.6258  -0.6003  -7.0254  -5.5973
+#>   -5.0572   4.0888   0.1128   6.7492  -7.8908  -2.0775   9.7692  -0.4405
+#>   -7.4791  11.3341   6.2327   6.9447  -2.4409  -0.2219  -9.6524 -15.3869
 #> 
-#> Columns 17 to 24  -2.0726  -8.5104   7.9019   0.8095   1.3915  -1.9690   6.3407  -4.2864
-#>    4.3306  -6.2841   5.3894  -0.8770  -8.1014  -1.7010  -6.8155   7.6810
-#>   -6.7169  -8.4859 -10.5882   3.0183   2.8342  -0.5263  -1.2124  -0.5939
-#>   -1.4485  10.2153  -6.5924   0.9652   2.0956   1.7560   1.1663 -10.9764
-#>    1.4974  -3.7323  -9.4498   8.6725  13.9418  -6.7380  -3.5900 -14.4211
-#>   -5.1598  -6.6805   1.9089  11.0177   4.9359  -1.2375 -18.6508  -2.8171
-#>   -4.3521   4.9877   7.8086   2.4810 -13.5333  -5.0737   5.3768   0.7935
-#>    5.4761 -15.1532 -17.5176   7.3348   3.8836   4.4217   1.4999   3.2297
-#>    4.6920 -13.0070   4.5573   4.2250  -7.9661   6.4834  -1.7304  -0.2558
-#>   -6.9633  10.6470   7.2614   2.0687   1.7736  -3.7101  10.6454  -6.3196
-#>    3.5066 -10.6950  -5.6799  -4.6649  -2.4139  -8.7156   0.1989   1.2605
-#>    0.2611   2.4521   4.0678   0.5756   1.4464  -7.7336   2.6385   1.9377
-#>    0.3724  12.6858  -6.6219   1.6642   4.2785  -2.6077  -4.8870  11.6064
-#>   16.4867  -1.3708  10.7355   3.2567  -2.8250  -9.6266 -13.2044  -5.2769
-#>    4.0348 -10.8766   8.0989  11.4609 -13.0163  -0.6370   1.8148  -1.7120
-#>    2.6839   7.6035   2.5099 -13.0648   3.8901   4.8382  -3.4283   2.3372
-#>  -11.1093  -4.6944  -5.4822   9.0872   7.4998  -2.4886  -0.8404   1.1495
-#>   -4.3792   0.9561   7.4340   3.5363  -3.4969   7.9804 -12.3729   6.5105
-#>   21.3925   2.3420  -2.7837  -7.9849  -2.1592   5.0746   0.0229  15.6099
-#>    5.2014  -7.5834   6.1664  -0.7725  -0.5959  10.4268  -6.7089   9.3841
-#>    3.9032   0.0352  -7.8161  11.2603  -4.6685   1.1181  -5.0832   3.3944
-#>    7.3372  -1.8604  -3.0359  -0.2803  -2.4817  -3.0330   4.7001   0.7510
-#>   -8.9387  -7.2561  15.3523  -0.1818  -4.5693  -1.8624   8.3489   3.5271
-#>   -1.2746  25.1526   3.3148  -6.3416   7.5979  -3.0577  -1.0300  -1.1902
-#>   -1.9080  -0.5866   1.9487   4.7672  -0.3371   1.0384  -0.0173   2.7061
-#>   -0.4439  -6.8372   1.0193   3.8842   6.6423   3.5682  -9.6387  -1.2007
-#>   -0.0374  14.5370  -9.4165   8.8781  -1.4394 -11.5466  -5.7869  -0.3195
-#>    5.4837  11.2849   1.7591   1.1738  17.6410   2.1348 -10.2499   1.9873
-#>   16.1238  -0.1729   4.4630   0.1696 -10.7953   1.3255   1.3351   2.5280
-#>   -8.5880  -1.6725   7.6080  -1.2273   1.6391   2.3870   7.2627   4.0279
-#>  -12.7845  -4.4068  10.2311   7.9620  -6.4136  11.8917  12.7687  -2.2985
-#>   -0.3770 -10.9905   0.7378 -15.5345   9.1464   1.6215 -10.1390   9.8305
-#>   -4.4512  -1.1555  16.8918  -3.8699  -9.2140  -3.4994   0.3889  -7.3029
+#> Columns 17 to 24   2.6361  -9.4123   3.6802 -15.0500  -5.1439   3.1893  -0.6530   9.0843
+#>   10.5934   0.2358   4.9103   5.9398  10.3799 -13.4461  -2.9386  -9.4093
+#>    4.0320  -0.0288 -12.2230   0.0415  11.9752   5.4533  -0.5251  -1.9834
+#>    0.5462  -0.1345   8.5135   2.8463   2.2393   1.9689 -12.0151   6.1393
+#>   -5.0602  -1.3962  -7.4218  -9.5314   3.4516  -5.5321   3.0937   3.8872
+#>   10.5453   4.7741   4.6247   3.4166  -1.3455   6.0257   1.8492   3.3361
+#>   11.7867  -3.4657  -3.0901   3.9521   2.2942   0.4662  -1.7708  -6.2033
+#>    2.5445   2.0228  -3.4097   1.4003  -3.0445   4.8202  -9.8775   5.2100
+#>  -16.0743  -0.1736   1.8009  14.2162   4.8747  -2.0714   8.3904  -5.4989
+#>    3.5552   2.2678  10.8353   1.7180 -12.3527   7.8974   4.5832 -14.5396
+#>   -7.3328  -7.7900   1.5407   3.2935 -12.4024  -1.0715   0.2774   0.0674
+#>   -2.6300  -0.6552  -1.9199   8.9205 -16.7103   3.4903   0.5024  -2.0053
+#>   -3.6374   2.1865   1.9466  21.3596   1.1995  -6.3210   0.3500   5.8160
+#>    8.0220  20.6556   2.3092  -4.1542  -4.0168  -6.2778  -1.0384 -10.4952
+#>    3.5660 -17.0017   0.4178   8.5593   9.2322  10.7303   4.4723  -9.7647
+#>    0.0986   3.0008  -9.3054  -4.7007   1.8947  -2.9916   9.1122   5.9285
+#>  -11.1414 -13.6340   2.2483   3.0558   2.6748   7.2134  -2.3264  -3.9131
+#>    0.0850  -0.6033  -6.5273  -2.4373   6.7109  12.2250   0.4376 -16.3410
+#>    8.9518  -0.2789   9.6188   2.3399   0.2835   8.9719   8.7331   3.9723
+#>    4.7040   4.7003  -5.9337  13.5381  -1.3971  -1.1050   2.8087  -4.5327
+#>    6.3325   1.8240  10.4689  -9.5013   1.1701  -9.1390   1.7532  -6.3633
+#>    7.6225 -13.6399  -1.2877  -4.3078   4.5141  18.9283  -1.0607  10.2141
+#>    0.8643  13.8970 -10.9306   7.5635   4.0442   0.0402   8.5154  -6.7378
+#>    2.1145 -15.8297   2.8630  -1.9691   1.0558   6.8279   9.9117   4.6583
+#>    6.5111  11.1032  -0.3492   1.2771  12.1997  -8.8836   9.8978  -0.4514
+#>    5.2945   7.6371   1.7623   1.8614  -7.6340 -11.7832   5.3925  -4.3533
+#>    8.7173   4.7956   2.9614  -0.8700   4.9186  -6.8387   1.7339   5.5832
+#>    2.1718   2.5207  -1.3494   9.3554  -4.9000   5.1328  -1.9538   4.1706
+#>   -4.0399  -7.3008  -3.6094  -5.8133  -3.9073  -3.4838   3.4059  -0.3304
+#>   -5.9197   9.8172   7.2579   3.9443  -1.1704   4.5221  18.8373   2.9012
+#>   -2.1499   4.9134 -15.6842   5.0810  -1.5658 -10.4101  -2.7212   0.0296
+#>   -5.6622   5.2506   4.0153   8.5021 -13.9100  -5.2145   0.8474   3.0206
+#>   -6.5415 -15.8894   9.0498  -8.4162  -3.5104  11.1390  -5.0427   6.8724
 #> 
-#> Columns 25 to 32  -1.4314   0.2024  -4.2907  -3.4588  -5.4996  -3.6940  -5.0281   1.0755
-#>  -11.5288  -0.3438  -5.6180   5.4355  -1.9867   8.7279  -1.3348  -3.2532
-#>   -1.7205  -2.4896  -6.8395   2.5132   6.8259   7.1714  -8.4811   1.8884
-#>   12.2358  -2.2164   9.4752  -6.3795  10.7066  19.2873  25.2370  -6.6958
-#>  -12.0759 -11.5659  -1.5740   3.2449  -0.4928   2.1976   7.9682  -3.1802
-#>   -5.4648  -1.1428   3.6683   1.4968  11.3532  -2.0583   9.4373  -3.6948
-#>   -0.8467   0.3965  -1.5380  -3.3360  13.1708  -2.0463   2.0306   9.3244
-#>   -6.1372 -11.2179  -2.1520   0.2010  -9.1823  -1.6848   9.5493   2.4082
-#>   -0.1230  -4.3619  -2.8306   2.8916   8.8175   7.8968 -11.3143   7.2510
-#>    3.9155  -5.1768   5.4694   3.1729  -1.6373  -1.1168  -6.9770  -2.4431
-#>   -5.5661  -2.9941   5.8947  11.3340  -0.4344  10.3948   6.8087  14.5704
-#>   -7.4637   5.9919  -1.1063  -2.8024   0.4877   0.9881   0.4987   7.7180
-#>   -0.1479   8.8453  -7.3753 -11.8225   1.0302  -5.8837  -0.5575  -0.0465
-#>   -1.1157  10.4909   9.3996   4.8179   8.5646  -2.8770  -1.1479  -7.2244
-#>   -7.7664   2.7722   0.2519  -5.8544  -0.2825   8.1229   2.5394  -1.9655
-#>    3.7868   6.7755   1.1934  -2.7120  -0.8109  -2.1745   9.7496  -7.8422
-#>   -6.0130  -4.9658   3.0462  -4.8569   3.0011   3.1681  -7.8576   4.7335
-#>   -0.8590  -2.2963   3.9233 -11.4663   2.8552   5.0722  13.3096  -0.2567
-#>    2.4902   6.0696  -4.5408   6.5379  -5.8495  -6.7323  -6.0372   3.8359
-#>   -1.7285   4.3793   8.7735  -2.9332  -6.4038  -0.0454  -4.6984  -8.4368
-#>    4.4295  -4.1835   0.2776   7.2391   8.5665   8.1895  -2.9724   1.7052
-#>    8.5844   0.4590   5.8992  -0.5390  -5.5647  -4.9448  -0.5048  -2.1230
-#>    8.4651   0.7857   8.0964   4.4390  -7.4403  -7.3722  -2.1246  -7.9744
-#>    9.2435   3.5409  -2.5928   6.3410  -3.9584  -1.3803 -16.1972   3.2548
-#>    6.9294  -0.4257   3.1535   1.8400   5.1561   8.7191  -6.5142  -0.4285
-#>    2.4707   2.4562  -0.8055  -6.3825  -8.4170   5.5621   5.7736 -12.0265
-#>   17.0837  -1.2967  -6.9036   3.5851   8.0978   1.0316   1.1240   0.9540
-#>    0.2170  -2.0506  -1.8686  -8.5422  -8.8884  -4.6117  -5.6362  -1.9346
-#>    4.5573  -1.4111   4.1964   4.8692  -5.5536   4.0321  -9.5204  -3.1594
-#>   -6.7277  -0.8789   0.3533 -11.3807  -5.0255  -7.8873 -17.7735   4.3360
-#>    4.5420   0.0373  -0.1702  -0.2021  -1.0652   9.4204  -2.9035  13.7808
-#>   -7.4720   3.0406  -4.3222  -3.9177   1.7697   2.4556  -0.5615  -7.5813
-#>   -1.4074  -0.0846   5.4931   7.8539   1.9790   6.8378  -1.2712   9.0633
+#> Columns 25 to 32   8.0032 -11.7964   2.5258  10.9881   7.6960  11.2029  -0.1921  -1.3995
+#>   -0.3967   2.8627   1.6747  10.5516  -1.5473  -5.7551   1.5399  12.9520
+#>    1.4094  -4.9826   7.2381  -1.0842  11.4178   4.7459   1.3283  -0.8630
+#>    5.8319   8.9319  -0.1512  10.0652  -8.8583   5.9899  -5.2435   0.8863
+#>   -3.2973   5.0795  15.1959   7.3075   6.6475  -3.4660  12.2190  -6.1263
+#>   -0.4660  -1.3674   6.3498  -2.3529 -10.7566  -6.0212   0.3009   3.8556
+#>    9.5261 -10.7244 -13.8307  -8.7597 -11.5763   6.3765  -1.6130  -0.5126
+#>   -4.5751   9.9278  -4.4040  -2.5034   7.4549   4.9258  -3.7550   0.6807
+#>  -26.6666   2.0031  12.2180  22.6127   0.8523 -15.8085  -0.4355  -0.9975
+#>    4.4551  -0.2536   1.0217   2.0787  -7.7166   3.1223  -4.8857   2.4261
+#>    3.8361  -2.2282   6.6116   8.1729  11.9204  -2.7291   3.3454   8.3342
+#>   -7.6496  -2.0285  -1.4510   1.2877   3.2973  10.5090   3.1704  -5.6285
+#>    3.2244 -13.2609   4.0548  -1.2995   2.4223   1.9072   1.3706  -5.6108
+#>    1.7014   2.1072  -1.6385   5.0523  -0.4813  -5.1208   1.3388  -4.3354
+#>   -8.2576   1.6686   3.1727   1.9465  -0.7740   8.9057 -10.5024 -15.5402
+#>   -8.6054   0.4861  -5.3284  -2.6231  11.5253  -8.7368   4.9920  -3.9115
+#>    1.8474   0.0106  -0.9431   9.5235  -4.9173  13.6484  -6.0853   3.0887
+#>   -0.9601 -18.8055  -3.8210  -2.0399  -2.6730  -1.1174   1.7746  -9.7763
+#>   -1.3466   1.0300  -6.4389  -5.8898  -1.6671  -9.7794  -3.2503   9.3921
+#>    4.3637   0.0824   1.2090  12.4997  -3.7938  -8.7367   5.1119   2.8845
+#>   -1.3058   2.6759  -2.0402   5.2329   8.1843   5.1306  -0.8960   8.4270
+#>   -5.1609  -7.3512  -7.1294   6.2029  -8.6638   8.7176  -4.9596   1.8840
+#>   11.3615   2.8806  -0.5232 -11.7841   3.6225  -0.5805   8.0442 -12.3476
+#>   -4.9003  -2.6468   0.9900  -1.4977  -1.1662   6.3457   2.1121  -5.4354
+#>   13.9385  14.3035  -9.1850  -7.1474  -7.4894 -14.0816  11.1249   1.0052
+#>   17.5312   8.5143   4.3163   5.2052   0.3356  -5.5745   1.4287   7.6658
+#>    6.6776   3.0296   1.3280   6.7349  -7.0024  -3.1787  -2.8548  -4.9663
+#>    8.2172   4.1557   9.3674  -5.3014  -7.6051  -4.9040  -4.4259  -8.3788
+#>   -5.6016   2.0360  -5.8811   5.0308  -2.7195   5.1137   2.5360   8.1114
+#>   -8.9261  -4.9704   6.6290   0.7140   4.8766  -6.7308  -5.3420 -10.8223
+#>   -4.4571   3.7214   7.8362   2.5191   5.5639  -2.6301  10.3899  -3.3793
+#>  -10.0100  17.6458  10.0663  -5.3321   5.1774  -0.9540  -2.3858  -0.9945
+#>   -8.7549  13.1753  -5.8074  -1.2596  -0.3728  -0.6953 -12.6268  -2.0990
 #> 
-#> Columns 33 to 40   6.9240   2.2739   7.0122  -1.2701  -2.0115 -11.0764  -0.4458   1.0459
-#>    7.3224 -17.3510   7.5953  -1.9614   0.2458   4.0317  -6.9640   1.3121
-#>   -1.6173  -3.9226  -3.5357   0.0570   7.2102   5.1422   3.8731   1.4483
-#>    9.0869  -4.2868  -9.8618  -3.8325  -0.4532  17.1419  -8.5024 -10.2601
-#>   -0.2259  -4.7216  -4.4411   1.1250  10.8359   7.9578   2.3752  -4.4357
-#>    5.8127   5.1264  -2.9787  -1.3527  -1.2789  -4.9458  -7.6917  -3.4897
-#>   -1.2848  -5.1894  -6.8718  -5.8907   1.3262   3.1774  -5.5343  -0.2647
-#>   -2.7601  -6.5327   2.7842  -1.9254   3.0175   7.3933  -0.0057   9.5916
-#>    5.9871  -2.5564  -4.1029  -9.8237  -4.6692   8.0873  -0.3651   7.6660
-#>    0.9929  -7.0597   8.7479   1.4223   0.3354   4.1855  -6.3449  -6.7046
-#>   -2.0061 -11.2205  12.1521  -7.9127  -2.7581  -5.9309   0.4322   3.8732
-#>   -4.0634   4.8246  -2.8899  -5.7091   1.0374  -2.4193   3.5607  -0.9288
-#>    5.1217   5.9972  -6.4678  -1.1522  -5.8698  -8.0975   0.9765  -2.1171
-#>   -7.7123  -9.3345   4.5372  -3.0058   6.6523  -2.0967 -14.2951  10.2028
-#>   -0.6436  -8.3312   0.9566  -1.0786  -5.6910   2.0966  -6.9138  12.0494
-#>   -4.8390  -4.3222   4.5728   5.7941  -4.1121  -2.2718  -9.8567  -0.8623
-#>   -3.5303   0.5670  -4.8218   4.8225   1.5968   4.4576  -4.7246  -3.1750
-#>    2.3410  -1.1211  -2.5534  -0.4316  -8.1122  13.7344  -6.8140  -3.7069
-#>  -10.9319  -4.5192  -4.2456   3.3167 -10.8028  -6.1140  -8.8725  11.7455
-#>   -4.2912   1.0876   5.6363  -0.4622  -8.5785   2.3193 -15.1438   9.9583
-#>    2.7829   0.9444  -9.0406  -0.5925  -2.3984   2.4452   3.7937   4.2564
-#>   -1.4780   4.3162   2.4416  -2.1974   1.2660 -11.5144   2.8115   3.2969
-#>   -1.6887   7.3647   3.4006   8.6264  -2.1062 -21.2366   5.4839  -7.3611
-#>   -3.4757  -4.6786   1.9789  -4.2565 -10.2247  -7.5134  -1.3938  -6.8941
-#>    6.3073  -2.8765  -2.7399   1.3770  -4.9203  -3.8809   6.7716   0.3234
-#>    5.7622   8.9038   4.6575  16.3209  -2.9244  -1.3163  -0.1654   8.7446
-#>   -4.1624  -6.9978 -15.7060  -0.7463   1.0695   0.1511  -2.9578 -10.5753
-#>   -7.4290  -1.1818   6.1719  11.9056  -2.4689  -1.7901  -9.7585  -3.2402
-#>   -2.9330 -10.4269  -1.6227  -3.9259  -4.4273  -6.0231   1.4791   2.9367
-#>   10.5194   2.4614  13.6208   1.2071  -6.8071 -14.5845  -7.7553   7.8994
-#>   -2.7698   3.2642 -13.1002   1.3493  -2.2242   0.5566   4.5718  -4.1912
-#>    6.4498  -4.5213   3.0324  -1.5308   1.1251  -1.1830  10.9511 -10.9109
-#>    2.9400  -4.1101   7.9595  -4.0359  -6.0525  -3.9387  -2.4284 -12.6910
+#> Columns 33 to 40 -22.0667   0.9400   5.4472   2.0393   4.1143   9.0760  -9.0910  -4.6369
+#>    1.0332  -7.7490   4.0268  -7.3917  -8.2659  -0.1511   7.4030  -5.3979
+#>    0.7237  11.6383   4.1008   6.7454  -6.3581  -2.7842  -1.4503   0.7231
+#>   -4.5894  -1.3522  -2.9072  -8.7338   2.5281   8.6765   5.7041  -5.7059
+#>   -9.9749  -8.8492  -3.6960   2.5723   2.3695  -3.9924  -1.9653  -8.3369
+#>    4.8991   2.2204 -11.9367  10.5248   8.0569  -4.3868   0.7721  17.0658
+#>    3.6578  12.2244   1.4273  -5.8624   2.6819  -2.7624  -2.8953   4.9717
+#>    4.3171   1.3951   2.8092  -0.3986   7.1502  -6.6138   7.9262  -5.1982
+#>   -2.8936 -12.6981 -12.8259  -5.2457 -12.0545  -5.8977  -9.6706 -15.5798
+#>    1.3049  -0.7115   4.6173   3.3217  -9.5191   2.3573   2.3755   8.1484
+#>   -8.5958  -0.0780  18.8421  -1.5876   5.9577   9.2416  -0.1654   0.1862
+#>   -5.5045   1.0510   0.5905   0.7304   5.0837  -3.9223  -3.8227  -7.8152
+#>    1.5532   1.8694  -3.5596 -12.2458  -9.7737  -1.4396   5.1892   5.6769
+#>   -6.9358   0.9797   4.5459 -10.2927  -4.5621  -3.6155   7.2474  14.4220
+#>    0.9170  -3.7835  -6.0728  -3.9294 -10.3809 -18.2403  -6.9323 -10.2960
+#>   13.1026   9.0039   3.3894  -0.2237  10.2792  -2.3542   5.4395  -4.1443
+#>   -4.4686  -2.3009  13.3682  -4.3334  -5.2948   4.2646  -3.8576  -9.9639
+#>    2.8922   6.9258   7.0938   3.3861  -2.4659 -14.6034 -11.7722   1.1025
+#>    0.7982  -4.3303   6.3976   3.0597  -2.2543  -7.1565   4.3338   0.0678
+#>    3.1602   0.5309  -3.1283  -8.2464  -7.2549  -3.6729  -3.1837   4.2661
+#>   -1.5452  -6.6099  10.5968   4.1484  -1.6138  -1.4313   6.6799   1.1826
+#>   -0.4683   8.1659  -7.7243   4.3027  -6.6899  -0.1859  -2.5647  -6.2542
+#>    1.1286   3.7235  -1.4043  -6.1850   2.9576  -2.6909  -7.6662   3.9362
+#>  -12.4645   5.2797  -4.6931   0.1677   2.8582 -13.9392 -16.1099 -20.1223
+#>   10.3361   2.2572   2.0199  -6.7932  -0.1536   4.0980   4.4018   3.3731
+#>    1.6968  -7.8575   0.3945   1.5132  -3.5100  11.9803   2.1581   9.6080
+#>   -1.4268  -6.7280  -3.6018   4.6838  -1.2171   1.0601   0.0963  12.2065
+#>    0.7501   5.7370  -5.0484   6.9951  -1.9868   5.4559   0.4505   8.7851
+#>   -3.3038  -8.6952  -3.9468  -3.3912  -2.7301  -6.4622  -4.9541 -12.6603
+#>  -11.6937 -10.4497  -1.3698  12.8531 -10.0808 -11.6848 -11.9295   2.8064
+#>   -2.8564  -8.1062  -0.3436 -10.5153   8.7385  -1.6354   6.2880  -4.6702
+#>    7.6629  -7.2868  -9.2056   0.8147  -9.2184  -2.4807   7.0501   7.8249
+#>   -5.2482  -7.6515  -0.5730   4.1781  -4.3705   0.6740 -11.5030  -9.2450
 #> 
-#> Columns 41 to 48  -6.0871  -3.5399   0.5556   9.9953   0.1490   3.1870  -6.8017 -13.3374
-#>    1.2691   2.3812  -6.8871   1.8500   5.0405  -0.9936   5.5278   5.6075
-#>   -5.6155  -1.3369  -2.0059   9.6355 -12.1226   1.6743  -0.1978  11.2916
-#>    1.6661   4.4906   7.2712 -12.7531  13.2853   0.7525   3.8068  13.7190
-#>    4.6584  -3.7663  -8.8265   2.4192  -7.8541 -10.5215  -4.8588   1.6292
-#>   -6.0489  -4.9913  13.1335   7.5758  -5.6704  -2.7146   7.0350   3.8747
-#>   -1.6261   4.0118  14.6895   3.5888  -2.2662   3.8648  -1.8610  -6.0246
-#>   14.2621  -8.8456 -21.6802   7.7011  -0.4258  -8.2232   0.4463   1.6795
-#>   -5.0160   6.0531   1.9887   7.9535   3.9957  -0.8742  -5.1014   5.0997
-#>    4.3507   6.6107  -2.5763  -5.0663   5.9065   7.2129  -0.1093   2.1234
-#>    8.6998 -11.3704  -9.0154  16.7581  -7.8162  -9.6847  10.0728   1.3776
-#>   -5.6878  -4.9777   0.8464   5.6317  -4.4443   1.0655  -0.7358  -0.4476
-#>   -3.2676   1.0310   2.7182  -4.5991  -6.6428  11.7684   0.0934   2.0296
-#>    3.3787   5.7968   2.6977  -6.3838   1.3135 -14.7291  -5.4446  -2.5756
-#>   -6.7851  -4.3474   4.3288  -3.5593 -11.8488  12.7276 -11.8330  -1.3451
-#>  -10.9471   2.5203  13.9947  -6.5298   6.3223  -5.1206  -5.3993  -0.0330
-#>    7.5481  -7.9991  -7.1928   0.4923 -10.9895   6.2631   4.2667  -2.1313
-#>  -10.8836  -6.3230   8.0348 -10.5145  -5.3140   1.6496   4.7261   0.3512
-#>   11.6409   8.2070  -3.0861  11.9172  -7.4494   0.6038  -5.9887   4.6739
-#>    2.8818  -3.6726  -2.8435  -3.0786  -3.5361  10.6113  -3.9672 -12.3372
-#>   -5.1373   2.7864   0.3852   2.0220  -4.7495  -2.6269   8.5026   2.4522
-#>   10.0219  -5.2154  -1.9534   1.5399  -0.1130 -11.5162   5.6648   1.9193
-#>   -1.1927  -3.9725   2.2673   7.0946   2.2641   5.1577   6.1204  -6.5758
-#>   -0.5557  19.6535   5.5160  -0.9664  -5.6467  11.3334  -3.9025   2.1416
-#>   -2.0937  -2.5151   8.3120   1.5805  -8.8088   1.0911   6.1394  -5.1742
-#>   -3.1773  -3.2192   3.7206  -6.5699  -1.1953   1.7972  -1.5603  -6.4786
-#>    8.5507  11.2789  15.4065 -11.7882   1.7775   0.7023  10.9679   7.2172
-#>    6.6951   0.6656 -15.2272  -4.2974  -6.1461  -9.1327  -0.0604   8.8080
-#>    3.9147   7.3136  -0.1624  -3.7806 -10.1947  -5.6692  -1.2823 -10.3802
-#>    4.9400  -8.4757 -12.2068   7.9412   8.0861  -6.6263   1.0023  -2.1691
-#>   -3.4307  -2.0088   1.9208   5.2437   4.9451  -4.2088  -8.7323  -1.8118
-#>   -4.3933   5.3883  -6.2951  -0.2386   2.5083  12.3352   5.4033  -1.1363
-#>   -9.1130   6.0769   5.5055   2.3297   3.4682   3.0630   2.3178  -1.0043
+#> Columns 41 to 48   7.9662 -13.7454  -1.0169  -5.5775   7.0638  -7.7525  -1.5533  -5.9468
+#>    5.9048   7.3728  -1.6593   6.9097  -1.9123  -1.8745   1.8214   0.0036
+#>   -1.1187  -2.0120   3.7279  -1.2286  -8.0900   4.0261  -1.3597   0.9028
+#>    9.1625   4.8416  -9.3799  -2.6597   0.7257 -18.1457   0.3595  -1.5322
+#>   -9.4205  -7.3622   8.9233  -4.1012  -4.0627   2.7612   1.0628 -11.7552
+#>   -6.8685  -1.9029   3.7424   5.5274  -8.8231  -5.5170   7.0308   1.7698
+#>    2.8859  -0.1491  -7.1103  -1.2714   8.6377   0.9600   4.2306  -3.2029
+#>    9.5702  -1.2062  -1.1239  -1.8485  -5.6695 -10.9140   0.4760  -1.1800
+#>    7.6003  -2.3418   2.0561   5.6033   4.9139  14.3200  -6.2849   0.4827
+#>    7.6720  10.6200   7.7725   1.0289  -0.8690   1.0928  -1.2726  -1.3336
+#>   -1.5738  -4.3034   4.7761 -11.3644  -6.3435 -13.6716   2.9580   9.5662
+#>    2.8213   3.8977   5.8461  -3.2699  -5.1327   5.5957  -4.1228  -3.2800
+#>   14.1444  14.4494 -12.9769   9.2490 -13.0667  13.7308 -13.6684   8.0853
+#>   10.9089  11.6345  -9.7478   3.4604  -3.2959  -8.4554 -11.8054  -7.1879
+#>   -4.5526  -7.7690   5.4055  13.3036   8.2348  -0.7017   3.3871  -0.0078
+#>    1.6512  -7.2042  -1.8150   2.1704  -3.7272   7.0310  -0.7151   3.0810
+#>   -4.3768   0.4102  -3.1572  -6.8379   9.4968  -1.7917  -1.0887  -2.9825
+#>  -14.3613   3.8702   3.5913   9.1168  -5.4368  11.6484  11.4087   1.3940
+#>    2.2346   3.6526   4.5928   0.9426  -3.9204   0.1750   8.6222   2.4408
+#>   -3.4692   7.1035   1.2109   4.9761   4.8151   3.0252  -3.9490   1.9902
+#>   12.0162  12.8137  -1.1962   1.6503   4.1553  -1.0130  -1.7365  -5.5606
+#>   -1.9209 -13.3138   4.3697  -7.7498   7.9286  -1.1156   6.9897  -4.5968
+#>   -8.5765   5.2894  -9.7852   6.2904 -12.1120  -0.0676  -6.8073   6.3118
+#>    1.1691 -11.0791   1.6748  -1.1146   8.3899   5.4649   5.1353  -5.1855
+#>   -7.3494  12.8866  -1.8187  -7.4662   1.9500  -4.6029   2.0345   9.8622
+#>    2.8446  11.8781   0.7513   5.4125 -11.6326  -0.7603  -1.0453  -1.4411
+#>    1.2697   2.6173  -1.3243   5.6600  -9.3858   1.9423   5.1690  -6.2484
+#>    5.2726  -2.7323   6.7922  -4.5550  -9.7473  -5.0790   6.9899  -0.5686
+#>   -5.5780  -2.9224   2.2087   2.7429   3.7300   1.1425   3.6194  -4.7255
+#>    1.6573   1.4485   1.9136   7.9484  -7.1654   8.4802  -4.1225  -0.2600
+#>    0.8855   2.5714  -7.3493  10.8064  -5.3623   8.0614  -2.2887   0.7669
+#>   11.3010   3.7844   7.2402  -8.7479  -3.5159  -4.1591  -0.7923  -0.8065
+#>   -4.4396  -7.5309   5.0241  -8.5806   4.3893   3.9590  11.7192   3.8639
 #> 
 #> (13,.,.) = 
-#>  Columns 1 to 8  12.7182   3.8064  -4.6155  -2.5688 -14.8029 -15.1014  14.4892  15.4805
-#>   -2.9289  -1.0909   3.3326   1.2205  -0.1754  -3.5472  -0.4407   3.1290
-#>   -3.2056 -17.0926   3.7162   9.3470   8.8201   1.5397  -7.4968  -8.2629
-#>   -2.9956  13.7263  -3.6488  14.7005  10.6438  -1.2256 -14.0772  -0.5874
-#>   -5.1724  -0.4424  -7.6078  -6.2448  -3.3594   4.1992  -4.1085  -0.3341
-#>  -12.0820  -4.6100  -1.2477  -3.0685   1.4967  -2.3838  10.4906   7.2323
-#>   -3.5632  -6.4135   0.5737  -3.8202  -7.1241  -1.6567   8.9361  -5.5403
-#>    1.4872  -3.9668   4.4526  -1.0609  -7.8944  -0.7267   6.2148  17.0953
-#>   -4.3076  -8.5738 -12.5724   2.4266   4.4529   4.6341 -14.1243   2.0411
-#>   -8.8410   2.2157  -2.9455  -2.8395  -4.8541   8.5633  10.4077 -10.2788
-#>   -4.8465 -24.2523   9.7135   9.3001  -7.8211  13.3860  -2.3863  -3.3853
-#>   -4.7057   5.7289   3.5920 -10.1702   0.9869  11.4714  13.8918  -2.4690
-#>   15.4570  -4.6192   4.4933   2.7143   0.9961   5.4573   6.6889  -3.3549
-#>   -1.1126   4.7426  -5.7291  -8.4460   3.5938 -12.8634 -16.3419   9.6687
-#>   -5.3567  -8.1428  -4.4861  -2.8718 -10.2998  14.9662   7.5159 -16.8273
-#>    0.2225  -6.3716   5.0470   2.5779  -7.1910   0.0347   2.0312   4.2226
-#>   15.0371  -5.1816   0.3470  -2.7460  -5.2628  -6.9144  -5.2145  11.9414
-#>    0.8363   0.7920   6.9030   3.1910   8.0712   7.6977   2.9686  -3.0270
-#>   -1.0038  -8.6622   0.0588   3.2239 -10.7892   3.4944   6.1863   7.9359
-#>   11.5861  -1.0449  -1.9407  -6.4830  -6.3589  -6.8171  11.7829   6.8568
-#>    2.6081   5.8065   2.0014  -1.1764   1.5566  -1.7988   6.6751  -1.2022
-#>   -3.4828  -8.0346   1.7535   5.6982   3.8348  -0.0052   2.4027   2.7767
-#>    1.1414  -1.1804  -3.3855  -0.6290  -5.3685   4.0934   9.0223  -5.5763
-#>   -9.7996 -10.3637   3.0964   2.5960  -3.6950  10.3639  -4.1379  -4.6375
-#>    8.5050   0.8158  -6.0417  -3.8965  -2.2206   3.6987  -0.5295  -1.9969
-#>   15.0140   5.7383   0.3618  -8.7803  -8.2044 -21.6215  11.0205   8.9326
-#>    3.8972  -0.7520  -4.1582   2.4096   0.7532   4.9532  -2.0873   6.8268
-#>   12.8697 -12.6970 -10.1723   3.7770  -0.2269  -6.4761  -2.3072   9.8770
-#>    0.8609  -3.8583  -3.4605  -3.7649 -12.9216   3.7425  -1.5771  -5.6844
-#>   22.6210 -12.0578 -11.3394   0.6404  -6.8014  -2.1969  11.6617   9.4068
-#>    1.5590   1.0693  -3.1193  -8.9169   0.2025  -1.1535  12.7848   5.9852
-#>   10.6550   3.2722   7.2799   1.1171   4.2774  -4.7724  -2.8974   1.9139
-#>  -10.4001   1.4934  -4.0810   1.3808   4.9810  -2.6628   4.0061   0.4019
+#>  Columns 1 to 8  -3.6634  11.7006  -2.3465   2.5491   1.2513  15.0872   3.9071  -3.7039
+#>   -7.5430  -5.0217  -1.1259   2.9532  -4.3064 -12.5227   7.4883  -0.7826
+#>   -4.8263   5.0260  -2.1014   4.9165  -7.7390   2.8510  -6.6097  -3.1135
+#>   11.0680   8.2539  -1.4301   4.0648   5.2830  -9.1697   1.5641  -6.9430
+#>   -2.8181   6.1959   3.1877   4.8425  -1.8609   5.7562  -8.1935  10.2056
+#>    5.3712   2.6640  -0.5245  -5.0170   6.1865   4.4139   2.2748   6.9259
+#>   -9.8872  -9.9068  16.6081  -8.7895  -3.0137  -8.5560   0.4983  -9.4284
+#>   14.6079   9.1200  -4.4497   3.7596  -4.4370   9.6875  -1.5132  -3.0632
+#>   10.4435 -11.5281  -2.9306   3.4770 -17.9612 -18.0787  -4.1950   3.8424
+#>   -6.5856  -4.6185  -2.1146  -8.8492   3.8233  -7.1640  12.0346  -3.3192
+#>    8.8703  -3.1801  -5.5119  -2.3467  13.0228  13.1376  -8.5730 -17.1273
+#>    5.6516   9.5670   6.8403  -0.2228  -3.6787   1.6586 -11.5520   3.9891
+#>   -6.4235   9.2340  -4.6301  -7.2694 -20.9493  -5.2339  -1.8552  -3.4232
+#>  -17.4046  -3.4950  -8.2909 -12.2312 -11.4055  -4.0846   4.9478   4.1859
+#>   -7.7053  14.7090   8.4482  -1.4595  -4.8895  -1.6412 -12.1121   6.3141
+#>    8.2675  -3.4990  -1.5989   7.2090  -3.2253  10.5338   1.3287   1.6864
+#>   -5.9271   3.3371   3.7844  -0.0344   8.0547  -7.4095   5.8454  -3.3631
+#>    6.0657   0.0643   7.9280  -0.9753  10.0336 -14.9738  -8.1182 -11.1720
+#>    2.3997   0.3197  -8.1412   5.0547  -1.1633  -0.0044   0.7921   5.1530
+#>  -13.5078  -5.3857  10.9510  -2.3409  -3.7326 -11.3581   5.8096   6.6168
+#>  -14.4874  -3.8764  -6.3581  -6.4819  -5.2883  -0.0434   5.8970   0.0169
+#>    0.4836  14.0193  -3.0191   4.5484   2.7025   6.5061 -14.6585   5.4071
+#>    2.8530  -2.7527  12.0503  -8.2553  -3.7081  -6.6519  -3.0411  -8.3584
+#>    5.8670   4.5923  10.8247  10.6554  -8.5501  -0.5768  -8.7984   3.2151
+#>  -10.4729  -4.2524   6.8356  -1.0749  -2.2912  -8.0098  -0.8256  -3.7473
+#>   -6.0066 -11.9215   0.1903  -1.3730   7.3203   1.8022  15.8698  -6.6902
+#>   -2.2189  10.0914   2.1397   1.4380  -3.0355   5.3256  11.9334   0.8750
+#>    6.0831  -2.2203  -7.9691   0.0413   4.8903   0.9257  -4.8753   2.7907
+#>   -4.2281  -8.3557   4.1414   8.6239   3.4848  -2.6406  -2.7133  -0.7676
+#>    3.7334  -0.0489  -5.2341  -4.3111  -9.4666  -0.5592  -5.1409   2.2493
+#>   -0.7118  -3.7026  -4.3064  -3.6553 -10.7432   1.3990   3.1702   4.6608
+#>   -2.4640  -0.9377 -13.0793  -5.0459 -10.4149  -2.9753 -13.3317  13.5061
+#>   14.6908   2.6278  -2.2211  14.1800   2.8116   3.7336 -15.0117 -11.2100
 #> 
-#> Columns 9 to 16  -3.8668  -2.7478   7.1392  -0.0054 -10.1722   0.1198   2.4732 -10.4295
-#>   -2.5754  -7.8994  -0.1849  -3.9888   4.4277   2.1233  -1.5317  -3.4512
-#>    0.1197  -2.1993  -2.9206   3.3155   1.5636  -2.1639  -5.8681   3.5642
-#>   11.7872  16.3354   1.8357   2.9643  -8.7039 -16.2158   8.4494  18.9837
-#>    0.9327  -6.3295  -8.8967  -0.9992  -1.9962   1.4210  -4.5254   5.3463
-#>    2.7183   2.6481   2.7299  -4.0675  -0.1711   2.8628   0.1096 -13.9721
-#>   -3.0392  11.4258  -5.2078  -6.1831  -8.3396   1.0591   5.2099 -17.4486
-#>    3.5829  -9.0933  -1.7016  -3.1307  -0.3316  -0.5869   1.8062   2.3150
-#>   11.4195   9.0115   0.8662  -4.4907   1.4685   2.5792 -11.6804   1.5862
-#>    4.9865   2.8401 -10.9968   4.7530  12.6697   1.5929  -3.2429  -4.4498
-#>    2.8203  -7.8873   2.5764   4.9312  -2.4513   8.1254  -9.4267  14.4767
-#>  -12.1972   4.5896  -3.4220   0.1365  -1.4710   7.8849  10.8458 -12.1589
-#>   -1.4284   2.1966   3.1238   6.6784 -10.8862  -3.4908  -1.3427  -4.6650
-#>   -7.8388  -6.3305  -7.3320 -13.8610  -4.9487  -6.9116   8.5775  11.5956
-#>    0.0853  -3.0662  -2.6750  -8.6276  -1.1991   7.8136  -2.3070  -9.7200
-#>    5.5640   1.9188   6.5125  10.3249   2.2903 -10.1161  -9.1294  11.1086
-#>   -0.1784  -7.3305  -7.4177  -0.3665  -7.3336   5.7114   0.8584  -0.4436
-#>   -1.0338   1.6243   3.5237   6.4708   2.4569 -15.7223  -2.4562   3.3643
-#>    7.7476  -0.8750  -7.3911  -0.5791   1.9732  -9.2091  -3.6274   1.1787
-#>    6.1295   0.0278   6.0033  -4.9993   9.2848  -0.0427   4.6808   5.4250
-#>   -3.9739   5.5625 -14.3787 -10.4563  -2.6791   5.7062   2.1011 -13.5959
-#>    6.9645  -6.5812   9.3948   5.4502   3.3648  13.1890   5.1092  10.3599
-#>   14.9967   2.4909  -1.4835   2.4225   1.3400   4.2230   1.5507  -4.6680
-#>    7.1955  -4.8003  -1.2917  -5.7070   0.1072  -8.7850  -0.7931   7.4616
-#>    1.6131  -5.1979  -9.2722  -5.3319  -5.0503  12.5062   1.3686  -5.8991
-#>   -6.1673   2.4002   6.9490 -12.5821   2.0743  -1.9734   4.7670 -11.5226
-#>    4.9347   5.6705  -9.3898  -4.2742 -13.3988   4.0754  -5.8353  -2.3527
-#>   -7.3341  -6.5663 -11.6332   2.3066  -2.2382  -7.5558   8.6617  -5.3038
-#>    5.8818  -6.0350  -2.6655  -7.3441  -0.0255  10.9176 -10.1541   8.6460
-#>   -5.3208  -9.5275  -1.1192   8.5280  -2.6452   5.6199  -7.0017 -17.5711
-#>    4.3405  14.7082  -0.3999  -9.3258  -3.6946  -8.6221   6.9970  -2.3698
-#>    1.7119   1.5103  10.6896   4.0890   0.2511 -10.5023   3.5156  -0.9019
-#>   -4.1529   1.8704   6.3679  -3.1304   8.9665   2.7667   1.1333  -0.4228
+#> Columns 9 to 16   0.7055   5.3372   2.3857   9.8498  13.9058  -1.0284   4.2575  -0.3985
+#>    2.4973   1.8814   1.1596   4.4882  -5.1098   3.5906   0.1416   1.4112
+#>   -7.1435 -11.3774  -1.7443 -11.5485  -3.1390  -0.4563  -7.0423  -9.4656
+#>   -5.4811  -1.8409 -21.5860   5.2348 -10.1543  -9.5078   0.7112   7.6442
+#>    2.3183  -5.3474  10.5340   2.5499  -4.9198   5.6376  -7.0210   9.2535
+#>    4.9089  -8.0855  13.9734  -3.2112   0.5272   8.6306  19.1600  -3.2919
+#>    1.3275  -2.5095  -4.0798  -3.9851  -6.7735   1.0907  -2.9488   5.1906
+#>  -12.1918 -12.6011 -15.2500  -5.5611   2.0174  -1.6917  -5.2859  -0.7239
+#>    1.6655  14.7827   3.5094  11.9244 -17.1345  -3.9618  -9.4608  -4.1386
+#>    5.2112  -3.1974   4.3246  -4.7389   2.4243  -4.4393  -0.2289   5.5442
+#>    5.9081  -3.8746 -14.7140   9.1677   7.9390   0.6412  -8.1849   8.4742
+#>    1.9444  -1.4311   1.6348  -8.1830   4.9506   3.5888  -3.6202   1.5792
+#>    1.8206  -0.7748   2.7631  -8.0890   7.5731   4.2226   7.0237  -4.2553
+#>    7.9008  -9.0680  -0.1227   3.2858   9.7920  -1.2807   4.0855  13.1322
+#>   14.9999  12.7574  11.5203  -3.0972  -1.5829   3.0574  -9.0936   4.8858
+#>   -9.0999  -6.6623  -3.5187   6.8552  -1.4794   0.8885  -5.8652  -7.6416
+#>    0.1801  16.2267   3.5628   3.7081   5.4119  -1.1915   5.8899  -6.3191
+#>   20.7212  -2.8291   7.0077   1.8045  -9.1430  -3.4606  -5.8282  10.7758
+#>    3.5529  -4.2529   4.7236 -11.7163   9.4926   3.0950  -1.2735   0.0445
+#>    5.7824   0.7473   8.2635  -5.5184   5.2261   1.3900   8.6179  10.1008
+#>    2.1830   0.5389  -1.0652  -1.5605   4.4187  -0.8938  -5.0422  -8.2918
+#>  -12.9665   9.9457  -1.7538  -2.6524  -0.5728 -10.8804   0.0585 -14.9092
+#>    7.6141  -6.1671  -2.4238   1.0189  -4.4932  15.9982   3.9502   5.1367
+#>    8.5227  -3.6223   5.9550  -2.7361  -6.9041   2.1936  -4.6941   6.8064
+#>    6.7095  -7.2098  -9.7687   4.7201 -10.1551  10.5384   5.3275  15.8047
+#>   -0.2212 -10.3496   6.1988   2.9295   4.3238  -0.4055   2.7564  11.3936
+#>    7.3169  -6.9206   8.5480   0.3347  -5.1331  11.5520   8.2046   4.4873
+#>    4.2834 -11.2113  -1.7950  -2.9543 -12.1670   6.6623  -4.1590  -2.7030
+#>   -2.7735   8.2522   0.3774   6.0110  -0.2993  -6.9696  -1.6683   9.7112
+#>    7.7624   7.7287   8.2828   9.5794   4.2534  11.3801   6.2064  -2.3607
+#>   -1.3668  -5.1105  -1.2431  -1.4789   1.0997   2.5763   4.8078  -8.8272
+#>   -0.6322 -10.1607  -3.6125  -9.1013  -8.0364   0.0231 -12.7076  -2.3615
+#>   -0.8883   9.1348  -6.1148  -3.4250  -7.3065 -12.2307 -13.2130  -0.6904
 #> 
-#> Columns 17 to 24   3.2644  -7.6745  10.2642   6.0378  -0.4222   6.0229  -1.2183  -9.0270
-#>    0.5475   3.1340  -1.7418  11.0928   0.0034   4.6541  -6.4449   6.8976
-#>   -5.2873   8.3848   1.2836  -3.6144  -0.5825   7.4089   5.6819  -6.9342
-#>   -6.3869   6.9111   0.6449  -4.9521  15.6333   1.1160  -3.0849   0.4828
-#>    7.6230  -1.8587   7.6517   7.5453  -5.5194  -6.6080  -2.5460  -4.6176
-#>   10.3962  12.6604  -3.6792  -1.5480   4.6448   9.8750  -3.0308  -5.3889
-#>    7.2503   0.8777  -9.2999  -4.8500  14.7870   9.0033  -5.2733  -0.1219
-#>    4.2802  -6.1003   4.3013   2.7553 -15.3193  -9.5015  -0.1899   8.7518
-#>   -4.2541  10.1026   0.5425  -6.4690  -3.4436  10.0342   1.6691  -7.5241
-#>    6.9315  -2.6069  -5.3847   1.8447   3.9693 -10.8297  -3.8406   2.8337
-#>    1.8448  -1.0881   7.9049   7.4700  -8.6152   9.5878   6.2947  -9.4251
-#>   -1.6250  -5.0960  -5.8780  -6.4262  -0.6168   3.6598  -5.6435   0.1190
-#>    8.8656  -3.5729  -0.7615  -2.3844   0.9248   2.6855   1.6193   5.6035
-#>  -11.0357   8.2507   1.4583  12.7547   8.3913   4.8109  -0.6845 -17.8735
-#>   -5.1945   3.3425  14.4335  -5.6128  -1.2405  16.7262  -8.0684  -3.8181
-#>    1.7073  11.1047   7.8915   2.4152   6.0107   3.5970   4.5400 -14.8674
-#>   -0.2061  -6.4413  10.6123  -5.4404   0.3608 -10.9615  12.0835 -10.8443
-#>    1.0752  16.0308   3.4707 -10.5946   1.7174   9.9684  -1.6525   3.1576
-#>    2.7748   6.7030   3.1177   8.2975  -1.7461 -10.9206   7.0825  -4.9354
-#>    1.3440  -1.1578   4.7978  -5.6843 -10.4707  -6.3842  -5.6464  -2.9741
-#>   -3.5364   5.4568 -13.7798  -1.0582  -4.4093   2.3835   0.6889  -0.1660
-#>   -1.9331   0.0593   9.4289  -1.3197  -3.2413   2.3622  -1.3254  -2.7177
-#>   -4.3343 -19.4646  -0.5988 -13.6729  -8.8478   8.7292   0.9266  -0.4544
-#>    0.7685  12.0428  -7.5370  -4.6764  13.1441  11.0089   2.3001   0.6684
-#>  -12.9065  -0.3823   6.3071   1.0944  -9.8442   0.6113   6.0180  -6.7031
-#>   -9.0167   3.9631   7.3313  -2.6333  -9.3185 -10.9269  -5.3837   2.5250
-#>    4.2730   0.4342  -5.3750  -1.3528  13.0142  -0.3168   0.0088   2.7591
-#>   -7.7178   5.6187   3.7903   2.4726   1.0198 -20.0105  12.6015   6.4381
-#>   -3.6189   9.8294   9.2189  14.6899   0.4935  11.5095  -4.7393 -10.4125
-#>   -4.1638  -2.9080   0.6444   2.5991  -8.8652  -5.8333  12.0345   9.4001
-#>   -9.8705   4.0219  -8.9801 -18.6822   6.6756   0.3598 -11.3057  10.5311
-#>    5.9611  -5.2889  -5.9852   2.2408  -7.3118   5.1187   5.4850   0.3128
-#>    0.6640   2.9601  -4.1693   2.8989   8.1751  13.8882 -11.8104   4.4661
+#> Columns 17 to 24  -6.8487   4.4435  10.4634  -1.6576  -2.6611   2.6581   5.2221   1.0016
+#>   -0.9367  10.5000 -12.4177  -6.7089  -4.5763  -0.6408  -5.7989   9.3414
+#>   11.1943 -12.4327   2.1724 -11.2070   5.6373  -3.5042   3.3512  11.4406
+#>   -6.2311   9.1892  -7.9499  10.8719  -3.5594   2.3633   0.8106  -1.7022
+#>  -18.6351   1.3245  -3.3700  -9.8865   6.7971  -2.3936 -13.8278  -1.3279
+#>   10.7476   0.6038  -4.1464   1.2876  -5.2170   5.2810  -6.4491  -7.7816
+#>  -10.9651   8.5084  -2.1616   4.4372  -0.6238  -4.9970  -6.8737   1.1720
+#>  -17.3835  -5.6508  -6.1543  -2.5702   4.8792  -5.1308  13.4418  -2.0235
+#>   -3.6095  18.0106  -3.7500  -1.5892  -4.3154 -14.7346  -8.9063  -5.4267
+#>    2.7424   5.2954   5.8235 -11.2857  -3.5601   5.1312   4.3286   1.4308
+#>   -4.9521  -2.3942   7.2079   8.4091  -4.0983  15.4002   9.3447 -19.0548
+#>    8.7038   3.1002  -1.5292  -4.9779  -6.0928   6.8519   6.8001  -7.6720
+#>    9.6998  -4.7902  16.0905  -4.7504 -11.9164 -14.8104   6.2689   5.6866
+#>    6.0375  12.7432  15.3665  -0.0719  -6.9700   3.2936  -7.2990  -4.7881
+#>    9.0116  11.9058  13.1974  -9.4618  -8.9103  -5.2014   4.6467  -3.2343
+#>   -0.8079 -11.8627  -1.1501  -0.9050   9.7038   0.0530   1.6431   3.3465
+#>   -3.3342  19.6242   6.7424   6.6249  -2.8769  -0.4599  -4.5489  -7.6756
+#>  -21.7216  16.5270   5.8606  -1.3811   4.7660  -1.6038  -4.1579  -2.6533
+#>    3.7908 -10.7424   0.8633  -3.6348   4.3650   4.3374   2.8766  -3.0366
+#>    6.3106   1.4443   0.9491  -1.2270  -6.9068 -14.7269  -2.1878   4.9022
+#>    0.1759   3.4991   2.0196  -6.8288 -10.9171  -0.7773   0.3181   7.3345
+#>    9.9469  -8.8342  -4.8429  -1.8343  -7.8642   0.5494   4.6327  -4.6239
+#>    6.3188  -5.4162   1.5947   0.8548  -0.6736  -4.1723  -9.7915 -10.8970
+#>  -20.9290   9.9974   1.7247   6.5055   3.5172  -8.3991  -0.0851   1.8686
+#>   -0.3862  -5.8277  -1.3904  12.1954  -3.6997  -3.5465 -11.3572   0.4942
+#>    0.1431  -8.7328   0.4494  -9.0969   1.5637   2.8361   5.8983   2.2474
+#>   -2.2032   1.0613  -2.0395  -2.8915  -1.1496  -5.0477   8.3861  -0.2060
+#>    4.5145  -2.0720  -6.4740 -12.9726  -0.9755   3.3538  -4.1977  -7.1319
+#>   -7.6895   0.5421   8.9445   6.5222  -0.1812   0.9959   1.8674  -4.9401
+#>    1.8714  10.3658  15.8881   0.7732   2.0168  -5.0220   1.8601 -10.9951
+#>    3.6769  -5.3409   4.3714  -0.6178   2.5703 -13.1579   2.2485  -1.8658
+#>    4.0850  -2.1553  -3.5993 -11.0591   1.0388  -1.5474   2.2893 -12.5559
+#>    1.8180  -6.4558  -5.8333   3.2604  -1.7808  -2.0268  13.2578 -11.1933
 #> 
-#> Columns 25 to 32   0.1069   4.4300   4.3003   6.5373  -7.5178  -9.7551  -7.0320  -9.3276
-#>  -12.5463  -8.7917  -0.6243   3.1369  -2.3819   1.8997   8.5821   1.8319
-#>    1.0214  -0.2199 -10.3648  -4.5515  -2.5590  -4.8727  -0.3350  -0.8497
-#>   -5.2845  -4.0951   0.4800   3.8095  13.0600  -0.3360  11.9384  19.4068
-#>   -6.6383   1.7713  -5.8471  -3.2602   4.2594  -4.0928   2.9030   8.8970
-#>   -5.2800   4.1837   1.3231  15.0332   0.6706  -8.4351  -4.1162 -10.4221
-#>    0.5118   3.6860   2.4320   8.9058   1.7754   0.0929   1.0673  11.7182
-#>   -1.4659  10.9084  -0.4592   7.5206   8.2967   7.0389   4.2406  11.6816
-#>   -1.0805 -12.3320 -19.3237  -5.8110  -0.9302   1.8380 -19.4156  -7.7184
-#>   -2.6221   1.7152   3.4518   4.9626   2.2381  -2.2154  -3.6776   5.4062
-#>   -8.0877  -4.6032   2.0279  -9.7583   7.7099   3.8745   3.0555   6.6926
-#>    4.7537   6.8879   7.1303  -0.9630   9.1403   2.7630   3.1092   1.5607
-#>    1.4150  18.0743  10.4641  -1.5044  -3.9003 -10.2854   3.7217  -7.7446
-#>    6.4209  -9.0526   6.0607  -6.2215  -6.8936  -5.0646   3.7337  -2.1049
-#>    2.4639  16.8365  -3.5789   3.9893  -8.1617  -2.3536   4.4018   2.1488
-#>    3.4904  -5.3955  17.2709  -0.1046  -0.7013   1.9910  11.2501   2.8145
-#>    5.8840   4.0392   4.6691   8.8491   1.7121  -9.7288   1.5834  -0.9072
-#>    5.7569  -5.7727  12.9906   6.4348  -4.4528   9.0758  -1.0589   4.9945
-#>   -4.2344  -0.3840  -0.8968   0.2907  -1.2674   1.3855  -8.0873  -9.2414
-#>    5.2562   2.3932  12.1631  13.1696  -7.7894  14.9373  -5.3492  -4.2549
-#>  -10.1516   1.2459   0.8897   5.6136   8.0912   5.3998  -3.4557  -4.1128
-#>    9.0228  -0.1128  -3.0950  -2.6131   2.9784   1.2623  -4.3874  -4.5560
-#>    3.7943   6.0817   6.3203  11.0212  -0.4039   7.1245  -0.3493  -9.0718
-#>   -4.3440  15.9696   4.4231   9.1531  -3.5382  -7.9130  -3.9667  -8.0826
-#>    3.7145  -1.6401  -0.5113  -5.6870   2.7285   6.6672   2.8719 -11.5719
-#>   -3.1369  -4.8979  -4.1840   0.1913 -13.0126  -4.8511  -3.0648  -7.7171
-#>    3.0135   1.6006  12.5806  11.2100   7.9489 -15.5392   3.0761   1.9750
-#>    0.2687   1.8509  10.7228  -3.3233   0.6185 -14.0387   1.9764  -4.6311
-#>    5.9052  -0.2443   7.0376   0.0236  -4.4985   7.8975  -6.8967  -6.6564
-#>   -1.9590   6.0037  13.4657  -8.3504   3.7786 -16.6422 -12.9441  -7.0071
-#>    8.0193   3.8135   3.1621  12.9735  10.7427   5.3991  -4.2870  14.5053
-#>    3.5541   2.5437   2.1426   3.5000   0.3929   0.2014  12.6902  -4.4364
-#>   -5.8694  -7.8271  -2.6009   5.2320   5.7658   9.6837  -4.6968   2.5444
+#> Columns 25 to 32  -8.0239  -1.2470  -0.6931  -1.4859  15.4803   2.7798  -5.4120  -0.8856
+#>    1.7940   1.8404   3.1563  -0.6655  -1.1949   6.9470   8.6517   6.3564
+#>    7.5723   3.2677   2.2348   3.9421   1.6255  -6.8559   2.3345   9.3224
+#>   -4.4171   0.7608   2.5544   0.0417   3.5979   9.5273   2.1453   0.3212
+#>   -1.5959  -0.0580   2.6072  -0.9632   0.9185  -3.4173  -8.7403 -11.4461
+#>    4.7117   6.5115  -6.0725   0.5120  17.3436   2.2232  -3.5680  -8.9147
+#>   12.3179  -6.1806   8.4467  -4.8787  -8.7924  -7.3739   6.0087  -3.3437
+#>    8.1436   3.9498  -0.7209   6.4006  -3.1987  -0.6735  13.6243  14.5705
+#>  -11.3278  13.3021   6.6153  12.0833  -3.5539  17.4280  -5.5479  -8.8259
+#>  -11.8505  14.9520  -3.0781  -1.0469   6.4941  -1.1333   2.2075  -7.1297
+#>   -3.0877  13.8003  16.1267  13.0234   6.5797  -6.0963   4.7527  -0.6197
+#>   -4.9823  -1.1061  -3.2840  17.3658  11.4418   6.4878   8.9719   4.7849
+#>   -1.0398   5.3661  -5.8047   0.2761   1.5251   9.0993  -1.7992  16.2079
+#>   -1.7283   2.0229  -1.4710 -13.5464  -1.2899  -0.3508   9.2304  11.1253
+#>   -1.9243  -3.3115   1.2582   3.5164   0.4513   6.6903  -5.5428  12.5095
+#>    5.0222 -10.4896  -2.1402   3.0046 -11.0875  -0.9612   9.9269   5.9036
+#>   -5.2882  -3.9958  -4.2295  -3.4205  -0.3779  -0.5442 -12.3781   4.2384
+#>   -1.9695   7.4295  -2.1455  -2.2733  -0.7245  -3.8250   8.4891   4.4323
+#>    8.9002   4.4686   3.9014   4.6856  -0.3516 -13.4372  -4.6156   1.1151
+#>   -2.6154   2.7633   7.3950  -4.2532   5.7675  -0.1448   8.2137  -4.7611
+#>   10.8310   0.6534  15.4860   5.6420   7.1490   8.5290   8.3403   3.2629
+#>    4.0982  -0.5285  -9.5515   4.7863   5.0920   6.7295  -8.6959  -7.3684
+#>   -3.4486  -3.0928   4.3251  -0.5811  -8.3932 -11.1466  -5.6941  -7.0080
+#>   -3.9545  -1.4796   2.7408  -6.0690   0.7845  -5.4991  -4.3873  -6.5194
+#>   -4.4769  -3.6620   9.0240 -22.6348  -7.2083  -7.6506  -3.4351  -5.3684
+#>   -4.9203   0.3637   2.9119  -4.9513   7.2147   3.5280   9.6197  -7.1984
+#>   -0.5157   3.0591  -3.0860  -5.5801  12.4945   5.8421  -8.9570  -4.0305
+#>   -1.2811  22.6866   3.6331   6.8412   7.4134 -20.7373 -17.5149 -13.5000
+#>    1.8131 -16.6703   3.9986  -7.1984  -3.3335   4.6610   4.4134   2.6929
+#>  -11.0258  11.6858   3.7568  11.6514   1.0455  -1.4648  -7.1173  -2.8521
+#>   17.4906  -4.3705  -0.7198   1.7958  -5.0022   6.4528  -1.6037   6.2115
+#>    1.2112   5.6321   2.8833   6.4522  -7.5531 -14.5363   1.1835   0.6486
+#>   -2.0771   8.8949   5.8341   7.3181  -6.4115  -0.5107  -3.2262  -7.8994
 #> 
-#> Columns 33 to 40 -12.4905   0.9962 -12.2520   1.8805   0.7729   2.2591  -2.4218  -1.1181
-#>    4.0905  -4.6726  -4.1660  -6.2877   2.1565  -9.2002  -3.7986  -5.0362
-#>    2.3994   8.7141   3.2630  -0.1725   4.8186   0.7792  -5.8155   6.4687
-#>    1.4119  -7.1807  10.3300 -12.6097  18.3856  12.5793   0.5493   6.3617
-#>   10.8638   7.3653   7.2373  -4.0725   4.0856   8.2117  -9.3570  -7.8061
-#>    7.3977   3.2087  -3.9545  -0.3392   0.3293   0.7637  -0.3166   5.9642
-#>    5.6641  -2.3012   1.2621  -1.1695   2.5621   4.1702   4.9207   3.2028
-#>    7.3067   5.3040   1.6473   1.7503   9.1175   2.8779  -2.2866  -5.0414
-#>    9.1455   1.1883   7.9984  -1.1010   0.7344  -0.3915   6.8009   8.0225
-#>   -7.8686  -5.7029  -1.8316  -0.0498  -4.6592  -1.9285  -3.9908  -2.4433
-#>    2.5765   4.2916  -0.1664  -1.0247   6.6853   3.0413   0.8368   5.5294
-#>    2.1822  -1.1567  -4.4557  11.3355  -9.0053   2.5235  -1.9342  -7.2046
-#>   -0.7974   0.6058  -6.8831   0.0058   2.0477   8.0620  -6.5564   0.8866
-#>   -3.5303  -0.4432   5.9479   5.8558   8.6230  -8.0827   2.2993  -6.1262
-#>    4.9180  -3.5954  -9.6872   6.2345   6.8421   1.4011 -11.3722   1.2919
-#>   -9.6036  -6.8975  -0.2126  -1.7491   3.3766   4.9670   5.3099   1.0471
-#>   -0.6523   8.0283 -13.2220  -1.0271  -1.3558   8.9063 -15.6677  -1.9965
-#>    9.0133  -7.1879   3.1848   3.3971   7.7392   3.0583   4.0695  -1.5547
-#>   -7.8588  -2.7530  -3.3350   4.8636  -6.2975   2.7418   2.4916   3.9969
-#>   -2.1845   0.3225  -7.1511   5.1200   1.0721  -5.5169  -3.6237  -2.7235
-#>    4.9371   5.6697  10.0409  -0.1665   1.5843  -3.9746   5.9828   8.9432
-#>   -8.0029   2.7323  -1.8968  -2.2308  -1.7877  -3.7023   4.5103   1.2744
-#>   -6.0240  -2.3316 -12.1196   6.0434 -12.3856  -0.0222  -0.2043   5.0812
-#>   -0.8801  -0.2558  -8.0960   0.6245   1.0336   1.1230  -1.4520  -2.2997
-#>    0.1147   3.5448  -9.3894   1.3536  -2.8737  -1.1762  -4.0554   5.0370
-#>  -12.9656  -2.1674  -5.5133   0.7504   2.1340  -3.4405  -2.6298   3.6028
-#>    6.0563  -4.7571  -5.6242  -5.9527   7.6094  13.6002 -14.7488   6.5359
-#>  -12.3398   1.0796  -5.4789   5.6667  -0.5361  -2.4495 -18.1071  -4.5523
-#>    2.5113  -0.5029  -0.0765  -4.5280   6.1293  -2.0885  12.7282  -5.5231
-#>  -18.0310   0.9654  -0.9127  -5.1784  -2.2538  -5.6351  -6.1726  -1.2225
-#>    2.4638  -3.7619   1.7139   7.3916   9.0185   7.7355   1.6119  -4.0585
-#>   -1.5231  11.2594 -10.2775  11.4900  -0.3246   6.5134   8.1654  -7.3673
-#>   -1.2108 -10.1927  -2.5560   2.4528  -2.8826  -7.8111   5.8999  -0.9200
+#> Columns 33 to 40  -3.9058   3.6330  14.6814  11.0294   0.6114  -7.4025  -8.1898 -15.6568
+#>   -1.5660  -0.1950   1.3365 -10.1040   4.6690   3.9809   4.8058   2.7980
+#>    5.4841   5.6827   4.3761   8.0758  -3.0589  -2.7941  -0.6464  -6.5529
+#>   -2.1475  -1.3394   6.7814 -10.1263   4.0161   3.5362   0.1569   6.3468
+#>   -4.9919  -3.3644  10.6606  16.6456   5.6330  13.9116   0.1069 -20.3199
+#>    6.1785   2.2155   0.4135  -4.5136  10.3880  -3.1039   5.6768  -1.1867
+#>    9.6005 -24.1643  12.1603  -1.2046   3.8662  -3.9055   9.2843  14.8498
+#>   -8.5824   1.5253   5.2585   2.6103   1.5645  12.8596  -5.5087   1.3866
+#>    8.6366   8.2602  -9.8375  -6.2958 -16.1229  -0.5396   3.2167   9.1022
+#>    2.4253  -3.1367   5.1562 -18.1027   8.9979  -0.9739   3.5797  -5.3982
+#>   14.0565   7.0063   1.7293  13.8184   3.9196   9.1474  -9.0444  -0.8980
+#>   -3.7577  -8.3676   3.9386  18.2545   7.4840   6.6019  -3.3903   1.3207
+#>  -12.2218   3.3944  -0.9306   0.7553  -7.0267   0.8121   7.5666  -2.7124
+#>    0.7766  -6.3066   4.8312  -3.3798  -5.3686   7.5447   1.8752   2.9230
+#>   -3.6166 -15.4443   3.0092  -7.7962  -5.7321  -1.4795   6.7907   8.4577
+#>   -1.4439  16.2603 -12.0475   0.2325 -21.4273  -5.2215  -5.5117   4.4395
+#>    3.9491  -1.8370   8.4841  -1.8794   6.2200  -3.1105  -7.4990   5.1101
+#>    8.6191  -7.4676  14.2140 -12.7239   4.8058 -11.9116   1.1672   8.1439
+#>   -4.1610   2.1837  -6.6788 -11.4972  -1.0558  -4.0919   6.2242   0.7378
+#>    9.6382  -0.5253   1.6212  -8.9249 -11.7176  -2.2420  18.8895   6.4936
+#>    5.0481   5.7827   9.8296   4.1209   0.6601  -6.0024   2.1525  -3.5833
+#>  -11.7774  -0.2523  -1.1469   9.3400   2.0623   4.7312  11.6909  -1.5328
+#>    4.9049  -9.2653  -6.2921  13.0919  11.4640   5.7578  -4.4693   9.4364
+#>    1.1265  -9.7010  21.3250  -3.3045  -5.2495  -9.7180   0.0209  -0.6504
+#>    8.1159 -16.3472 -11.7493   1.3023  -4.2803   8.0947  10.3524  14.3685
+#>   -0.0148   0.6821  -0.0359  -7.6832   5.7399  -7.4555   0.8439  -0.1664
+#>    0.2682  -7.6822  -3.1223  -9.8920  -1.6114 -12.7770   5.1609  -6.7563
+#>    0.7045   7.4599   5.1447   6.1177   6.6432   1.7982   1.0804  -5.2116
+#>   -0.7470  -1.7846  -5.2651   1.7306   0.4855   3.4184   1.0711   3.4388
+#>   -8.4033   6.6338   5.5670  -9.8963 -10.7894  -5.7310  -1.9054  -0.8739
+#>    3.4799  12.8977 -12.5416  17.0875  -4.7295  10.3302 -14.4967   3.3181
+#>   -8.4333  11.1517  -3.9651   1.0370  -7.0633   5.5265   6.3791  -1.7160
+#>   -4.2158  -2.3132  -8.1403   8.0781   9.8984  -6.8006   4.2172  -5.6759
 #> 
-#> Columns 41 to 48   0.1988   5.2504  -3.0760  10.2908  -1.3008  -9.9980 -13.6230   5.6009
-#>    1.1537   3.1284  -4.4897   3.9502   5.0897   3.6015   7.8427   1.9832
-#>    5.0285  -1.6221   4.5983 -10.6953  -3.2972   8.4126  -4.6680   2.0519
-#>    4.9383   6.7629   0.1700  -0.7731  10.6456   2.6704  -4.1326  -3.7576
-#>   -6.5911   6.1939   7.0798   1.6225  -1.5633   3.8868   5.1865   9.6929
-#>    3.2894   0.1017  11.9715   0.1416   8.5866  -7.0269   8.0777  -6.1043
-#>   -2.9275 -10.6820   1.3487  13.4299  -3.0858  -4.1574 -12.7612   3.0961
-#>   -3.7740   1.8306  -1.6566  -0.4461  -2.3291   4.8333   4.8621   3.8554
-#>    4.4658   1.1858   8.9472   2.9284  -4.3364  -3.1499  -4.1935   0.7509
-#>   -3.4900  -1.9808  -0.9273   8.6051  -0.6985  -1.2492  -1.7975   3.1656
-#>    9.5229   5.4037   5.7429   2.6790 -12.2003   6.4440  -5.3810   4.4119
-#>    2.7466  -4.2808  -1.5434   7.0435  -1.9691   0.1578  13.3589  -2.7863
-#>   -0.4999  -5.5772  -2.0889  -3.4647  -0.7473   3.2818   4.4717  -5.4836
-#>    3.9357   5.8264   2.2869  -2.3583  -8.4141  -7.6839  -1.1675  -4.9500
-#>   -3.5572   2.3844  12.0013   1.1560 -10.2125   0.8103  -1.5860   4.9254
-#>   -5.5393  15.7903  -2.9637  -7.3790  -2.8508   0.4124  -2.9728  -2.5130
-#>  -15.2763   1.5354  -2.9154   5.6464  -4.8810  12.7598  -8.5844   1.4449
-#>    0.0455  -4.2933   3.1696 -11.3167   5.7426  10.5657   5.2644   9.0828
-#>   -3.8138  -0.8780  -6.8863  -2.3013  -4.3458  -3.6998  -8.0492  -6.8730
-#>   -2.9428   6.1095   3.6243   0.9395   1.2437   1.5961  -2.6437  -7.6080
-#>   -2.8307 -14.9884  -3.5193  -1.7380  13.1939   1.2391  -1.5804   6.7580
-#>    4.3376   1.4819   5.9095 -10.5479  -6.1397  -1.9740  -3.3975 -11.0190
-#>    6.2649   0.7111  13.0831  -2.3153  -0.6452   1.3613  -1.6137  -3.0733
-#>   -2.5901  -5.3288   7.2456  12.8651   1.4870  -3.5211  -5.8653 -25.0905
-#>   -0.2713  -8.3647   0.0763  -5.4667   5.4971  -1.8255 -10.7636   0.0127
-#>   -0.4668   8.2574 -11.0812  -7.1201   1.5301  -6.1162  -2.5051   0.0130
-#>    0.9573  -5.0825  -1.6988   8.7437  -5.5978   2.2083 -11.9892  -2.3081
-#>   -4.2024  -0.2054 -11.7608   0.5975 -10.1964   9.2613   6.0544   5.0097
-#>   -3.8999  -6.2661   8.1442  -2.5761  -2.0750 -13.3615 -13.5262   0.3246
-#>   -4.1377   4.2118 -12.6908   7.3387  -3.0250  -1.6286   4.4283   8.4087
-#>    1.0502  -1.8882   2.5351   5.0288  -0.8796  -3.3671  -5.9324   0.6171
-#>    6.7530   5.1505  -5.3810   3.0831   8.8983  -7.8994  19.5764  -7.6865
-#>   10.5669   6.0169   2.3651   7.6554   3.8816  -5.4518  -0.1022  -2.6415
+#> Columns 41 to 48   9.4739  -2.4871 -11.3898  -6.2782   2.2322 -15.6750  13.3813  -9.2363
+#>    2.9246  11.3030  -3.7889  -6.0741  10.5975  -7.4606  -6.8567   0.2646
+#>    4.7538   8.0857  -4.1892   7.2678  -3.3109   8.2137  -6.0604  -1.6080
+#>   -1.5788 -10.4323   0.5596  -2.8241  14.4969  -5.3987   1.5820  -3.6046
+#>    8.9440  -8.8995  -1.2210 -11.4799  -2.2984   5.0576  -5.7322  -0.0743
+#>   -7.6970  -2.1091   6.3578   3.6874  -4.1044   0.6602  -0.5166  -1.5042
+#>   -9.6716  11.9752   4.4680  15.2822  -4.9844   0.1639  -3.2687   8.5479
+#>  -12.9347  -9.1832  16.0518 -12.3684  11.5679  12.1763   1.6678  -1.6048
+#>    9.8337  -0.2622   1.8546  -7.2773  -1.6749  -2.5461  -3.6256   4.3443
+#>   -5.8384   9.5115  -1.6364  -1.0145  -3.5868  -2.8844  -0.9321   3.4537
+#>  -16.3595 -11.9573  -5.9198   3.6488   0.3895   7.7617  -3.1785  -5.8786
+#>    0.4726   6.7204  -0.4686  -3.9788   6.2379   3.9203  -0.2650  -0.2112
+#>   -4.6015  -1.7512  11.0462   2.8313  -6.2434  -4.6101  -0.7652   4.9493
+#>   -6.3871  -0.2391   3.9989 -12.8485  -3.8938  -5.5514  -6.0448   0.6949
+#>   -3.6988   5.1299   1.5247  -3.4503   0.1101   8.9090   5.2390   1.7370
+#>    4.6947   1.3253   2.4065  -5.1609   0.0391  16.1757  -0.3270   4.9306
+#>   -3.5305   3.2545 -14.4031   6.5018   3.8694  -6.9241  -1.9808 -10.2522
+#>   -5.7138   3.0211   6.1957   1.8028   8.7582   3.8991  -0.8794  -7.2811
+#>    2.2139  -1.7236   1.2650  -8.8652   8.1364   7.5462   3.5493   5.6968
+#>   -9.5784 -11.6312   4.0365   2.2844  -9.2006  -8.8440   2.5961   7.6096
+#>    0.7704  13.0104  -6.0015  -9.8596  -3.2601  -4.3436  -6.1417   6.1274
+#>   13.2206   8.8281  -5.4809   0.9209   4.3793  -1.0726   9.8446   4.3888
+#>   -6.6724   2.1674   3.0973  10.2949  -0.4234   7.4996  -5.8836 -10.7790
+#>   -2.8081  -5.2632   6.5798  -5.0252   2.1289  -6.0331  -0.0873   9.5376
+#>    7.9091  -2.2422   6.4234  -1.8565   5.3989   5.8702 -14.2319  -0.6525
+#>    2.4293   5.9469  -3.8970  -1.9631   1.3240  -9.1889  -2.5622  -3.3236
+#>   -3.9343  -7.3933   4.6675  -6.5705  -2.7564  -3.9535  -1.6035   9.7085
+#>    3.8070  -9.2476  -5.6887   1.4793  -0.3372   1.1336   2.3916  -0.3128
+#>   -5.4129   0.3016   6.2480 -14.1575   5.3201 -10.1676   3.0214  -2.6561
+#>   -7.8239   4.1868   5.5191  -0.8306  -5.6963  10.4358  -2.3267  -3.0777
+#>   -8.9461  -0.8685   5.8140  -7.0942  -3.3415  -5.1304  -2.9065  -2.1565
+#>   -1.8962  -5.7789   7.2716 -10.1762   0.1679   4.7211  -7.0600  -7.6150
+#>   12.2349  -4.6248   5.0205  -4.7099  10.4281  -0.6178  -0.5726  -0.6882
 #> 
 #> (14,.,.) = 
-#>  Columns 1 to 6 -2.9470e+00 -8.6971e+00 -4.9650e+00  4.1232e+00 -3.1464e+00  5.3055e+00
-#>   1.5427e+00 -1.1095e+01  4.2713e+00  5.7637e+00  3.6362e-01 -9.4469e+00
-#>  -7.6291e-01 -3.9613e+00 -1.6844e+00 -2.7396e+00  1.8311e+00 -4.1143e+00
-#>   8.9186e-03  1.9440e+01 -8.9361e+00 -2.8924e+00 -3.0730e+00  1.9352e+01
-#>  -9.7225e+00 -4.7628e+00 -6.7697e+00  3.6149e+00 -8.2467e+00 -2.5238e+00
-#>  -7.0066e+00 -2.8066e+00 -6.3864e-01  1.5807e+01 -3.9110e+00  3.8801e-01
-#>  -6.5141e+00  1.6159e+00 -2.5237e+00  2.6393e+00  1.0126e+01  2.1174e+00
-#>  -1.9377e+00 -3.7156e+00 -1.0927e+01  2.3730e+00  3.7326e+00 -1.6164e+00
-#>  -1.1518e+01  1.2488e-01 -8.5711e+00  1.2285e+00  1.5453e+00  1.2867e+00
-#>   1.4090e+00  1.2069e+00  4.5120e+00  8.8675e+00 -4.1212e+00  2.0452e+00
-#>   2.1451e+00  7.7938e-01  9.6499e+00  4.1449e+00  1.4574e+00 -8.9375e+00
-#>   1.0404e+01 -3.8630e+00  7.2910e+00 -5.0135e+00  8.1828e+00 -4.1867e-01
-#>  -4.0174e-01  6.0947e+00  3.0210e+00 -1.5495e+01  2.5243e+00 -9.6399e-02
-#>  -1.4876e+00 -3.3305e+00 -8.6169e+00  6.6952e+00 -2.1132e+00  2.6100e+00
-#>  -2.6931e+00  5.3624e+00 -4.1022e+00  5.6431e+00 -2.5500e-01 -9.9579e-01
-#>   6.6913e+00 -1.3960e-01 -3.6253e+00  5.5914e+00 -1.5811e+01 -5.9588e+00
-#>  -8.7709e+00  9.3600e-01 -8.2002e+00  4.3308e+00  6.0203e+00 -5.1867e+00
-#>   2.4328e+00  7.7536e+00  2.4390e+00 -4.8175e+00 -8.0653e+00 -2.9132e+00
-#>  -7.8568e-01 -3.3420e+00  7.9374e-01  1.0131e+00 -5.6944e-01 -1.9089e+01
-#>  -3.7811e+00 -1.9507e+00 -1.2322e+00  4.1061e+00 -8.2291e+00  6.5997e+00
-#>  -7.1566e-01  2.7382e+00  2.6303e+00  3.4786e+00  2.8957e+00  5.6836e+00
-#>   9.6807e+00 -6.2116e+00 -3.0417e+00 -2.4916e+00  3.7252e+00  2.3739e-01
-#>   1.0791e+00  6.4898e+00  1.3307e+01  1.0551e+01 -5.9581e-01  2.3691e+00
-#>  -5.3603e+00  1.0517e+01  1.5184e+00 -9.7263e+00  4.9366e+00  4.8630e+00
-#>  -2.2348e+00  5.4156e+00  7.0300e+00  1.3209e+00 -4.3836e+00  3.8853e+00
-#>  -5.0869e+00 -8.2025e+00 -8.6510e+00  1.0470e+01 -5.3927e+00  2.7191e+00
-#>  -1.1621e+01  4.2484e+00 -4.0592e+00  1.0454e+00  1.1135e+01  2.2191e+00
-#>  -8.1427e-01  3.7419e+00 -6.3552e+00 -9.7194e-01  5.5231e+00 -8.6829e+00
-#>   3.1891e+00 -4.9019e+00  7.8427e+00 -1.7551e+00 -2.0898e+00 -5.0366e+00
-#>  -2.5993e+00 -4.6363e+00  3.5349e+00  2.1700e+00 -3.2316e+00 -3.7960e-01
-#>   8.6088e+00  1.6032e-01  3.2837e+00 -4.5223e+00  1.6399e+01  7.8730e-04
-#>   5.7161e+00 -4.1015e+00  2.2706e+00 -7.0973e+00 -2.1299e+00  6.9960e+00
-#>   7.8803e+00 -2.1816e+00  1.0224e+01  2.5879e+00  1.2398e+00  7.2957e+00
-#> 
-#> Columns 7 to 12 -1.1646e+01  1.0131e+01  2.9991e+00  1.1064e+01 -2.1592e+01 -1.3639e+00
-#>  -7.7052e+00  1.2533e+01 -2.7843e+00  1.1178e+01 -7.1699e-01 -3.4950e+00
-#>   1.7887e+00 -4.3862e-01  4.8924e+00  1.2163e+01  4.5573e+00  3.6067e-01
-#>  -2.0127e+00  9.5348e-01  3.6808e+00 -7.9013e+00  1.7339e+00  1.1636e+00
-#>  -2.2187e-01  3.9322e+00 -8.6138e+00  1.6170e+00  1.3332e+00 -5.1143e+00
-#>   7.7466e+00 -2.5890e-01  6.3755e+00 -1.1040e+01 -1.2306e+01 -6.3907e+00
-#>  -9.5735e+00 -2.1791e-02 -8.1075e+00 -1.0824e+01 -1.0851e+01  1.0848e+01
-#>   7.0934e+00  1.8923e+01 -9.2418e+00 -1.6874e+00 -3.7214e+00 -1.5075e+01
-#>  -2.3427e+00 -8.0402e+00  1.0169e+01  6.0570e-01 -5.0686e+00 -2.0361e+00
-#>  -1.3110e+01 -1.7934e+00 -6.1507e+00 -1.4862e+01  1.0235e+01  4.1763e+00
-#>   5.6747e+00  9.7487e+00 -1.0725e+01  1.5667e+01 -4.6575e+00 -2.7561e+00
-#>   2.7392e+00  3.2462e-01 -6.5484e+00 -1.1757e+01 -1.2866e+00  1.7478e-01
-#>   2.4737e+00  3.8282e+00  1.9346e+00  1.3481e+00 -2.8944e+00 -1.9211e+00
-#>  -2.5990e+00 -1.9938e+01 -1.8334e+01  3.8725e+00  8.6759e+00 -5.0472e+00
-#>  -7.9813e+00 -7.0264e+00 -6.9558e+00  7.8006e+00 -1.2365e+01  3.5747e+00
-#>   5.1817e-01  1.5110e+00  1.7366e+01 -1.8837e+00  9.2961e+00 -5.0517e+00
-#>   3.5365e+00 -2.3325e+00 -2.8275e+00 -3.1046e+00 -5.2681e+00 -8.9198e+00
-#>   6.5027e+00  4.3644e+00  1.2701e+01 -3.7251e+00  2.0427e+00  7.9933e-01
-#>   7.8586e+00 -7.0188e+00  5.9492e+00  2.5074e-01 -3.2924e+00  1.8061e+00
-#>   8.8907e+00 -5.8894e-01  1.5509e+00 -3.8481e+00 -7.0443e+00 -1.4297e+01
-#>   7.1570e-01 -4.8762e+00 -1.1524e+01 -1.3736e+01  2.4542e+00 -1.8681e+00
-#>  -1.7810e+00  2.3527e+00 -6.2339e-01  4.3841e+00  3.7593e-01  2.7804e+00
-#>  -9.2921e-01 -1.0520e+01 -4.9004e+00  4.5912e+00 -9.6276e+00 -5.5966e+00
-#>  -1.2594e+00 -1.3813e+01  1.0828e+01 -1.5079e+01  7.6320e+00  9.8324e-01
-#>   1.4521e+00 -1.4763e+01 -2.9935e+00  7.2191e-01 -9.4360e+00  7.4886e+00
-#>  -2.6939e+00 -6.2316e+00  6.4547e+00 -1.0803e+01  4.7456e-01 -1.2776e+01
-#>  -7.0297e+00 -3.4002e+00 -3.2316e+00 -2.2789e+01  1.9848e-01  9.2831e+00
-#>  -9.8949e+00 -1.2377e+01 -3.3984e+00  6.5185e+00  8.3058e+00 -4.8508e+00
-#>  -9.2559e+00 -9.8128e+00 -1.0571e+01  4.7695e+00 -4.0007e+00  9.6223e+00
-#>  -1.2972e+01  1.3614e+01 -8.2897e+00  1.0589e+01 -6.8666e+00 -5.1787e+00
-#>  -2.1274e+00  2.2466e+00 -4.8599e+00 -1.7663e+01 -1.2687e+01  6.8946e+00
-#>   9.7860e+00  7.6324e+00  8.4055e+00  7.6672e+00  4.1579e-01 -1.3755e+01
-#>  -9.5256e+00  5.1509e+00  5.7313e-01  3.8565e+00 -8.5037e+00  6.5861e+00
-#> 
-#> Columns 13 to 18 -5.8376e+00 -2.9279e+00 -5.7467e+00 -5.5014e+00 -4.6681e+00 -1.1295e+00
-#>  -9.8382e+00  4.1628e+00 -4.8965e+00 -6.0329e+00  1.7361e+00  2.7454e+00
-#>   5.3213e+00 -9.8942e-01  3.3805e+00  4.8124e+00  7.9408e+00 -4.1334e+00
-#>  -8.4554e-01 -5.8992e+00  3.3015e+00  9.2698e+00 -6.1940e+00 -1.8760e+00
-#>  -3.3602e+00 -6.5618e+00 -1.3458e+01 -1.0804e+01 -1.0276e+01 -2.3611e+00
-#>  -3.2807e+00 -4.7024e-01 -4.8106e+00 -7.1463e+00 -1.2021e+01 -2.0456e+00
-#>  -3.3972e-01 -6.5393e+00 -7.5630e-01 -1.8538e+00 -3.5975e+00 -3.9039e+00
-#>  -1.5135e+01  7.3035e-01 -8.3808e+00 -4.8221e+00 -9.3744e+00 -3.2798e-01
-#>   2.4560e-01 -7.5552e-01  1.1924e+00 -3.4284e+00  1.9044e+00 -2.9079e+00
-#>   6.4364e+00 -2.0622e+00 -2.1329e+00  2.0296e+00  5.0129e-01  1.3035e+00
-#>  -7.6127e-01  1.3828e+01 -9.9733e+00  4.2783e+00  2.4848e+00 -9.1430e-01
-#>   6.9285e+00 -1.0299e+01  2.3943e+00 -3.8257e+00  1.1201e+00  1.4560e+00
-#>  -4.5045e-01 -4.0313e+00 -4.1263e+00  5.8071e+00 -2.0225e+00  3.4909e+00
-#>   6.6008e+00  2.6956e+00  1.0403e+01 -1.1447e+01 -5.6352e+00 -5.7858e+00
-#>  -3.8587e+00 -4.4260e+00 -3.5954e+00  1.2241e+00  3.7192e-01  3.6454e+00
-#>  -2.1584e+00 -7.1288e-01 -1.5161e+00 -2.7456e+00  1.8898e+00  4.4897e-01
-#>  -1.1219e+00 -3.7443e+00 -4.8727e+00 -8.3364e+00 -7.5283e+00 -4.1012e+00
-#>  -1.0698e-01 -1.2422e+01 -2.1764e+00 -1.7482e+00  1.9705e+00 -6.7154e+00
-#>   4.0076e+00 -8.2704e-01 -2.5556e-01  2.2082e+00  4.1201e+00  3.2584e+00
-#>  -4.1204e+00  1.1085e+00 -3.8030e+00 -3.9246e-01 -6.4055e-01  7.0182e+00
-#>   4.5501e-01  1.9863e+00  4.2138e+00 -3.2505e+00  2.1790e-01 -6.2447e-01
-#>  -2.7618e+00  8.8997e+00  3.3469e+00 -1.5589e+00 -2.7696e+00 -9.9674e+00
-#>   7.7602e+00  4.1035e+00  6.3377e+00  8.3649e+00  1.8985e+01  1.2069e+01
-#>   8.8029e+00 -3.1696e+00  6.7168e+00  1.4785e+01  8.6215e+00  2.7457e+00
-#>   7.1574e+00 -1.0295e+00  2.9361e-01  3.8908e+00  6.6932e+00  3.7401e+00
-#>  -3.9838e+00  1.7488e+00 -2.1708e+00 -1.1437e+01 -4.0910e+00 -7.1037e+00
-#>  -1.6132e+01 -8.5650e+00 -2.4694e-02 -1.5977e+00 -7.3895e+00  8.8435e-01
-#>   5.3712e+00  1.7691e+00  2.4260e+00 -1.8620e-01  3.9535e+00  1.0418e+00
-#>   9.8902e-01  9.6299e+00 -3.1638e+00 -6.1946e+00 -4.0484e+00 -7.3948e+00
-#>  -7.4685e+00  7.6784e+00 -7.6226e+00 -9.7231e-01  4.6998e+00  1.2277e+00
-#>  -2.1309e+00 -9.5858e+00  3.8287e+00 -4.5513e+00  1.1547e+01 -8.7646e+00
-#>   5.6137e+00 -1.0663e+00 -2.6638e+00  7.5905e+00 -2.1023e+00  4.7568e+00
-#>   2.8300e+00  6.5272e+00 -2.2828e+00  3.1876e+00  6.7941e+00 -1.4367e+00
+#>  Columns 1 to 8   1.8120   3.7617   0.4471   6.9333 -12.9029   1.5540   5.6503  -7.1980
+#>   -2.5197   9.9323  -1.8481   5.1337   4.5225  -3.0721   7.1803  -5.2650
+#>    0.6766   3.9011  -5.2145  -1.1104  -2.6207   1.9313   2.8473   1.3332
+#>   -4.1116  -0.5486  -0.9061  -9.4907  -0.0606  -2.8519  -3.7295   3.4588
+#>    5.0245  -5.5788  -8.9182   3.7319   8.3697  -1.0909   0.8366  -0.6395
+#>   -0.8158  -0.0995   1.2385   5.0583  -2.6876  10.0157  -9.2965  -3.6235
+#>    0.8441  -1.0265   2.4561   4.4603   2.9985   3.5421  10.6706   7.0625
+#>    1.9724  -6.2498   5.1469  -2.8947  11.4586  11.2741  -7.1414   8.9407
+#>  -10.9602  -0.1255  -7.6967  -1.8333  -8.7321  -4.6374  -0.8599   8.8685
+#>   -2.8211  -0.0814   7.7133   5.2489 -11.1519   1.9016 -12.6401  -6.0678
+#>   12.5198   7.3402   4.4746  -7.9494  -3.3943  12.6510  -7.7838  -1.6605
+#>   -3.2784  -5.5715  10.8100  11.7527  -6.6415  13.3171   0.1200   5.8478
+#>   -8.6510   4.7206  -7.7298  15.1382 -10.8644  -6.7093  -8.7067  -8.7603
+#>    6.2818  -1.1763  -6.6250   2.2117  -3.6052   0.1065  -5.4839  -2.7576
+#>   -5.4923  -8.3758  -2.2596  -4.5804   9.3766   5.7054   5.5243   1.8644
+#>    5.4238  -5.1292   9.7032  -7.3221   6.1139  -3.9237  -6.5717   2.9857
+#>    1.2697 -13.6312  15.9850 -12.2223   3.7401  -8.0154   2.3675  -0.8374
+#>   -8.3809  -1.3908 -10.8221   6.5877   6.5755  13.0431  -1.0928   1.1669
+#>    3.6379   5.5428   2.4451  -5.0665  -6.0208   0.5615  -0.8265  -4.3748
+#>    6.1441   4.7829  -0.6880  10.4632 -12.4652   1.5572  -5.5885 -13.2753
+#>    2.3854  10.9534  16.0951  12.1824   1.0762  -9.4443   0.3599  -3.0608
+#>   -1.4715  -4.8643   9.5817   1.8899  -4.6848   5.3230   4.2168   5.1630
+#>   -7.8870  -3.5397  -8.6147  -4.7593   3.6831   5.0845   4.2286   8.5966
+#>   -1.7009  -8.2563 -19.8460  18.3021 -14.0808   8.9529   4.7158   5.1620
+#>   12.2938  -2.9541  -5.9204   3.6288   6.4764 -13.4344   2.2028   2.1978
+#>   -0.1014  10.9994  -2.7530  18.7125  -6.3077  -5.6898  -4.1101 -17.2708
+#>    7.2069   1.5405  -8.8842   5.7010  -2.3828 -13.0868  -3.4182  -3.5180
+#>    1.3883   9.2243   8.9420   5.4042  -9.1066   0.3937  -2.3768 -11.3954
+#>    1.5710  -2.6089  -7.3017   2.3454   6.5143  -6.4729   7.6334   3.7750
+#>   -5.4822  -4.6137  -8.1065   1.4911 -12.6661   6.1865  -5.8872  -6.2860
+#>    3.2892   0.3209  -2.7690  -4.5741  16.8951 -12.6602  -3.5407   4.6877
+#>   -3.8372   5.7957   5.6769  -1.9891  -7.7453  -7.8117 -13.0259 -14.0559
+#>    6.1158   4.4327  -9.5881   2.1524  -5.3699   5.2145   3.0018  10.7193
 #> 
-#> Columns 19 to 24  3.9527e+00  3.9152e+00  8.9049e+00  1.1294e+01 -9.8714e+00 -1.3542e+01
-#>   7.7918e-01 -3.5703e+00  1.1471e+00  1.0779e+01 -4.1714e+00  3.7584e+00
-#>  -1.1080e+00  9.3968e+00 -5.0234e-01 -7.4000e+00 -4.7738e-01  4.4831e+00
-#>  -7.2076e+00 -3.4422e+00 -6.2024e+00  1.1907e+01  1.0278e+01 -1.3093e+01
-#>  -8.0908e+00  4.3010e+00 -3.2117e+00 -8.4518e+00  3.5677e+00 -1.0008e+01
-#>  -1.2001e+01  4.0682e+00 -4.7979e+00  1.7572e+00 -4.5670e+00  3.5043e+00
-#>   5.7016e+00 -8.2803e+00  3.3730e+00  3.0721e+00  5.9141e-01 -4.8445e+00
-#>  -3.6571e+00  3.2611e-01 -9.7575e+00 -3.3793e+00  8.9044e+00  8.9033e+00
-#>  -2.7785e+00  3.7773e-02 -7.7068e+00  4.1643e+00 -1.1703e+01  3.0570e+00
-#>  -3.7972e-01 -6.2760e+00 -2.5783e+00  6.8392e+00  1.0227e+00 -1.8564e+01
-#>  -4.7111e+00  5.1840e+00  1.6555e+00 -1.8181e+01  1.1991e+01  9.1902e+00
-#>  -1.3548e+00 -6.1443e+00  5.9713e+00 -2.2839e+00  3.6700e+00  1.3198e+00
-#>  -6.7875e+00  1.3635e+01  6.8890e+00 -1.0232e+01 -9.0848e+00  5.8770e+00
-#>  -2.5420e+00 -5.9944e+00  6.9125e+00 -5.3103e-01 -1.1211e+01  2.6487e-02
-#>  -1.0139e+01  1.1467e+01 -5.1473e+00  5.1925e+00 -1.4699e+01  2.3066e+00
-#>  -7.9126e+00  2.2568e+00  7.3034e+00 -5.2624e+00 -5.4210e+00  4.4153e+00
-#>   3.0570e-01 -1.5279e+00 -1.7311e+00  7.4561e+00 -8.5627e+00 -1.4265e+00
-#>  -3.0795e+00 -3.2056e+00  2.3707e+00 -3.7382e+00 -5.6777e+00  1.3584e+01
-#>  -6.0551e+00  9.6878e+00 -2.8827e+00 -1.5638e+01 -6.0660e+00  1.1794e+01
-#>   5.2208e-01 -9.5151e-01  4.8781e-01  7.1150e+00 -1.1985e+01  1.1426e+01
-#>   5.2291e+00 -5.9671e+00 -7.7412e+00  7.6153e+00  1.0399e+01 -2.9500e+00
-#>  -6.2040e+00 -2.7724e+00 -2.0706e+00 -9.4660e+00  2.1540e+00  3.0201e+00
-#>   4.2770e+00  8.7830e+00  5.0159e+00 -4.2996e+00  9.8929e+00 -2.7062e+00
-#>   2.9477e-01  1.0222e+01 -4.2887e+00  5.1453e+00 -4.7670e+00 -2.9534e+00
-#>   5.0972e+00  6.2548e+00 -8.7009e+00  9.8776e-01 -2.0427e+00 -6.0557e+00
-#>   3.7945e+00  1.5340e+00 -4.9280e+00  1.0356e+00 -1.1497e+01  1.8980e+00
-#>  -5.5207e+00 -2.3598e+00 -6.4203e+00  5.3196e+00  1.6321e+00 -7.9663e+00
-#>  -3.3121e+00  4.5133e+00  4.7246e+00 -1.1493e+01 -1.7462e+01 -2.9324e+00
-#>  -5.5510e+00 -7.6881e+00  3.9163e+00  2.0472e+00 -4.8133e+00 -8.8644e+00
-#>   3.4029e+00  3.8217e+00  9.1463e+00 -6.9773e-01 -1.1475e+01 -8.3752e+00
-#>   1.2595e+01 -9.3585e+00  2.2784e+00  3.9545e+00  1.0027e+00 -6.0553e+00
-#>  -1.7755e+00  4.8426e+00  3.1131e+00  5.8507e+00  1.4758e+00  8.1132e+00
-#>   8.1158e+00 -2.6878e+00  3.8420e+00  9.7280e+00 -2.0917e+00 -1.7866e+00
+#> Columns 9 to 16   0.3597  -2.0903  -3.1581  -2.0367  -3.0659  13.5757  11.8259  -8.2849
+#>  -14.3246   2.8480   2.4120  -6.6259  -2.9739   0.4189  -9.0361   1.6881
+#>   -8.4891  -7.2466  -3.5202  -2.7671   2.6824   3.1614  -2.5880  -6.5934
+#>    0.2683   2.2314  -8.0590   3.0173 -13.5310  -8.3040  -2.7080  10.9972
+#>  -11.2204   1.0337   4.6042 -11.4552   0.0898   8.9120   3.3409  -9.9265
+#>    9.9767  -7.0705  -3.3332 -10.8903   5.7525   2.4714   5.6048   4.9469
+#>   -2.4297  -4.3130  -2.8449  -7.0217 -10.1437  14.2901  -8.3152  -0.4260
+#>  -12.7395  -2.0215 -15.3991  -3.6943  -9.9595   3.8962   2.0800  -1.4782
+#>   -3.1190   6.1817  -2.4657  13.6280   4.8562 -19.5169  -3.0502  10.2227
+#>    5.5958   6.6090   6.2062  13.5058 -11.4794  -5.1918   4.0016   0.6111
+#>   11.3145 -10.4141 -13.3045  -2.2231  -3.4931   0.2439   2.3371  10.4773
+#>   -5.2823  -1.5924 -18.2333  -7.5242  -2.9962   5.2234   5.7785  -6.7293
+#>  -14.7249   5.7797  -0.6724   4.2688   4.8974 -11.4439  11.5410  12.9653
+#>   -8.6522   7.7081  16.2287  -0.3754  -8.5992  -2.4765   6.0954   3.9052
+#>  -11.1785  -1.5492   2.7302   2.6521  -5.8620   5.7717   6.8828   9.1204
+#>   -4.5918   4.7367   0.2843  12.5741  13.0995  -2.5704  -7.2906  -6.7709
+#>    3.8466   9.3663   3.1537   5.9368  -5.7631   4.0011  -2.5978  -0.6000
+#>    5.6944  -4.0560  -6.6739   7.5072   5.3467  12.8864 -12.6360  -6.3561
+#>    2.2674  -1.5447  -5.0801   3.5843   4.8275  -0.2540   5.1684  -1.0358
+#>   -0.4786  -7.0435   5.8918   2.1709  -1.1985 -12.5241   0.3920  11.7516
+#>   -6.8908  -7.7343   2.9669  -3.6853  -3.1499  -2.1967  -5.0498  -3.4463
+#>   -0.5249 -11.5338 -14.0674  -2.7252   0.1619  -1.8788   5.8070  -1.8265
+#>    5.7027   0.7319  -7.4653 -20.6158  -4.1757   6.5519  -1.4161   0.5598
+#>   -6.4897  -0.5664  -6.9819   4.3203  -1.1149   7.4771  -3.2735   4.2626
+#>    8.4064  -3.9333   2.8795  -4.9561 -10.6564 -11.0564  -6.0721   1.1060
+#>    0.5055  -1.4520   8.7027  -2.5157  -0.4962  -9.4471   0.5019  -3.4809
+#>    0.4523   0.8833   7.0779  -1.0875  -1.6334   6.1787  11.3117   1.1238
+#>    5.6203  -6.2314  -6.3620  -3.6415   0.8934   0.3930   8.0999   2.3680
+#>   -1.9187   0.1178   2.8303   1.0446  -4.8628 -13.3341   4.5485  -1.1984
+#>   -2.1462   6.8713   1.1878   4.4796   9.8793  11.9375   4.3436   5.2784
+#>   -8.1113   2.3671   3.5521  -3.1801   5.7325  -4.4532   7.3262  -2.4309
+#>    1.4593   1.9454  18.0287   7.6329   5.2203 -17.9666   0.1984  -5.6458
+#>   13.4660 -12.4678 -11.6993   0.1278  -7.4379   1.2361  -2.4902  -4.4603
 #> 
-#> Columns 25 to 30  2.7514e+00 -2.9044e+00  3.1635e+00  4.8970e+00 -2.7153e+00 -2.1692e+00
-#>   4.0094e+00  5.8292e+00  1.2055e+01  9.6828e+00 -3.7371e+00  3.6239e+00
-#>   1.4078e+00  4.0304e-01  1.2239e+00  8.0703e+00 -9.6928e+00 -6.7939e+00
-#>  -7.4295e+00  7.2254e+00  2.9496e+00  3.6850e+00  1.9936e+01 -7.6404e+00
-#>   7.7423e+00  4.6614e+00  3.0677e+00  1.6276e+00 -8.0058e+00 -1.4904e+01
-#>   2.7324e+00  4.6477e+00  2.9174e+00 -7.6481e+00 -1.5952e+00 -7.8083e+00
-#>  -2.1986e+00  5.1477e+00 -4.2553e-02  1.3089e+00  4.7724e+00 -9.3814e-01
-#>   1.0899e+01 -4.5767e+00  7.8206e+00  4.1006e+00 -9.6803e+00 -9.8559e+00
-#>  -2.5533e+00  5.8232e+00  4.3523e+00  3.5645e+00 -2.8284e-01 -2.3316e+00
-#>  -8.0524e-01  7.9485e+00 -1.6328e+00  6.4514e-01  9.6695e+00  3.4099e+00
-#>   1.4511e+01 -1.5813e+01  1.3674e+01  9.2899e+00 -1.7942e+01  1.4025e+01
-#>  -4.0874e+00  5.8157e+00 -7.6620e-01 -1.0892e+00  6.5480e+00 -3.9128e+00
-#>   3.3873e+00 -1.3534e+01  2.6775e+00  6.6464e+00  5.7631e+00 -6.4381e+00
-#>  -1.1190e+01 -4.2002e-01 -1.3070e+01 -4.3599e+00 -7.0482e+00  2.7458e+00
-#>  -2.0814e+00 -8.8541e+00  1.6759e+01  5.5173e-01 -4.2523e+00  1.2654e+00
-#>  -4.2751e+00  1.3150e+00 -1.3943e-01  3.2242e+00  1.1298e+01  1.2557e+01
-#>   2.9336e+00 -4.9230e+00  6.7140e+00  2.0939e+00 -6.5652e+00 -1.6155e+01
-#>  -8.0575e+00 -1.4924e+00  3.4084e+00 -3.2414e+00  6.2329e+00  5.5945e+00
-#>   3.6416e+00  7.1970e+00  2.9832e+00  4.8062e+00  3.9785e+00  1.2869e+01
-#>  -2.4926e+00 -5.4260e+00  6.8716e+00 -3.1893e+00 -4.6895e+00  1.4379e+00
-#>  -8.6248e-01  4.2699e+00 -1.4311e+01 -6.6221e+00  8.4722e-01 -1.9782e-01
-#>   6.0904e+00 -8.3998e+00  7.4810e-01  8.4135e+00  8.9536e-02  1.5779e+00
-#>   9.0231e-01 -1.2763e+01  2.2154e+00 -6.3118e+00 -5.7779e+00  2.1693e+01
-#>  -7.9907e+00 -1.0028e+01 -2.4887e+00 -2.3234e-01  1.3156e+01  9.6231e+00
-#>   4.0964e+00 -2.1144e+00  1.2411e+00 -2.9359e+00 -2.9314e-02  1.0143e+01
-#>  -3.5980e+00  3.4881e+00 -2.2463e+00 -1.3874e+01 -1.0153e+01 -5.2240e+00
-#>   1.0538e+01 -2.3452e+00  1.2710e+00  7.5885e+00  4.9355e+00 -1.0769e+01
-#>  -2.4105e+00 -3.5217e+00  4.9055e+00  1.9954e+00 -4.5076e+00 -1.8899e-01
-#>   3.2111e+00 -7.7566e+00 -4.4094e+00  9.5524e+00  6.6475e-01  1.4449e+01
-#>   1.2576e+01 -4.6264e+00 -2.9808e+00  4.9721e+00 -7.8618e+00 -2.9451e+00
-#>  -1.2270e+01  7.4681e+00 -8.6775e-01  3.1733e+00 -1.0157e+00 -4.1103e+00
-#>   6.1041e+00 -7.8737e+00  7.6910e-01  2.6178e+00 -2.1165e+00 -2.4539e+00
-#>  -2.9781e+00  5.1067e+00  9.0291e+00  3.7386e+00  4.0225e+00  1.1059e+01
+#> Columns 17 to 24  -0.8807  -6.8757  -4.9313  -7.2179  -2.7915  15.4274   6.0164  -6.6741
+#>    0.9477   1.7979  -6.3627   4.4939   6.2186   1.5902 -11.6395  -3.5445
+#>    7.9012   4.5292   7.2227  -1.0916   9.0658   5.3200  -5.7882  -5.3311
+#>    4.3502   7.8103   7.9528  13.1282  -5.2297  -0.6976  -1.5083  -8.7624
+#>  -13.8298   3.3520  -3.1743 -18.8409   9.0000   1.3979   3.1690   6.7249
+#>   -4.2754 -10.9421   2.2851  13.1925   0.6630  -2.1236 -10.3466   1.8604
+#>   15.2846  -3.9597 -12.6631   2.4750  -4.1738  -3.3280   8.4616   1.3307
+#>    6.3590   5.3857   2.8926   2.1786  -6.7312  -0.7581   1.0523  -3.7789
+#>  -13.7418  11.9056  13.6120   2.5728   6.4213   5.6939 -11.1140  -0.4415
+#>   -6.8191   4.5158  -7.8159   4.3063   0.4596  -0.0760  -2.0341   3.8460
+#>   -0.5926  -9.4973   8.0374  -6.0093 -12.5298   1.7373   1.6441  19.0382
+#>   -9.2623   5.8766   4.1363  -3.2158  -9.5395   1.7469   6.7436   1.4874
+#>   -0.2246 -11.9674  -3.3364   9.8491  -2.7533   3.9118   5.7652   6.6789
+#>    4.5272   0.4888  -9.6354  -1.6830  -1.9183   8.2584  -3.5511   0.5338
+#>   -1.9234  -7.4784   5.2854  -2.8972   2.0119  -6.1031   2.9352  -5.9270
+#>   -1.0020   2.6322  15.1039  -2.4978   1.5712   4.8497  -1.9763  -1.4250
+#>   -2.7766  -8.9166 -12.5303   2.3104   3.5020  -0.2225  11.0040   3.1141
+#>    3.6221  -0.0765  -8.2000   6.2186   0.7653  -0.4866 -10.3200  -1.4989
+#>   10.7927   2.6817  -1.8595  -1.8303   1.1885   2.6971   1.4769  -4.0710
+#>    4.1746  -4.2341 -12.4317   4.1027   2.7154   2.4214  -5.6199  -2.7166
+#>    1.3276   0.3343   1.0940  -5.9334  -6.1352   5.5198  13.6717  -8.4329
+#>    3.9061   1.7449   6.7268  -2.8043  -2.4510   3.9353   4.1274 -12.8426
+#>   -0.4724  -2.4116  -3.0013   1.0078  -1.8346  -1.3911   4.9137  15.8103
+#>    6.3488   4.6773  -2.9455  -2.1355  -1.7662   1.3958   0.5978  -4.8514
+#>    4.0734  10.6386   3.3235  -9.7826  -5.2383  -2.9818   6.3445   7.3067
+#>   -3.0872   8.4646  -2.7031   4.6974  -2.4227  -0.9212  -4.9382  -1.8930
+#>   -2.7740   1.2674  -3.8433   3.4049   2.2112   1.5422  -3.4015  -8.1686
+#>   -1.1354  -0.5527   1.1582 -10.9276  -2.6106  10.2117   2.6492  17.8093
+#>   -0.7095   6.3531  -2.1500  -2.2012  -7.6940  -7.6331   0.4168  -6.9157
+#>    3.5068  -6.9159  -1.8968  -2.1031   4.7683  11.9701  -4.3158   4.0219
+#>   -4.6503  -5.6369   7.3009  -0.4046  -5.3646  -0.5556   2.6237   5.3386
+#>  -10.0209   8.0023  10.9669  -7.7269   0.4845   3.9636   9.8087   5.9099
+#>    4.0444  12.4672   7.9136  -7.6656  -9.4817   1.4621   6.5596 -11.4023
 #> 
-#> Columns 31 to 36 -1.0024e+00 -2.6713e+00  1.2578e+01 -1.7690e+00 -3.4575e+00  3.3586e+00
-#>   6.5265e+00 -6.1522e+00  3.4934e+00 -4.9728e+00  5.8530e+00 -2.0366e+00
-#>   2.4769e+00 -4.4414e+00 -7.5334e-01  1.5265e+01 -1.1129e+01 -2.2469e-01
-#>   8.2506e+00  1.4354e+00  4.4313e-01  5.4406e+00  1.2897e+01  4.4803e+00
-#>  -2.8658e+00 -1.8296e+00 -2.1623e+00 -2.4244e+00 -3.0937e+00 -7.2425e-01
-#>   8.4132e+00  7.2456e-01 -5.0226e-02  5.9712e+00 -3.5478e+00  3.6569e+00
-#>   2.0938e+00  3.0349e+00  1.0649e+01  3.8103e+00 -3.3931e+00  5.1124e+00
-#>  -5.9067e+00  2.8918e-01 -7.7774e+00 -6.2789e+00  2.6692e+00  1.3624e+00
-#>  -7.5225e+00  5.4142e+00 -1.2569e+01  1.1863e+01 -8.2005e-01  9.7496e-01
-#>   4.8118e+00  9.3095e-01  5.2597e+00  5.2339e-01 -2.7255e+00  8.6351e+00
-#>  -2.6015e+00 -9.6194e-01  3.9729e+00 -1.5552e+00  4.7174e+00 -4.7509e+00
-#>  -5.2280e+00  2.3861e+00 -6.1787e+00  2.1090e+00 -8.7541e-01  1.8596e+00
-#>   4.1943e+00 -2.1511e+00  7.2290e+00  1.4783e+00 -6.3846e+00  3.8103e+00
-#>   6.9841e+00  5.9577e+00 -8.0214e+00  3.0639e-01  1.8434e+01 -7.8145e+00
-#>  -2.7008e+00  9.5668e+00  2.2890e+00  2.7696e+00 -1.1703e+01  1.4588e+01
-#>   6.2605e+00 -5.3954e+00  2.2707e+00 -2.5968e+00  4.0614e+00 -6.1080e+00
-#>  -2.1722e+00  2.8522e+00  5.6883e+00  2.5314e+00 -6.3639e+00  4.9474e+00
-#>  -9.4953e-01  1.5049e+00 -3.5119e+00  3.2323e+00  4.7714e+00  2.7848e+00
-#>  -1.2248e+00  5.2855e+00 -1.1353e+01  8.8063e+00 -4.5217e+00 -2.6649e+00
-#>  -2.8687e+00  3.8691e+00 -8.2179e+00 -6.2951e-01 -3.1783e+00  5.1316e+00
-#>   4.0843e+00 -8.5289e+00  1.2830e+00  1.2650e+01 -5.2441e+00  8.3415e-02
-#>  -5.9715e+00 -1.1104e+01  1.9260e+00 -3.7978e+00  3.4070e+00  2.1377e+00
-#>   1.4444e+00  2.2540e+00  3.7112e+00  8.3033e+00 -8.9732e+00  1.7013e+00
-#>  -7.3074e+00  1.8956e+01 -1.8431e+00  1.0475e+01 -8.2182e+00  1.1970e+01
-#>   2.3107e+00  1.4038e+00  8.8203e+00  4.4088e+00 -4.7749e+00  8.1737e-01
-#>   5.5478e+00  4.3362e-01 -3.9417e-01  5.8719e-01 -3.4224e+00  8.3111e-01
-#>   1.9707e+01  1.6573e+00  4.5278e+00  6.3618e+00  6.5622e+00  2.3959e+00
-#>   9.7553e+00  1.3196e+00 -3.7189e+00  1.0158e+00  4.2140e-01 -5.3399e+00
-#>  -1.0143e+01  1.5711e+00  7.4052e+00 -3.8900e+00  1.1622e+00  1.5689e+00
-#>   8.2355e-01 -1.3308e+01  1.7697e+01 -1.3180e+01 -1.5565e+00 -6.2004e+00
-#>   1.5589e+00  3.6957e+00 -3.5663e-01  3.4026e+00  9.4058e-01  8.3490e+00
-#>  -9.1588e+00  3.6904e+00 -1.9694e+00 -5.0691e+00 -1.4893e+00 -2.4870e-01
-#>   1.5074e+00  4.2083e+00  6.5068e+00 -1.4737e+00  7.8155e+00  1.9302e+00
+#> Columns 25 to 32  -6.0222  -2.5225  18.6794  -1.9022   0.0288   0.1890 -12.0331  -4.6590
+#>    7.9806  -6.4788  -6.1458   1.9264  -2.6169   2.2489  -0.1440   7.5822
+#>   -1.1021  -2.6544   9.7899   3.3499   6.7148  -6.5702   6.2299   5.6617
+#>   -6.2453   1.0456   7.5186   5.3260  -0.4289   8.5268  -1.1748   0.9125
+#>    0.9165  10.8179  -5.7658  -9.7494  12.5624  -2.2520   4.1101  -3.6776
+#>    4.4642   4.5141  -0.9595  -0.9528   6.1274  -7.9298 -11.2766   2.0582
+#>    9.7725 -25.7100  -8.8137  -2.2008   0.1551  -0.7973   9.8084   1.2838
+#>  -12.8132   4.4471   4.9165   1.6298   1.1680  11.3918   4.7620  -1.3822
+#>   -7.6801  11.2273   9.5392  -6.5460  -1.5583 -21.2909  -1.6984   4.6187
+#>    9.8602  -9.5653  -0.5661   2.2599  -6.6182  -2.0556  -2.2713  -2.2365
+#>  -11.1825  -5.2334  11.5033   3.7391  -9.5969  10.6885   4.3899 -15.0028
+#>  -11.3795  -6.1763   6.2081   1.2671  -0.5915   3.4431  -3.1682  -2.6503
+#>  -18.4274  -3.3459   8.9185   9.3857  -9.9528  -7.2578   7.3259  -2.2813
+#>   -1.3254   3.3307  -2.5424  -2.6175   1.0168   0.7376  -4.4100 -11.7450
+#>   -2.9995   1.9990   4.8363  -3.6735  -5.7607 -18.0266  -8.4709  -6.5698
+#>   -9.5125  19.1244   4.1371   6.8073  -2.6230  14.8248   1.3387   1.2955
+#>   11.7142 -16.4838  -2.2590  13.0867  -5.6591  -1.9879   1.6286  -2.7937
+#>   -4.3823   3.8865  -2.0699 -10.4855   9.0669  -8.6802  -9.2607   0.2720
+#>    1.3276   4.0587 -12.2692  -5.6160  -9.3784   6.8906   1.0773  -7.5102
+#>   19.9966 -13.4888  -1.3764   9.2722  -6.4324 -11.7465  -6.4369   3.9438
+#>    1.8748 -12.9575  -0.1238   8.8789  -7.3453  -0.0752   0.7718  -1.9769
+#>   -2.9430   1.2230  -3.3981   1.3450   7.5772   6.8048   2.6787   8.3449
+#>   -3.7040 -15.1341   0.6486  -7.6604   2.4353   7.3543  -2.4480   7.8708
+#>    0.3476  -4.1923   6.6404  -6.4607   6.8673 -13.9570  -4.9220 -10.8102
+#>    2.2574   3.9855 -15.4048 -11.4323  -7.1003  14.8612  11.6437   4.7460
+#>   14.6156  -3.7702   4.1366   4.8487   0.2202  -7.1611  -1.1738   8.7857
+#>    5.6938   6.3797  -4.9098  -2.6667   1.5749 -17.5671  -3.0667   2.3424
+#>   -3.2931   5.9301  -8.8754  -7.4322   7.0949   3.0345   2.9480   6.5884
+#>    6.8072  -2.5864  -7.3007   3.4889  -1.4750  -6.7630  -9.3298  -8.1758
+#>   -1.8700   8.0152   5.3331  -7.1700 -10.8139 -22.1410  -5.6817 -12.2239
+#>   -6.3280  12.9446   0.1962   1.4002  -3.2044   3.6930 -16.2343   9.5315
+#>   -6.5399  12.6252  -2.6233  -5.3308  -0.1140   4.8691  14.7716  -5.8068
+#>   -0.3855  -0.3596  -1.5771  -8.5168  -1.1172   1.3504  -1.1572  -0.5796
 #> 
-#> Columns 37 to 42 -6.0443e+00 -5.0038e+00  2.4085e+00  6.1652e-01 -2.9455e-01  4.1388e+00
-#>  -2.1589e+00  7.5638e-01 -1.4299e+00  5.4934e+00  1.1379e+01 -1.7734e+01
-#>  -5.5570e+00  1.4518e+00 -5.1437e+00  5.8455e+00 -1.1211e+01 -1.0749e+01
-#>   8.0975e+00  2.3521e+00  3.8920e-01  7.0470e-01  1.1417e+01  5.2706e+00
-#>   2.2747e+00 -2.5634e+00 -2.5810e+00  2.8128e+00 -1.2677e+01 -9.6755e+00
-#>  -6.1550e+00 -4.8737e+00 -1.0272e+01  8.9978e+00  4.5288e+00  2.6653e+00
-#>  -6.3205e+00 -5.1040e+00  3.3478e+00  1.1458e+01  1.1980e+01  1.3942e+01
-#>   2.7540e-01 -1.3097e+01 -1.1894e+01  8.7668e+00 -6.5043e+00  1.0282e+00
-#>  -1.1896e-02 -1.3126e+01 -8.0822e+00 -2.1835e+00  4.4062e+00 -8.4565e+00
-#>  -5.2831e+00 -2.2819e+00  1.3993e+01  6.1044e+00  1.3993e+00  2.7036e+00
-#>   5.0564e+00 -2.4792e+00  2.1099e+00  1.6402e+01 -9.3117e-02 -1.0041e+01
-#>   1.6624e-01 -1.1526e+01  1.0828e+01 -5.7474e+00  4.6739e+00 -3.1839e+00
-#>  -5.9014e+00  2.9813e+00 -1.6901e+01  2.9944e-02 -2.5500e+00  1.0309e+00
-#>   3.1118e+00 -7.3564e-01  5.5841e+00 -1.4666e+01  3.8443e+00  6.1601e+00
-#>  -9.0465e+00 -3.6688e-01 -1.7310e+00 -6.8496e+00  6.2570e+00  5.2079e+00
-#>   2.4793e+00  5.8129e+00 -6.8916e+00 -1.0394e+01  4.2798e+00  4.3335e+00
-#>  -8.6506e+00  1.2264e+00 -4.2477e+00  2.0104e+00  5.3704e-01  5.4668e+00
-#>  -4.2562e+00  1.6511e+00 -7.5951e+00 -1.8699e+01 -4.9848e+00  2.8267e+00
-#>  -4.0240e+00 -6.9409e+00 -3.4937e+00  1.0665e+01 -1.2527e+01 -7.0438e+00
-#>  -2.1840e+00  4.4412e+00 -1.4393e+01 -1.0654e+01 -3.5939e+00  1.2485e+01
-#>   7.2340e+00 -3.2706e+00 -2.2007e+00  6.2254e+00 -6.8434e+00 -1.3621e+00
-#>  -4.5564e+00 -4.0301e+00 -2.7368e+00 -4.7971e+00 -3.8564e+00 -4.0555e-01
-#>   5.1519e-01  1.7264e+00  4.8336e+00 -3.0540e+00  1.3621e+01  2.4650e+01
-#>   3.8365e+00  1.0922e+00 -3.1472e+00  1.0390e+01  9.9348e+00  7.2009e+00
-#>   4.4151e+00  1.0959e+01  1.6859e+01 -2.6747e-01  3.2473e+00 -2.3686e+00
-#>   3.5090e+00  1.3791e+00 -5.6040e+00 -1.0725e+01 -4.0518e+00  7.6260e+00
-#>  -7.0225e+00 -6.5785e+00 -1.0424e+01  9.5121e-01  1.7987e+01  6.9230e-01
-#>   3.2599e+00  4.0881e+00 -1.3803e+01 -1.1379e+00  5.3533e+00 -1.5402e+00
-#>   2.1776e+00 -4.0299e+00 -7.1673e-01 -3.0291e+00 -2.9262e+00  2.9943e-01
-#>  -2.6078e+00 -3.6891e+00 -9.8233e+00  5.5719e+00  1.2748e+01 -3.7681e+00
-#>  -5.5408e+00 -1.4093e+01  4.5600e+00  4.5790e+00  1.8872e+01  1.6317e+01
-#>   1.8432e+00 -5.3269e+00  4.9184e-02 -1.3810e+01 -3.9466e+00  1.8990e+00
-#>   1.1511e+00  1.7908e+00  2.0034e+01  8.6886e+00  1.1405e+01 -4.1812e+00
+#> Columns 33 to 40  -0.8611   1.0568  -9.0654   5.3277   4.1641   4.4664  -6.3162  -3.7858
+#>    4.1445   1.2710   6.9080 -10.6709   6.6070   8.0267   2.1055   3.5532
+#>    3.7797   5.5512  -7.8304  -1.8071  -0.8741  -4.7816   0.3294   1.1418
+#>   -1.7175  -3.0894  10.3689  -5.6529  -8.6062   4.1273   3.9762  -7.6929
+#>   14.5131  -6.0969  -5.2956   3.3127   6.8256   5.7329  -6.6279  -0.1204
+#>   12.7190   1.6159  -0.0140   7.5648   3.0746 -10.5060  -9.0135  -4.9237
+#>   -3.6573  12.0284  -2.2545  -9.4216   8.5257   6.2418  -4.3268   2.3996
+#>    4.2874  -2.0749  -9.5551  -3.2306  -8.7069   1.5910   8.9562  -1.1663
+#>    0.8192  11.2540  20.1876 -12.9242 -10.9081   8.7150   8.3009   1.9462
+#>   -0.7929  -2.0514   7.4391   7.8435  -2.3212  -4.4998   0.5574  -2.7245
+#>  -14.1838   0.0514 -14.8192 -10.1813  -7.8843   5.9334  14.7841 -11.0592
+#>    4.3808   9.1414  -9.0137   7.9655   8.0009 -12.7833   6.9089   7.5214
+#>   -5.2681  20.7088   3.2978   4.0480  14.2196   1.7780  -2.1520  -6.3127
+#>    5.8338   7.5677   1.0579   0.7008  10.7610  10.9471  -2.2719  -4.1100
+#>   10.0725  13.0931  -4.6233  15.3859   3.8250   0.1703  -5.1177  11.1111
+#>   -0.7654  -7.1043  -6.0416   0.2592  -4.7001 -15.8419  10.7481   2.4375
+#>   -5.4736  -3.9784   6.6280  -0.1362   2.2109   0.2694  -2.6858   6.7014
+#>    5.5710  -0.7875   2.5261   7.4350 -11.4584   0.8176 -13.5017   5.9151
+#>    7.8448  -4.9647  -6.1809   8.5925  -1.7261  -5.2174   1.3180  -6.7331
+#>   -4.3430   6.0933   8.7123   1.6033   3.2952  11.4633   6.3643  -2.0127
+#>    2.9574  -3.4387   2.7256 -12.6858   4.4055   3.7674  11.1957   8.3339
+#>    9.4475   2.7473   1.5837  11.0178  12.8108  -3.7032   4.0755   4.9520
+#>   -4.9123  13.2245  -0.6136  -0.0522   2.2529  -3.7863   0.2554   0.1037
+#>    4.5806   3.2915  -7.4074   1.4762   6.6544  -1.2248 -13.4508  -0.6399
+#>    2.9039  -1.6340   6.9400  -7.5700   6.8986  15.1708  11.1973  -2.6747
+#>   -6.9221  -7.5585   7.5320   1.6463   0.6203   7.4859   4.6856   6.5935
+#>    5.7284  -1.6975   5.4235 -11.5777   2.5505   6.7303 -14.7043  -7.0326
+#>   -0.7517  -1.1776  -7.2456  10.2495  -2.1493  -2.8518  -0.1505  -3.0757
+#>   -4.3981  -5.4743  10.1640  -2.4632   3.0505   7.5561   2.0925   7.4318
+#>   -0.0157   9.1111   0.1285   6.9749   6.4397  -4.5390 -13.2734  -5.0305
+#>   -6.0661   7.3484  -3.0283  -8.9787  -4.3967   6.0583   4.7974  -2.1257
+#>    2.0119  -4.1015   2.2100   6.4800  -5.0461  -6.1380   7.4747   8.5014
+#>    0.3777   1.4523   0.8882  -4.0412  -6.6179  11.7257   6.5241  -0.8750
 #> 
-#> Columns 43 to 48 -5.4550e+00 -8.2143e+00  4.7637e+00 -2.9903e+00 -7.2637e+00 -9.7950e+00
-#>  -5.4919e+00 -9.6887e+00  4.7355e+00 -6.6842e+00 -9.0013e+00 -1.1458e+01
-#>   1.1702e+01  6.5723e+00  4.7653e-01  2.5971e+00 -2.8433e+00 -8.7995e+00
-#>   1.3533e+01  2.4328e+00 -3.6331e+00  1.5955e+00 -2.2165e+00 -1.7262e+01
-#>   3.8044e+00 -2.0567e+00 -3.6479e+00 -4.1294e+00  2.9667e+00 -5.5330e+00
-#>   8.1044e+00 -8.6189e+00 -5.4637e+00 -1.1795e+01 -2.1088e+01 -4.0552e-02
-#>  -2.9690e-01 -6.5130e+00 -9.3919e+00 -1.9817e+00 -6.7502e+00  6.5811e-03
-#>  -1.9122e+00 -1.1089e+01 -7.5356e+00 -4.9767e+00  1.1579e+01  9.7925e+00
-#>  -4.7898e+00  5.8781e+00 -1.0547e+00 -5.0681e-02 -7.6112e+00 -6.2750e+00
-#>   2.2555e+00 -3.7843e+00 -4.7802e+00 -8.5480e-01  1.4987e+01 -7.9747e-01
-#>   1.0052e+01  3.6775e+00 -1.0574e+00  1.0857e+00 -2.2764e+00 -7.6102e-01
-#>  -1.0788e+01 -9.3425e-01  1.2396e+00 -1.4440e+00  3.6199e+00  7.8463e+00
-#>   8.0964e+00  4.8538e+00  2.7574e+00 -5.8478e+00 -6.2870e+00 -2.7797e+00
-#>  -1.7868e+00 -1.0333e+01 -2.0574e+00  4.5673e+00 -2.6122e+00  3.1900e+00
-#>   5.2439e+00  5.9873e-01 -1.0739e+01 -5.0399e+00 -2.8870e+00 -1.0834e+01
-#>  -6.6098e-01 -6.5914e+00  8.4657e-01  8.7373e+00  9.3333e-01 -1.7683e+00
-#>   9.2482e+00  8.5724e+00 -3.8986e+00 -9.4177e+00 -6.1733e+00 -4.7650e+00
-#>   1.3976e+00  5.4746e+00  6.8451e-01  1.0722e+01 -6.5352e+00 -5.5735e+00
-#>  -6.8063e-01 -7.1734e-01 -1.0688e+01  3.1244e+00  9.7000e+00  4.6155e+00
-#>   1.4267e+00  1.2498e+00 -3.2097e-01 -3.9869e-01  6.3889e+00  1.3758e+01
-#>   4.5269e+00 -3.7422e+00 -4.6177e+00 -3.7689e+00  5.7216e+00  5.8025e+00
-#>  -2.6467e+00  2.3303e+00  1.0984e+01  1.1895e+01 -8.3679e-01  7.5611e+00
-#>   1.0457e+00  8.9304e+00 -6.0809e+00  7.7563e+00 -9.1621e+00  1.1348e+00
-#>   2.5585e+00  7.1815e+00 -3.8037e+00 -5.7326e+00 -6.0681e+00 -2.6218e+00
-#>   8.5473e+00  8.0866e+00  1.9267e+00 -5.5515e+00 -1.0811e+00  5.9082e+00
-#>   4.0650e+00 -1.3018e+00 -1.4162e+00 -5.3452e+00  1.6563e+00  3.4752e+00
-#>   3.5318e+00 -2.5060e+00 -1.2967e+01 -1.5486e+01 -1.1734e+01 -7.6932e+00
-#>   1.0912e+01  1.1026e+01  2.3755e+00  4.6339e+00  1.2357e+01 -1.4554e+01
-#>  -5.2169e-01  1.8166e+00  2.7753e+00  6.4007e+00 -4.7057e+00  1.1531e+01
-#>  -1.6006e+00 -7.4824e+00  7.2213e+00 -7.9190e+00  6.7513e+00 -8.9247e+00
-#>  -6.4194e+00  1.5810e+00 -4.7193e+00  6.2955e+00  7.0297e+00  6.8899e+00
-#>   2.6142e+00 -1.6475e+00 -2.5523e+00 -1.0377e+01 -8.8961e+00  6.2604e+00
-#>  -1.8070e+00  6.4933e+00  9.0026e+00 -7.7026e+00  2.2729e+00 -6.4668e+00
+#> Columns 41 to 48  -6.2253  15.4813   5.4665   1.7043  -6.0134  11.2739   3.4207  10.5951
+#>    1.7634  -4.2631  -3.8672  -0.8163   0.1975  -6.5871  -2.4852  -0.5717
+#>    5.6023  -3.1966  -0.3015  -6.8502  -5.4750   3.8639  -2.1092   3.5328
+#>   -7.9157  -3.4104 -15.3541   2.3773   5.5924 -19.1730  -2.5667   2.2097
+#>    9.9243   5.1434  14.3880 -14.6639  -2.6193   4.4196   3.6730 -12.1041
+#>   -1.9230 -10.9819   9.2371   9.6889  -9.2786 -10.2420   8.5802  -1.4945
+#>    0.6482  -4.0058   4.6641   9.1679   7.9664 -11.4321   8.6949   6.1002
+#>    0.0771  -6.0239 -15.3810 -10.3905   1.2433  -7.9479   1.7519   8.1579
+#>    3.7030   7.0018   3.8837 -18.1678   2.3074 -12.9184  -6.3444  -7.0652
+#>    0.2832 -12.5081  -1.6503 -12.7904  -0.7906   4.5055  -2.0274  -7.0966
+#>   -1.1560   7.0276   2.4356  10.6392  11.2842   3.4248   4.4802  -1.5989
+#>    3.3875  -1.8682   6.3087  -3.9602   1.7666   2.2462   5.6006   2.6341
+#>  -14.4572 -10.4930  -8.8586  -1.9750 -12.9727  -4.7007   3.8266   8.8408
+#>    1.6228  -5.1363  -4.4258 -10.8498  -7.5557  -5.3916   6.5801   8.8889
+#>    2.8427   7.2956  -8.0200  -1.5578  -5.0323   5.3388  -5.9133  -0.9252
+#>    8.8019   1.7589   4.1468  -3.7787  -1.4694  13.9980  -1.7290  -9.1751
+#>   -2.4335  19.8203   4.8795  10.1656  11.7867  11.2572 -12.0224  -6.1928
+#>   13.4877   2.4026   0.1878  -1.2813   5.0872  -6.6813  -0.9762  -5.9967
+#>   -5.5528  -9.3455  -2.1656  10.8958   0.7286   2.3748  -5.7412  -9.6578
+#>   -6.0045  -8.5685  -0.5930  -5.4759  -1.8792  -7.3016 -14.8040  -2.4047
+#>    1.8737  -7.0144  -2.5284 -11.7359  -6.8526   6.9042   9.9385   1.6060
+#>  -12.9047  -0.9142  -7.1893   1.6719  -5.1746   1.1802   7.3469   0.9788
+#>   10.8455   1.7521   8.3575   9.0212   9.4134  -4.4178  -9.7777   8.4185
+#>   13.0378  -2.6627  11.0654  -2.6261  -1.4146 -16.2292   4.7065  -8.3873
+#>    9.6175  -8.6752  -2.4736   5.6130  13.7771  -4.7949 -15.8744  -0.6387
+#>   -1.2921 -11.4872  -1.7810 -10.5419  -5.9407   2.5456   1.6647  -4.7831
+#>   -1.9197 -10.4179   0.3066  -2.0262  -6.5656 -10.9171   7.0313  -0.0362
+#>   -5.0607 -11.0022  -4.7930   4.4554   6.1903   4.9043   2.5441  -6.7533
+#>    3.5607   5.2236   0.7005  -2.0445  -6.8758  -6.7025   1.6693  -9.5756
+#>   -2.7160  10.0189   9.0864   0.9660  -1.2260   4.2049   2.8616  -6.1965
+#>    3.0842  13.8484  -4.8341  -2.2035 -10.7796  -2.2567  -3.8369  16.7756
+#>    1.0556  -1.7092  -3.7212  -7.8805   2.5508   1.4942  -2.1015  -7.4245
+#>    5.8052  -1.8614 -13.0850   1.6800   4.2464  -4.5503  -0.7407   0.9004
 #> 
 #> (15,.,.) = 
-#>  Columns 1 to 8   0.8048  -7.1302  -5.1982   4.7184   2.3049   0.1458  -2.9323   0.9045
-#>   -6.2981   8.2727   2.8437   3.7729  10.3314   2.1071   0.9795   7.1802
-#>    5.0801   1.4905  -5.0455  -4.1448   7.4240  -0.2848  -4.4704   6.6526
-#>   -4.3386  -0.6268 -12.2354  15.0675 -11.1815   7.1535   0.6211  -2.3970
-#>    6.0735   2.6415  -7.8096   0.1928  -6.0289  -3.4852  -1.8490  -6.5341
-#>   -5.5020  -4.1683 -21.8447  -7.1531 -10.1589  -3.6331  -6.6435   8.8253
-#>   -5.4820   1.1505  -1.7518  13.0789   5.8027  -6.2200  -4.9524   6.7853
-#>    3.2055  10.5159  -3.1530  -4.1114  -8.2848   2.9294   9.1991  -6.8600
-#>    0.7853  -0.2040  -2.6513  -8.2570   4.7800  -6.0429  -1.0372   4.5380
-#>  -10.0597  -1.5282   3.6515   4.6160  -6.1629   7.2925   1.3604  -2.4042
-#>    5.5991   8.1511  -5.1106 -12.7090   6.2902 -14.5277   8.0083  -2.9098
-#>   -1.2806  -1.3863   8.2275   2.2868   7.8467   1.3516  -3.8672  -3.2717
-#>    1.9597   3.3709  -8.9261   7.0613  12.2671  -7.5790  -3.4860   9.1355
-#>    0.7325  -5.4724   6.2299  -8.6116 -21.3077   3.4404 -13.1769  -1.2893
-#>   -4.7072   7.5819  -2.1514  10.3851   2.1876  -4.8796  -9.3614   3.8329
-#>   13.7107   5.2684   6.4132  -3.1331  -7.7471   9.2193   3.7722 -10.6658
-#>    6.7938 -13.5632  -3.4073  -6.4364   6.0194  -6.4552  -3.6073  -2.3002
-#>   -3.2604   7.2644  -6.9982   3.0262   0.3497   6.5678  -3.8303  -2.5425
-#>    9.5488  12.8055  10.9672  -4.7713  -4.8149   2.6481  14.4671  -0.8024
-#>   -0.1062   4.0740   0.1192  -2.8975  -3.5445  -4.3332   3.5695  -5.8257
-#>   -5.9688  10.4781  -3.7207   0.8464   3.3031  -2.0499   2.5568   2.4314
-#>    6.8408  -2.4286  -2.0812 -13.5789 -11.9642   1.9043   1.6696  -5.7336
-#>   -0.3294  -4.0051   4.4825   8.7400   4.2389   6.1872  -4.8511   3.3600
-#>   -7.3160  -2.2606   0.7169  -5.0866  -0.0417  -2.8604 -12.7086  11.6121
-#>   -1.7032  -1.4316  11.1245   0.1705   9.2713   1.7239  -2.6730   3.3785
-#>    0.5772  -2.7065  -2.8858  -4.2171 -10.9184   3.6095  -0.5072  -4.4745
-#>   -8.3830  -5.4281  -6.8301  -2.5117  11.5640  -9.2411   5.6765   8.3588
-#>    2.6178  11.9343   9.2576  -3.4401  -3.8225  -3.3271 -12.3327   1.9006
-#>    5.9909   2.2253   5.8577  -8.6208  -2.9306  -6.7949   1.7518  -8.0359
-#>   -3.3776   4.6775   6.3503  -5.2518   6.9190  -4.8524  -8.8138  -1.2051
-#>   -6.7259  -4.4902   7.2270   7.9758   4.6113   2.5120  -4.4357  -6.3322
-#>    7.7574 -13.7695   3.9471  -4.6606   5.5693   7.0922 -10.3325   4.2199
-#>  -10.8634   1.3126   2.9160   2.5213   6.1139  -5.9908  -5.1855   2.8433
+#>  Columns 1 to 8  -4.2747  11.2209   5.3836  -1.4030  -5.9563  -0.9162  17.2545   4.5411
+#>    3.2806  -2.3690   1.9042   8.4447   8.6115  -5.9054  -2.2237   2.2706
+#>   -4.5532   3.4310  -7.5214  -2.8339  -1.9221   4.4026  -0.9524  -4.1235
+#>   -1.8922 -12.9170  12.2323   7.9637   1.5879  -1.4672   2.6015  -2.3395
+#>    1.7052  14.0402   7.7936  11.8928  -3.0232   2.0031  -1.2634  -8.4372
+#>   -9.4591  -1.3585  -1.0784 -14.4314  -5.6974   7.1326  -9.7584  11.2040
+#>   -6.7563  -3.1252   0.9994  -7.5600   6.6968   4.9823  -6.4021  -2.5075
+#>    1.2933   5.8467   6.7220   0.6309   6.9515   7.2260  -4.5833 -10.7179
+#>  -14.1855   2.3998   3.8708  18.3877  10.7002  -5.7762  -5.2950  10.0083
+#>   14.0508   3.0589  -9.3336  -6.5368  -3.9655  -0.9708  -0.6588   6.5599
+#>   -2.6133  -5.8309 -14.9207  -2.0724   0.5182  -0.1810   6.9787   1.1528
+#>    2.0274   9.1401   3.8578   5.4996   4.6371   9.2297   5.1012   0.9382
+#>    6.5537  15.8814   5.0876  -4.8474   3.5665  -4.4088  -4.2359   2.9431
+#>   10.6382   3.9061   5.5868  -2.7831  -1.9625   2.9950  -0.3136  -2.8474
+#>    9.9382  12.4973  10.1195   2.9316  16.6530  -1.8438  18.3835  -0.8610
+#>   -3.9924  -2.8585 -17.5996  -3.5616   1.5926  -2.5963  -9.7335  -2.8453
+#>    4.4835 -10.9601  -9.5063  -6.7245   1.4008 -17.1639   9.7064   0.8067
+#>   -5.6122   8.1782   2.0082 -17.2075  13.1548   9.5857  -5.4116  -9.3046
+#>   -2.0921  -4.4457  -5.2193 -14.1155  -0.5663  -0.8361   2.7828   5.6409
+#>   -4.9276   4.1458   0.8858   2.4367  -1.2432   3.1711   0.9293  -0.4187
+#>    0.0853   4.1620   2.4880  -4.1946   2.0009 -10.6949   1.2272  16.6223
+#>    1.4169  -2.6146  -0.2968   4.4078   2.2711  -1.5178   2.2351   4.9065
+#>   -4.3045   0.0909  -1.2028  -0.6383  -3.2996   5.6568  -6.0963  -5.8985
+#>  -20.9009  10.2937   9.3113  -3.3180  -3.0311   7.3176  -2.3713   3.4409
+#>   -9.3364 -13.6321  -4.6866   4.3415  -9.5569  -4.7031 -13.1400  -2.8893
+#>   -0.9151   3.0460   4.6602  -4.3565   1.2116  -0.4645  -4.1205   3.3254
+#>   -6.5715   9.9600  14.7939 -13.0391   1.7620   4.8612   3.2664   3.8959
+#>    5.6417  -0.4934  -6.0265  -8.6849  -9.7323   0.3718  -0.3412   6.8578
+#>   -9.4668  -4.3314   6.1339   6.5446   7.3414   5.5083   3.1474   1.9006
+#>    1.7123  24.7829   1.2456 -21.5154   9.4511   3.0373  -2.3033  11.0565
+#>   -3.8991   1.1865  10.2420   7.7787   3.3465  -8.0858   3.4400  -1.7019
+#>   16.4395   6.8679 -12.3294   0.1245   0.7837  -6.7297  -8.5040   0.9146
+#>   -8.2886  -5.1431  12.2854  10.0587  10.4088   4.0534   8.5897  -2.7896
 #> 
-#> Columns 9 to 16  -2.6243   0.4328  11.1646   8.5593 -12.1996  -7.4265 -12.9429   7.7398
-#>    3.5453  -0.1579  -4.2605  -9.5878  -3.6614   5.4460  -3.9349   6.1239
-#>   -3.2706  -6.6456 -13.3301   1.4546   6.8946   4.3316   8.8452  -5.5344
-#>    8.2053  -8.7390  13.9397  24.9905   4.2868  -0.4157  15.1577   1.2894
-#>    2.3995   5.4825  -5.9720   1.8767  -4.0682  -6.2251 -14.7602  -7.7949
-#>   -2.3086   8.8263  15.2860   5.3245  -0.6444  -6.3166  -6.5798  -2.2219
-#>   -0.9974   9.5613  14.1790  10.5027  -4.9540 -13.3136   3.9889   0.6169
-#>    4.0747  18.7485  -9.3609 -11.3180  -9.3307   2.9839  -9.8000  -4.7789
-#>   -1.2814  -5.5160   5.1901   6.1370  10.5471  -2.6609   5.4535 -13.1699
-#>    8.7097   4.1678  -5.8543   1.4070   4.3947  -2.2228  -2.4664   8.3158
-#>    4.0793   5.4742 -15.4222   1.8686  12.2919   6.3672  -7.8565   7.6350
-#>    3.6448   6.1630   1.4843  -8.0472  -2.3761  -7.7439   0.7194  10.8694
-#>   -0.4451   7.7842  -7.7088  -6.5198  -5.2164  -7.2539   8.5703   9.0153
-#>   10.1100  -9.1459  23.6299  -3.6850  -6.6768  -9.6936 -10.3292 -13.5748
-#>    9.9733   8.9384  -9.7610   8.3322  -7.7173 -10.8625   0.3825   4.6803
-#>    6.4227  -1.0801  12.4142   1.6283  -1.2001  -4.3134  -6.8145  -0.7343
-#>    4.0345   9.0029   6.5919  -3.8529  -7.8487  -6.0298  -1.6113  -8.0233
-#>    0.3470   8.4495   3.4920  -5.8563  -9.9557 -10.6460   7.1741   9.1540
-#>    0.2047  10.9090  -1.1599  -6.6670  -5.3418   0.5331 -15.7860  -3.4584
-#>    2.2713   7.9691  -2.9790 -11.3593  -3.0517   5.9772  -3.5811   3.4882
-#>   -1.9983   5.4185  -0.5448   1.1243   3.6822  -5.9691   1.3919  -3.8577
-#>   -6.0237   1.4433  -0.1638   0.7065  -6.3657   2.9962   0.7895  -7.5776
-#>   -7.1163   6.1097  -2.6729  16.1532   1.7291   5.4875   0.1021   5.3662
-#>    0.2978   3.3122  -3.9261   2.0635  -0.0566  -5.4560  10.9724   9.1025
-#>    1.3748   4.8877   8.7258   1.8500   6.0718  -2.0596  -7.8329  -2.4864
-#>    2.1345   1.4062   8.9350  -0.1680   0.5001  -3.2611 -15.8572 -11.3055
-#>   10.6560  20.8887  12.0689  -4.7632 -11.5247 -18.9330   7.3607   5.1821
-#>   11.8770   0.2722  -3.6730 -15.7264  -0.4359  -5.6856 -11.3708   0.7707
-#>   -3.0294   9.8799  12.4789   0.2152 -15.0768  -6.8379  -9.5639  -8.6209
-#>   19.6157  -3.2185  -6.2283 -15.6918   4.8533  -2.8065  -9.8387   7.6355
-#>    3.2747  15.9104  18.6214  12.1918  -7.1561  -8.6773   5.4100  -6.6609
-#>    0.7128 -12.3952   4.7757 -10.6673   2.7547  13.9214  -3.6430   9.8891
-#>    6.0475  -2.3505   2.2329   1.5311   8.0533   3.1398  -1.4593  11.9712
+#> Columns 9 to 16   2.5876 -12.9979   7.7099  -6.2017   0.9518  10.2987  -6.6717  10.0881
+#>    1.4349  -0.8321  -0.5845  -1.1423  -0.3030   3.4329  -5.6098  -3.9766
+#>  -11.3756  -3.2481   1.2326   0.2942   7.2329  12.1213  10.2287   3.0747
+#>    2.1961  -0.1428   1.1030   2.1117  -1.7190   1.1502  -2.0437   5.3869
+#>   -2.5092   9.4117  10.9734   4.1672   3.3402   2.4821  -6.4350   3.0019
+#>    0.5083  -8.8514  -6.7338   1.5207  -2.0410  -9.5737  -1.7273   9.9099
+#>    8.3670   1.2663 -14.3858  -8.4964 -10.4892   9.1447  -3.2528 -10.1467
+#>   -2.4563   2.7110   0.4576  -5.5571   7.6568   3.7240   4.5972  -0.6151
+#>    0.5692   1.1357  -3.2869  15.6175  -0.7590  -9.1329  10.9456  -2.4756
+#>   -4.1670  -1.7299   7.5588   6.5154  -5.2341   3.7069  -1.7579   5.6123
+#>   -8.6358   3.3087  11.5492   5.9000   0.7000   5.6203   9.5818   5.8684
+#>   -8.3734  -9.0231  -7.2242   0.5823  -5.4014  -0.2007   6.0797  -7.0188
+#>    1.4658  -5.6203  -8.7261  -1.4989   7.8541  -9.6464   5.6319  -2.0984
+#>    2.3988   5.6698   1.9776  -5.4519  -4.6376   2.6983  -5.7701  -7.8565
+#>   -3.5355  -7.5361  -6.0313  -6.8370   0.7085  -1.8911  -0.8698 -12.2137
+#>  -10.3301   1.1440  -3.3687   0.3721   9.2846  -4.4569   5.2276  -4.2738
+#>    5.9532  -2.5153   3.9358   3.5199  -7.2063  -1.2784   1.4520   6.9584
+#>    0.8911   1.6841  -5.1877 -10.0137   9.5660  10.9914 -12.5710   6.1001
+#>   -1.3461 -10.9042  -0.2153  -6.0340   1.0096  -2.3690  -2.5191   1.3392
+#>    5.1408  -5.5534  -0.6703   7.6078  -6.4556  -6.8282  -3.6198   2.6698
+#>    3.8401  -2.4434  -5.5857   5.3737  -7.8142   6.1887   5.7830  -3.0767
+#>   -9.3553 -20.9373 -12.4017   4.6379   1.8046 -10.1554  -3.8313  -2.8380
+#>    2.2838  17.2925  -5.7846 -13.6586 -10.0679   6.2485   0.6100 -10.6211
+#>    9.4378  -0.9839  -5.7292   0.7477  -2.4847   6.3036  -1.3421   2.5304
+#>    9.0686  14.1414 -10.0648 -10.1142 -15.5967  -2.1761 -15.5058 -11.2852
+#>    3.1401   5.3112   2.2429   5.1124 -10.7240  -0.5680  -8.4167   2.0790
+#>   -1.0588  -1.6112  -8.4336   8.7863   2.5908  -2.1235  -3.2482   6.9029
+#>   -5.1237  -3.6513   5.2006   0.4776   3.2072  -6.1609  -3.1893   9.6612
+#>    9.7545   5.0189  -0.9617   7.2267 -15.4872  -0.7203  -3.4099  -4.8678
+#>   -3.8151   0.4245  -3.3436  -6.5632  -1.0615  -2.0232   7.5643  -1.0563
+#>    3.0647   9.8594   0.1297   0.1789  11.1698 -11.7139  11.9365  -0.2432
+#>   -3.7420   9.6675   8.3256   9.5601  -6.0883  -1.5649   3.6960  -4.0880
+#>   -1.6746   2.7384  -0.1467   1.4261   1.1921  -2.6601   2.2365  -4.1107
 #> 
-#> Columns 17 to 24 -10.7269  -7.0341  -4.5691 -19.3047  11.3635   2.1366 -11.1086   9.7337
-#>   -1.3675  -0.4500 -11.2419 -17.4118   1.6026  -4.6512  -3.6940  12.8833
-#>    1.3415   2.8172   3.8470  -3.8784  -0.9783   9.3358  -1.6777   5.7717
-#>  -15.0082  -2.9175   4.9480  -1.9858  -1.1864  17.0469   8.2187   7.8884
-#>   -2.2238   2.6507   1.9936  -4.5235 -12.0722   8.0846  -5.2894   9.9181
-#>    3.4597  -5.0310 -18.5341  -7.4792  -1.1131   4.3999  10.0050   1.9466
-#>  -10.2704   0.7731   1.3454  -2.3996  20.8438  -5.3910   1.9644   8.1886
-#>    6.5566   1.6228  -7.1471  -9.1481  -2.6575  -5.3536  -5.5653   5.4137
-#>   -1.3179   4.3835  -3.4015  -1.2802  -4.4014  -6.5830  11.9631  -9.0388
-#>   -1.6018  -4.5212   7.1977   7.7312   2.8460   4.3718  -3.1728  -3.9669
-#>    5.3512  -3.0614  -7.0596  -3.8677  -1.1902   2.3101 -14.7512   8.5772
-#>   -4.0062   0.7587  -3.6666  -4.0924   1.1767 -12.3443  -1.2912   0.3650
-#>  -10.4419  -1.7139   2.5798  -6.1205   3.7598   7.3134  -6.3118   9.1148
-#>    4.5062   6.7279  -0.1350   3.8005   7.8222   8.7671   3.8129  -8.1235
-#>  -17.0484   0.8677   5.0648 -11.1856  18.0983  -2.6071   1.6093  18.9739
-#>    4.1095  -4.1844  -1.1886  -8.2871  -4.1897  15.7950   0.3164   7.1537
-#>    6.0536  -3.2139   3.1367  -4.3572  -3.2948  19.5925  -6.7900   7.3236
-#>    9.3605  -2.2390  -7.1153 -11.1644   2.4283  -5.9793  14.6581   4.1310
-#>   11.8628  -5.5734  -1.3126  -0.1468   0.7981   4.7050  -6.4429  -5.4245
-#>    3.4164 -18.6147  -5.6152  -7.5493  -2.3179  -8.2711  -0.4826  -5.3061
-#>    2.7276   0.1413  -2.7000  -2.6496   2.5378  -9.9883   7.9165   2.5381
-#>    6.2961   9.9005  -2.7543   2.1022   2.6281  -0.8926  -2.7783 -13.4023
-#>    0.7441  -3.5912 -10.0708   7.8162   3.0236  -3.2449   1.3602 -10.5133
-#>   -7.4043  -8.2482   1.9733   4.5619  -1.3130  12.2857  -3.8850  -7.3048
-#>    1.8633   0.2468  10.0071  13.2876   2.1962   6.7229   6.5548  -1.1695
-#>    5.3615   1.4604   7.1541   3.6082   6.6734   3.7640   3.4410  -0.6691
-#>  -11.0977  -0.3524  -5.1584  -6.6272   7.4835  18.1811   1.3001  18.6657
-#>    9.3068  -2.9136  17.1541   3.0779  -8.9603  21.3401 -16.4021  -6.1953
-#>   -2.7839   3.9002  -1.7010   4.1217  13.8331  -5.2049   7.4136  -2.0544
-#>    4.3481  -7.7533   4.3164  -6.1902  -6.0672   8.6225 -16.4504   0.4909
-#>    1.8774   3.1294  -6.3901   2.4610  15.0536 -11.9390  11.1065  -4.4631
-#>    2.7281  -3.2621  -4.2025   0.7014 -20.1567   3.1274  -1.7161 -10.5776
-#>    1.0278  -7.0991  -9.2778  -4.2494   1.0259 -11.1924  -0.1329 -13.5445
+#> Columns 17 to 24   6.6184   1.1030   2.0648 -10.7212   5.4404  10.1149  -8.6124   2.8432
+#>   -7.0805  -3.1861   9.1699   0.0404  -5.1002  -3.7227   4.1894   3.4530
+#>   -1.3490   2.6690  -7.0640 -10.3683   3.5994   4.2780   3.5495   2.2408
+#>    7.7307  -7.0473  -3.1147  -1.7412  -2.3436  -0.6346  -2.6097  10.7840
+#>    8.1260  14.5299  11.3759  -5.2874   4.2734   7.3765   6.1671 -11.4377
+#>   -0.7681  -0.0344  -5.9003  -3.9739  -0.6213  -8.2776   2.9905  -0.3785
+#>  -15.7247   0.9331   1.6161  -1.8922  -3.6438   2.3390  -1.5774  -3.8441
+#>    3.8893  -4.3369 -10.8468 -10.6531  -8.9493  -2.2181  -4.9513   1.9359
+#>  -14.2442   1.5807  16.8771  11.2701   3.9620 -11.5948   8.4619   3.7181
+#>    1.9457   1.4270   4.2615   9.2280   2.8886  -0.5203  15.4807  -4.5500
+#>   -2.7279 -15.9951 -13.8036   7.8027   5.3600   8.5416   6.4683   0.5478
+#>   -2.9861   4.3129 -13.5209 -10.1400  -4.1135   8.4483  -7.0568  -7.2461
+#>   -0.2546  -2.3467   4.5596  -2.6388  13.6028   7.3492   3.1828   0.8961
+#>   -6.7606   4.7067  23.2489   1.9132  -2.8624   2.3499  10.2202   1.2802
+#>   -2.9727   4.5097  -0.6156  -8.9329  -8.0880   6.7563  -9.0279  -4.5531
+#>    5.1356  -1.5607  -5.4880  -5.0374  -2.8647  -4.0657  -3.0986  -4.5374
+#>   -4.0223  -8.0928  -1.8474   8.2301  -0.7528  18.4746  -9.7340  -3.6031
+#>   -0.9243 -18.7331  -3.9924 -11.7917   2.9637 -14.7175   4.8231  -9.0357
+#>    7.1258 -11.1329  -9.2663   0.7886  -0.6687  -2.3403  -3.1528  -1.9531
+#>   -5.4994   1.4657  11.8501  -2.5899   5.3709   2.2074   6.4882   5.5628
+#>   -0.0761   2.5887   5.3327   5.6399 -10.0858  11.0612   7.8867   2.7175
+#>    4.8096   8.3581 -19.6857  -7.2523  -8.9552   6.5619  -9.8403   2.6775
+#>   -4.4180  -1.6368   6.2281  -5.5233   6.8090  -1.5552  -3.1684 -11.6280
+#>   -4.9894   1.5259  -2.0899  -8.9529  -0.1386  -6.0879  -0.8879  -0.1137
+#>    2.6106  -2.8868   8.2288  -1.8687   5.5861  -2.8573   4.1611   0.6215
+#>    7.9863   3.1406  11.3741  -0.3891   3.8970  -5.1409  12.6278  -0.3163
+#>    3.2934  -3.3294   9.9129  -8.2976   1.8597   0.2873   7.9849  -7.9432
+#>    5.7547   4.0399   1.2295   2.5538  18.4518   1.5167   9.7285  -6.8311
+#>   -2.4178  -2.9201   4.9999   0.3869  -4.8894  -3.3738  -0.9356   7.1583
+#>   -2.7385  -2.6651  -1.8665   4.7754   3.9906  -4.9644   3.7301 -10.9044
+#>    2.8843   6.5246  20.2371   1.0709   0.0653   8.1621   2.2304   7.0330
+#>    4.4385  10.2909   3.4981  21.4640  18.0731   2.7552   4.3355   4.4733
+#>    2.2048  -7.9052  -5.1967  10.6472  -4.4152  -2.3398  -4.9826   3.4552
 #> 
-#> Columns 25 to 32  -1.7462  -1.6269  -0.7915  -0.8197  -3.9971 -10.4806 -10.2680  -2.7534
-#>   -0.2765   6.7450  -2.0712  10.6486 -13.9731  16.2608  -5.7437   3.4435
-#>    7.3143   4.7691   0.7026   2.9372  -7.7866  -4.2075   6.0117  13.2946
-#>   -5.4815  11.9231   6.8967   9.7563  15.3568  -1.2720  16.1653  -1.1744
-#>    3.4551   1.6304   4.1245   0.6843   4.5899  -6.6570  -6.3757   3.0269
-#>    2.4357  -3.9644  -7.9284  -0.0059   2.5904   4.4955   7.8125  -0.6694
-#>   -5.8358  -7.0826  -3.6999  -0.5470  -1.9090   1.5121   7.6959  -6.5233
-#>    6.5241  -0.8224 -16.7985  -0.5287  10.2887  -3.1625 -11.5654   0.9984
-#>   -0.1712   0.7338  -4.5138  -9.9664  -2.2649   0.5123  -5.3444  -5.0459
-#>   -5.5832  -4.8549   5.5569  13.3486  12.9264  -5.5433   1.9425   4.5420
-#>    1.1549  -0.1853  -6.9617   1.7310   1.1553  11.2502  -7.4437  12.6783
-#>    0.9435  -6.3769  12.0910   9.3257  -1.9542  -4.7526  -1.3705  -0.8283
-#>    5.5251   9.6266  -0.4007  -1.7914  -9.0324   1.4803  18.9263   0.3953
-#>   -8.7347  -4.0501   8.7921  11.5538 -12.4976   1.0826 -19.7760 -10.8927
-#>   -2.3679   4.2283   3.5410   7.1489 -13.3737   2.6299  13.3520  -5.4859
-#>    2.0640  -0.0326   2.0723  10.3406  -2.1520   3.9185   1.0878  -4.0766
-#>   14.8852  -2.2712 -14.5972  -7.5811  -3.3774  -6.0105  14.9241  -8.3811
-#>   13.5728  11.6081  -1.0403  11.6453  -8.1370   3.6349  24.3273   3.0830
-#>    3.2080   5.0707  -9.2790  -8.8123   4.9685  -1.6484  -4.7333   5.8462
-#>    2.9742 -10.7546 -12.3970  -0.1404   0.0318  -1.9171   5.8307 -11.9025
-#>    1.2260  -2.8130   0.4420   1.9131   3.8702  -5.9096   1.6282   2.5812
-#>    1.4588   5.4399  -0.8877  -2.4271  -5.0846  -3.9904  -6.7946   1.9089
-#>   -4.8377  -9.2531   5.5385  -5.5002   3.8937   7.2787   7.1057   2.6130
-#>   -8.5997  -3.5724  -3.3184   3.1191  15.3997   9.4976  10.3288  10.3277
-#>    3.6108  -1.1064  -4.6679  -4.5344  -8.6885  -1.0672   8.9834   4.1420
-#>    9.4999   1.1589 -11.8681 -11.2911  -9.8568 -14.2836  -5.4645  -7.7589
-#>   -4.2400  -2.4742  -1.2214   4.4757   1.6532   0.8790  14.6292   0.4674
-#>    8.4445  15.9800   4.0901  -7.3633  -3.6679  -3.6021   9.9330  12.6796
-#>   -7.9000   3.8494   1.4938   0.4920  -3.1299   4.9682  -6.1952   0.9764
-#>    8.7349  -1.2985  -2.5513 -14.5349  -6.8205  -1.1748  -7.4282  -1.3516
-#>   -3.6078  -7.8039   2.0198   1.2103   8.5414  -3.6797  -5.4959  -2.0035
-#>    8.1339  -1.8808  -8.5537   2.8481   5.4491   7.7438   2.7956  -0.6117
-#>   -8.0023  -0.9707   6.7169   0.4876   6.8987   8.7232  -2.7651   8.5866
+#> Columns 25 to 32  -6.3050   4.7238 -17.8670  -0.5880   8.0363  -8.8384   6.7973  10.1670
+#>    0.7819  -8.7086  -0.5493  -1.5067  -6.3630  -3.1176  -3.8060   7.1644
+#>   12.4999   0.5950  -7.2478   4.9652   0.2354 -10.5477  -8.1457  -8.5105
+#>   -9.0250   1.0340  -5.4598  -3.3421  -1.3794  -7.9057 -10.9088   0.5491
+#>    0.3745  -7.6168  -0.4208  -6.3234  -5.2446  -0.4377   9.6929  -3.7409
+#>   -3.3252   9.8781   0.0665  -2.0162   1.1815  10.7428  11.6444  -6.6607
+#>    9.4381  -5.2888   5.7532   0.7087  -8.6443   5.2028   0.3598   2.4486
+#>   -3.0277   3.3279 -12.2310  15.2771   4.9515 -13.7606  -2.0435  -3.9806
+#>   13.1074  -9.4906  -3.3137  -7.0977  -0.6819  -3.5135  -1.4732  13.4122
+#>    3.2798   4.9833   6.4333  -2.1565   7.3589   1.2299  -2.4760  -2.0664
+#>   -5.1800   0.7813   5.0688  10.7303 -14.3422 -13.1902  -5.4290  -0.1870
+#>    5.4465   2.4001 -12.8189  -8.4338   8.4190   1.8156   0.8274  11.9054
+#>   -2.0857  10.1048   4.2619  -6.7065  -3.3476  -1.1451  -2.0377  -1.7887
+#>   -5.3566  15.0664  -7.1205   5.9788  -9.1359  -7.9899  18.9300  17.3375
+#>    5.4603  -1.4439  -7.1876  -3.2696  -1.5934   7.0721  12.3014  17.3448
+#>    7.9303   1.3446   1.5933  12.3317   4.6062   4.2817  -4.2334 -12.1762
+#>    8.1221   3.9732  -0.2661 -15.1008  -0.9461   1.0207  -7.1194  -1.4479
+#>    9.7294   1.1051   7.1813  20.9401 -14.5844   3.9706  14.4483  -1.4965
+#>   -3.9381   5.8195   2.3962  11.8602   4.9107  -2.3144  -1.4896 -12.3850
+#>    0.5819   5.5068  12.9107  -5.0659   0.3248   7.8091  -4.1615  -0.1643
+#>    6.0217  -1.3741  -3.2747  -1.1051   8.2488  -1.2873 -11.3907   4.4985
+#>   -5.7118   4.0587  -0.3100  -5.7755  11.3952  11.5330  -1.9617  -9.1594
+#>    5.7893  -4.9262 -10.4986  -9.1852 -10.4091  -6.0330   0.7095  11.6377
+#>    2.5615   6.0328  -3.8714  -2.1092   2.2874   3.8614  -0.0351   3.4032
+#>   -4.6734   3.5800   0.3835  -5.4194 -11.5737   6.3313  -6.8266  -9.3864
+#>   -1.4295  -7.0318  -4.3435   2.8048   3.7325   0.2020  -0.6500  -0.7863
+#>   -6.9036  -0.0105  -5.3543   1.8927  -0.5046   5.5343  10.8065  -1.0212
+#>   -3.9819   0.0611  -4.3249   9.5066   5.6024   6.1746  -5.7912 -14.9529
+#>   -2.0487  10.6287   1.2395  -4.4636  -0.7882   0.2121   5.6188   1.0587
+#>    3.3456  -3.7282  -4.3858  13.0972 -10.8682  -2.4497  10.0444  16.6966
+#>    2.6152   7.4653  -5.2857  -2.3116  -4.8892  -0.5763   5.0725   1.6763
+#>    4.0750  -2.5667   1.5579   1.7010  -2.0998  -4.2782  -8.7313  -6.9343
+#>   -9.7400  -5.8549  -4.2980   1.2974   5.3345  -1.1588  -7.3566   6.2945
 #> 
-#> Columns 33 to 40   3.4080  10.7186   5.5730   4.6531   5.7616   3.4705 -11.4040  12.9549
-#>    7.7377   6.0998  -1.5554   4.7076  -8.1016  -9.8182   3.8232  -0.1717
-#>   -5.6604   1.0921 -11.5067  -0.9772   2.9314  -7.4592  -9.5098  -3.6219
-#>   -2.3808  -4.4176  -0.4573   5.1452  -4.5173   2.6218   4.3377  -9.7166
-#>   -3.7069   5.9415  -9.7631  -3.8982  -8.0165  -2.5303  -1.2263   0.7284
-#>   -0.3598  -6.7104  -0.3492  11.4228  11.0449   3.6593   7.6630   4.0337
-#>   -5.1844  -7.9579  -0.3715   3.9422   3.8204   7.4046 -11.1925   0.5115
-#>   -7.0582  -0.8884   6.2280  -2.8426 -14.4377   7.6894   4.5536   2.4210
-#>   -1.4600 -10.2162  -8.5577   1.7303   4.5618 -11.8549   1.7316 -11.9648
-#>   -4.1447  10.3017  13.7237  -4.0415  -4.9265   2.7495   3.8622  -6.7376
-#>  -11.5153   2.8323  -6.5427  12.0739  -0.6204   4.8624  -1.5153   3.3605
-#>   13.2823   5.9974   1.2621  -4.7486   1.5167   5.9744  -0.3291   0.6503
-#>   -1.0654   0.5911  -4.0941   8.9973   1.0366   6.0193   0.5471   0.4281
-#>    0.2312 -10.8132   1.8276  -0.5442  -4.3519   8.0915   4.7438  -0.1606
-#>   -0.0113   2.5344 -11.3341  -2.8751  14.1932  -4.1816   3.7660   1.8073
-#>   16.3061  -0.7111  -8.0503   2.8067   6.5130  -9.8521  -4.1031   1.1970
-#>  -10.5519   0.9086  -8.5823  -0.0814   8.8444  11.8856   1.2298   4.7355
-#>   10.0114  -5.0213  -6.0665  -1.6630   7.1819  -0.7356   4.0499  -2.1666
-#>    0.6757  -0.3162  -3.5780  -4.7094  -2.8122  -1.5863  -1.3931 -10.5494
-#>   -2.0870   0.9603   7.7240  -4.6320   5.4591   0.2743  14.8810  -2.1514
-#>   -1.8110   1.1190  -2.2812   2.8365  -0.6039   1.2504   2.5801   2.9844
-#>    2.4130  -9.4174  -8.4426 -12.0919   7.1439   6.6067  -6.2722   2.3164
-#>   -4.1834  11.1998   3.0447   2.6957  22.4190   7.6576  -5.2527  18.8177
-#>  -20.2723   1.3253  10.8418   9.0811   6.4476   3.7484  16.8607 -14.8302
-#>   -2.7513   6.6503 -12.5883  -1.9143  13.6749  -2.2026  -0.0576   7.5686
-#>    0.3314 -11.3378  -0.9537  -6.7157   3.6694  -7.5760   5.5724   6.0488
-#>   -8.1499  -5.8161  -9.8587  19.9884   2.0863   1.5576  -4.2066  -0.4436
-#>   -7.1017   1.5927   7.4132  -3.1600 -16.2266  17.7469  11.1453  -3.6420
-#>   -3.5196  -6.1302 -17.1096   1.9950  12.6488  -7.2172   1.1653   8.3086
-#>    3.9420   1.1401   4.9253   8.3876  -3.8281   9.5859   8.0676   0.9702
-#>    2.5050  -6.1517   4.7386   5.9677   9.2732   2.9076   0.0195  -0.0915
-#>  -10.9789  -1.3337  12.6203   8.5702  -4.3448  -3.6564   4.6815  10.8041
-#>    3.2875  -0.0805  18.5864  12.3418   1.3776   0.4639  10.9487  -5.0897
+#> Columns 33 to 40   8.4627   9.6192   0.5056  -4.7102  11.6024  -2.0951   4.1913  -1.5111
+#>    2.8340  -6.4928   6.8795   2.9201  -2.1075  -1.8531   0.5551   2.0819
+#>   -7.5933  -2.2260  -9.1166  -3.2124   4.8456  -0.2283   1.4762  -0.7194
+#>    6.2248  -8.5167   3.5322   3.2227  -5.6294  -0.3967  10.2963  10.5078
+#>    3.1985   6.7279   4.6912   6.3275   0.2253   1.7753  -8.2086 -12.8037
+#>  -16.7505  -2.0902   3.7017  -5.0439  -2.6029  10.1196  -1.6105 -14.3003
+#>  -15.0534   3.9078  -5.5869 -11.5304  -3.5092  -7.4240  -3.7589  11.6518
+#>    2.5050 -12.6827 -12.8706  -1.9666  -5.5736  -1.6785   1.3554  19.1239
+#>    1.7287   1.2349  14.3629  13.1778 -16.3137   9.5405  14.7577  -9.1000
+#>    0.4104  -5.2002   4.7632   0.3833   0.8436   6.1623  -3.5593  -4.0756
+#>   -3.1540   6.8193  -8.8962  20.6007   1.0029  -7.6812  -7.1488   1.4358
+#>    6.6526  -2.8659 -10.8030   1.4553  -1.1354  -3.5005  -2.7119   9.1697
+#>    6.8097   0.8033 -10.0922  -8.6253  11.5531   7.4769   8.1791  12.6088
+#>   11.3124   1.5183   3.8205  10.4646   5.2921   0.5983  11.7163  18.4572
+#>   10.9357   2.8577  -6.7735  -3.1426   2.6630  -1.0412   5.0757   0.4454
+#>  -13.6616  -7.8840  -3.0935   2.3106  -2.6839   3.9880   4.8708   1.0188
+#>    7.5740   5.5523   5.7893   3.9466   5.6309  -6.2144 -10.4352  -3.0217
+#>    8.2918  -6.7735 -13.7922   1.1264   1.9380  -8.5805  -1.6173  10.0197
+#>    0.9718 -16.4808  -4.5497  -5.4669   2.1980  -0.1691  -9.4305   0.6820
+#>   -5.4696   1.2961   8.7297  -2.2501  -3.3067   4.1099   4.4256  -8.5122
+#>    4.1438  -5.0817   2.8268  -2.3660   1.0834  -5.4129   4.7049   8.0334
+#>  -14.3751  -6.6880  -6.2389 -13.0992   0.7792   7.2334   4.0696  -1.9419
+#>   -7.2310   5.2424  -4.8411  -1.7995  -4.1335 -13.3840  -7.6249   3.8943
+#>    3.3899   0.2378  -3.0897  -2.7173  -9.5046  -7.9173   2.6721   2.9327
+#>   -6.8260  -5.3483  -3.2417  -2.5692  -8.2699  -9.4297  -3.3118  -2.2543
+#>   -7.0935   2.9322  11.5962  -3.1335  -2.4853   3.7401   1.9402  -8.2695
+#>   -0.4618  -1.1653   3.3451 -14.2864   3.5546  10.7668  -0.5330  -6.3669
+#>  -11.9906  -2.9861  -3.1784   7.9281  -6.7173  -1.0408  -4.9628 -16.1489
+#>   11.3069  10.8252   8.9816  -5.3561  -4.4633  -2.5525  -3.4003   3.8927
+#>    9.1441   3.4465  -8.9527   6.7487   6.2379   0.9032   0.3956   0.1699
+#>   -2.6357   9.3995   8.7429   1.9425   1.7303   4.3151  -0.6646   7.0418
+#>    0.5546  -4.8317   0.9649  17.4945  -7.4146   3.6819  -1.2988  -6.8112
+#>   14.4165  -3.7656  -0.7285  -5.3543  -3.2077 -15.4640  -3.7080   2.8010
 #> 
-#> Columns 41 to 48   4.5416  -4.9024   8.8311  10.3463   0.2651  -8.4339  -6.6832   7.2281
-#>  -10.1922  -8.5999   0.4567   1.2905  11.9904   8.3649  -0.2820  -0.5000
-#>    2.1755  -3.7282   1.3097   2.5777   7.8753  -2.0035  14.1423   6.6787
-#>    1.1655   0.5492   4.4501   0.3171  -5.4586   2.6379   0.0697  -9.4739
-#>   -6.2303 -14.1027   0.0100  -3.9314   5.2509  -8.1181   2.6001   1.2095
-#>   -6.8354   1.3839  10.1818   6.8880 -11.1387 -13.3290   2.5360  -2.4812
-#>    8.3362   9.4515   4.1199  -9.3917 -14.5917   1.4332   0.4153  -3.2342
-#>    0.0806   0.2949  -9.1927 -16.4414   5.4621  -5.5275  -6.3721   2.0919
-#>   -1.5245   1.7350  -2.6612   9.0850  -1.4021  -0.8051   8.9440   5.5216
-#>    2.2669   1.4136   2.1153   1.3907  -1.9028   3.3937   5.5590   1.6864
-#>   -9.5550   9.3133  -0.5917  -6.6588   8.5990  -9.0556  -1.1651   0.3522
-#>   -0.2138   1.6576  -7.4984  -7.4907  -8.1598   2.7208  -5.3047   4.1849
-#>   -8.7061   2.1911  11.3166  -7.0701 -12.3447   0.3809  -8.7935  -1.9093
-#>    5.8310  -3.7924 -14.1226  -0.9867   1.1463 -12.4631  -7.0898 -12.4898
-#>   -4.3149   6.3384  -2.0539  -0.0828 -16.0012  -1.3459  -0.2730 -11.1067
-#>    0.2034  -1.7032  -7.7734   4.6387  -4.2106  -1.3526  -4.4212  -3.4719
-#>  -12.2013  -1.7224   3.9268   3.3508  -8.4421  -6.3359 -12.8927  -2.1470
-#>  -21.1804   6.3041  -4.5008  -0.5402 -20.8187   0.5318   2.9913 -13.6855
-#>   -3.2797   7.9700  -0.3886  -4.6745   2.1036  -4.6107  -4.7608   7.6923
-#>  -15.8307   8.7412  -0.0962   7.8266 -13.6592  -0.8376  -7.8022 -13.3426
-#>    3.4127  -3.5618  -6.9771   2.4669  -2.9717  -8.7935   2.0971  -7.9943
-#>   -5.0785  -3.3369  -9.3461  -5.6587  -0.6787  -3.1871  -9.3324  -3.0281
-#>    2.5578  21.7349  15.3899  10.4132  -2.5514   1.7782  12.5657  -6.7548
-#>    8.6913  11.6463  18.1946   0.7303  -4.4140  -0.7748   4.9841 -15.1897
-#>  -11.2238   6.7093   4.6444  12.6038  -5.5794 -10.4248   4.1176  -1.7744
-#>    1.6525  -3.1180  -0.3574  17.7211  -0.1144 -12.3549  -4.0452  -5.5704
-#>   -4.9651  13.9408   8.6249 -11.5936 -16.6941  -5.3931  -3.6881   6.0155
-#>   -5.4065  -2.5217   1.4385  -0.3922   1.6123  -0.9188  -7.3587  -2.3773
-#>   -8.6857  -5.0989  -6.1459  -7.0026  -8.0787 -14.5593 -10.4636 -12.8228
-#>    2.6677  -4.7651   1.0568   1.3425   2.7920   2.0309  -8.7960   8.8150
-#>    9.0281  12.5455  -2.5788 -12.3628 -11.9505  -4.1404  -0.8740  10.5614
-#>    3.1092  -4.0124   7.5946   5.5313  -0.2117  -1.3845   8.1836   4.1690
-#>    2.6316   0.1588   6.9737  10.9898   3.5931   3.1511   4.9429  -1.8327
+#> Columns 41 to 48 -10.8241 -12.5592   9.8794   8.1459  -8.0249  -5.3232  -7.3662  -6.6561
+#>   -1.9464   8.5595  -3.7384   1.7510  15.6552  -1.0824  -5.7959  -3.1648
+#>    1.6102  -9.2710   8.5078   1.5640   8.5394   6.6464   5.9841  -0.9512
+#>   -6.0430  10.6005  -3.3871  -0.5440  -7.9448  -3.5804   2.1058  13.3017
+#>    2.1819  -4.2182  -4.0471   8.4911   7.0756  -7.7260  -7.3000  -8.5854
+#>    2.1067  -7.5725  -8.9682   4.3029   2.1925  -1.4281  19.4983  14.4373
+#>    2.9312   6.4773   1.3713 -10.6200   6.4025   2.9545  -8.5119   8.9705
+#>   -1.7926   5.3045   5.7370   0.5968 -10.0502   2.1086  -0.9249   9.5904
+#>   -1.8064  14.1006  -1.9522   0.5508   4.0472  -1.6355  -2.1794 -10.7252
+#>    6.0747  -8.3286  -7.3287   4.2511   0.1022   4.0733   7.1620   8.2707
+#>  -14.8316   0.0931   3.3676   1.4202  -6.7653  -7.9898  -6.8231  -3.1254
+#>    2.4596  -1.6326   6.1167   8.8923  -3.3707  -6.6921  -7.9243  -6.2614
+#>    0.7403  -8.1644   2.0377   8.2480  -6.3439   0.9885  20.8536   5.7158
+#>    1.1119   3.6225   0.1497 -11.9439 -12.5551  -5.3223  -1.1399  10.8148
+#>   -0.8456   3.9264   8.1905  -1.7234   3.0473   3.4385  -2.2770   0.2595
+#>   -7.0555   2.3944  -2.5763  -5.5024  -8.9512   6.8928   4.5017  -5.0469
+#>   -6.1114  -7.5485   8.2868   4.4023 -10.1372  -1.9716 -12.8082  -6.7290
+#>    1.0195  11.0753   0.1624   5.6988  -9.9563   1.7751  -5.6086   7.5327
+#>    2.3055  -4.6038   1.0535  12.0757   4.9606  -2.2908   2.2219   3.1930
+#>    0.1517  -8.4319  -9.5501   5.2974   5.1445  13.2255  -5.7966  10.5574
+#>   -6.7888  -6.4484  -1.8910  -2.4853   8.7156  -4.2482  -0.8505  -6.0441
+#>    3.6000  -9.1188   2.1149   2.0131   9.8010  -0.2025  16.7531   0.1040
+#>    3.8869   6.0433   3.6712   2.9468  -7.8337   5.9737  -9.2323   4.2390
+#>   -2.8802   6.6061   3.0407   4.1871  10.1265 -10.4854  -5.2429   2.2165
+#>    2.8970   4.7978  -7.9631  -3.6446   3.4185   9.8806 -11.0710   5.4011
+#>   -0.0199  -3.1293 -11.8688   4.2835   2.7631   8.6910   3.0006   7.1971
+#>    4.3071  -5.4259  -6.5849  11.6391  -6.4536  -0.3236   2.7658   6.5286
+#>   -0.8604  -4.1629 -11.3665   7.3753   2.2787   0.6569   5.8722   1.9402
+#>    3.4090   6.8764   1.7934  -1.8218   2.9799   5.3587  -8.9751  -6.0064
+#>   -2.4390   1.8613   7.0550   8.3929  -4.1681 -14.5310  10.5581  -1.6691
+#>   -9.1057  -1.2082  -1.4793   1.9302  -7.6886  13.2929  -1.5715  -7.4436
+#>   10.1393   5.8404  -3.8208  -5.8047  -4.8160  -3.8602   4.4829   0.5871
+#>    2.3434   6.6981   7.1134   3.3371   1.3401   1.0295  -8.4443  -9.2548
 #> 
 #> (16,.,.) = 
-#>  Columns 1 to 8   2.6537  10.0281  -1.3741   0.9900 -12.0867  -4.0296  -7.6636   0.1825
-#>    2.4319  10.9279   6.3170  -3.5904  -3.7944  -9.5351   3.8456   9.2553
-#>   -7.8803  -3.3544  -1.3375  -3.0423  -1.6107   9.5581  -4.5955   2.3276
-#>   -4.4451  -0.2926   3.6284   7.7152  -2.6448  14.0143  24.4411 -11.8132
-#>   -7.9960   8.9860  -4.5813   7.6282  -6.9701   1.1402  -2.3306  -4.5071
-#>   -4.4226  12.0704  11.6664  -6.9571   3.1467  -4.9787   5.0473 -10.9933
-#>   11.2864  -1.5729   1.7127   9.4951  -2.6290   8.0230   0.0378  -8.6601
-#>   -9.4625   3.9488  -2.1094   9.5814 -13.2363  -5.8638  -6.0246  -2.4959
-#>   -0.1048 -13.5206   6.7469  -4.3198   3.5434  -0.1837  -0.8146   6.3350
-#>   -3.9342   7.8975   1.1074  10.9452   2.6244  -1.7462   6.9768  -5.0688
-#>    1.7446  -0.1351  -0.6821   2.8836  -5.9138   7.9013   4.5623  14.5249
-#>    6.4212   0.7026  -1.7461  -4.0589   2.0305  -1.0969  -1.0820  -0.8338
-#>    0.2226  10.2770  -2.7988  -6.5288  -9.7603   3.7378  -2.5321  -3.4819
-#>    3.0488 -11.7935  10.9536  -1.8877  11.9208   2.3212  -2.7537   1.0941
-#>    2.1382   5.3092   7.1833  -6.4094   1.9917   3.8928  -3.1746   0.3565
-#>    8.1730   0.7460   8.6897   3.0966   5.1147  -2.4684  -0.6720  -0.8584
-#>    9.6087  10.6647  -0.1727   1.2635  -4.7348   1.4423 -10.2314   2.2201
-#>    5.6906  -5.4915   6.5053  -7.2581   1.7664  -2.1022   2.1175  -0.0348
-#>    9.1430   1.9296  -7.5327   8.4665  -3.0810   1.2409  -4.6988   4.8817
-#>    1.9465   2.5159   1.7587  -3.4185  -3.5874  -3.4295  -1.3687   2.3190
-#>   -6.6130  -9.0796  -1.8347   2.6115  -1.1207   9.5405  -3.5588  -4.4271
-#>   -4.1785   2.8673   4.9747  -6.1164  12.9784   2.4537  -4.2924   3.0161
-#>    8.0232   4.7341   1.0049  -3.2322   8.3828   4.6395  -7.5344   8.6833
-#>   -4.2088   2.6784   0.1928   0.3867  -0.6369  -3.5143  10.9183   5.2169
-#>    8.9835  -6.3049  -4.8523 -13.6846  18.6929   5.5845  -3.0672   5.3202
-#>   -2.9213  -3.7387   5.5182  -3.6154   1.3478  -1.7320  -1.5216  -6.9537
-#>    9.2671  -3.2407   7.5714  14.1982  -3.3357   1.1475   6.5716 -10.1794
-#>   -4.1577   8.2047   4.1041  12.3912 -11.5370  -5.1664   2.5799   3.8036
-#>    3.8338  -5.2952   6.7403   4.5124  13.3406   8.5143 -11.3196   7.2957
-#>    3.7297  14.7927  -2.2709   1.0681 -16.8867 -19.4837 -11.9069   5.0476
-#>   11.2957  -5.4608   0.8200   7.4871  -1.2263  -0.4746  -6.9441   2.1174
-#>   -9.0418   0.4256   5.4519 -12.3165   6.7599  -6.1433  -4.6055   1.1761
-#>   -1.4819   4.0796   0.9065  -0.9019   5.4088  -7.9440  14.5377   9.0100
+#>  Columns 1 to 8   5.1261  -2.8450  -3.2382  -8.7989   3.0773  -7.4989  -4.2167   3.6598
+#>   -1.0038   0.9312  11.1684   7.0661  -8.1220   6.7760   2.8751  -2.0495
+#>   -2.1045  -2.1887  -0.3678   3.3220  -2.1537   4.2073   5.3580 -13.6370
+#>    3.2350  -4.1579  -2.9122  -5.1370  10.1330  -3.5221  -8.0732   1.8269
+#>   -4.7509   8.8498  -8.5420 -10.2327 -19.4137   0.6527   3.6232 -10.9135
+#>    5.5381  -5.6384   3.9632   3.2175  -3.2445   3.2333   5.2998   8.4650
+#>    4.0765  -7.3636   4.8385  12.6403  -8.2223  10.1804   8.8325  -6.7026
+#>   -3.9838  -4.9200 -10.5703  -2.4202   7.1313  -4.8207   3.3515  -9.3286
+#>   -8.5621   9.8314   4.7020   9.0598   5.0971 -13.7285  -9.2223  13.1704
+#>   -0.1157  -4.1365   5.5928  -9.7817  -1.4935  -5.3224   2.8611  15.7890
+#>   -5.2081  -1.9686   2.7150  -3.7723  13.0990   3.6701  -0.1524  -6.8796
+#>   13.2258   8.9368   3.3121  -7.0790   4.7623  -2.0034  -2.6128  -1.4503
+#>  -11.2120   7.4422   5.5518  -0.1588   4.8228   9.8168  -6.8340  -1.7672
+#>   -3.4332  -3.6753   8.2436  -1.9458 -16.5379 -10.9161   5.3190   5.8212
+#>   -1.1222  11.4132   9.4151   5.9887  -8.0350  -5.6568   1.0801   2.7832
+#>    1.9269  10.6649  -6.1271   7.9513   9.5201   0.8032  -1.9908   2.2831
+#>    8.1346   2.7858   7.9123  -6.5829  11.5281  -4.9781   3.8680  -2.0012
+#>   10.2440  -0.5996   4.3847   7.8699   4.6680   1.2474  -6.1953  -6.1151
+#>   -2.1528  -5.0708  -4.6581   9.6572   9.5949  10.4371   1.1159   3.0433
+#>   -1.8566 -12.8853   3.6096   1.2526  -6.7440  -1.4534   1.3010   3.9254
+#>   -1.6968  -3.8088  -0.5291   2.9351  -5.4835   4.5089   0.3231   6.0435
+#>    7.1190  -0.9704  -2.2517  -4.2518  -0.0369  -0.2925   0.0045   6.5996
+#>    1.3046   4.8211   0.8576   7.0307   7.2529  10.4599   8.3072  -6.0055
+#>   -3.0841  -6.9428  -9.5165   3.0634  -3.5058   2.9692 -15.5600  -5.5499
+#>    1.9411   5.5068  -6.3341   9.3663   5.9734   8.1237  -2.7955   1.3979
+#>    3.1859   1.0869   0.1808  -3.4309  -8.1068   1.9914   0.2076  12.4701
+#>   -4.9744  -1.5919  -6.1288   4.3464  -8.4726  -1.8149   5.4670   3.9777
+#>   -6.8355  -5.9781 -11.1834  -9.5106   0.8818   7.5806   7.4133  11.7849
+#>    2.6733   0.1011  -4.3735   3.0455  -8.6630  -7.8654  -8.5211  -1.5228
+#>   -7.2386   4.1866   7.5937  10.0426   4.5374   6.7831  -0.5036   6.4681
+#>   -1.8181   5.4128  -2.0206   0.3848  -6.7987 -11.6497   0.2210 -13.1029
+#>    3.9093  13.9236  -6.4055   3.4992   1.0158   0.5141   1.9188  13.7409
+#>    3.8692  -3.9149 -11.5660  -3.1436   4.0488  -5.6748  -9.2204  -0.3520
 #> 
-#> Columns 9 to 16  -6.8426  -4.7429  -2.6128  -4.6095   2.6961  -0.8184 -12.1658   2.0165
-#>    3.9022   5.0333   6.4491  -2.1842  11.7931  12.3021   3.1147   6.1278
-#>   -6.9729   2.7342   4.6440  12.5455  -3.9127   5.4592   4.1639   1.4389
-#>    8.6323   6.5326   5.2528  -3.1248  -4.5754   1.5873  -5.0231  -3.1225
-#>    2.8939   0.1219   2.8283   3.6899   2.2707   1.9394   0.6548   5.5807
-#>    0.0017  -3.8882  10.1598   7.6993  -0.0444  -7.9066  -4.5745 -12.3957
-#>    0.7785   5.4766   3.5906  -7.7531   1.0911  -5.9815 -12.2547   7.7808
-#>    1.0523  -5.5980   3.0210  -0.2250   1.8311   5.0031  -2.5257   1.5206
-#>   -4.9046  -6.8045   2.1573  10.7057   0.4956   7.5452  -4.8635  -4.9029
-#>    6.7992   6.8604  -8.2919 -13.6156  -2.8000   5.0487  -7.0615  -1.5223
-#>  -12.9161   2.7160   8.2835  13.1736   3.0676  -4.2209   0.4369   8.7412
-#>   -0.3872   1.1803  -6.1056   4.2021   1.5244  -6.7314  -1.8443   9.4874
-#>   -3.6800 -10.8111  -3.7154   7.3191  -1.2210 -15.8530   4.8161  -7.8834
-#>   10.7230   0.1590  -2.8018   5.6006 -12.5767  -6.6004 -16.2261   7.2019
-#>    0.4843  -5.2169  -1.0586   4.1696 -10.5364  -8.4845   2.7401   3.2174
-#>    0.4920  -0.2655  -8.6780  -8.1274  -1.6072   2.6221   0.2542  -3.7309
-#>   -8.9139  -4.1685   0.6980  -4.3601  -0.0753  -2.4713  -6.2920 -11.3251
-#>   -2.9412  -2.9277  -0.9006  -1.0688  -3.6055   7.9323  -7.5708 -12.4720
-#>   -9.2443  -2.1692  -6.7854   1.7093  -8.0463   6.3150   1.6444 -13.0162
-#>   -6.2650  -1.9294  -7.3652  -5.3038  -1.9747  -0.5525  -2.9674  -6.6390
-#>    8.1219  11.4121  -0.0902  -3.8964   6.4237  -0.1665 -11.2574  -0.7778
-#>   -2.7504 -10.8360  -1.8227   4.4445  -2.9894  -0.9454  10.1992   5.0257
-#>   -1.9871  -8.9973  -1.5199  -7.0012  -4.1810  -9.0287   1.5581  -2.0986
-#>   -6.9044  -1.8868  -0.8582   5.7889  -7.4550  -9.6949  -4.8682 -18.4330
-#>   -1.8776  -1.4501  -3.1208  -1.5706  -8.5306   0.0210  -0.4809  -8.2916
-#>   -2.3846   0.7277   2.7183 -12.9379  -4.1543   1.4221   3.2912 -10.6667
-#>   -0.3494   7.2240  10.9552 -11.7152   4.5154  -6.7795 -11.9837  -3.2178
-#>  -13.4474 -15.3883  -3.8726  -5.5541 -11.9312  -2.3773   1.6819  -5.5822
-#>    0.3630   4.3099 -16.3680  -0.3777  -2.2528  -4.0222  -1.3204   0.7607
-#>   -4.4686 -19.8499 -10.0706  -2.9009  12.4972  -4.3342  -9.9224   3.8666
-#>  -10.1040  -6.5496  -5.6094 -16.8005  -4.2389  -6.8117 -18.5713  -1.8870
-#>    1.7998  -1.0995   1.8782  14.4492   0.5544  -1.6617  12.2205 -10.4153
-#>   -6.9645  -1.6298   8.3926   5.3486  -1.4503   2.8527  -1.8521   6.0394
+#> Columns 9 to 16 -11.0716   7.8926  -1.5192  11.1751  -5.6606  -1.6120   7.3872  -0.3674
+#>    3.3136  -6.1990   5.8662  -3.0578  -7.9727  -3.6502  -9.8288  -0.0878
+#>   -5.7984   4.8727  -1.0836  -3.3400 -11.9352   0.2132   3.3643 -12.2731
+#>    4.5368  -8.4935  -0.8240  -5.3146  -0.0723   1.8665  -9.9431   4.7557
+#>   -6.0098  12.1897  -0.4899   3.5732   0.6463  -1.9791   0.6883   2.7912
+#>  -11.0578  -7.7644   0.7946  -0.5218  -5.7665 -24.0823   3.7099  -3.5103
+#>   -2.8711 -14.5286   7.0938   5.6453   7.7450  -3.5059  -0.7206  -4.1606
+#>    9.2334  -4.7448  -4.0372   2.4442   0.9533   8.6785  -8.9648  -4.5411
+#>   14.7544  -5.0897  -2.8168  -8.4430  -5.6290  -4.4548  -5.3709  -2.3346
+#>   -8.9407   5.7096   2.4833  -6.0196   7.3999  -6.8425   1.6314   1.9350
+#>    0.5937   0.8597  -5.8163 -17.2911  -0.6945   7.6235   0.4653  -0.2495
+#>    1.1909  -5.4046   3.9597   0.5139  -2.2671  -8.6106  -2.2423  -3.8151
+#>   14.6383  -5.4473   8.4067  -3.0250  -4.9272  -4.6375 -13.0177   3.0851
+#>   13.9487  -3.8829  -2.5261   2.9440  10.4917  10.7231  -2.7004  -7.8963
+#>   -4.8642  -1.9137  -0.4015  -0.3533  -3.2627   1.9848  -8.1712 -13.0388
+#>   13.6087   0.5360   2.8865  -5.0504   1.4859   3.3201   4.1823  -1.6874
+#>   -5.5746  10.4820 -13.4285   5.2714  -0.5863   1.1479   5.2240  -0.3015
+#>   -1.0061  -4.7373   5.6559  -6.7207  -1.5914  -5.0994  -4.4543  -3.2439
+#>    0.8013   1.1788  -2.2453  -5.3738  -8.8849  -3.0521   5.7252   7.9563
+#>   -4.9584  -4.3350  -5.5550  -3.0845  -8.0257 -12.6339  -2.9030  -4.3063
+#>    2.3830  -0.0033   1.6715  -3.5396  -5.6952  -8.7490  -1.2171   0.9261
+#>  -18.7848  -1.9961   4.5367   6.1949 -11.8174 -12.2647  -5.3147  -4.0750
+#>    9.4608  -6.6229   2.3834   2.1527   7.4591   1.0893   2.5343  -9.5999
+#>    3.7772 -14.9920   4.2505  -3.7771   2.8131  -4.4641  -0.6396   3.4831
+#>   10.6338  -7.9093 -11.4937  -4.3720  11.6796  -3.0354  -0.3707  -7.6393
+#>   -3.8859  -1.4952   0.4492  -1.2558   6.8768  -9.6030  -5.7386   0.8783
+#>   -7.3742   4.1089  -1.1013   3.4691   3.2846 -10.6651   4.2856   1.6132
+#>  -14.8183   2.1003   7.7073  -5.5211  -2.1223  -9.9208   7.1001   5.6111
+#>   -2.2851 -10.0254  -7.1918   0.0860   3.4937   5.2893  -8.3288   5.1660
+#>   10.0078  -2.0671   9.0152  -3.1767  -3.0365  -1.6561   3.9795  -1.5960
+#>   13.1446  -4.6746  -5.7532   3.5775  -5.9741   9.7639  -1.5420  -0.7079
+#>    0.7970   8.8009  -0.6542  -4.3686  -3.3261   1.8994   7.8499   5.4588
+#>  -11.9677   2.4533  -6.6983  -8.2325   4.5343   5.4002  -3.8636   0.0917
 #> 
-#> Columns 17 to 24  -3.1629  -0.0568   2.9113  14.5280  14.7589   3.1293 -12.1515  -1.2125
-#>   -2.5092  -9.6162   4.5885   2.9099   8.3468  -4.3173  -0.8217  -1.2561
-#>   -6.6908  -9.5136  -6.0419  10.0233  -3.2612  -4.1823  -1.2707   1.4180
-#>  -12.0190   7.8139 -13.8348   0.5338   5.1126   4.1994 -21.2723  -7.0667
-#>   -7.8274  -4.9705  -2.1304   4.1353  -8.3330   1.0937   0.1881  -4.3878
-#>  -12.7670   5.6393   0.2593  22.5342 -10.9020   5.1398 -12.4250  -2.1257
-#>    5.8816  -4.6940  -8.5924  -8.4002  12.8787  -7.0274  -6.9082  -0.0870
-#>    7.1171   3.6475   9.1813  -9.7746 -10.4951   0.8722   1.6396  -7.0983
-#>   -1.2520 -11.8828  -8.1181   8.3765   6.4687  -6.8427   9.5647   6.9806
-#>    6.4404  -2.9231 -11.3833  -3.3352   0.4295   2.9849  -0.1354  -1.9745
-#>   -1.8990  -8.8391  14.1990  -4.9804   4.0018  -5.7063   6.9528 -12.5414
-#>   10.7867  -2.3682  -2.0609 -10.6862   3.9373  -3.7516   7.8622  -7.7914
-#>   -3.6083   9.3223  16.5962   1.2439  -1.8558   3.5668 -11.2451  -6.5508
-#>   -3.8310   5.8341  -5.7072   6.0198   7.0461  -4.2640  -0.8619  12.6715
-#>  -13.2245  -0.7354   3.9689   2.9254   7.2269  -8.9177 -11.8530   4.4396
-#>   -6.9530  -0.6109   2.6992   0.4603   9.3217  -0.9783   0.3191   0.5973
-#>   -2.3645  -0.7434  14.0873   0.1423   2.8636   1.0353  -9.0741   0.3920
-#>   -5.8839   1.9932  12.2294   0.8054   6.2002   2.5218  -4.2763  -7.6965
-#>    0.8513   7.0868   7.3695  -0.3998   7.6253   7.4863   5.9059  -3.1743
-#>    5.8895  11.1340  16.8882   2.2584  -4.4054   2.7326  -6.0199   1.4574
-#>    5.9178   1.1990  -9.1185   6.5885  -5.9489   2.0860   3.2412   2.0335
-#>    2.9457  -4.2214   2.5596   0.2399  -8.8712   3.9793   8.2923  -2.3617
-#>   -1.2055   9.2519   0.7123   9.6024  -3.8783  -0.4447   2.7004   2.2629
-#>   10.3553  16.5592  -0.2477   6.4830   6.7097   1.7149  -4.0558 -19.0939
-#>   -2.5081   3.0439   2.1285   8.5253 -10.4950   0.9928   4.8042   9.3855
-#>  -12.9153   4.9772   3.6428  -0.2269   0.5490   2.9480  -7.1561  11.2938
-#>    3.8155  -3.4244  -3.3108   8.2549   9.9998  -4.2891 -21.9953  -8.6520
-#>  -10.2267  11.0747   8.1871   0.2964   4.0452  13.9275  -8.5610   1.6311
-#>    3.8581  -6.7254   4.5158  11.5645   3.9171  -6.2779   7.3628  -2.6553
-#>   -9.4337  -2.1890  12.6193   0.1468  -3.0054  15.4094   1.0468   9.5217
-#>    2.4823   0.7290 -10.0452  -5.2096   9.9998   3.4954  -4.4458   2.5061
-#>    2.7536  11.1029   3.4073  -1.4913 -11.4818   0.4184  -4.7221  -0.0167
-#>    3.4516   1.3483   3.1828   2.0351   3.8541   7.9194   1.7277  -8.4165
+#> Columns 17 to 24  -3.7604  -2.1218  10.2643  11.3296  -4.1961  -7.8892  -2.9116  21.3383
+#>    2.7713  -2.3177 -10.6566   5.0730   6.5690  16.2669   1.7761  -6.2731
+#>    5.5539   0.2088   1.2515  -3.5379   3.1489   0.5724   3.5163  -3.9862
+#>   -7.1346   0.8066  -8.0818   4.8729   0.9078   3.1405  -6.1009   1.4360
+#>    4.8925  -1.4863   4.0143  -1.4077  11.2512   3.4900   5.6795   4.4313
+#>  -10.3108  -5.9039   0.1143   6.4796  -0.4888   2.5535  -3.9071   0.0379
+#>    1.7902  -1.7095 -12.2436  -4.1077   4.1913  -6.9233   5.3244 -20.7955
+#>   -4.1053   5.2045  -4.0090  -0.8069  -0.7462  -3.5991   0.7425  -6.0462
+#>    6.1688   1.8786   1.8677  -3.9815  10.1993   7.9289  12.0169   5.9694
+#>    2.7438  -2.0493  10.2124  -7.8059   6.6905   8.8442  -0.7276   5.9513
+#>    6.3550  -2.4962  13.5204  -0.9947  -7.6063   2.3687  -4.9018   7.2481
+#>   -6.2239  -1.4190   5.9511  -0.1006  -1.1228   0.9932   1.3151  -2.4219
+#>   -9.0519  -8.6618  -7.7160  26.2304  -1.2801  -1.8879 -10.7122  15.0455
+#>   -8.2650  -5.6845 -13.4935  10.5413  -7.0751  -0.2893  -5.5108   6.9336
+#>   -2.0213   0.6318   3.9644   6.7782  -3.5172  -1.5176  16.9523   3.6875
+#>    0.4846   4.2697   8.7208  -8.7379 -18.5525 -11.8871  -2.2346  -1.8242
+#>   -0.1416  -2.4817   2.6448  -4.6931  -0.7918 -11.4044  10.0879   2.1032
+#>   -4.9870   7.9952  -0.6473 -10.9716   7.5003   6.8877  -1.5101  -8.8006
+#>   -3.6111  -0.5425  -0.2632   0.7723  -5.4256   5.6935  -4.5463   6.0522
+#>   -0.2661 -11.4720  -1.4190  -2.0422  10.2721  -5.9003   5.1691  -0.2372
+#>    5.8354   4.3365  -1.8049  14.4065   7.4103   2.3427  -2.9091   2.7755
+#>    0.3120  -4.0293  -6.8875  -3.5073  -9.2093   0.1938   1.9776   7.8692
+#>  -10.2363  -2.0231   1.7242  -8.8492   0.3548  -5.0216  10.2280  -9.8666
+#>   -2.4458  -3.1471  -7.7955   7.0580   3.7621  -2.7298  -3.7527  -1.7677
+#>    0.2750   3.0765 -13.2988 -14.2917 -10.4668  -8.8943  -6.4412  -0.5008
+#>    3.7819  -2.5216   6.3017  -3.1720   2.6112   4.1207  -0.2363  -2.5184
+#>    1.3873   5.3469  -1.5839  10.3203  12.6063  -8.8295  -2.7760   2.3282
+#>    8.4450  -2.8892   5.9488  -7.4181  -0.0546  12.6295  -1.2329   5.8607
+#>  -11.4617   1.2900  -3.1312   3.5400  -8.6505   3.3382   0.1915  -2.8183
+#>   -3.7683  -6.8829   4.0607  10.8703  -2.3494   2.1553   5.7061  10.8609
+#>   -0.4346   6.6904   1.8583   9.9106   2.3102  -6.4464   2.3357   1.9055
+#>    3.1398  -1.9802  16.7641  -1.4764  -2.5059   2.7730   1.5432   1.2536
+#>    7.4855  18.2824  -5.1477 -13.7752   9.0591   5.4391   3.2000  -3.2036
 #> 
-#> Columns 25 to 32   4.0505   8.9516   8.9033  -7.6845  -8.8602   1.5821  -8.4482  -6.3353
-#>    2.2318  11.5904   1.8198   3.8042   2.1653   2.6246  -1.8867  -2.0695
-#>    2.4190   6.7486   1.8535  -1.3479   5.0385   9.5143   5.8353  -9.7707
-#>    0.3160   0.1693   5.0241   2.3182  -5.7190   9.2947  -3.2825   9.5995
-#>    8.3221   0.3005   0.5396  -2.9437 -11.8083   7.2209  -2.4893 -14.6031
-#>    0.1779   4.2406  -7.3055   3.8547  -6.1461  -8.7149 -10.9975 -25.8239
-#>    5.6674  -5.5394   6.8157  -2.5307  -3.3665  -4.5521 -11.3748  10.3743
-#>   10.0272  -0.1362  -2.2936 -12.8996  -9.6990  -3.0641   5.2434  -3.1504
-#>   -4.6833  -3.7969  -2.9460   7.8764   6.6890   1.9937  -0.5641  -7.1264
-#>    2.1160 -10.3251  14.0864  -5.1683   0.7573  -4.8519  -4.4410   3.3829
-#>    4.6316  -0.5813   6.1136  -6.9190  10.1993  -5.3839   3.2224  -3.4258
-#>    5.1394  -6.1845  -3.6422   2.5954  -3.9365  -7.0041   1.5143   0.0143
-#>    7.2168   0.3847  -4.3440  -3.7469   1.3919  -7.6400  -0.1277  -2.9737
-#>  -19.3379   3.5289 -15.4244  12.3707  -1.3818   8.6222 -18.1597  -0.9059
-#>   10.2140   6.9681  -2.4340 -15.3828   8.3304  -4.1101  -3.6007 -12.4290
-#>  -10.1915  15.1152   9.9963  -4.5331  -3.1466   1.2303  -2.1024   0.4509
-#>    1.8998   5.4893   1.7368 -15.3842   0.2894   0.6480  -8.8291 -15.4097
-#>   -7.6948   5.5280  -9.6765  -4.6556  15.5769  -6.7131 -10.0724   2.7616
-#>   -1.0178   4.2935  -0.9750   9.4335  -6.8237  -1.9242   4.2828   6.9149
-#>    0.5209   4.0523  -7.8269  -9.1706  12.9358 -13.0383   0.8386  -5.0926
-#>   -6.7296 -15.0976  -5.1138   2.4820  -1.8629   2.3847   2.7592  -3.1532
-#>   -5.8144   1.5334   9.3806  -8.4229   7.0976  -1.4525  14.7540  -1.5626
-#>   -7.7576  -7.1915   6.7963  -7.5794  13.1287 -11.2520  -5.9283  -4.3028
-#>  -10.0635  -2.0883   8.4976   2.8765   9.4888  -7.2230  -2.2691   6.7774
-#>   -6.2538  -2.5045  -5.7848  -0.1290  11.1655   0.4228   1.2409  -6.2375
-#>    8.9935   3.3222  -6.6574 -15.2159  -4.5156   3.4840  -1.4515 -11.9367
-#>    7.8913  -3.0932  12.0142 -10.1397  -6.4581  -3.4470 -10.4847  -0.8180
-#>   -8.3068   2.8102  -5.8828   8.7412  -5.1551  -0.1998 -16.8988   3.1662
-#>   -6.2529   7.3636   0.1179 -10.5478  11.0667   0.3758   2.9674  -6.9911
-#>   -2.4338   0.0214  -1.6522   0.4852 -12.8848  -3.1333  -9.7729  -2.2721
-#>    0.7034  -8.9684  -5.5336  -6.7640  -2.4479  -1.6852 -10.6143   3.1703
-#>   -0.4357   8.9272  -9.1191   0.3087   0.8216  -0.8100   0.8083 -13.8424
-#>   -6.3769  -3.2665   4.3365  15.7927   6.6798  -6.6067 -13.9550   3.5308
+#> Columns 25 to 32  -8.4375   5.8681 -12.6815  -2.7522  18.9005   8.1561  -1.4711   0.6721
+#>    5.1552  -2.2365  -3.1831  -1.5080   2.2412   6.0099  -4.4138 -13.4387
+#>   -1.3817   1.2058  -9.0776  -8.1363   2.5808   6.8256  -3.2355  -4.8606
+#>   -6.4562   3.8584   2.9348   1.5592  -1.6639 -13.3910  -6.1613   4.9691
+#>    2.2316  -7.7670  -9.4927   6.6779  13.2614  12.4068   3.5718   4.5467
+#>   -7.7871  -1.7283  11.7222   9.2901  -6.0210   1.9537  -6.2021   3.6399
+#>   17.4142  -3.5749  16.7383 -12.1120 -12.2599   8.6659   2.8077 -10.2484
+#>   -4.9452  11.4132   2.6347   0.2709 -10.3623  -8.7913  -2.0598   1.5628
+#>   12.4483  -7.9866  -9.5169   5.7624   2.8241  -7.3315   6.1326  -4.1919
+#>   -8.2068   2.6452   0.1561   2.2225  -6.3467   2.1432  -3.3846  -1.5751
+#>    6.4031   9.4122  -6.2390   5.3088  -3.2782 -17.0041  -2.8831   4.5766
+#>   -4.6749   0.2783   1.0030  -2.0822   7.1267   8.4780  -0.8306  -8.8267
+#>    2.8908   4.6225   1.7612 -10.2183  -2.0849   1.9763  -3.1229   2.9150
+#>   -3.4476   7.1192  -7.8110  -2.4038 -11.9407   7.3889   8.5001   1.2828
+#>   -1.5388 -18.7380  -1.6629 -10.2506   9.6399  16.3542  11.1127  -5.5020
+#>    4.1398  -2.2345  -0.1368  -1.3296   3.0933  -8.4330  -2.1578  -1.0009
+#>   -5.5802  -6.4512   5.4265   2.5174  15.3016   1.4209   5.7125  -7.9210
+#>   -8.4848  -5.3513  -2.1688   7.8100 -15.1759   2.2116   7.0934  -2.2250
+#>   -1.8009   1.7342   5.9228   8.2168  -6.5668  -6.1326 -16.3601   0.6873
+#>   -1.8678  -4.7639   0.9619   0.4322  -0.1557   6.9369   6.6921  -6.9461
+#>   17.0084   1.9508   3.7898 -15.8282   0.4422   8.6399  -8.9750  -3.9676
+#>   -9.4709  -5.4104   3.8096 -16.2421  12.5024   6.5037  -6.6907   8.3447
+#>    6.7814   1.9486   6.7006   9.7204  -0.5838  -4.7788   8.7128 -13.3932
+#>   -0.3491 -12.4743   1.1914   2.4251  -2.5598  13.9371  -0.8433  -0.6254
+#>   -1.5615  -8.1052   3.7613  15.6856  -3.0549  -5.0018   3.2002  -7.6677
+#>   -0.2573  -1.8555  -5.5380   3.9263   2.2249   2.6079  -6.4966  -4.3779
+#>   -5.1127  -2.9139   9.7634   3.4248  -1.8733   0.1754  -5.3005  -0.8686
+#>  -11.2679   9.0640   5.1368  15.3644   2.9642  -5.0790 -10.0058   0.6656
+#>   -5.1098  -2.9547 -10.8836  -6.8406  -5.1310   6.3634  11.1377   5.6923
+#>    8.4288  -4.1183   3.9715   2.0472   7.2207  -2.7269  -3.8387  -5.4165
+#>    2.6938  -2.6882  -6.6016  -4.5369  -1.6985  -1.4199  18.8089   7.8581
+#>    2.3302   4.4734  -0.5091   5.6254  -3.7959  -9.1441 -12.4383   3.9693
+#>   -7.7333  -2.1194  -0.9992  -3.6487   0.7344  -5.6113   2.9804  11.8883
 #> 
-#> Columns 33 to 40  -6.9297 -11.0017  -7.3290  -0.9733  -4.0245   0.7207  -0.8083 -11.6688
-#>    0.9887   6.7787   9.3630  -0.1089   9.4970   1.2405  16.4210   5.2920
-#>   -1.9242   1.4191  -1.4146   0.6657  -1.8452   4.3975   6.8781   4.3683
-#>   -6.2923  -5.6739   2.7374  -5.7019   4.6604   8.9459   1.3710   5.9450
-#>  -11.2656  -1.8411   1.3547   6.1710   0.0621  -7.3169  -2.9158   6.4003
-#>   -0.6419  -4.1902  -7.3557  -7.8505   6.1350   9.9960   5.9794  -7.8682
-#>    0.3170  -0.8213  -2.7116  -1.0315  -3.5961   7.2136  -5.3460  -5.7550
-#>   -9.0557   1.3421   7.3307  10.7694   0.1778 -19.3088  -4.8544  14.3715
-#>  -10.4370   7.7783  -7.5868  -8.5351   2.8893   7.1334  14.0060 -15.1075
-#>   11.9832  -7.7026  -6.9726   1.5130  -6.5501   3.8620  -5.2957  -3.4377
-#>   -2.8358   2.7340  -7.3989  -0.4648  -2.3398   5.1737  13.2490  11.8018
-#>    3.8140   6.2382  -2.4453   5.1233  -0.4008  -5.5641  -6.0084  -3.2443
-#>    7.0816  -1.5572  -3.5374  -2.6719  -1.3513  -0.7589 -18.0983  -1.5163
-#>   -4.1255  -3.3912   7.7073  -9.9341   4.0253 -12.1809  12.0770  -8.5870
-#>   -2.8708   6.5475   1.2193  -6.1879 -14.8457  -1.3914   6.6650 -12.9305
-#>   -0.4384  -6.7738   0.2258  -4.2270  -1.8498   5.0849  -4.2075   0.1013
-#>   -2.4174   6.0093   0.3795   5.1943 -10.8393  -5.2192   1.1280  -1.1510
-#>   -0.2956  -3.8886   5.1311  -9.1582  -2.3592   2.3350  -3.5444  -2.0641
-#>    6.5872  -0.8988  -9.7604  11.0533   3.7212  -6.9216   2.8120  -2.2940
-#>    6.3180   0.1680   5.9209  -2.4832 -12.1692  -3.6259  -7.8507  -6.5393
-#>   -3.1110  12.7384   9.9380  -5.2919   6.5667  -6.4021   5.7608  -1.6890
-#>    1.0232   0.6634   0.9311   4.7265  -3.2909   0.7162  -6.8898  15.5118
-#>    9.0084   7.1687  -3.9944 -12.5334 -15.4884   0.3428  -5.3218 -13.0022
-#>   14.7850  -3.2750 -18.7341  -5.3742  -9.2112   4.6256 -11.5047  -9.0995
-#>    6.2066   9.0095   4.8608  -8.1920  -3.6004  -5.2042  13.9607  -7.7939
-#>   -7.7216  -3.3453   5.6436  -1.4099  -4.4756  -4.7878   6.4228   1.2972
-#>   -1.9253  -5.2992   8.9140   2.5100   3.1209   3.7009  -0.3293   2.2241
-#>    4.0245  -5.1316 -12.5800 -14.4542  -8.1623 -18.2699   0.8162  -7.9554
-#>   -4.4564   6.1181   2.9378  -5.8014  -4.5335  -0.8213   0.8772   1.1595
-#>    3.6789   1.1213  -8.5050 -13.9654  -1.0107 -13.9829   0.1980 -15.7043
-#>   -1.7556   1.8067  -6.1722 -10.4958  -7.0932 -15.9986  -7.6157 -13.4282
-#>   10.2085  -5.0717  -4.6299   6.3475   8.3986   0.7032   2.2475  -8.1552
-#>   12.8033  -4.9932 -17.7588 -13.1457   2.3762   9.0886  12.7045  -8.7254
+#> Columns 33 to 40   1.2831  -3.0478   4.4763  -7.8062   5.2588  -0.5161 -14.6356  -2.2508
+#>    0.7360   5.7460   1.5698   5.3998  -3.5024   5.3727   2.5852  -7.4993
+#>   -3.5804   3.4000   0.5893  -3.1498  -0.6533   3.0402   0.6515   2.6806
+#>    3.8781  -3.5728   2.4448   2.4720   2.6148   3.8832  -7.4060  -9.1886
+#>    0.3891  10.8568   0.6245  -7.3897   1.0589  -0.3865   2.1085   1.9306
+#>   -8.2267  -9.1467   5.0025  11.2596   3.9119 -14.7153   0.6415  11.7746
+#>   -5.8712  -4.4464 -12.1352   4.3904   0.8929  -3.5592  -4.1323   5.0740
+#>   -5.5298   6.3088  -0.6864  -0.2968  10.1380   7.5405  -5.6006   8.3173
+#>    4.2371  15.4334 -11.2456   0.1605  -9.2520  12.9677  -0.1820 -22.2803
+#>   12.5241  -1.6203   4.9386  -7.2035   3.0404   5.0285   8.5747  -3.3096
+#>   -3.9372  -8.7109  -8.4101   2.9968 -10.5678  11.2122  -0.2524  -3.8859
+#>   -0.6715   9.4249  -2.3240   0.2733  -9.8237   8.4368  -3.1764   3.7562
+#>   -2.8956  -8.5119  15.9026   9.9389  -0.1198   2.3390   4.7742  -5.8192
+#>    2.8115   5.4684   0.0520  -1.4681  15.0784   4.7902  -8.3521  -7.0152
+#>    0.5207  -6.3620   3.3055   3.2268  -8.6868  -9.3651   1.9347  11.8192
+#>   -6.8734   7.2728  -1.5241   2.6622   0.1594   2.4368  -1.2347   6.6607
+#>   -0.0684 -11.5418  -3.2310   1.8720  -9.9884  -0.6127   7.7466 -13.1663
+#>    8.2251  -6.1936   2.3715   2.1034   9.7459 -17.6537   9.1238  23.0090
+#>   -9.2687 -13.9224  -0.1046  -8.9311   2.5911  -2.3880   4.6002   3.0171
+#>   -2.9448   2.7912   7.4211   1.3856   4.2842   7.6593   7.8723 -11.2764
+#>   -7.5781   8.1443  -8.1191  -5.5758  -4.2076   9.1548   1.4482 -10.2092
+#>   -2.8872  -6.7994  -4.4007   4.3353  -3.5851  -4.8136  -1.8761   8.0400
+#>   -0.4290  -3.1065 -14.5496   0.5649   2.6692   1.9439  -7.1964   6.0803
+#>   -5.9097  10.5603  -8.1681 -10.1826  -1.2288  10.4813 -11.9262   8.6101
+#>   -3.9755  -1.6607 -13.4679  -8.4485   4.3782   8.9802  -6.5016  -9.2057
+#>    7.8079  10.0995   4.3641  -2.4094   7.4531  -2.1450   3.8124 -10.7495
+#>   -6.8749   3.1467   5.7576  -2.9737  10.1921 -10.5106   1.4517  -3.2319
+#>   -1.4083 -16.2801  -3.4105  -4.7959   4.4371  -2.9939   9.0503   3.7294
+#>   -2.1158  14.4688   4.0173  -3.6502  -1.5406   0.8567   3.8255  -1.6407
+#>   -3.6414   3.8361 -12.4512  -0.9657  -1.2168  -7.1060  -4.4479   6.5655
+#>   -4.0126   8.1649   7.2663   4.2748  -2.1237   3.2451   2.5936 -11.4434
+#>   -0.7338  10.4724   3.8298  -1.1074 -10.3991   5.8741   9.9483  -2.8942
+#>   -0.0887  10.6792  -4.6705 -18.2520   0.6415   5.9036  -1.8907  -0.8449
 #> 
-#> Columns 41 to 48  -7.4805  -1.3145 -13.7431  11.4765  -0.9648 -12.0847   4.7179  -1.4272
-#>   -2.2829   2.5950 -10.7235  -2.1609 -13.8357  -9.4849   4.9866  -4.8983
-#>    2.5329   2.3297  -6.3099  -0.2002   9.5547   0.6236   0.4863  10.6733
-#>    6.6941  -8.6938  -2.0557  -5.7574   1.9355  -6.3002   1.6139   3.4015
-#>   -2.2127   4.6527  -3.2228  -6.1964   3.3754  -3.8984   5.7287  11.5439
-#>    0.4154 -14.2939   3.1326  -4.1760  -2.6193  -3.7922   3.6896   7.2133
-#>    3.8841   5.6008  -0.8244   7.2883  -3.8380  -6.6507  -3.1420   4.8811
-#>   -6.9786  13.0368  -1.1869   6.9741  -2.7768  -2.5288   1.3483  -3.5707
-#>    6.9048  -5.1701   4.1549   5.8017  -8.5916   2.9603  -7.7201   7.7871
-#>  -11.9918   3.8684  -2.6689  -6.1250  -9.2973   1.2899   3.8853  -2.2765
-#>   -7.7719  11.2667 -19.5185   4.6244  -1.3942  -8.3421  -8.6290  -1.2741
-#>    0.4772   4.3330   5.6445  -7.2651  -7.9712  -2.0373  -5.2405  -1.3881
-#>   -2.8972   5.4049  -5.5721  -3.8811  19.0670   3.8932  -2.1869   0.7545
-#>   15.8109  -8.8330   7.4110   1.7833  -0.2962  14.3180   2.0180 -11.7855
-#>    2.8560   6.9780 -17.5480  10.3052  -3.1295  -4.2072   1.4636  -6.0688
-#>    0.8833 -13.2746  -8.3860  -4.9834   3.1834   8.0150   1.5190   0.3426
-#>   -4.9003   1.4852   0.1459  -1.5525   0.3936  -2.1157   2.1987   7.1499
-#>    0.3587  -2.1121  -0.5518  -1.9261  -9.1747  11.4873   6.3786   5.7065
-#>   -6.8265  -2.9418  -4.8807   0.7788   2.3878   3.5582  -4.0625  -3.5805
-#>  -10.6284   3.5065  -4.6024   6.8657 -11.1292  15.1727   3.2767  -8.7483
-#>   -5.0826   2.9713   8.4317  -5.3287  -8.3769  -4.4589  -5.1314   8.1267
-#>    0.7844   0.2151  -0.4487   8.8005   7.6252  -0.6333   5.5800  -3.0729
-#>  -11.8212  -7.3095  -0.3658   0.8800   1.5696  -7.7159  -9.3507  -8.9309
-#>    5.5200  -1.2981   3.8761  -1.1395  14.0902  13.1584   0.2560  -4.1000
-#>   -2.5606  -2.6205   2.2323  -1.8492  -9.6116  -7.1594  -3.9362  -3.7241
-#>   -5.2702 -10.7750  -0.7620   6.2300   0.7716  -0.5113  -4.7611  -2.4473
-#>    0.7879  -8.2632   5.1632  -4.9078   3.8705  -0.7683  -0.0107   7.0924
-#>   -1.8081   0.7194  -2.0832 -16.3230  15.7687   9.7112 -12.0240   2.1642
-#>   -7.5212   1.5560  -3.9287  11.9568  -9.6883   0.9000  13.2467  -7.7876
-#>   -9.0240  15.0953  -5.1962  -7.3965   5.9510   4.0735 -10.1522   0.6561
-#>   -4.0246  -1.4343  12.8208   5.0154 -10.6280  -4.3388  -6.2407   0.0779
-#>    0.7169  -1.2498   4.6823  -0.6321   6.2968   3.7195   3.3005  -6.5767
-#>   -1.1701  -0.4908  -7.7588  -6.1339 -17.1114  -5.4716  -5.5306  -2.6368
+#> Columns 41 to 48  11.0287  23.9223 -10.9727   5.7250 -15.1863   0.6890   6.0917   5.5327
+#>   -7.7970   8.1815  -2.3361  -4.9361  -1.7301   1.2229   1.6241   2.1373
+#>    2.4145  -5.5961  -7.0176 -12.3579   5.8794   4.3002   6.9620  -2.4586
+#>   -4.9267  -2.8628  -1.2118   3.4540   4.4838   4.0440   2.1566   0.3314
+#>    1.5155   7.3794  -6.5086 -12.0071  -3.3859  -1.0735  -1.3640  -1.2261
+#>    4.9562 -12.3253   3.9348  -0.1426  -2.9609  -4.5936   8.6444  -0.1615
+#>    2.7995  -3.8605  -2.9861  -2.6860   8.0441 -13.4360  -0.3204   1.2595
+#>   -3.1475 -10.9079   8.1268  -4.6683   3.2842  -4.5466  -3.6702  -6.3878
+#>    0.2912   4.4247  -0.3868  -2.9161   6.7283  12.1133 -15.0527  -6.1177
+#>    3.8399  -2.5606   2.1613   5.9011  -3.0837  -5.0822  -0.7802   0.6256
+#>   10.4074   5.0157   9.0405  -4.5233  -0.1893   7.4060   2.1579  -5.0729
+#>    6.5912   5.1064   5.6458 -13.1088  -3.7531  -2.8180   5.5297   1.9090
+#>   -3.9427  -8.9145   5.9143  10.2941   4.0567  -9.5471   1.9832  -3.8064
+#>   11.1200   5.2249  21.4086   0.7067  10.2527 -11.2665  -2.8074   2.0100
+#>   -3.9126   4.0027  -8.0435   3.7892  -4.5951  -4.0826   4.4880   5.1268
+#>  -12.5270  -5.4311   2.5436  -6.9655   5.6668   9.4169   0.8917  -6.0900
+#>    7.4471   6.1326  -1.2263   4.5595   0.6203   5.1312   4.0651  -1.7175
+#>    0.8906  -9.8464  -7.6223  -4.2308   5.1840   1.4342  11.6056  -3.5907
+#>   -9.3793   1.4549   1.7463  -5.5863   2.4171  -0.3159   5.8861  -1.6003
+#>    0.3980   1.5253 -18.8303   4.1457   8.2941  -0.4017   0.3058  -3.4936
+#>   -6.5450  12.0834  12.9839 -11.8848   4.0145  -9.3704  -2.7580   4.9590
+#>   -2.4938   2.1248 -12.9813  -4.5869 -16.2373  -1.9499   4.0333  11.7855
+#>    2.9872  -6.4620   3.3748  -0.0389   4.9888  -6.5724   0.9417  -5.2329
+#>    4.7134  -4.7245 -13.5988  -5.3534  16.2090  -1.3043   1.9982  -3.2839
+#>  -14.0372  -6.9747  -0.3115   1.7478  19.5211  -0.7504 -10.7181   0.0177
+#>   -4.7587   0.5045  -1.1509  -2.5681   2.8321  -5.8245   4.5838  -1.5990
+#>    3.8367  -1.5328  -0.2166  -5.3373  11.3631  -7.3298   0.7675  -0.0700
+#>   -5.0838   0.8220  -7.0111  -3.8552 -15.1995   0.5013   3.4304  -6.1871
+#>   -2.3995  -0.0312   3.7932  -2.7801   6.4899  -1.4659   3.6165  -1.1040
+#>    7.4466   5.0951   6.2980  -0.5562   6.1656  -3.1261   0.7208  -4.7690
+#>   -4.1971  -0.0518  11.8565  -1.4557   4.0945  -0.9249  -7.2542  -5.5782
+#>  -20.0819  -8.0617   8.4675   0.3628   5.5791   4.2936   0.1786 -10.7128
+#>   -2.8940   9.6314 -16.2302  -6.9059  -5.7656   7.3872 -12.2754   2.8481
 #> 
 #> (17,.,.) = 
-#>  Columns 1 to 8  -2.7704  10.5603   0.7267  -7.7291  -3.7433  -1.6578  -3.3570  -1.1029
-#>    2.5729   2.4487  -2.5715  -7.8141   4.2293 -11.1039   1.5680   8.5610
-#>  -13.6032  -0.1561   2.3610  -0.0955   2.1723   4.9194  -0.1423  -4.7875
-#>  -18.6177 -16.6249   7.0370   0.4203   5.7234   8.8069  -4.4259  -3.4729
-#>   -3.0531  -0.4844  -2.4866  -2.8341  -2.3717   0.2669 -10.3285   1.0651
-#>    6.0293  -2.8649  -5.4014  -7.0805  -7.5987   0.7204  -1.6050 -10.0098
-#>   -3.7357   7.8525   3.0803  -3.4952   0.9365  -1.8273  -1.7159  -4.0662
-#>    7.0489  -2.4002   0.2068  -1.4921   1.5017 -11.9677 -13.1278   9.2036
-#>   -2.5369  -4.1299  -4.7285   6.1634  -4.5615  -4.8606   1.4767   5.1493
-#>  -10.4036 -10.7121   4.2236  -9.0293   6.1111   7.8108  -7.0556  11.0722
-#>    0.9993   4.4852  -8.2668  -3.4930   0.3539  -4.8302  -1.4111   9.8303
-#>    0.9946   3.3462  -5.6483   1.6089  -5.8641   1.4127   2.5571  -4.2720
-#>   -0.2602   3.9641  -1.0204  -2.6129  -3.2721  11.8840  -2.5988  -5.9541
-#>    2.0741   1.7521   1.4423   7.5366   9.0240 -22.3413   1.5828  -1.8651
-#>   -0.4998   3.5266   0.7295  -2.1794   1.4527  -2.7818  -8.7797   1.6922
-#>   -0.0240  -3.9744   2.0120  -8.4228  -0.8473   2.9716  -0.0982  -9.6429
-#>   -6.8080  13.1794   0.6546   1.0582  -5.0821  -9.3842  -4.4272   3.9259
-#>   -2.1990  -5.3386  -2.8094  -4.7910   8.2978  -9.5896   7.6713  -2.5908
-#>   -3.1451   1.2174  -2.7183   0.9241  -3.2326   7.2417  -0.7122  -7.7775
-#>   20.0796  -6.6055   2.9293  -1.0359   2.0235 -13.0951   9.0062   1.1787
-#>   -6.9559   2.6484  -3.8156  -2.2437   1.5578   1.2313   1.7346  -4.0805
-#>    1.6198  -3.9982   5.2072   5.4066   1.4270  -3.5072  -7.0815  -2.8528
-#>   -3.6409  12.0129   5.4795   0.9914   1.0420  -4.8794  12.5829   1.7580
-#>   -5.3814  -1.0697   2.6472  -0.6693   9.2690  18.7628  -1.3712  -0.7372
-#>    0.2149   9.5615  -6.8986  -1.1008  -0.6883   5.1239   1.9762   2.9490
-#>    6.4925  -3.5990   9.6852  -8.9323  -1.3355  -3.7827  -0.8172 -10.7852
-#>   -5.4988   2.4479  -4.5648  -4.2705   7.0228   5.1410 -20.2350   6.6242
-#>  -14.4974   7.3483   5.1486  -2.0932  -1.0024  -1.4598  11.5936   1.4477
-#>    4.3321   7.3866  -2.9594   6.7808   2.5951  -7.6477 -11.4815  -1.3703
-#>   -4.0412  11.8890   1.3561 -10.6676 -13.7145  -2.2311   8.0976  10.0307
-#>  -15.1334   5.8670   5.7254  -1.1431  -0.2199 -11.0346   5.3607   1.5764
-#>    9.1307   0.9232   2.6315   0.2508  -1.3830   4.0534  -3.5289  -7.6598
-#>    5.0985  -5.2785  -1.8614  -4.2947  -5.6576   1.2913   9.4377  10.3827
+#>  Columns 1 to 8  -6.7101 -11.4777  -1.0029   2.4885  -4.9770   0.7825  -4.2027  -4.5052
+#>    0.3459   4.8314  -5.4571  -4.7593  -4.6608   0.2986   3.2795  -1.5996
+#>   19.8249   4.1899  14.6791   0.5789  -2.0847   0.8952   1.9519  -1.4155
+#>    6.2983   9.3254   0.1402   6.9671  -2.2408   1.8283   3.0115  -0.0823
+#>   -3.2566 -10.4584 -12.1326  -9.2020  -0.8667   6.1097  -0.3324  -9.6863
+#>    5.9892  -6.9177   2.0852  12.3194   8.2921  -7.2529  -3.1114  -0.5145
+#>    6.6196   5.2669   0.6946   0.8671   3.2244  -4.9872   0.5402   6.9031
+#>   -3.2299   6.8019   3.9077   1.6574  -2.3924   0.4771   0.2643  11.1557
+#>    0.3952  11.4992  -5.9060   0.5920   2.9335   1.6913 -11.2096  -2.7676
+#>   -3.7975  -1.5835   4.3984  -2.6634  -0.7476  -6.5118  -3.8307  -2.6033
+#>  -20.7084   3.8114  -3.2204  -6.0314  -4.9041   5.1997   6.2849   8.7627
+#>   -1.4334  -2.1929  -6.3171   0.1353   3.6732   0.7619  -1.3566  10.4293
+#>   -1.8562   3.7916   6.7467   2.6822  -7.7007   4.6581   0.4162  -7.4894
+#>  -10.2735   8.7310  -0.5912   1.6107   5.4785   3.7882  -1.1886  -7.1015
+#>   14.4727  -3.8329  -1.4278   2.7269  -4.4700  -7.4967  -1.9738   4.5130
+#>    6.8452  10.1308   7.3494  -2.0400   3.6543   4.3195   3.1355   9.7870
+#>   -5.9459 -11.1414  -0.2729   3.5982   6.4495   4.1605   0.2170  -5.8012
+#>   -4.5511  10.9316  -4.1601   7.4514  -6.1557   2.0782   8.1392   2.4922
+#>    0.0213  -3.3917   4.5134   1.4458  -2.2109  -0.8068  10.0207   8.8992
+#>    2.7701   1.8638  -0.7078  -7.6232   0.3998   2.7054  -9.8599  -5.1184
+#>   -1.2312  -4.3599   0.9120  -7.0603  -6.7828 -11.1520  -3.7187  -1.8712
+#>   21.5495  -5.9258   6.5790   2.8749   1.1707  -3.6663   7.2855   3.8327
+#>    2.2530   0.2939  -8.3601   5.5444  14.1916   1.5324  -1.2279  10.7738
+#>    4.2856   2.8972  -1.3524   7.9621   7.4640   4.4297 -14.1368  -1.5305
+#>   12.5635  -1.7788  -9.6753  -4.2210  11.5715   7.2179   1.0202   6.6613
+#>   -0.7550   0.4094   3.4700  -2.4425  -5.9166  -5.9489  -5.8825  -7.2441
+#>    6.2465 -15.3493   1.6315   0.1454  -4.2356  -2.0912  -2.2255  -7.1869
+#>   -6.7266 -13.0365  -1.2436   4.4574 -10.2964  -7.8975   1.7467   5.7052
+#>    6.3476   6.2439  -1.2488  -3.0344   2.2518  -0.3197  -4.7615 -11.2520
+#>   -7.9013  -0.3427   8.5573   4.5705   2.0897   4.0988  -0.9844   2.5164
+#>   -2.0412   6.2283  -9.9075  -1.1132  -1.1746  -1.0053  -6.9266 -11.6848
+#>    1.5173   4.3332   7.6094  -3.5713  -4.6466  -0.5351  -1.7853   5.7034
+#>    6.0532  -8.2225   0.3326 -11.5499  -7.2081   1.5923  -1.4379   5.2997
 #> 
-#> Columns 9 to 16   6.7341   1.9863   8.9667  12.0119   5.6970   0.6868  15.2604  -4.1424
-#>   -4.2998  -8.5678   9.4961   2.9845  -3.5134   1.8394  -0.9021  -2.1620
-#>   -3.2300   5.0553  -2.9733  -1.1748  -0.8557   1.1872  -3.1148   3.1607
-#>  -11.7453  -1.7068  -5.4682 -12.8065  -2.6870   7.2390  -0.2386  -3.2514
-#>    1.0902   0.9017   1.4176   3.7273  -0.6858   1.1306  -0.6308  -6.2185
-#>    5.4508   1.4973   0.2619  -8.4212  13.0744   3.6572  -0.3702   6.1075
-#>    0.2905   4.0318 -10.3974  13.1124   2.7247  -4.9409   5.5627   7.1809
-#>    2.8171   6.4971   1.1240   0.0898   2.5301  -4.2880 -21.7980   2.0108
-#>  -15.7529   4.7049  -3.7587 -10.8786   9.4058   5.3792  -2.8985  -5.8478
-#>    0.7016 -13.5083   2.7154   1.1747  -2.4312  -3.8385   9.1867  11.6649
-#>  -15.5568   4.4197  -7.8619   0.2513 -10.2832  11.4538 -14.1472   5.2680
-#>    8.7746  -9.0890  -3.8961   2.9851   4.9018 -19.8198  10.5791  -2.6983
-#>    9.1059  11.0467  -2.0032  -1.5611  -0.6221  -3.1742  -0.9975   1.7780
-#>   -0.7256   0.6946   3.3463   1.4316   5.4650  -0.5997  -3.0255 -15.4508
-#>   -0.2171   5.6657   9.0344   2.1456  -4.4352  -4.1476   5.2174  -4.4732
-#>    2.8979   7.3370   0.8185   2.5894  -6.4739   3.8603   5.8764   4.0880
-#>    1.1180  17.6122  -5.3422   5.0036   4.3687  -5.0587  -9.5809  10.2824
-#>    3.1349  11.4819  -2.5447  -8.5379  -5.8880  -7.5417 -13.4420   5.0741
-#>    7.3487   4.7572   0.9641   0.0457   8.7562  -0.4766 -11.2908   3.6942
-#>    2.3793   3.5841  -0.3000 -11.0359  -0.0331  -2.5764  -7.8028  12.3705
-#>    1.1415  -4.4475   4.1464  -4.4200   4.0291 -16.3471  -6.8999   3.4234
-#>   -1.4660   1.7638  -7.1693   4.2245  -4.1514   8.3268  -8.7105 -10.3372
-#>   -0.6831  -7.3303  -3.0960  -2.8838  -2.9398   3.8557  12.8259   2.2771
-#>   -1.6265   3.4067  -3.4404 -14.4183   7.1554   9.3766  -4.0037  18.9329
-#>   -0.8861  -2.8863  10.9595  -3.0864   1.1135   0.2013   1.0840   1.5685
-#>   13.0322   4.8567  13.5926   3.8107   8.6678  -0.9883  -7.2145  -1.4742
-#>   -1.9749  -1.3267   1.1079  12.0055  -0.5795   1.0858  -3.1108  -3.2832
-#>   -0.5156  11.9009   5.1915 -12.5264   5.8314   4.6921 -14.1464   8.2093
-#>    0.1146   4.5829   6.4958   5.2190 -10.4601   6.2816  -5.5749 -10.3199
-#>   -2.6049   5.9377  12.4423   2.7328   1.6565   4.3421   2.3708   3.0174
-#>   10.3795  -6.6289  -8.6209   1.4962   0.4001  -4.7952   1.3191  -1.1169
-#>    8.8352  -4.9195   3.9531 -15.5500   7.0633  -5.7947   4.2496  -3.8436
-#>   -7.5820 -15.2724  -0.5123  -9.0253   3.9423   4.5669  13.7463   5.4912
+#> Columns 9 to 16   3.6851  -5.5823  -0.8896  -7.6001   1.9750   3.4856   6.0084   1.1896
+#>    1.9349   7.1622  -1.3940   1.6941  -3.8618  -7.1291  -1.7178   7.9887
+#>    8.2891  -1.4174  13.3630  -1.4146   1.4920   1.2760   3.0477  -1.3736
+#>   -4.7718  14.5363   3.3013  10.9345 -13.1247   3.2536   0.7368   2.8326
+#>   -4.5275   5.6231  -2.3968  -6.3981  -1.2304  14.1423   4.9638   0.7035
+#>    1.2616  -1.0991 -11.4668  -2.9844  -2.7750  -4.4085   4.4696   3.6687
+#>    3.4103   4.0733  -0.6153   1.6811   3.1296  -5.2233   3.9391   2.8672
+#>   -5.0149  12.5380   6.2359   9.5709   7.7510   8.4097 -14.2098  -2.2919
+#>    3.4180  -9.5783   4.3574   1.8264  -3.5829 -14.2357 -10.9522   9.3056
+#>   -5.5409   4.9382   4.0326  -4.1310   6.6968  -7.6930  -4.8090   1.4086
+#>   -7.1578  -2.2782  -4.3995  -4.5459   3.2513  -2.9472   5.0713  -9.2009
+#>    5.3264  -7.4082   3.6674  12.6978  -4.0372   0.2962 -12.3301  -5.6356
+#>   13.3602   4.5467  -2.8874   2.7407 -10.3076  -5.0587  -3.3660   2.8274
+#>   -7.0421   7.4721  -8.9882  -1.9446  -7.3005   0.0265  -2.8710   2.8790
+#>    2.2557  -3.8773  -4.1271   1.0536   1.7114  -5.9833 -15.0314  -8.0907
+#>  -11.9969  -1.1218  -0.0272   3.2888   9.6942  -0.4746  -4.2843  -8.4002
+#>    2.5310  -8.5808 -17.4459  -2.6292   5.3209  -1.6922  -0.0420  11.5135
+#>   -5.9935   8.3489 -13.8455  -1.4210   7.8496  -7.3958 -18.0197  -8.2921
+#>   -5.5460   2.1932   0.4417  -3.3319  -4.8748  -3.5933  -4.1547   2.2401
+#>   -9.7388   0.6479   4.6112 -16.9316  -3.9626  -8.7223  -7.2928  11.5077
+#>    3.8072  -3.8731   4.8798  10.9572  -5.7474   3.7387  -7.3638  -3.6598
+#>    1.2881  -3.3529  18.1911  10.6599 -10.7666 -11.0110   6.3142  -4.1415
+#>    9.2746   0.1981 -11.8911  -2.9984  12.6167   3.8637  -2.1943   7.6611
+#>    4.4887  -5.7109  -2.8101  -2.9143  -5.9146  10.7833  -7.6034  -2.7181
+#>   -7.0522   6.6725 -12.7007   7.1897  -0.2077  -5.6401   4.4739   6.9485
+#>   -0.4787   0.1785   9.9144  -2.9775   7.3733   2.1053  -7.2497   4.2792
+#>    1.5835  10.4767 -12.8557  -1.0089  -4.9936   2.5060  -0.8951   5.7842
+#>    0.8011  -1.0961   6.0491   0.8477  -4.1108  -4.3715  14.1876  -1.3003
+#>   -4.6499  -1.4993  -2.6450  -1.6413   0.1944   3.7243 -11.6397  -1.9370
+#>    5.6309  -4.3383  -5.9791 -16.7630   3.9101  -0.1012 -16.4182  -2.9557
+#>    0.5716   1.1852 -11.0379   3.0770   0.1567   0.1318   6.1487   8.6028
+#>   -5.7520 -11.0449  16.2766  -1.6632   0.8697   5.4534  -3.7527  -0.9934
+#>   -0.8892   3.5720   8.9218  10.0915  -3.2897   2.8101 -10.1093  -6.3926
 #> 
-#> Columns 17 to 24   2.8659   7.9970  -2.2369  13.0924   8.6808  -0.2852  -9.3335  14.6500
-#>    7.0788  -7.9463  -3.4163  -1.3608  -4.7428  -0.0517  -4.0457  -4.6531
-#>   -3.1597   1.8588 -12.6884 -11.0860   4.6732   2.3709  -9.3827  -0.1688
-#>   11.8203   2.5997   4.2648   1.1745   5.2093   5.3027   1.5585 -16.1315
-#>  -10.1067   1.5029  -5.0066  -9.1026  -9.8618  -9.7336 -11.3681  -7.6435
-#>    3.4721   1.6037  -5.9775  -4.3065   0.5484   4.4562   1.1557   0.8167
-#>   -0.5257  -0.5474   3.3103  -3.4944   4.4801  -8.4647   0.1863  -5.7290
-#>   -4.6142  -1.2074   1.7776   3.7075   7.3313   2.6778   0.3457  -1.0475
-#>    0.2307   5.1051  -4.3815 -12.3826  -3.9321  13.1424   1.0713 -13.4604
-#>    0.2598   0.6921   4.9805   7.6117  -8.1925  -8.7037  -6.8049  -0.0327
-#>    7.0166  -0.7439   2.9224  -9.7845   6.8724   9.8846  -2.1503  -5.7923
-#>   -2.1224  -1.3258  13.4410   1.4991  -2.4490  -4.1900   4.0207   4.3657
-#>   -3.2526   4.4835   3.9203  -3.7430  11.5783   1.6549   4.3792   6.2467
-#>  -10.0576  -3.4088   4.0432  -5.1732 -17.3550  -7.1546   1.4905   0.1845
-#>   -5.6070   3.0486  -5.6468  -8.1206  -5.4535  -6.7772   0.8812   8.7500
-#>    1.3125  -4.4642  -3.8045   7.3684   1.5014  -5.6692   8.8631   5.4753
-#>    1.1991   8.7489  -6.4687   1.2454   2.9509   0.9164  -8.4742   7.6956
-#>   15.4080  -4.4415  10.2667  -4.4999   5.5235 -13.4351  15.9167   7.7456
-#>    1.6523   4.7800  -0.0088  -3.5715   3.0276  -3.7797  -1.8262   4.8513
-#>    2.5692   0.6369   4.6692  -0.8968  -3.0227   4.5375   7.6932  13.8799
-#>    0.5910  -0.4237   9.7608   4.6418  -0.1699  -7.6320  -2.6444 -10.0515
-#>    1.9812   2.0879  -2.8302  -3.6234   7.9835   9.1231   5.0533   6.6013
-#>   14.6054   4.1328  -6.3627  -7.4908  -1.6861   7.2527   8.0787   7.1441
-#>   -2.2543  -3.0008 -10.7692  -5.7324 -13.2544  -1.8763  -2.6397   8.1258
-#>   12.1308   5.8818   0.9452   7.1992   0.6095  -3.3979  -6.7545   2.6578
-#>    0.9111   8.0921  -0.1917  21.9292   4.9044  -2.3156  -1.1944   3.9495
-#>    9.2807  -2.8576 -10.0671  -3.6933  -3.1867  -0.5104   2.8762 -10.1065
-#>    8.1040   7.1307   5.4396 -10.0216  -8.6300   0.2725 -10.2342   4.1169
-#>    1.8882  -7.2394   1.6500  -6.4690  -6.5163  -8.4244   0.7696   2.0009
-#>   -3.4141   1.8392   4.7830   4.7468   3.8604  12.8414  -2.3081   2.1538
-#>   15.9406   7.2814   5.9098   7.0757   2.5140  -1.6559   7.7894  -2.8529
-#>   -6.7661  -9.5295  -6.3038   9.4841  -4.0221  11.3960  -6.2655   6.3740
-#>   15.0542   2.3747   4.9200   6.3924  -2.9990   8.0179  -2.8307  -2.4978
+#> Columns 17 to 24 -13.5687   6.5127  11.2827  -2.7570  -8.7587   2.8355   0.5006   2.2251
+#>    0.3867   2.3722  -1.3737  -6.3242  13.0167 -10.9999  -7.8048   3.2972
+#>    1.4502   6.9294  -3.0276   2.9232  -4.3504  -1.6233   1.3814   3.7063
+#>   -0.1825  -0.8234  15.9316  -5.2203   4.8262   1.5230  -6.0799   7.2343
+#>    9.2279  -8.6581   8.5543  -1.0704 -12.5591  -0.7235   6.1389   8.5998
+#>   11.8555   1.8300  -8.1740  11.3302   3.3347   1.1953   6.3902   1.4844
+#>   -5.4598  -1.9664 -18.0901   4.5571  10.7894 -12.4336  -5.0217 -13.9397
+#>   -4.9063  -6.6183   8.7664  -9.4895   1.5648  -2.6755 -11.3531  -3.5207
+#>    8.5009  10.1183   8.8329  -9.2066   4.4645  -0.3128 -11.4763  -1.0231
+#>    9.5467  -0.5454   3.4559   1.3236   2.0064  -0.0237   5.8554   2.2589
+#>    8.2596   1.9679  -1.2458  -0.0506   3.9848  -2.3507   3.3796   0.5996
+#>   -1.7898   3.4860  11.4615   3.0012 -10.2115  -5.6955   3.2178  -7.3044
+#>    4.1323   4.1176   6.8611  17.9405  -5.3548  -7.5897   2.6224  -1.3363
+#>    8.4927   3.7228  -2.9584   4.0962   5.2812   2.8066 -11.7284  -0.6806
+#>    2.6983  11.3749  -2.7474   4.4368  -4.0580  -6.6421   0.0997   1.3248
+#>   -5.3917 -10.6345   6.9907  -1.5771   2.0337   2.5822   9.5103  -4.7180
+#>   -3.3416  -0.9225   4.6518  -5.3008   4.8100  12.7337   3.0646   4.0448
+#>    1.3542  12.9571  -7.7277 -10.3433   3.5621  -8.1302   0.3377   8.7763
+#>    6.1591  -5.8663  -0.5179  -6.2179   4.6257  -0.3382   1.1006  -6.1489
+#>    5.0665   4.8555 -10.8439  11.4757   7.8647   4.7505   0.4624   4.7066
+#>    1.4569   0.5079   3.2724   1.9506  -0.0630  -2.3472   0.8453 -11.6049
+#>   -0.5043  -6.1222   7.6402   9.8744 -15.7752  -1.2555  -1.5286  -3.3984
+#>   -5.6876  10.6584  -9.5881  -1.3011   8.0969   0.6792  -4.3767  -9.1040
+#>   -1.2803   0.9476  -5.1278  -5.5527   0.3106  -5.8792  11.3634  -5.4370
+#>    3.5373  -7.1395  -3.9328  -2.0724  13.0769  12.7756   5.0851  -8.1487
+#>   -4.8530   5.1399  -4.0207   8.6512   4.5221   1.5550   2.2901  11.6727
+#>    5.3800   0.3494  -2.4451   0.0102  -0.5794   0.6976   0.8997   6.2500
+#>   12.4039  -2.2611  -3.5274  -1.9516   0.0779   3.7180   1.7358   4.8573
+#>   -6.8875  -6.1839   3.8829   0.6373  -1.9786   3.3023  -6.6698   5.6416
+#>   14.4934  13.9035  -0.6031  -1.7887  -5.0468  -1.1163  -2.7402  -4.0482
+#>   -8.8617   2.8904   1.8729  -2.2741   2.8444  -0.9365  -4.3174   3.7160
+#>   11.9332  -2.7173   4.4408   6.4466  -7.2732  10.7196   2.5931  -1.9942
+#>   -2.1627   8.7172   3.3949 -13.9914  -7.7207  -0.9197  -0.0031  -6.5949
 #> 
-#> Columns 25 to 32   2.1070   1.1357  -0.6737   1.5143  -8.0808   1.4888   4.5449   1.9640
-#>    4.4534   2.5352  -0.6067   0.8445  -0.0012   1.2110   0.8128   0.5631
-#>   -4.9796   3.3211   6.7698   7.0588   6.1402   3.4425  -6.0022  -3.4105
-#>    1.5599  -5.6939   3.6210  20.0176   0.4168   3.2349   0.4631  10.5732
-#>    9.3376  -4.3873   0.4279  -3.8398  -7.9957  -7.3808  -0.1321   0.8090
-#>    6.2631  -2.7879  11.4126  -0.0802  -5.3166  -7.7050   0.0382  17.2720
-#>   -8.1011   6.2337  -2.9383   4.1201  -4.2928   3.6873  -0.4019   2.7793
-#>    1.7744  -5.0834  -1.6528  -6.6120  -8.9883  -0.6799   0.8348  -0.0160
-#>  -13.1987  -0.0450   0.9701  -1.2428   2.4142  -1.3553 -12.7588   9.6190
-#>    7.0441   6.1168  -3.5449   4.0805  -5.3083   5.9797   8.8975   2.7549
-#>   -3.8628  -1.2028   0.1499   9.4044   5.8880  -0.8799  -1.2509  -5.9398
-#>   -0.9154   2.7582  -0.1849  -8.6003   1.1950   4.5682   7.6503  -1.1930
-#>    1.5326  -8.2111   4.0244  -8.2650   6.7523   5.4653   2.4483  -7.4292
-#>   -4.2883   0.2562  -2.8330  -7.6422 -20.6673  -4.0809   1.7666  -4.3616
-#>   -8.0310   0.3609  -3.1955  -5.4921  -6.0570  18.4650 -14.8922  -2.0463
-#>    0.1960  -3.4853  -5.3803   6.3456   0.3314  -3.9994   2.6963   3.8593
-#>   -1.3784  -1.7650   0.1733  -3.8867  -5.2378  -5.9713   5.2499  -4.7100
-#>  -13.8746   4.6234  -6.7364   8.9808  -0.9970   9.9604   1.1777   2.4911
-#>   -7.9730  -5.1767  -4.4298  -5.4043   6.5203  -6.3957  -7.0252   7.0823
-#>   -7.5692   0.2801  -6.0745  -7.7463 -13.9535   5.2160  -5.9360   0.5628
-#>    1.3956   0.3247   4.9877   0.6447  -8.8323  -2.6314   1.5729  12.2125
-#>  -11.6241  -8.0160  -9.6314  -5.0019  -3.2613   4.8675   3.5103  -3.1493
-#>   -2.5003   5.0246   3.1325  -0.2385   9.1923   2.3005  -0.7771  -2.0385
-#>    4.8006  -2.8842   7.8990   1.0820   7.9377  -3.5297  -0.4941  -3.6435
-#>   -3.0593  -1.8972   0.7686   3.1528   1.8651   3.5969   0.1643   3.4832
-#>   -1.2656  -3.8606  -6.7093  -0.4224 -10.3953  -8.6362  -0.9552   4.5440
-#>    0.2829  -5.4841   2.0222   0.5593  -0.8317 -13.1011  17.1616   8.9623
-#>    9.4220  -2.8666  -5.2726   0.5688   3.8026  -8.5452  -3.6207 -17.8513
-#>  -15.7801  -7.5726  -5.9551  -7.1169 -13.2869   0.5127  -2.9001   0.7436
-#>   17.4660  -1.4932  -7.7020  -5.5903  -1.6124   6.7695  -0.5188  -9.5186
-#>  -13.4290  10.1790  -6.9775   2.2547   5.4104   1.8314   2.4659   7.4010
-#>    9.0881  -1.5618  18.9436  -6.0187   7.7381  -9.9503   2.6436  -4.7875
-#>   -1.2500  11.6404   3.5587  10.2993   6.2472   5.6981  -3.0613   1.0673
+#> Columns 25 to 32  11.4745   6.9269   4.8774  -2.0958  -9.7285   5.9198   0.9012   5.1188
+#>   -4.8288   0.2665  -2.8089  -0.0742  -2.4463  -2.8109   4.7577  -3.8448
+#>    6.1666  -4.2836  -9.8506  -7.3292 -11.5697   6.0078   0.9813   5.2271
+#>    3.1652   1.2859   3.9376   8.1368  13.0757  -5.7888   3.5054  -2.8935
+#>    9.6099  -3.2063  13.8705  -6.0158  -3.9418  -3.1853  -0.9793  -1.8307
+#>    5.2521   2.6800   9.4957   0.5549  -1.0532   3.2449  11.0121   6.9269
+#>    1.2321   4.1570   0.2087   0.2731   0.5162  -7.2604   8.5382 -16.8329
+#>   -0.2639   2.5039   0.8767   7.3226   7.9762 -15.7648  -3.7707  -0.3491
+#>  -14.7445   9.4461 -10.0301  -4.5420   7.2404   2.6139  -3.8939  10.2656
+#>  -12.3670   1.6378  -6.1903  -7.0113  -0.2487  -3.1509  -1.4754   8.9767
+#>   -0.6004  -6.3564  -1.7146  10.5536  -4.6391  -5.5797  -5.9850  17.0420
+#>   -1.0708   4.3684   4.5661   5.5823   2.9018  -0.3809  -9.4206   0.5866
+#>   -8.4398   2.9568 -10.3268  -5.6140  -7.1608   6.9569   4.7336   4.1830
+#>   -1.6025   0.5531   4.3948   3.0332  -2.2063  -3.1956   5.3363   3.8775
+#>    2.3409  -2.1857  -0.7678   0.0617   5.0413   2.5835   1.8820  -0.0052
+#>    6.5754  -4.3946  -1.5501  -1.1785  -2.1480  -1.2932   2.8489   0.5282
+#>   -9.1387   8.4629  -9.6648  -4.6575   1.5888   5.4846   8.5441   6.5225
+#>  -13.0260   5.2306   3.8946  -3.7950  -5.6353  -0.3656   5.5660  -5.4506
+#>   -2.7561  -9.4117  -1.8517  -1.9591  -2.1227  -7.7888   5.4212   8.4974
+#>    4.4705   2.4724   0.8234  -9.2992  -1.6666   7.2636   6.8622  10.0846
+#>    4.6718  -2.2679  -5.2187  -6.3119  -7.6130 -13.8678  -0.6389   0.5356
+#>   18.1821   2.3081  -5.9324  -4.6010  -0.3936   0.9049  -2.9524   1.3514
+#>  -11.5694  -3.8010   5.0844   7.3215  -1.7548   3.1009  -2.0693  -9.2262
+#>    2.3637   0.5598   5.5983  -7.4253   1.3303   1.8550   4.2324  -0.1752
+#>    1.6527 -11.6107  10.7182  -4.1860   3.1728   5.8682   1.4406  -5.4344
+#>   -9.0393   5.0287   5.8522   1.7742  -4.9223  -2.5139   2.7156   1.1177
+#>    1.1954   6.5779   5.5969  -3.3282   2.6744   1.2588  14.6031  -5.5260
+#>    0.8213  -1.4125   4.1455  -2.2743  -0.2482   1.2909  -6.7753   7.8871
+#>   -8.9178   0.2694   3.5532   4.8254   0.4340  -2.0997   7.5575  -2.5730
+#>  -11.7140   5.0675   1.7414   1.8056  -9.8308  -1.8322   6.3071   7.7251
+#>    0.1694   8.4420  -4.7378   4.9153  -2.4625  -0.1151   5.8404  -0.8228
+#>  -12.1115  -8.8196  -5.7316   0.4696   1.2350  -1.6596  -7.9293   8.1358
+#>    7.1959  -3.4427   6.5927   3.2206  11.5673  -1.1585  -7.7801  -6.9818
 #> 
-#> Columns 33 to 40  -6.0871  -7.8560  -5.1843  10.8660   7.2719  -2.5899  -5.5856   1.1496
-#>    0.0269   4.3973  -2.5121  -1.8696  -6.6044   3.7320   3.2216   7.9404
-#>    2.6763  -5.6407   1.6508  -4.2718  -4.0539  17.6827  -4.3344  -7.8905
-#>    7.9267  15.5898  -0.7649   6.3951  -6.6746   5.9853   5.0476  -9.6068
-#>   -1.3657   3.8838  -2.8952   0.2352  -1.4886   0.8382   1.1905  -0.9388
-#>    1.1199   0.8771  -0.5850  16.6982   9.6759   1.5541  -7.3544  -0.4983
-#>   -5.8925  -3.7602   2.7142   7.4090  -1.1657  -4.8527 -10.1923   9.1371
-#>    4.8444  10.4564 -11.3587  -8.9570  -0.4252   8.3873  -5.0638 -12.8834
-#>    4.1751  -4.8812   3.1374   6.2684   3.2427   4.9984   1.3892  -8.1252
-#>   -3.6175  -2.0191  -2.8556   6.7477  -5.4764   1.6779   6.3614   9.1818
-#>   10.9193  10.6641   0.7074  -2.9042   1.2107   8.3301   5.3420  -7.7657
-#>   -1.8419  -9.6558   9.0585  -6.4754   6.5303 -11.6757  -4.2685   9.5693
-#>   -8.4170  -0.1091   9.5069  -4.8290  -7.7341   3.9342  -4.8279  -8.3420
-#>    2.0843   5.6943   2.3081   2.1958  -1.7479  -8.2845 -17.9574  -1.3221
-#>   -0.1133   0.1275   2.5143   3.3279 -13.4875   3.4412   2.3122 -11.5105
-#>   -0.3208  -0.4993  -2.3072  -3.3562  -9.6989   7.0908  14.1526  -1.9749
-#>    4.1466  -5.4791  -1.0779   2.0537   0.3336   0.2411  -5.6312 -10.7543
-#>   -3.3062  -6.7042   5.1662  10.0127  -9.5516   1.1809  -6.6059 -13.0325
-#>    2.3824  -5.0299  -1.7504 -11.8770   5.7312   2.6594   5.1171  -4.1069
-#>    5.5454   1.3222  -4.5464   6.1858 -11.6493   1.1564   2.2874  -3.6223
-#>    4.7625  -1.0953  -4.0312   1.7370   3.8948  -3.4990 -13.1045  -3.7373
-#>    5.1932  -0.9977  -4.4916  -6.2493   1.4565   8.7194   7.8811  -4.6788
-#>    5.1834  -1.3572  -2.1052   6.3175   1.3448  -7.9595   5.8325   7.4940
-#>   -3.9020  -0.5996  11.2718   6.6009  -4.9164   5.5207  -6.5937  -5.3866
-#>    5.5396  -9.1300  -2.2336   3.5168   2.0850  -6.4280   0.9162  -6.8538
-#>    5.9051   3.5531 -14.0328   7.0267  -3.1132   4.9326   2.0346 -10.9407
-#>  -11.1485   6.5715   6.9071   1.6247   1.6972 -17.4898   3.0868  -1.3412
-#>   -2.8866   6.7616  -4.6754  -1.8187  -7.1617  -3.5344   0.5114  -7.9344
-#>    0.9396 -10.4522   1.2673   2.3761  -4.5045  -5.8762   2.5294  -5.7347
-#>  -11.7426   0.7682  -4.7147   1.3250  -2.2800   8.3357   6.3525  -7.6078
-#>   -4.0112  -7.7059   3.6581   3.6947  10.7464  -6.6490 -12.4564  -3.1245
-#>    0.4887   1.7864   3.1950  -1.9047   1.3532   1.7572  -4.5550  -3.3779
-#>   -3.7773   3.4754   4.8371  10.9474   3.7645  -7.2888   1.0882  11.2958
+#> Columns 33 to 40  -6.0692   2.0793   4.3350 -12.3235   3.0037   1.1288   2.5813   2.6110
+#>    0.5308  -4.3238   3.5379  -1.9671  -6.8099   3.1928   1.1679   2.0679
+#>   -0.3491  11.8161   6.8303  -0.2416   3.8417  -7.2661 -13.6116  -3.0796
+#>    2.6402 -13.5588   3.2563   6.5625   4.2423   3.3360   5.5788  -3.7050
+#>  -10.4350  -4.8099   0.2079   0.3846  -6.8584   4.3211  -1.0947  -4.5229
+#>    5.6016   0.0254  13.1502   0.7369   0.4473  -2.5565  -4.7408 -10.8955
+#>   -3.4334   1.2943  -0.3995  -5.9715  -7.7765   6.2900  -2.2444  10.3684
+#>   -7.1959  -0.6748   3.9565  -1.2811   5.5968   4.1287   7.8194  -3.8093
+#>   -6.4766  -8.6170   5.2415  -3.6940  -7.9119 -11.8260   4.7828  -2.1268
+#>   10.6714  -6.6086   5.9842  -3.2699   7.3213  -0.6271   7.1429 -15.8847
+#>   18.2600   6.0113  -8.8513   5.8896   2.6609  -0.6577  -6.3455 -11.8124
+#>   -2.2230  -3.2465   2.5827 -12.6842  -6.5214   3.3242   0.9225   5.8058
+#>    4.6609  -5.2502   4.9229  -2.7032   5.7908   7.7743   5.2959  -3.2931
+#>   -4.9631 -10.0461  -0.5561  11.6973  -0.6897  20.1431  15.2375   3.0726
+#>   -5.5589   5.7592  -5.1090  -5.8342  -8.6684  -7.5513  -3.2661  -2.3548
+#>    3.4268   6.9213   4.6162  -5.5217   7.6247   0.0046   4.1052   0.3439
+#>    4.3462   4.1992  -2.5999   0.1596  -8.8188   2.0628 -12.0417  -0.2866
+#>    4.9834  -1.3398   3.3698   4.3658   1.0337  -2.3643  -8.3714 -10.8860
+#>   15.4978  -0.0545   1.0423  -3.8502  -0.2772   4.2026  -2.4977  -5.5629
+#>   14.3952  -4.3112   4.2074  -1.1560   3.3123  -0.8910   1.3908  -9.3950
+#>    8.7375   4.9504   2.1718  -1.5356 -11.9605   8.1222   0.3876   6.7565
+#>    1.9322   6.0241  -2.8762 -10.9811   2.6327  -4.1093   0.5120   0.0892
+#>   -7.0380   4.0626   6.4581   0.9974  -1.0643  -8.1721  -4.3548   6.2531
+#>    1.9169  -0.0199  14.0205  -4.7170  -6.8027  -8.3949  -8.1569   1.8699
+#>   11.2147   0.0931   5.2265   9.3769  -9.3777 -10.1494   1.1812  11.0793
+#>    5.9984  -0.8354   2.9185  -0.0188  -2.8839   0.5440  13.5881  -3.4950
+#>    0.2542  -5.4256   5.4084  -0.3341  -2.0828   5.2383   9.3085  -9.4333
+#>   12.6739  -2.6515  -2.8827 -16.4875   2.2863  -0.0353  10.7818  -6.4361
+#>    0.6490   0.5729  -4.1580  10.4702  -6.2550  -5.1557   1.2929  -7.2775
+#>   -2.0955   1.2751   0.6529  -5.3223   2.2847   7.3465   0.5465  -4.8587
+#>  -12.9309   4.2264   4.9639   2.3792  -4.9550  -5.7829  -0.2025   0.9620
+#>   -3.3703   0.5198 -11.8149  -0.9858   5.5318   9.6888  14.9919  -9.3329
+#>    2.8768  -7.5404 -14.6591  -4.7665   3.9690 -14.6539  -6.7533   2.7032
 #> 
-#> Columns 41 to 48  -6.0387 -11.2056   9.6341   2.8017 -12.2098   1.0781   5.1328  10.4557
-#>    7.2590  -8.2040  -2.9756 -13.0488  -3.0499  -5.4872  -1.5956  10.6881
-#>    1.2324   4.3335  -3.6939  -3.1393  10.2875  -1.5746  -9.9184  -6.8965
-#>   -2.4542 -13.3423   3.5852   6.7524  -1.8796  -4.5611   7.9572  -1.0148
-#>   -1.3396   4.4177  -0.2213   6.2529   3.7517  -0.7370  -3.7494   1.9093
-#>    2.6653   8.3401  -3.1142  -8.5770  -2.8581   5.9668  -4.6497   3.4966
-#>    7.7550  -8.6419   5.5229  -6.6355   3.0054   2.1490   5.7685  10.6066
-#>    7.2441  12.5011  -0.4768  -3.0536  -0.6593   1.0614  -1.2096   7.1685
-#>    5.6518  12.7438   2.9351 -12.3455   2.5277   1.2141  -7.0547  -8.5420
-#>    4.3700  -6.5105  -4.3187   3.5244   9.4576  -1.7260   4.4559   4.3307
-#>    5.7719   1.3263  -4.4880  -9.1984   9.7216   0.2186  -1.0691   5.3772
-#>    1.1191  -1.0495  -0.4789  -0.2497  -2.2224  11.6902  -0.4531  -0.3812
-#>   -6.3349  -0.9310  -4.5166  -0.9552  -2.1166   3.4339   2.0261  -2.2041
-#>   12.0080  -5.1870  13.9815   5.0460  -4.5699  -8.4260  -5.1223  -2.8219
-#>   -3.1362  -1.5496   1.5033 -14.7490  -5.9815   2.0039   0.5765   5.5446
-#>   -5.5697 -14.0296   0.7314  -3.6337   6.9127  -2.6981   5.8657   2.1826
-#>    7.7615   4.0952  -2.6710 -11.4045  -1.7372  -2.0555   9.9285   1.1991
-#>    8.1680  -6.1382   5.9534 -15.0505   4.3782   9.7085   3.4996   4.0257
-#>   -2.0523   0.5244  -3.8419  -0.8932 -11.1999   2.5236   0.9160  -3.5783
-#>    9.2817   3.7083   3.4058  -3.5454   2.9044  -1.7373   5.6485  -4.9491
-#>    2.7605   7.7002  -2.6598  -6.8164   2.5014  -1.5200  -6.9747   0.8509
-#>   -5.1617  11.3389   8.3555  -4.8374  -0.2337   0.2449  -4.0933   3.7114
-#>   -7.2699  -0.3516 -10.9980  -6.3405 -14.9887   2.6767  -5.0563   3.6578
-#>   -1.8291   7.8125 -13.2615   6.5019   5.0765   3.9017   2.8786 -14.1297
-#>   -8.8028  -0.7751   0.2333 -13.4476  -5.6117  11.4009   4.5270  -1.2784
-#>   -6.5362   5.0214  12.6471  -2.5547  -4.7357  -9.1172   0.4524   8.0845
-#>   -0.1702   1.2163  -0.2177 -12.3578  -8.4017  -3.1552  -3.2782  -1.3680
-#>    6.3253  -4.4724   3.3072   5.0575  -4.4235   0.6756   1.1694 -15.7861
-#>   -5.6443   7.3664   4.6533 -11.5215 -10.3234  -0.0212   3.2675   7.5162
-#>   -4.0903  -2.8207  -1.8034  -8.4524   6.7105  -4.1710   5.3431  -0.8284
-#>    1.0200   4.1784   1.0915  -6.5063   0.6506   9.9118  10.2774   0.8494
-#>    2.7498   3.9871 -11.0478  16.3057  -6.8933   4.1712  -5.2481  -2.9344
-#>    9.1198  -6.8364 -11.0031   2.7434  11.8762   8.8112   0.1716   0.3366
+#> Columns 41 to 48   7.8763  -9.7199  -4.4690   9.6089   4.8405   3.3520  -2.7235  -6.4251
+#>    2.9170  -3.8034   1.1004  -6.1312   2.1313  -4.1998  -3.7231  10.7095
+#>    2.2840  -7.5836  -7.1392  -3.8762  -3.8631   4.9332  -5.1161   4.4003
+#>   -6.2286  -6.8129  -4.6185  -2.2939   1.1028   3.5578   2.0399  -1.0122
+#>    4.6452   2.0789   9.9866  -0.0370  -0.8002  -1.6254  -8.5539  -1.6654
+#>   -1.4131   7.1423  -0.6537   2.8323  -2.1313 -10.4077  10.6749   5.2659
+#>    1.9635  12.4574  -3.7505 -11.5363   6.1292  -5.1505  -3.4485  14.4767
+#>    7.1639  -8.9321   3.6841   2.2668  -3.3532   4.5838   1.1326   0.4709
+#>  -13.8005 -14.1317   5.9086   5.7031  -7.8651  -2.1370  -5.4105  -0.1254
+#>    1.0900   3.2574  12.4159  -3.6929   1.1065  -0.8303   2.6351   6.7417
+#>   -3.3045   9.9247   5.5519  15.7988  -7.2889   7.8238 -13.1443 -14.0744
+#>    2.8655  -1.1382   2.3960  10.8223  -4.9765   7.1994  -2.2211  -0.3499
+#>    1.7840  -2.8242  -9.5109  -1.2672  -4.8167  -0.6918  17.9051  -1.9648
+#>    2.3403  -3.6133   1.1202   6.5131  -6.1245  -4.9689  -1.4683   8.1377
+#>   -6.3661  -3.4269   7.7904   6.9561   2.0698  -4.1060   8.2306   2.8670
+#>   -4.3051 -14.7653  -5.2190  -5.9767  -8.1131  -1.9674   0.9269 -14.8752
+#>    1.0752   5.2407  -2.9673   1.1213  -2.4811   0.1844  -5.8937 -12.1171
+#>    4.8922 -12.5024  14.2966   1.7855   5.8768  -7.2377   1.2134  16.1354
+#>    9.1479  -6.0209   8.0430  -8.3879   1.8758   1.8084   1.4744  -2.3208
+#>  -11.5399   8.7769   1.3258 -10.4887  -1.2018  -9.7503  -0.7932   8.2395
+#>   -4.6584   1.4419  -2.6616  -4.1353  -0.1446  -3.7500  -3.2242  -0.7995
+#>  -11.7634  -4.2080  -8.4132  -4.4086  -8.5494  -0.5896   9.2247 -15.2114
+#>   15.8126  12.5480  -6.7916   8.2607   1.7568  -7.0495  -5.7526   9.3061
+#>    2.2299  -8.6338   3.8069   0.2027   9.7364  -1.7221  -9.2119  21.1579
+#>   12.4882   3.2241  -9.5911 -10.9391  -6.2601  -6.1291  -9.0527  -1.0655
+#>    0.6824   1.7477   0.7488  -6.5637  -1.7814  -8.5923   1.9031   2.8719
+#>    5.5882  -4.5095   4.4700  -1.6041   7.5863  -5.9989  13.6049  -0.3071
+#>    5.2941   2.9734  12.7298  -2.0739 -14.8459  -7.2190   0.6030 -10.8661
+#>    6.0007   3.8168  -0.8319   3.5197   0.6885   5.1498   0.6077  -3.1995
+#>   -1.9940   2.7808   8.2217  14.2816   4.6736  -6.6666   3.1037   9.3884
+#>    5.0540  -1.0323  -4.9521   6.5307  -6.0250   1.3596   6.7707  -9.4023
+#>   -0.0900   2.9771   2.5626  -9.2355 -18.0492   2.6934  -0.3286 -13.9506
+#>    4.2294  -1.5628   7.5533  -3.8401  10.4743   0.2519  -5.4022  -5.3539
 #> 
 #> (18,.,.) = 
-#>  Columns 1 to 8  -3.8668  -5.2559  -1.3421  -3.4314  -5.5902  -1.2348   7.5022   5.1467
-#>    7.3846   6.2321 -11.4451   1.7548 -20.0545   6.2218  -3.4738   5.3867
-#>   -7.4439   8.5497   4.9470   3.4129  -6.3982   6.3543   4.4766  -1.0839
-#>    1.8430   2.1162   6.6763  12.9154  14.1324  -3.0693   2.5126   4.9445
-#>   -0.5132   6.6500   5.4475  -7.0266   4.8493  -3.7801   6.1027  -9.6499
-#>  -19.8086  -7.2166  -5.5340  -1.8946   0.1660  -2.3499  -2.5856  -1.3366
-#>   -0.6751  -0.9848  11.5909   7.2691  -8.7376  -6.1050   1.9485   4.0363
-#>    8.8061   5.0337  -2.1156   0.1317  -2.0671  -4.1119   7.4026  -2.9720
-#>  -11.1357  -4.2862  -1.5577  11.4097 -10.9833   5.1710  -7.6701   3.8730
-#>   -2.0210  -3.4727  -6.0439  -6.3764   4.9350   0.0568  -0.2317  -5.7015
-#>   -8.7964   8.9918   1.7091   1.7820  -1.7458   2.4526  10.1842  -0.5978
-#>    1.5600  -7.0254   6.6511  -0.7881  -2.8247  -1.2636  -6.7303   0.7457
-#>   -9.9962  -3.3429   2.2513  -2.2121  -3.8652  -2.3950   2.6836   1.1698
-#>    5.8844  -0.5286  -6.5471  11.2424   3.0500  -0.0954 -16.5267  -0.3183
-#>   -5.6467   7.9594 -12.9433   3.7634 -18.3734   8.7222   2.4835  -1.1996
-#>   -5.1361   0.8167  -4.7019   3.1790   7.8726  -1.0832  -2.2899  -2.2924
-#>   -4.2281   2.7615  -4.2521 -13.8693  -2.5246 -11.5597   8.7394  -0.5678
-#>   -1.4691  -0.6020 -18.1740  16.6846   0.2870  10.0524  -3.2751  -0.9453
-#>   -1.9186  -1.3545  -4.1410  -4.9974  -6.8474  -1.3620  -6.9289   9.6157
-#>   -1.6624  -1.9641 -24.7416  13.0001  -6.9840   4.9895  -7.7299  -6.8454
-#>    7.8452   0.0362  -4.2189   1.4746  -5.9102   8.0202  -0.0475  -1.3546
-#>   -3.4357  -0.9328   8.5267   1.9319   0.5512 -16.5238  -5.4286  -6.3495
-#>  -17.0909  -5.6792 -10.8661  -6.1793   7.4291  -7.0865   5.0553  -8.5706
-#>  -27.6425  -7.6394 -10.0787   2.3910   9.3487  -0.0833  -4.5750  -1.5715
-#>   -0.3811   7.1165  -9.3529 -10.1394  -2.6536  10.3981   7.1494   6.2481
-#>   10.2249  12.1080  -6.7486  -1.7461  -4.8264  -0.0810  11.8476  -2.3056
-#>  -10.0728   6.0447   4.7626 -13.1478  -1.3342 -20.2861   4.2710   2.8576
-#>   -5.9852  -5.6862  -6.5108  -7.5676  11.4240  -8.8640  -1.1609  -3.7681
-#>   -1.8751   1.1401  -9.4994   5.1197 -14.1007   0.8723  -8.6336   3.2089
-#>   -5.1206 -13.8672  -0.9405 -14.4086  -7.9146  -5.0967   5.1145   1.7116
-#>   -0.0410  -8.1056   8.9243   4.9892  -2.0418   0.7855   1.2673   3.1330
-#>  -12.9567   3.8870  -2.1128  -2.1108   6.1276   7.9823  -2.7277   2.6696
-#>   -9.4991  -8.8730  -2.5337   6.7111   2.3819   9.8990  -4.2177   3.4382
+#>  Columns 1 to 8  -3.6453  -2.6579   1.0205 -19.2537  14.9200  17.0821  12.3673   5.4109
+#>    1.7824 -10.3962  -6.6121  -4.9261   3.5545 -13.9317  -0.7564   4.6510
+#>    7.0070   1.9875   4.7275   4.3578  -2.6972   4.3838  -5.9402 -12.5366
+#>   -4.5100   2.4487   6.3459  -8.7580   5.3897 -12.2287  -8.3092  11.3734
+#>    6.5372  -6.4463   2.9906  -6.3791  -9.6312  -2.3495  14.0604   0.5863
+#>   -9.2287  -0.4555  -7.1418  16.6878  -8.7363   9.0154  18.1642  -0.3775
+#>    1.1358  -2.4334 -10.1680   5.9642   0.6275 -10.5348  -0.9737  -0.7177
+#>    8.6772  -0.3514   9.1072 -18.3253   0.7804   4.9254  -2.5228   5.2816
+#>    6.8709  16.4975  -4.1184   1.4430  -6.6037 -25.3350   3.9478  -8.7836
+#>   -1.7890   7.4037  -5.0774   2.9740  -6.1798   9.9556  -2.9724  -5.7923
+#>    3.3146  -3.3497 -10.4625   6.0432  12.0398  14.4923  -0.7702  -0.6348
+#>   -0.5661  -8.0927   5.3426   3.2935 -12.1543   7.3922  -1.3355  -4.0372
+#>    5.2187  -1.9892   4.1576  -8.0040 -12.5869   7.1160  -5.3116  -4.7617
+#>   11.4069  -7.6713  -8.6458 -11.0721  -9.2120   2.7215  -0.6181  18.3522
+#>   11.9974 -10.3743  10.9011  -1.0668   1.0419   2.8335   4.5804  -2.0573
+#>   -1.0470   1.7463  15.0426   7.5185   6.7436  -3.6470  -9.4602  -8.8461
+#>    3.3860  -2.4703  -4.4880  -2.9963   7.3801  13.3359  -7.5165   0.5895
+#>   -6.7674   5.0262   3.1154   6.2015   6.4862   0.3410  -2.0817  -2.1105
+#>    1.3559 -10.0833  15.8514   0.1794   3.6656  13.0581  -7.3427  -5.6156
+#>  -11.1527   9.4416   2.6333   1.1133  -6.5177  -4.0262   4.1868   4.2921
+#>   13.1061 -11.6603   1.1239  -7.1197  -0.8983   9.0235   5.3988  -4.4506
+#>   -2.6373  -0.2666  10.8509   1.2169   0.2335  -0.5019 -13.0790 -20.3252
+#>   -5.7588  -0.2518 -18.9002   3.2621  -7.7811  -5.8618   2.6940   3.2461
+#>    4.1567  -0.1518  17.3141  -3.6499   0.7910  -4.0651  20.5443   2.2293
+#>   -2.3704  -0.5042  -1.2511   4.9921  -5.6246 -15.1894 -10.7815  -7.7068
+#>  -10.8883   4.1255  -8.3133   2.5646  -1.1387  -1.6310   6.8993   0.2765
+#>   -0.9623   1.7098  -0.7709   0.0830  -3.0090  10.9351  18.2482  -3.0070
+#>  -11.5276  -2.1557 -12.1451   8.9624  -0.3518   0.6111   3.6210 -13.9250
+#>   -0.5594   7.5094   8.0810  -5.0133   5.7311  -9.4000   3.2041   9.8999
+#>    1.6788   0.8314  -5.9028  -6.0460  -4.1476  20.1882  20.4712  -3.5843
+#>   14.0586   3.3318   3.2964  -7.2740  -6.7567  -5.0984   0.8962  10.1492
+#>    0.3662   1.8839   7.5564   4.7655 -11.8075  -9.0917  -5.9243 -22.0228
+#>    0.0666   5.9959   5.2991  -7.3632   8.8079  -7.0946 -12.1705 -11.2574
 #> 
-#> Columns 9 to 16   2.6155  -3.1598   0.5561   0.9017  -0.8640 -13.1992 -10.6243   0.8509
-#>   10.1564   4.2755   4.0640  -2.1607   5.6714  -4.9804  -1.0634   0.5981
-#>   -2.5515  -1.1962   0.4510  -7.8476   2.5789  -0.8973   7.3582   4.2570
-#>    7.0324  -5.5265  -3.5624  -5.5224  -4.6802   4.4413  -5.8475 -18.2055
-#>    1.4783   3.6747   5.0357  -3.0983   2.1574 -12.1662   0.8275  -6.6068
-#>   -3.2798  10.5581  -2.9460   4.3101  -4.8227  -1.8283   1.5126   3.0180
-#>    8.6264   3.7093  -6.5137   1.8973  -3.4125   1.3862  -1.5053  -1.1352
-#>    6.5437   7.7399   3.0553  -2.0195   6.2153  -9.7967  -6.6664   7.3130
-#>   -2.9593  -0.3026   3.8410  -4.0726  -4.7563   5.4368   8.2580   3.8967
-#>    2.4642  -4.0941  -6.1839 -10.0695   3.2336   1.1617   3.3472  -2.6711
-#>    6.9811   2.8503  -5.4271  -6.5792   7.9685  -8.0903   5.3719   5.1022
-#>   -5.9984   5.1151   1.4408   6.2052   0.7562   1.3832  -1.8367  -1.3511
-#>    0.7537   2.3841   0.1785   2.3359  -7.0487   6.0956  -9.5434   0.6965
-#>  -10.3330   8.9429   7.5808  10.8931   4.8602 -14.7879   4.3269  -9.7338
-#>    9.5932  -5.9092   3.4381   4.2429 -11.2346   0.6286 -10.5995   8.1673
-#>   -2.3872   2.5932   4.0034  -1.2594  -9.1051  -5.9047  -7.7488  -2.8257
-#>   10.0295   5.3519  -2.4834   2.4865  -6.3106  -6.8691  -0.9673  -1.8831
-#>    0.0335   4.8028  -5.9487   2.8925 -16.2756   5.4352  -4.0029  -3.7733
-#>   -1.5558   5.3827   2.6516 -13.2862  -0.6100   0.2447  -4.9061   6.3422
-#>    3.9959  -1.8448   1.9519  10.5935 -14.5738   1.7463  -4.8509   2.2005
-#>    0.3484  -0.1279   4.0334   1.7024   3.9858  -3.5049   2.5102  -0.7298
-#>    1.4837   9.8924   3.6415   3.7319  -2.4233   1.6162  -5.2266  19.6050
-#>    2.7476  -7.0835 -13.0863   4.3597  -0.2027   8.2926   9.9258   0.5601
-#>    0.0557 -10.4625   0.9327  -7.9863   9.9888   5.2492  -0.1595  -0.1648
-#>   -0.5021  -1.4746  -5.3854  -3.6369 -11.8902  -5.9471   3.5275  -2.5313
-#>    3.1709  -2.3712   2.9537  -1.2457  -8.2462 -14.8765  -9.2953   7.6170
-#>   11.9909  11.4902  -4.1442  -5.9850  -1.6904   3.0835   5.1076  -0.1874
-#>   -7.7562  -0.5789  -3.5707 -10.4800   5.7294  -4.6781  -9.5600 -12.0115
-#>    3.4318   0.3919  11.0936   2.4332  -6.3888  -5.5107   2.1030   9.6941
-#>    5.8946  -0.0350  -0.2174  -5.8357   8.9972  -3.4707  -8.0726  -5.5292
-#>    4.1600  -6.9609  -3.7461 -14.6550  -0.8750   0.0199  -3.6804  -6.1085
-#>  -19.3142   3.0839  -0.7835   8.6145  11.9654   5.3337  13.8425  -3.3817
-#>   -0.5039  -4.3831  -2.1489  -7.5294   2.8402   2.1701   1.9791  -5.4358
+#> Columns 9 to 16  -0.4150   7.3297   4.7385   6.0212  -5.8500  -2.6622 -15.9227  -2.3034
+#>   -0.0915  -9.7693   4.5898   1.5310   0.7251   9.4142   1.3755  -9.1093
+#>   -6.2384  -4.8587  -1.6812  -0.8036  -2.7377   7.6864  -4.7117   4.1426
+#>    5.9765  -0.2671  -0.9736   3.6647  -6.0316 -12.8932  -1.9193  -6.5710
+#>    6.5148  -2.3052  13.0146  -4.8592  10.9307   3.6934 -14.8444   3.0526
+#>   -4.3232   0.9341 -14.3279  -1.2807   4.8322  -6.9900   5.1972  -2.0346
+#>   -7.6589  -4.0042  -9.1963  -4.7624  -1.2916   0.7860  14.1884  15.7203
+#>    3.9638  -1.8889  -1.4839   0.5603  -6.1389  -0.4716   0.1734   1.7695
+#>    6.0370  -6.2876  14.9213  -7.2597   8.7252   0.7768  -7.5648 -10.4185
+#>   -8.0372   1.1112  -2.7146   7.3474   2.6524   0.5096  -6.8336   4.6407
+#>    8.3613  -2.7376   0.5527  -3.4527 -14.2165  -7.5961   3.4256 -10.0965
+#>   -2.5307   6.9927   3.5505  -4.5900   4.5145  -2.2089  -7.3691  -3.8217
+#>   -8.3336  -7.3397  -1.3364  12.6717  -6.1033  -4.6683   7.5983  -8.9458
+#>    4.8714   4.9646   0.3718   0.1617  -8.6646  -9.7267 -11.3852  -2.7581
+#>   -6.7464   2.5849  -0.2127   7.5808  -1.9457  10.3010  -4.3726  -4.5917
+#>    0.5463  -9.5342   6.6990  -1.3655  -3.5564  10.5332  -0.2083  -2.8623
+#>   -4.4357   4.4824  -5.2298  10.3264  -7.3772  -5.4804  -6.4382 -10.8502
+#>   -7.6610   2.8621  -8.2088   0.4311   1.1014   1.8976   5.4160   2.7136
+#>   -1.2590   1.6385 -11.3218   2.4333  -2.3540  11.8564   3.9640  -2.7040
+#>   -1.8074  -0.6682  -9.7941   6.2760   8.0811  -3.7647  -0.2252   1.1985
+#>   -8.5855  -4.5691   0.9683   4.5863  -1.2817   2.2082  -7.8620   2.3120
+#>    2.0347   3.4235  -8.9792  -2.7356  12.5742   3.2900   9.6394  12.4246
+#>   -5.8081  -2.6720   1.7077  -8.4530  -8.8805  -1.8012  10.4114   2.5574
+#>   -2.7157  -2.1973   3.0315  -8.8928   9.2534   9.4810 -13.3899  -2.1078
+#>   -5.8773  -2.9140   2.9319 -13.1199  -5.0528  10.1364   1.6731  -1.5246
+#>   -8.3230  -2.3498  -0.7791   7.5833   9.7928  -3.4929  -5.6999   0.9562
+#>   -3.8060   8.0338  -0.3327   0.9759  -0.1954  -7.6175  -8.6705   6.8945
+#>   -1.7274   3.0938   0.8773  -5.9809   8.0806  -1.8621   1.9676   5.9436
+#>    3.7119  -6.8380   2.9771   3.9365   3.8053   1.9199   2.9381 -11.9290
+#>    1.7151  -2.1160  -0.3891   4.3032  -0.3586 -12.8600  -0.9216  -4.0962
+#>   -2.3794  -0.9357   8.1649   8.0264  -3.1704   2.7305  -1.3373 -16.8143
+#>   -0.4313  -9.5439   9.6550   2.3922   5.4265  -7.7437  -0.8577  -1.9617
+#>    1.3139  15.5181   7.2527  -8.5931  -1.3176   4.9786  -0.7705   8.0786
 #> 
-#> Columns 17 to 24  -0.7860  -5.2558   8.8485 -20.9941  -9.0655   3.5294 -10.1439 -15.4498
-#>   -9.5280  -5.9107  -4.1893  -8.4292  -4.4653  -0.5671  -8.2579  -5.4353
-#>    0.9009  -5.9605  -5.2960  -0.0580   4.2290   6.6839   4.1097  10.2514
-#>    7.4349  -0.9851   8.0691  10.2494   5.0533  -8.2979   2.1494   8.2970
-#>    2.4778  -6.8777  -4.8544  -1.9120   2.3932  -2.1848  -2.1859   1.3520
-#>    6.7057 -11.6855  -4.0378   3.1691  -2.3374  -4.3761  -7.1103  -8.3558
-#>    1.2603  -5.3465   7.3288   0.0823   3.4294  -5.4725  -1.6565  -1.1264
-#>    4.1951  -0.9868   8.4957  11.0593  -0.4520  -5.0130  -7.8880 -13.4144
-#>    1.7785   2.6223  -8.1713   4.1476   8.1134  12.6581   9.3881   7.1014
-#>  -11.4726  -3.0691   3.9983  -6.6128   2.8902  -4.2356  -8.2007   9.8636
-#>   -5.5642  -5.4402  -7.3251   3.8875   4.0312  -2.7634  -1.9829   9.1520
-#>   -0.9521   6.8743  -6.0237  -2.6349  -3.3604  -9.5427  -6.5401  -3.5823
-#>    5.0215  -0.8272   0.7952   3.4548  -3.2609  -6.3531   1.7328  -9.2392
-#>   10.4847   3.6584   4.0901  -0.0507  -9.9594   5.5010  10.7630   7.5907
-#>   -5.7430  -3.7902  -1.0271   0.4131  -8.0020  -3.5380   2.1057   3.7887
-#>   -3.0929  -0.8988   1.4206  -3.6087   2.6007   2.3756   4.4542   5.3190
-#>    4.8061  -2.6334   6.0383  -3.5185   0.1744  -6.8400  -1.0821 -11.2657
-#>   -3.1119   5.3678   2.1130   0.3014   2.6299  -6.9807  -8.1485   2.6010
-#>   -1.3879  -0.9456  -4.7146  12.1872   0.9405   5.8407  -3.8907   0.7099
-#>   -1.2914   4.6096   4.2702  -5.0991  -2.4684  -3.7089  -1.5956  -3.0278
-#>    0.9443  -3.4175   5.8033   2.0396   2.6150 -13.5490  -5.6807   9.9841
-#>    6.0496   5.3381   2.1143   4.7890   6.5540  11.7075  10.9428   2.0711
-#>   -8.6852  -0.1733   0.9401  -4.7324  -4.7015   2.6617  -5.5006   5.2733
-#>   -7.6679   5.3552  -4.1996   6.3112 -13.5730  -1.9100  -8.2240   6.4587
-#>    1.4878   2.8627  -3.5439  -5.4882   4.8272  -1.4257  -2.8101  15.8103
-#>    6.8095  -4.1825  13.2839  -1.0319   0.8730   6.4668   0.7699  -2.7591
-#>   12.1832  -2.5764   0.0200  10.0529   9.1707 -10.8723  -5.3037   6.6403
-#>   13.7693 -13.7331   1.9829  -0.2371 -11.3306  -1.6697   3.9548  -9.9244
-#>   -8.8938  11.2271  -1.6461  -1.5718   3.9387   6.0064   4.2124  21.8599
-#>    8.8075 -13.4189   1.4054  -8.9115   2.0479 -12.0521   4.1982 -24.0073
-#>   -3.6085   6.9181   7.9440   1.9889   4.7326  -6.7069  -8.5142  -5.6065
-#>    0.1416   6.5440   1.8905   4.0071  -8.3706   7.7118  -0.1617 -10.8803
-#>  -10.0878  -4.8186  -8.6435 -13.7511  -7.2138  -1.0321  -5.4247  -8.6376
+#> Columns 17 to 24  -3.3802 -16.5795  -4.8073  -0.5404  -7.1475  -3.1667   2.7997  -2.9929
+#>    9.3943   3.1952   4.0808  -5.7957  -4.4734  11.8072  -7.5091   4.1751
+#>  -12.4842  -3.0911   3.9793   0.8904   3.5557   2.9183   7.4246   4.0798
+#>    5.7370  -0.7496   1.9616   0.6393  -0.2739   3.2794  -5.9360  -1.8160
+#>  -14.9989  -7.0250  -7.3100   3.4486 -18.7775  -2.6968   0.8530  -1.9575
+#>   -7.5552   1.9067  -1.1024  -2.5404   7.9428 -13.8391   2.1016  10.3724
+#>    2.1475   2.5338  11.0880 -13.6220   3.8781   5.2712 -16.2984   8.4264
+#>    6.1028   4.9763 -11.7471   3.8455  -3.8900  -2.1165  11.4968  -2.9493
+#>   11.9565  19.0238   4.0692   1.9450 -18.8761  12.4061   2.2446   4.8582
+#>    5.0876  -2.1769  -0.7845  -0.2564   6.9307  -3.8769   4.6191   4.7758
+#>   -1.3357  -6.3469 -10.4244  -7.4268  14.7157   3.8146   6.1740 -11.4708
+#>    5.5352  -0.7440  -5.8605  -3.8285  -1.9345  -6.0109   4.3798  -6.5518
+#>    5.7718  11.8434   4.4963 -15.0592   8.9086  -5.8297  17.4436  13.1879
+#>   11.3384  -1.6285  -6.2323   1.5047   5.4067  -4.8151   3.6423   1.7930
+#>   -1.0057   0.7231  -6.8517  -2.8450  -3.5516  -5.5350   0.4510  11.1484
+#>    3.6108   5.5331   2.1428  20.6810   5.8252   0.2176   6.0349  -8.0966
+#>   -4.2849   1.6567   3.4039   2.6399  -5.0305   7.7684  -5.0250  -3.7902
+#>   -6.2979  -3.3068   9.3983   6.2687   2.8225  -4.8381   4.6628   5.7471
+#>    9.4056   2.8777  -7.2065  -0.9392   8.3859   3.1559   9.6467   2.4561
+#>  -12.5639   7.1517  11.0490  -5.4041  -9.9790  -2.4337  -4.0309  -0.5188
+#>   10.2878  -1.3573  -5.4229  -4.0446  -6.4615   4.1894   9.0969   2.4457
+#>    0.7747 -12.4015  -0.7291   3.3445  -7.4514  -2.5345  -1.4617  -1.6583
+#>   -1.1796  10.3824   1.1136  -7.8257   6.1854  -1.8923  -4.0510   3.0664
+#>   -5.5701  -1.1843   2.0442  -4.0832  -0.7427  -9.5607  -6.9620  11.4737
+#>    7.9242  16.1232   4.9848   5.9106  -2.2443   0.0575 -10.4117 -10.2186
+#>   -2.5571  -2.4189   3.8435   5.7330  -2.2310  -5.6454  -3.5400  -7.1145
+#>   -2.6510   6.0102   0.7565   6.3312  -9.3734  -1.4229   7.3555   7.7973
+#>    1.9639  -5.7450  -4.8747  -4.4045   3.4776  -2.4852   6.6250   3.6135
+#>   -3.6018  -1.9261  -2.1462   8.4476 -13.4601  -3.1309 -11.1919 -11.7957
+#>   -3.1745   6.5586  -0.7759  -8.8976   3.5520  -1.2292  14.6989  15.3786
+#>    2.7685  -0.6769  -2.5516  -1.6768  -7.6459  -0.3419   6.6799  -7.8551
+#>    2.7665  19.3113 -11.6959   8.2523  -1.7164  14.3424   9.1995  -7.8113
+#>    6.8839  -2.9863  -9.9830   3.2360  -2.1380  -4.5266  -4.6093  -6.9626
 #> 
-#> Columns 25 to 32   5.8348  -7.2655  -1.5712 -10.6256  -7.4372  -1.4391 -11.4552  -7.0477
-#>    0.3226   3.4154  14.7550  -4.5340   4.0858  -3.4755   3.5059  -4.8757
-#>  -11.4289  -0.3527  13.9969   2.6939  -3.1083  -3.0465  -0.0892   7.0621
-#>    5.9597   8.9556   8.5256   2.0979   1.2178   6.0288  -5.4063 -13.5261
-#>   -3.1176   0.5134   8.1586   4.2564   3.3197  -8.7756  -1.9437   8.7018
-#>   -2.9178   2.5326   6.1470  -0.5083  -4.6868  -7.8694  -6.4656   0.8148
-#>    5.0851   4.6391   2.5312  -6.5178  -1.5761   1.3420 -15.3398  -7.1221
-#>    3.2214   2.9499  -5.3047   0.9502  -2.6619  -7.0484   6.5908   4.1343
-#>  -19.2554   0.6993   0.6807  13.9843 -11.8171  -6.7210   0.4342  -8.2857
-#>   -0.2347  -2.5530  -5.5027   6.8211  -3.2994   6.2850  -0.9099  -0.9908
-#>  -10.3087  11.8646   7.4830  -5.8137  -0.1379  -6.7583   8.5932  -4.0547
-#>    4.7290   3.4967  -4.3611  -1.3191   9.5178   3.1170  -4.6229  10.6171
-#>   -2.6505  -1.3561   7.5753 -12.7398  -1.2293   0.8599  -7.6208   9.2033
-#>    4.8080   0.7353  -2.8968  -8.4572   4.6661 -13.1305   1.8542  -9.5437
-#>    0.0877   1.0816   7.2096   3.7373 -13.8221  -5.2305  -8.0411   0.1576
-#>   -0.5292   0.0716  12.2252  -8.5118  -5.0807  -5.6914 -10.2480  -9.0764
-#>    0.1724  -2.2463   3.3778  -3.3526 -14.6650   2.1788  -4.2253  -2.9203
-#>   -9.5554   3.8573  12.3497  -0.4372  -4.4459   4.8477   3.3281 -11.1750
-#>    3.9233 -13.3457  -1.5832  -7.1649  -1.5820 -11.0861   3.7966   0.0886
-#>    3.8153  -7.0028  -6.0608   2.2563 -10.7381   5.2756   1.5506  -0.0917
-#>   -6.7651  -2.4536  -6.6022   6.8422   8.3161   0.5805   2.1232   0.9424
-#>   -0.4326   7.7848  -4.7841   3.1087  -5.1200  -4.0048   0.2341   9.0584
-#>  -11.2660  -2.3502   2.1099  -0.5210  -7.9694  -0.1528   3.6844   0.5896
-#>  -10.2571   7.9452  -2.3368   1.3291  -4.2773   9.0338   1.8009   1.2802
-#>   -7.7180  -3.6896  -9.0833   2.9145  -2.3874  -5.3301   8.4110  -5.9553
-#>    8.5449 -13.3886 -12.8695   0.5592 -11.1546   0.8784  -2.8044 -13.2828
-#>   -4.9942   1.9179   5.1382  -0.4088  -1.0402 -13.1228  -6.7094 -10.3743
-#>    1.4642 -17.0698   3.6303  -5.0720  -6.8388   4.6268  12.0566 -10.4237
-#>   -6.5882   3.0184  -3.5185   0.3690   0.4860 -16.3141  -6.1981  -3.1381
-#>   -4.9649  -4.1968  -2.9713 -10.4720 -15.6504   0.5116   1.8237   1.1983
-#>    2.2559   0.6364  -7.3063  -3.4943  -8.5275  -0.2126   0.0538  -0.7336
-#>    2.4349  -1.4577   7.1449  -1.2379   2.1649   0.5370   1.3001  -8.0382
-#>    2.7743   7.5586  -6.7374   3.7699  -7.0262   9.1465   6.9093  -9.5367
+#> Columns 25 to 32  -3.2291  10.7030  -2.8386   7.5368  -3.6817   0.5457 -12.1813   1.8038
+#>    2.8274  -4.3918  14.8735   7.7960   1.7378  12.6509   6.7462  -0.0910
+#>   -2.9892   5.6809   6.3600   4.2879  -5.3626  -4.5011  -7.1817   6.2683
+#>    4.9344   0.0673  -1.6913  -1.4739   2.3819   8.6632   7.8988  -4.1273
+#>    0.6691   4.4379  -4.4961   0.2016  11.0722   5.8352  -4.7882  -0.5294
+#>   -7.4656  -6.6462  -0.6537   5.1728   6.3793  -7.0337  -7.6226  10.6279
+#>   11.3895 -18.2443   0.8376   3.1360   1.6836   2.7688   7.8754 -16.5484
+#>   -0.7623  -0.0741   3.6667  12.7978   2.2446   4.0945   8.1088 -17.3363
+#>   12.5698   9.8655  10.7244  -5.5314   9.2732   9.5926  12.5526  10.3565
+#>   -3.0678   4.9167  -1.2763  -5.2317   2.9389  -6.2865   6.7550  -0.5897
+#>   -2.7771  28.5695  13.9840  -0.6955  -1.6058  -7.0847  -5.5598 -11.4288
+#>   -0.1134  15.3724   0.9233   4.6458  13.2193   5.5101   1.5288  -2.5896
+#>   -8.3060  10.4701  12.4269  -2.5010   7.5621   1.3840   6.5992   6.4586
+#>    1.8848   0.2941 -12.8773  -7.3082   2.6298  17.5829  20.0359  -3.0621
+#>    3.7175   1.8944   1.5716   2.8683  -0.2183   2.2398  -1.6514   1.9437
+#>   -8.7121  -6.7781  -6.0320  -8.0381  -7.7988  -8.1748   0.4033  -3.9238
+#>    0.4005   3.0571  -1.9216   0.4900  -7.7397 -12.0784   4.6738   0.2380
+#>    3.6112  -6.0575   5.1367   4.2013   1.9351  -8.3423  -1.3090   0.6960
+#>   -1.7518  11.4851   7.0932   1.9152  -4.6589 -15.6921 -11.3339  -2.4502
+#>    4.9115  -2.6085   7.2744   2.0863  -4.5220   1.1088   9.4088   8.8456
+#>   -2.4644   7.4740  14.9799  20.1974   7.3838   6.7452  -1.2426  -9.4013
+#>   -4.2160   9.6512   3.1419   5.0515   7.3958   3.0585 -15.0977   2.1559
+#>   12.8328 -10.2693  -4.5694   1.4815   6.9491  11.4255   0.1258  -8.2059
+#>    8.6309   1.8218  -4.0163   1.0393  -5.3220  -5.2826  -6.3134  -4.6092
+#>   -1.5395  -4.6064 -15.4316  -7.3492  -3.0026  10.8899   7.8679  -5.7423
+#>   -4.0104  -9.9856  -2.4603  -3.2330   3.0295   2.2225   2.0814   8.4577
+#>   -8.6324  -3.2244   1.5286  -4.1569  -0.5881  -5.1474  -0.2732   5.2499
+#>   -2.8235   2.4385   2.4365  -3.3335   8.6081 -10.5246 -23.2637  -4.6503
+#>    9.6978  -0.1554  -3.5184  -0.3277  -3.2259   4.4653   7.0787   6.0881
+#>    1.0039   6.9666   8.5641  -4.3593  -4.6308  -7.4341  -2.8288  -1.2116
+#>    2.0377  -7.5815  -4.1055  -3.9629  -0.0647   3.4498   6.6076  -4.4810
+#>    4.2784   2.8188  -7.2315  -2.3046  -0.8632   3.4735  -1.2786   5.4659
+#>    0.0191  14.2118   4.6856  -1.2515  -0.4736   3.1603  -9.1913  -1.5629
 #> 
-#> Columns 33 to 40   7.5027   6.5333  -0.0116  -5.6049  10.2883   3.3521  12.5631   1.5284
-#>    3.2782  -5.3731  -0.3792  -0.1593  -3.4329   4.7939 -11.5708   0.0229
-#>   -0.3696  -6.6871 -12.1762  15.2650  -5.1596   9.6189   1.4396  -5.4593
-#>   -5.5469  -9.9894  -2.5025  11.4326 -19.5473 -12.5112  -1.3405 -14.6335
-#>    3.3988  -3.4589 -13.5032  -6.4191  10.6713 -10.2992   1.6308  -3.1208
-#>    1.4465  -2.1320  -6.9581   6.5072   3.0123   3.9350  -3.9943   2.0850
-#>    4.2501  -5.4135   0.6682 -17.2987   2.2371  -4.3746  -5.0197   1.8515
-#>   -3.4838   2.9367  -1.8368  -2.3554  13.1617 -18.1611 -11.9097   6.4819
-#>    4.7710  -3.2932  -7.1364   1.5846  -0.7487  19.7362 -11.5068  17.0332
-#>    4.7742   5.7331  -7.4818  -2.4472  -9.6425  -3.7683  11.4337   3.0286
-#>    2.7958  -6.7184 -14.5433   1.4455  20.1422 -12.3778 -10.6355   3.6799
-#>    2.7496   2.2887   3.8542 -11.3247   3.0819  -9.4470  -1.8825   5.2718
-#>   -1.6504   3.3703   0.0674   9.4359   3.2942  -4.4481   6.6880  -6.9526
-#>    9.5634 -12.0699  11.6217   3.5743 -21.1038  19.2175   3.4960  -1.7261
-#>    8.5224 -13.1445 -10.6559  27.6880 -13.1003  -4.8193   3.7349   2.6295
-#>    5.7360  -4.9928  -0.6631  -1.0594 -14.4707  13.4307   3.8149 -11.6830
-#>    4.8149  -2.1025  -2.8097   6.9264   1.9033 -14.3990   2.6695  -4.9869
-#>    4.8526  -9.6141   2.1947  13.7938 -20.2344  10.8255  -9.7898  -8.3773
-#>   -7.9815  15.2409  -3.7938  -5.3458   6.4106  19.3269  -3.7322   5.5608
-#>    9.5753   5.0733  -3.7613  17.2755  -4.4581  -1.9309   2.1579  11.7440
-#>   -0.0213   6.9128   5.5643  -1.6708  -5.6212   3.4767 -15.9278   5.8476
-#>   -2.4029  -7.9804  -5.8736   2.8209   3.1041  -4.5196   1.0162  -6.9719
-#>    2.7896   5.2301   5.4749   6.9678  12.5461  -0.9798  12.0340  -2.3895
-#>    1.9614  12.2263  -2.0617  15.6980  -6.3249  15.8998   7.9298  14.7955
-#>   -1.4827  -4.7149  -2.3174  12.9012 -15.4193   1.5327  -5.1090  -1.9725
-#>   -3.0517   2.2253   0.3231   6.1099  -6.0135  -1.0435   4.3048  -0.0366
-#>   -1.8208  -2.9453  -9.7977   2.7620  10.3637 -10.1950  -0.5822  -7.4750
-#>    3.1300   9.3976   5.5887   0.2071  -5.8449   8.8785  15.9674  -6.2410
-#>   -2.3326  -0.7725  -5.7696   3.1336  -5.6467   4.5828 -10.7735   6.1770
-#>   12.2428   4.0546   7.2391  -9.2279  15.3162   5.3007   8.9519   5.0663
-#>   -2.0020   4.3573   5.1197  -6.6489  -1.9023  -3.3735   0.3393   5.5041
-#>   -2.6711  15.2617   6.2822   1.1592   0.5775  -3.1309   7.5555  -1.9678
-#>   -1.8282   3.8881   2.8608  -9.5459  -5.4770   4.2785   2.9235  10.4418
+#> Columns 33 to 40   5.8220  -5.9585 -11.1234  -3.1603  -4.6316   8.4807  10.5010   5.5024
+#>    6.1066   7.2097  -7.3635  -0.5837   4.2434  -5.8463  -9.9194   0.7706
+#>    7.3368   4.5503   4.5224  11.4279   9.0379   6.6149  -0.8279  -2.2151
+#>    9.6338  -3.2460   1.2729   1.2180   2.5470   1.4183   0.5273  -1.5177
+#>   -7.2292 -13.9029  -1.5003  -7.1057  -0.3813  -4.4605  10.9278  -6.7865
+#>    4.0703  -7.5708   0.4387   9.7501   2.1490   3.6674   0.4522   1.4119
+#>   -1.1331   6.6840  -4.8100   1.1518   5.0439   1.6307 -14.7160   5.6886
+#>    2.5314  -2.3441  10.3273  -3.8606 -13.4257   5.5911  -8.2073  -2.9753
+#>   -3.6279  -1.4415   0.3305 -10.7637   8.4690  -3.6397   5.8529 -16.6335
+#>    3.7892   4.0938   6.8738   0.8232  -4.8434   1.1250   4.9787   6.2021
+#>   18.9465   3.1542  -3.0669  -4.7318  -8.4856   3.6816  -1.6007   3.5327
+#>    9.5519  -5.9685   2.1909   2.6486  -0.7384   3.8348  -0.6606   0.6095
+#>    1.4465   0.5264  -1.1730   6.3559 -10.9870   2.2555   7.5011   4.6379
+#>    5.1074  -0.0674  -7.0179  -5.2227  -9.8421   5.2808  -1.8671   8.2903
+#>   -1.3154 -12.9196   1.5084   8.8700   3.7121   6.2527  -5.9607   7.2043
+#>    5.3534   1.3893   7.6863   4.0875  -8.4294   2.6728  -3.6680  -1.9937
+#>    7.3930  -2.4865  -0.6695  -5.1212   8.1023   3.6920   8.4380  -0.4609
+#>   10.3266  -2.1785  11.2308   7.1412   9.4480   4.0511 -12.0939  -2.9586
+#>    8.0646  -2.0491  -3.3040   2.6962  -7.3753   2.2280  -4.0516   4.4782
+#>   -3.9627   3.3026  -3.5714  -3.1277 -14.1868  -7.6111   3.9365  -1.8307
+#>    2.4939  14.9946  -6.9179  -2.8400  -7.8571  -7.6483  -5.0890   7.9494
+#>    2.6553  -3.1783 -10.5153   1.5278   2.4003   2.6209   6.6390   3.3985
+#>   -5.5345   1.4169   0.0556   0.9806   3.8910   8.3930  -6.8202  -7.9651
+#>    0.8440  -8.0188  -1.9172   7.6415   0.6154   2.6541  -7.1366   5.4497
+#>   -1.2573  10.5624  -3.2536  -4.0046  -7.8857  -1.5806  -9.9374   1.6591
+#>   -7.5206   9.3328  -1.2461  -0.2772  -8.1923  -7.2622   2.0394   5.6648
+#>   -1.0974  -3.7975  -0.4945   0.1787  -7.2873  -0.7737   2.3343  -1.1023
+#>    2.4373 -15.1106  -2.2588  -1.5399  -3.4147  -5.9233   0.5065  -4.0432
+#>   -9.2517   6.0256  -3.9838  -6.5566  -5.4267  -0.9787  -4.2490   1.3722
+#>   -0.9727  -1.7382  -0.9337   2.2735  -0.5028  12.1543  10.1493   2.0821
+#>   -2.3873  -0.8254   4.9093  -1.7384  -8.9157   7.7980  -3.3649  -5.1609
+#>  -15.6301  -0.9252   7.8800   1.5679  -4.8594  -8.6393   1.3922  -3.7080
+#>   -2.8121   1.1704  -4.7964  -5.1482   1.4181  -0.2449  -7.5492  -6.0980
 #> 
-#> Columns 41 to 48  10.6075   8.1955   6.3594   9.6548  -3.9281  -5.5283  -4.3969  -5.2311
-#>   -0.0549  -6.8490   6.1117  10.3936   0.6194  -1.7077  -2.4671  -8.3272
-#>   -0.7429  -0.7171  -5.5075  -6.5202   4.8573   3.5552 -13.5905   0.2427
-#>   -2.6324   1.8097  -6.6340  15.7721  15.1312  -0.8379  -8.1369  12.4140
-#>   -1.1339   1.6051 -11.0083  -4.0315   6.4343   5.9605 -11.9441  -0.4597
-#>    8.0709  -5.0276   6.3454   1.9127   6.5007   0.9900  -8.9867  -7.4269
-#>    1.6719   4.0843   2.6772  11.4787  -1.9626  -4.5868   6.2614  -4.5262
-#>    5.7108  -1.7419 -15.6123 -16.1967  -6.1014   2.6904   1.5191   5.5925
-#>   -5.1404   5.7229  10.0120  -1.0770  -4.9319  -2.1359   4.9363 -14.8991
-#>   -6.5668  15.5995   1.9847  -2.6831   3.6583   0.6838   2.2724  -0.4155
-#>    0.1617   0.1575  -4.6440   3.1532   1.0411   8.8002  -9.9338  -3.5080
-#>   -9.0213  -0.1910  -1.7430  -2.6874  -3.9154  -1.9654   8.2127 -10.7969
-#>    2.2560   1.8975   0.7868   5.4140  -2.2687  -6.4610  -7.9165  -0.2637
-#>    4.2571  -5.9356   1.2642   2.6764   4.9651   2.8574   3.9194   1.9587
-#>   -3.9367   0.4815   6.5117  14.2914   4.0803  -9.3343  -6.3550  -8.5403
-#>    1.8754   8.8220   4.3447   3.7271   8.3342  -0.7177  -6.6892  -9.8216
-#>    2.9226  -0.6007   2.7407  10.7963  -1.4039  -2.7413   0.1470   0.0305
-#>   -2.0551  -8.6517  -1.0070   0.2237   3.9467  -3.5014  -1.3727   1.9502
-#>    7.4919  14.7980   6.8258 -15.9342  -2.7612  -9.0467  -0.2408  -6.1911
-#>    4.9543   0.7682   8.2976  -7.3017  -1.8134  -4.6807   1.0936   2.2199
-#>    0.2476  -8.2005  -5.5142  -8.9249  -1.5899   1.5298  -0.2571   6.9172
-#>    3.8945  -2.5934  -1.4054   3.2700   1.6870  10.3187  -2.0064   6.1178
-#>   13.7704   1.9819   8.4786  13.2931  -0.3295  -6.7888  -4.4256   3.2921
-#>    3.7184  10.6178   7.6598  -0.3294   0.1958  -7.1434  10.6028  -3.0216
-#>    2.6404  -1.8616  13.4526   8.1869  -2.2343  -6.3750  -4.1366   6.2893
-#>    2.4454  -5.7420   5.2624  -0.4159   2.7483  -5.1336   1.8708   1.7837
-#>    7.8029   6.1585  -6.6585  17.4982  -1.5582   1.0887  -5.1489  -7.9415
-#>    5.6980  -1.8182   0.0233  -0.6164  -0.6737  -2.6375   0.4123  17.0397
-#>    1.3305  -3.6551  12.2228  10.3632   1.2346   3.8860   1.8377  -0.7569
-#>    8.7651  10.0290   5.7246  -1.3062 -12.2864  -5.9924  -4.5115   2.5171
-#>    8.9476   2.4347   4.3081   1.3072 -11.2390  -5.1412   6.4073   2.6543
-#>   -2.9282   0.0720   4.3174  -8.4152  -1.8963  -8.3885   5.4650  -5.5437
-#>   -7.5952   8.7533  17.1642   4.6203  -1.5054   1.8863   1.8967  -3.7182
+#> Columns 41 to 48 -11.1306   0.6919   4.8512  17.3918  -4.0362  -8.9822  -1.9174  -2.6997
+#>    0.9836  -5.5430  -0.1749   1.3844   3.0945  -6.2191   4.9300   0.9478
+#>    0.7872   5.1292  -4.1745  -2.1452  -9.6652   0.5110  -3.5500  -1.8006
+#>   -5.1029   0.4365   9.5328  -4.4164  -2.7142  -2.2890   1.1373 -10.2070
+#>   -4.2318  -1.1601  -3.0408   6.4436   7.6427  -9.5761  -1.1885  -8.6383
+#>   -5.5641   7.0299 -11.1836   3.9675   1.9286   3.1148   6.4438  17.2768
+#>    7.1689   7.2436   1.2148 -15.2031  12.8163   6.5868 -10.4234  16.1784
+#>    9.0185   3.9652   4.8276  -6.6401  -2.6230   9.7936   4.8730  -9.0933
+#>   -1.2321 -18.4359  10.3138   2.3768  -3.4326 -17.6045  -2.0458  -5.3509
+#>   -6.4869  -8.0318  -0.0468   5.0349  -7.1426   0.6006   9.4161  -5.5588
+#>   -7.1758   8.7406  20.8345 -10.1867 -10.1063   1.3316   4.4973   4.5348
+#>    2.1176   0.6602   3.7209   4.3050   3.2850  -3.1070  -1.2137  -0.2957
+#>    4.2311  -5.2565  -8.1201   1.8208  -2.8954   4.1180  -1.6627   1.6529
+#>    1.9328   3.8919  -5.6913  -3.1739  -6.6300   0.8863  -5.7888   0.2582
+#>    1.9327  -0.9987  -0.9545   6.2232  -5.2541  -7.0955   1.0247   6.0885
+#>    5.6303  -5.0227  -8.3033  -1.4571   2.2641  -0.4002   2.8168  -1.3227
+#>   -4.9022 -10.4092  10.1600   3.7506  -9.8174 -10.2585   4.4503  -3.0098
+#>   -0.0012   6.7839  -2.8909 -14.9251  -5.1689   2.1843  -4.4906   3.3159
+#>    1.2218   8.8697  -1.0477   7.6454  -6.7688  -0.0112  14.1941   8.3535
+#>   -4.9380  -6.6958  -4.8488   6.4205  -3.2710   3.3907   0.6245   8.4166
+#>   -5.9622  -2.3321   1.4829   8.3279   7.8688  -6.5363  -5.5891   8.1517
+#>   -3.8473  -3.1054  -5.8659   7.4631   2.2225  -0.2593   6.2682   3.9445
+#>    7.4341   7.8476   5.7572  -7.3124  -2.7070   1.4620 -10.6436  13.1956
+#>   -5.1010   4.6871  -4.1689  -1.5598   8.9147  -9.3880 -11.0180   2.8470
+#>    0.1611   8.2333  -6.9146  -2.0030   3.1563   2.3113  -0.8183   9.7559
+#>   -4.3718  -9.2927  -2.0732   9.2403   3.7783   7.1507  -4.1151   2.7890
+#>   -5.4616   3.8117  -4.1010   4.3467  -3.4613   6.2492  -1.5053   4.1627
+#>   -7.9354   5.9562   6.9274   9.0557  -7.5317  -1.0009  14.8822   9.4534
+#>    2.8936  -9.4752  -0.4965  -3.5914  13.2439  -0.7371 -10.0037  -1.6373
+#>    2.3857   5.8190   6.1638   7.8189  -6.2333 -10.4108  -6.1803  11.9791
+#>    7.0986  -9.0875   2.2925  -0.2476  -1.9190   8.7548  -7.0915 -10.2016
+#>   -0.7546  -2.9609  -2.9446  -0.2993  -0.6294  -8.1636   1.1149  -0.7690
+#>   -5.9044   8.9972   7.6192  -2.5183  -6.4683  10.9135  -4.8663 -10.6644
 #> 
 #> (19,.,.) = 
-#>  Columns 1 to 8  10.8395  10.7757  -3.0870   5.3283  12.8517  -5.9433  -2.2106  14.2127
-#>   -2.8759   1.0786  -4.1254   0.8018  -0.2598  -8.2426   3.3772  -7.8797
-#>    0.0327   5.8181  -4.2337  -7.1393   7.7752   2.3379  -9.4840  -3.3952
-#>   -4.0998   8.0561   2.5215   0.6297   0.6777  10.5896   9.7402 -16.0670
-#>   -1.0089   8.3787  -2.9934  -2.6356  -0.0877   7.0668   4.5577  -0.7420
-#>    9.5502   9.5713   4.4936  -0.6498   6.3097   3.3118   2.3432   8.6385
-#>   -4.0563  -1.2055   3.8899   9.1231   6.7793 -12.0974  -3.1299   9.9029
-#>   -0.7493   0.1963   4.1077  -0.6971   8.1487  19.8076   6.8223  -5.6851
-#>   -6.0644  -7.6939   6.7336 -12.1034  -5.3430  -4.1995  -5.5885   2.5117
-#>   -0.8601   1.1812   5.3597   3.9339  -3.5057  -0.7212   3.2272   3.0324
-#>    1.7426   0.3936   4.8376  -2.4991  -0.5326   2.9792   7.5664   0.8225
-#>   -3.7376   3.5097   2.6515   8.5253   5.7521   8.0427  -4.7524  -1.5751
-#>   -0.2459   5.9787 -11.0733   0.3648  10.8524   4.1613  -1.0832  -7.1325
-#>    1.4894  -3.5983  -5.5288 -13.8901 -10.3781   6.5673  -5.9233   3.2369
-#>   -3.6380  -4.0456   2.1913  -5.4734  18.1547  -3.7933   5.0162   3.9899
-#>   -0.3314   7.9992   4.3374  -0.0042  -9.5690  -1.3394  17.0652  -4.3626
-#>   -5.2768  -3.1344  -1.1358   1.3520   9.2251   5.6593  -5.9296   9.0785
-#>    9.4193  -6.8123   8.6052  -4.0533   6.9688   1.3662  15.2029  -7.0616
-#>    3.9928  -2.9939  -4.9972  -8.6826 -18.3501 -11.7910   5.2548   3.0088
-#>   -5.7589 -14.4873   7.5612  -9.8233  11.5037   4.0116  11.8311  -4.7630
-#>    4.2859   0.5745   3.4160  -4.1035   3.7191  -4.9911   3.2578 -10.4416
-#>   -4.0827  -9.8458 -10.4471 -12.1190  -1.5617   0.8391 -12.4993   4.9041
-#>   -2.0138  -9.3498  10.1790  10.3041  12.8580  -2.0940   7.0812  16.6040
-#>    1.3709  -9.2536  -3.8364  -1.8204  -9.9328   6.5994   4.5961  10.5244
-#>    6.4022  -8.8969   3.6429   0.9117   0.9441 -10.0114  -6.0480   5.8424
-#>   10.7966   5.0398   1.4386  -5.0239  -1.1596  -8.2526  -3.6071  -0.0900
-#>    3.9841  -3.3743  -4.2212  -1.0025  -4.8112  -5.1390  10.2893   2.6183
-#>    4.2903   8.8365   0.8650  -2.0502  -0.1431   6.2520  -4.8942  -2.7032
-#>    0.3704 -17.7415  -5.7719 -10.0108 -12.9640 -12.5472  11.2287  -0.2070
-#>   -1.0709  17.7364   4.6136   4.4929  12.2768   0.8676  -6.2423  -3.1522
-#>    0.5952   1.1386  14.9558  17.7456  17.0263   1.8315  10.4364   9.7502
-#>    1.5720   1.1694  -3.9902   4.9676   0.4082  23.6846  -3.0325  -1.5709
-#>    1.2193   4.3040  12.7433   8.2747   4.9245  -3.6125  -1.0537   7.9518
+#>  Columns 1 to 8   0.0268  -0.3757   6.0375  -8.7826 -17.0956   4.2531  10.9680   2.5448
+#>    0.0495   7.0581   9.0656  -1.1926 -12.1456  -1.8420  -3.2290  -0.2959
+#>    7.9777  11.4858   7.2739   3.4636   4.9199  -5.3706   2.8446  -9.9530
+#>    2.6601  -0.7499  -7.2544 -12.9090  -7.2110   0.1346  -9.9113  -3.0219
+#>    8.7960   6.8082   3.6763  -7.8660 -13.6202   4.0760   5.1185   1.1282
+#>    1.8266  -1.7872  -2.9200  -3.7922   8.7956   2.3854  -0.2362   7.3092
+#>   -4.3778   0.7753  -0.7186  10.7946   5.8511  -0.7413   2.2387   4.5521
+#>    4.3605 -11.7745  -2.2958  -0.2394  12.2962  -5.1317   2.4964 -13.2891
+#>   -0.0563   3.3435   8.0466  -7.8452 -13.0751  -4.5739 -18.4470   4.7742
+#>   -6.2856   0.9669   8.5684   7.8602  15.9807  -1.2085 -14.2517  -1.0861
+#>  -11.3462 -18.3101  -6.4877  -6.8844  -2.5102   3.2966  -2.2104 -10.3777
+#>    0.8654  -2.3130   4.0081  -6.3313   1.3738  -0.9346   3.7568  -5.4381
+#>    3.9105  -7.3376   8.1461  14.7779  11.0156 -14.6919  -7.4955 -13.1915
+#>   -4.6555  -8.6328   5.3334   0.4184 -13.0719   2.2033   5.9864   2.7905
+#>    0.5894   4.7579   1.6137   1.7790   2.4720   1.7490   3.0407   5.4525
+#>   -0.4121  -8.2638   0.8681  -6.9749   7.6413  -7.4411  11.3417  -1.8989
+#>   -3.1684   3.9039  -4.1948  -6.7739  -0.6042   5.5872  -4.8040   8.3046
+#>   -3.0163  -4.2427  -6.6639   4.5024   7.8388  20.9367  -3.3742  -4.9199
+#>    1.4918  -3.7849   5.9119   2.1112   5.5031  -4.4142   2.5144   1.1172
+#>   -4.5655  -3.6217   5.7729   4.2136   7.1163   6.8666  -6.9217  -3.8492
+#>   -9.3415  -5.5559   5.1257  10.2804  -8.1302  -6.8186  -5.0925  -3.9069
+#>    6.4905  12.9940  14.2940  -3.2576   2.2758  -2.1263  -1.7688 -13.0083
+#>    2.7562   7.0061  -4.2407   1.0654   0.4941  -1.3026   8.0126   8.0354
+#>    3.7603  -2.4793   2.4157  -1.3281   2.0871  13.4004   3.3887  -5.8957
+#>    7.4692   2.4086   3.8459   4.8023  -9.1148   6.8317   7.5146  -2.9114
+#>   -0.6001   1.1131   0.3110   4.1938  -4.3754   5.3183  -7.5500   5.1761
+#>    6.2630  -5.1274  -4.8614   1.0958  -1.2175   3.6862   0.5034   1.6907
+#>   -0.4244  -0.9863  -4.9482 -12.9478   2.3922   0.8760 -11.2191   0.2017
+#>  -10.1134  -0.1794   2.0449  10.0937  -1.9397   4.8323   3.8194   1.5351
+#>   -6.2615 -10.7897   0.1184   3.3322   0.8641   4.0431   6.7040   4.6131
+#>   -0.4006  -1.5759   3.9827  -2.8200  -0.5042 -11.0836  10.0118  -1.2860
+#>   -3.4027  -8.7748  -4.8088   2.7602  -2.1160 -10.3954 -12.6993   5.7771
+#>    3.6088  -2.6600  -2.4538   3.3201  -5.9343   1.4111   0.2936 -13.5548
 #> 
-#> Columns 9 to 16  -4.7201  -3.1236  -4.3389  -1.9527   4.6023   6.3952 -12.6313 -21.1348
-#>   -0.6620  -3.7689  -4.2902  13.6811  -2.3209   6.6041  -4.8710 -17.3554
-#>    5.1904  10.6001  -6.5149  -4.8909  -9.0926 -11.8929   5.2871   2.2837
-#>    0.7013   4.9385  11.7092   2.8884  13.2337 -17.4017  14.9012   9.3459
-#>    6.8411  -1.4300  -1.6803  -2.5781  10.4844  -4.2093  -3.0250   1.0544
-#>   12.9819  -4.7711 -14.4137   5.0339   8.2432   0.5383 -11.8861  -2.3156
-#>    2.7924   1.7839   2.2759   1.9873  -5.4555  -1.7162  15.9806   6.6636
-#>   -4.0410 -15.5223 -13.1287  -2.8102  -5.5585   0.1104  -2.2128 -12.1074
-#>   17.3425   8.5051   3.8251 -12.2377  -1.1293  -5.4657   0.8050   4.6297
-#>   -4.8510   6.7570   6.4678  10.1318   3.9934  -8.2748  13.6497  13.6396
-#>    9.3899   0.2428  -1.8257   5.4489   5.2872   3.9363   0.1621  -8.0930
-#>   -9.2866  -7.1185   2.7267   0.8633  -1.3314   0.9149  -9.6874   5.3744
-#>   -1.1862  -7.3784  -2.1169   6.4714  -8.7672  -7.9466  -5.3680 -11.5957
-#>   13.7131  -4.5149  10.2006  -6.1466  12.6197  19.8093  -3.2519 -13.6451
-#>   -5.5197  10.4320   0.0603   5.5517  -6.6388  -7.9648   1.7688   3.0486
-#>   -7.3826   0.1136   1.5554  -5.5288   7.0923  -0.7994   1.9077  -8.2893
-#>   -4.4383   0.3800  -2.3695   1.3786   6.1753  -4.8534  12.6246 -18.0979
-#>   -9.0982 -13.8421  -5.1902   6.5919  -3.6211  -3.6110  -0.9803  -8.3294
-#>    8.1154  -3.1195   0.4546  -1.1779  -3.6951  -4.3140  -1.4386   3.9892
-#>   -4.9093  -4.5519   0.8064  15.6809  -2.5004  -1.2447 -16.7001 -13.4800
-#>   11.5612  -2.9265  -1.7209   2.6694  -0.4203  -5.8078  -1.4483   1.3615
-#>   -5.2648  12.7933  -4.3861  -0.3763  -1.6852   4.4957   4.9136   4.4373
-#>   -3.9523  11.9814   1.7973   1.4953  -2.6835   0.2446  -4.5562  17.3886
-#>    5.1934  10.6655   0.6115  11.1418   3.1180  -8.9435   6.6520   3.4674
-#>    5.1238   8.8625   4.3751   3.1573   2.5184  -5.1505   1.8224  10.4806
-#>   -2.4792   6.4549  -6.0669   0.6875   5.4914   3.0087  -2.3207  -5.9153
-#>   -2.4603  -2.0192   0.0122   7.6925   3.2375 -23.4830  10.0626  -7.1549
-#>    4.1439  -5.9518   5.2762  -1.1040  13.5939   0.5049  11.1101 -11.0639
-#>    8.4713   7.5715   0.9777   8.2896   0.3026  10.2661   4.7805   4.8929
-#>    7.5424  -5.8435  -1.1392 -13.0447   1.6035   0.5726   3.4629 -18.9380
-#>   -6.0775   3.4473  -5.3616  -4.7318  -5.5823  -0.2472  14.1388   7.6101
-#>    0.9798 -11.0886  -8.4849  -3.8576  -9.8913   8.9775 -13.6314 -10.7274
-#>    1.0321  -0.2241   4.4221  10.9133  13.4562  14.3194  -7.3366  -0.7615
+#> Columns 9 to 16   4.9225   5.5600  -2.2441   7.6603   1.8716   0.7380   7.2255  -1.5654
+#>   -6.4574  -6.3887   6.6581  -8.1694  -8.8087   2.1191  22.9908   0.1538
+#>  -16.1547   1.0222   4.1697   2.2358  -8.9400   4.3203   5.4363   1.0150
+#>    8.6192 -12.6086 -10.3566   3.1265  12.9864   6.6163   9.2424  -3.4180
+#>   -8.4447  12.5822  -4.7761   3.0417  -7.6632  15.3903   2.5236  11.6599
+#>   -0.3860 -17.4155  -2.9785  15.5705   8.6716  -9.0990 -13.0978   1.3521
+#>  -13.1463   0.9251  -4.9657  -3.8783   6.3122  -6.5268  -2.2784   1.4956
+#>   -7.6302  -8.5406   4.6711   1.8267   0.2083  -8.9586   0.6774   4.4917
+#>    3.9469 -11.8175   1.4958   3.1491 -10.3108   3.7795  21.8913   5.5163
+#>    0.7508  14.7510  -0.4539 -15.7338   0.1425  -5.7490   1.3307  -5.3326
+#>   11.6687  12.5395  12.0068 -10.5433   7.4114   9.1138  -1.9093  -8.9764
+#>    2.9976  -2.9200   0.0076  -6.3508   4.8695  -6.1133   7.2441  10.0213
+#>    4.3059  -2.4060  -1.9804 -12.7432   3.4749  -2.4262  -8.1165  15.6262
+#>    8.3939  -0.1630   0.8528 -11.0749   3.5464  -2.9716  -2.2068  10.6706
+#>   -9.1988   3.5714   3.2794  -1.6498  -2.0392  -7.5501  -2.0919   5.6080
+#>   -6.0028  -2.9252   9.3408   2.7734 -12.8275  -8.1112  -2.5071   2.4990
+#>    5.6441  17.8487 -10.9258  -1.2157   9.4649   1.7805   3.5160  -0.4948
+#>  -14.3434   1.2701   8.8466   0.4224   6.4197  -4.1490  -9.6471  -6.0742
+#>   -6.7458  -0.9015   1.2883  -5.7606  -5.1605  -1.3763  -4.4218  -0.3204
+#>   -3.5323   7.4151  -0.1791  -8.4691  -0.2713  -6.7305  -1.2468   9.6714
+#>   -2.4161   4.7951   5.5261  -2.3472  -5.4762  -8.4961  13.9995   8.3659
+#>   -2.8123  -4.4264 -13.0146  -0.2199   1.3056   1.3500   5.1790   4.4767
+#>    0.0135 -17.3878  -3.4409  -1.0390   4.9462   1.7660   0.0994   0.8319
+#>   -1.2961  -2.4429   0.1393   4.6054  14.3563  -7.6293  -5.0934  -5.9340
+#>    3.8720  -2.3441  -9.3442 -13.2364  -3.7195  10.1455  -1.2176   7.5793
+#>   -1.0601   0.7780   6.8379 -13.3178  -8.6855  -0.9878   9.4483  -5.2107
+#>   -9.6257   5.1942   0.7178   2.6521   0.2496   4.0240 -14.6032  -0.5868
+#>    5.7428  -2.1691  -2.3975  -0.0737  -4.1814  11.5471  -1.0604  -2.2776
+#>    3.2835  -0.1539   6.7294  -4.2859   0.3658  -8.3379   3.8094   2.9161
+#>    0.0561  -5.0773   8.4428  -1.5961  -7.1156  -4.5487  -3.9622  -1.4178
+#>    3.7956  -9.3947  12.9836   4.7540  -2.3314  -3.9858   0.4320  10.9241
+#>    4.3397  -4.2929   2.2238  -6.8958 -11.9045   6.8032  13.4853   4.5593
+#>    3.1307   9.4933  -3.8672 -11.1129   7.2185  18.0700   0.4513 -10.4555
 #> 
-#> Columns 17 to 24   3.2404  -7.3528  -4.0721   7.6482  -2.2974  -1.6315  -8.5402  -0.2590
-#>    9.0915  -5.5299   7.1584  -6.4244  -6.2699  -4.5453   0.4297   1.3497
-#>    5.0526  -2.3287  16.0916  -8.3235  -1.0199  -0.6070   4.0202   4.3790
-#>   -7.1261  11.7021   0.9335   1.3722  -7.4772  -8.3906  -0.6237  -9.8234
-#>   -2.8154  -1.1493  11.3990   5.7868  -9.0052  -8.9318  -0.5868   1.0273
-#>   -7.2551  -2.0263  -0.2881   3.6759  -5.8995  -0.1478  -3.0632  -8.5266
-#>    3.9098  -1.3945   3.8332   7.5970   3.7336   0.7507 -11.8417  -5.6555
-#>    0.2132   8.4414   3.7995  -9.1395  -8.8469   7.1621   7.9866   5.6705
-#>   -8.7067  -4.8448  -1.2625   3.3080   0.7969   1.8951   1.2556   1.2725
-#>    3.4546   6.4591  -5.8498   4.8281  -0.9510  -5.3729  -1.1176   3.3406
-#>    8.2234 -12.0190  16.8075 -19.5644  -3.8102  -0.2338   3.2206  -3.4571
-#>   -0.3564  -5.0141  -4.0951   7.5192   3.4239   0.3308  -5.1510  -4.0848
-#>   -0.3188   4.4719  10.9368 -10.5394   0.3799  -0.0291   3.2460   0.9790
-#>   10.2404   0.1642  -5.1547   8.4400  -8.4119 -10.9495   2.0886  -1.9074
-#>   -0.9937   5.1533  19.3627  -7.2296  -9.1919  -4.4116   3.1740  -3.1250
-#>    1.2888  -1.0215   8.0473   4.1319  -5.1494 -14.1310  -1.6532   1.7811
-#>    0.3926   6.3051   3.6569   0.1893 -11.3633   5.9868   0.7072  10.7594
-#>   -7.1072  -3.0477  12.2373  -4.7590  -6.2750  -3.4573   7.7633  -8.3014
-#>   -8.7176  -2.8906  -0.7154 -14.9467   5.2296  -7.2645   5.2165  10.9547
-#>    2.6475   0.4443   1.2017 -12.9848  -5.7442   7.7503   1.7651   6.4125
-#>    0.2760 -11.0673  -3.5735   1.7614   8.2979   1.2129  -2.4041  -1.7983
-#>   -5.1471  -7.7904   5.4553  -3.4230  -8.1335   7.8878   6.8528   2.7138
-#>   -2.6071  -6.1638   5.2968   3.1989   1.4251  11.1042   3.8692  -6.5996
-#>  -15.7746  15.8617  -1.6893  -7.4314  11.2806 -12.9195   0.9744  -5.6331
-#>    4.5438  -5.6933  -0.7325   2.9716   1.4225   0.3591   2.3317  -0.8351
-#>    4.9513   8.1065  -9.9296  -2.4630  -5.4124   5.6269   1.6865  12.9014
-#>   -0.4381   7.9463   5.5066  11.0034  -7.7295 -13.9815   6.7824   5.4234
-#>   11.2191  12.7392  -2.9574  -5.2218  -9.9673  -5.6994   4.9155   5.8823
-#>   -5.3755 -14.1145  20.0468  -2.6852   0.5977 -10.9694  -1.0630   6.6494
-#>    0.7967  -1.2790   0.7432  -5.6644  -8.0744  14.7843  -4.1934   3.5868
-#>   -8.6367   3.4889   1.5427  -0.7841   6.2432   3.9866  -7.5451  -3.6884
-#>   -1.1392  12.9018  -9.7556   3.6774   4.3340   1.9292  -0.7439   9.6608
-#>   -0.7898   0.4567  -9.0658   0.7583  -0.5643  -2.7536 -12.0739 -13.5161
+#> Columns 17 to 24  -4.5403   0.8627  -5.5399  16.7652  -3.3005   0.3912  -2.2864  -0.1981
+#>    0.0705  -6.0419   4.7120   3.4012  -3.2563   4.4886  -6.5086 -10.7036
+#>   -2.2246  -8.1928  -7.3928   0.1447   0.5969   3.3527   1.4047   3.0847
+#>  -20.8609  -0.2642  -1.0628  -3.9638   3.2940   2.4475   0.8673   5.0464
+#>    0.4571  -7.2577   0.3695  15.4552  -2.3910  -1.7355  -5.4584  -0.1109
+#>    3.0723   7.3549   1.5265   2.2355  -2.5095  -4.8412   3.4412   0.9306
+#>   14.4901  -1.5159   1.9517 -13.8063  -4.0890   0.3825   0.3586  -4.0783
+#>  -12.4548   5.4844  -0.9215   1.2928   2.0309   1.0115  -0.9829   6.1028
+#>    2.3406  -4.1557   1.3609   6.1630 -13.2867  -2.1212  -2.2850   6.6842
+#>    8.1618   5.0559  -0.2792  -7.3086   6.6220   2.8348  -4.4975  -4.3933
+#>    1.9959   4.5803   6.4670  -4.8582 -12.1061  -0.3311  -1.8318   0.2960
+#>    1.2757   5.0919   4.1235   3.6204   4.7513  -1.6185   2.2168  -5.9940
+#>    0.8802  -0.7256   7.6243  -7.6476  13.5457  -6.1795   3.7220  -0.3460
+#>   -0.6865   3.3372   6.5970  -0.0107   0.7561   5.8117  -5.2296  -5.3790
+#>    5.0755   6.1097   1.0909  -0.6166   9.5765  13.2437   1.5516   3.9194
+#>   -8.0604  -5.9008  -6.0673  -1.4995   2.3548  -2.3083  -0.9492  15.5202
+#>    2.4620  -0.0056   2.9948  -5.8729  -4.7114  -4.3107  -3.9687   3.2706
+#>    7.1155  14.5256  11.7819 -10.3028   0.8881  -3.2271  -7.0647   1.5374
+#>   -4.3952   2.6930   1.8190  -0.8021  -4.3241  -5.7911   0.3484  -9.6943
+#>    2.0471  -1.0732  -4.2291   1.7310   5.0389  -2.3117   7.2055   6.4297
+#>    5.6211  -2.2049  -4.5974  -6.0471  -4.8597  -1.2184  -6.1280  -7.1372
+#>    2.6220  -5.4533 -15.3593   0.0618  -3.3244   1.9433   6.7403   9.9059
+#>    2.6229   6.7076  11.8256  -4.5230   0.7833  -6.2725  -3.9163  -8.9228
+#>   -4.3641   5.8146  -0.3310   0.7695  -5.1522   8.2024  -0.9187  -0.4964
+#>  -11.3966   0.7391  -4.1380   1.0357 -12.4213  -3.6986   2.0429  -2.5611
+#>   -2.7803  -3.9230  -2.0136   4.3543  12.3046   2.3671  -1.8022  -3.0155
+#>   -3.9181   3.3108  -3.0345   6.7626   2.7906  -9.2037  -3.1566   6.3978
+#>   -1.6257   6.3275 -10.3144  -4.6663   0.8116  -0.6236  -3.0478  -0.3901
+#>   -1.1515   6.2684  -3.8367   4.8982   1.9917   1.1414   6.9544   1.9681
+#>    3.9706  10.2930  22.5673   7.9787   3.0717  -2.1829 -13.0866  -1.4238
+#>   -0.8366  -5.9752   7.0010   0.9696   1.7413  -0.7517   7.2532   1.0929
+#>   -6.1598   7.1434 -13.5498   3.0724  -3.9919  -1.2601  -1.1236  -1.4062
+#>   -1.0377  11.7243 -11.7727   0.4309  -1.7176  -1.1098   7.8816  -4.5404
 #> 
-#> Columns 25 to 32  -7.7718  -0.9114   2.4102  -2.2426  -8.2447  -8.6054   4.0331   4.6473
-#>    1.8486  -5.2750   7.4340  15.8399  -5.6456  -5.6514 -10.1373   3.1087
-#>   -9.4465   4.7849   1.6913   3.6872  -6.2903   8.9057   6.9255  -5.0254
-#>   14.8307  -4.0019   0.4373 -15.9645  12.4092 -11.6289  -2.6368 -14.3868
-#>   -0.2235   3.8137   3.8534  -1.3829   2.1283  -1.9937   4.9293  -5.9287
-#>   -2.5982   0.9202  11.7816  -7.2020  -6.4888  -4.1582  -5.2661  -7.1377
-#>    4.0172   1.0735   2.9697  -3.6743   1.0773   9.7277   3.3830  -1.5863
-#>    0.8849   1.0239  -5.2432   0.7883   5.8000  -3.4362   8.1262   7.7836
-#>    2.9349  -5.4463   0.5602  10.1004   1.6811  -1.1345   9.2585   5.7046
-#>   -6.0164  -1.3181  -0.4065   1.5494  11.7829 -14.4608   3.9402  -5.7432
-#>  -17.4480   5.4720   1.7400   5.2470  -9.6330   4.0045   4.5097 -11.8818
-#>   -2.0877   2.5754   2.0526  -2.6236  -5.7712   3.4179  -0.6518   3.2757
-#>   -6.4103   3.7287   4.6183 -17.0621 -15.5844   5.8730  -3.4688  -5.3081
-#>   10.6664  -2.7789 -12.8203   3.8798  -2.2326   5.6436  -5.2723   0.9585
-#>    3.0664   1.0020  -7.0297  -2.8026   4.1825   0.0879  -3.6404  -1.6307
-#>   -1.5760  -0.0641   2.7851   8.6238  -5.8379 -13.0112  -4.4457   4.8467
-#>   -3.5930  -0.6990  -7.8559   8.1605  -8.8669   3.5128  -8.1397  -6.4161
-#>    4.2051  -4.6264  -2.4763  -4.6992  -7.7277 -11.2350 -10.4281  -6.7836
-#>   -2.7288   5.3781  -0.7707   3.8182  -4.2097   2.8139  -7.3372  10.2148
-#>   -7.5773  -4.9700  -8.8037   1.7967  -2.2666  -4.4975  -7.7480  15.4940
-#>    4.9859   0.0118   1.7544  -7.8191   4.6211  -0.8371   2.3063   4.5158
-#>    2.3803   3.3274  -6.8530  -3.5117   3.6384  -7.1110   5.9185  -9.9121
-#>  -12.0744  -5.7320  -8.6097   9.6187   6.9884   0.7670   4.1691   3.6504
-#>   -9.9666  -1.6004  -6.3201 -10.2167 -10.4199   1.9198  -1.2735  -9.4717
-#>    1.4589   4.1667  -2.6370  -1.6384   4.4989   4.7677  -0.8968   0.3371
-#>    8.3363   0.9305  -3.7238   6.0712   4.8480  -1.6509  -0.6134   7.1978
-#>   10.3574   5.0577   1.0890 -12.0661  -0.4778   3.2472  -7.8195  -2.8836
-#>   -9.1254  -9.6882  -3.2917  10.9773   0.0025   3.2516  -6.4559  -5.2064
-#>    6.9633  -0.8168 -10.9703  -0.6805   3.4517  -5.9585  -4.0768   1.5045
-#>  -15.8190  -6.2055  11.6044  13.8867 -15.3518 -11.9771  19.2955   3.1466
-#>   -0.1233  -9.5387  -9.2900   5.5027   1.5485   3.0759  10.2926  10.8285
-#>   -9.8313   0.3688  -3.3588   7.8980 -14.6756   7.7726  -2.7930   3.4806
-#>  -11.8913  -9.0171   5.8413   3.9101   1.3863  -8.5099   0.7576  -9.0370
+#> Columns 25 to 32  -3.3639   3.7089  -3.5293  -1.8418  -6.5532   4.9634   9.3293  -1.1334
+#>    1.0921  -1.6256  -0.4790 -10.5491   1.3899   5.8573  -9.6310   3.5270
+#>   -3.6231   7.3822  -5.0488  -5.2696  -5.8890   6.7486  -1.3528 -10.1037
+#>    6.5933  -4.4395  -0.8298   0.7569   4.5329  -4.3295  -3.3510   3.3305
+#>   -7.3630  -0.8983  -8.4753   2.5676 -11.0820  -2.3869  -6.1060  -2.5663
+#>    3.7198   5.8540   4.1679  -0.4445  -1.2934  -0.0859  19.0920   6.4379
+#>    5.1275  -1.0242   5.4139  -5.2090  -2.3867  -3.1714   7.5581 -10.5045
+#>    6.1018  -8.0742   1.3731   8.5287  -6.1802   3.1121  -4.1473  -0.1143
+#>   -7.5814  -5.9677  -5.7110  -3.1808   9.9725   0.3796 -17.4838  -2.7362
+#>   -2.9835  -6.5368   8.3865  -3.4417  -2.8261  -7.9488  13.6199   5.6744
+#>    5.8382  -1.7312 -11.4873   5.2917   9.1595  -6.1192  -1.3211   5.3039
+#>   -5.6978  -1.7276   5.9020   3.5445   1.1818  -7.1539   4.9696  -4.9703
+#>   -8.1501 -10.6983  -0.6169  -3.6448 -10.5499  -0.0997   1.1262  17.6429
+#>    4.5481  -9.6244   1.3043   5.5085 -11.3478 -10.8300   2.1328   8.0171
+#>  -15.1748 -17.6798  13.9718  11.2357 -14.5809   2.7135  -6.1892 -11.7734
+#>   12.1469   3.9165   9.3138  12.5281   5.2217   2.0857  -6.2557   3.6950
+#>   -5.3670  -1.0265   7.8035 -10.8065  -6.0649   0.5879   3.3191  -9.8317
+#>    6.6331 -15.9322   9.1446  17.5641  -8.7397   3.9684   5.7379 -14.6294
+#>    9.5837   3.9262  -1.9871   5.7472  -1.9088   1.6816  -0.1372   6.5131
+#>    1.8731  -2.9632  11.2780  -7.6983  -6.4604 -10.5026   5.7501  13.8107
+#>    2.5895  -9.0015   1.3132   1.4014  -0.4176  -2.5248   2.3322   3.1885
+#>   -4.3353  16.3129   3.2437  -7.7630   5.1971   1.7249  -1.4453 -12.7346
+#>   -7.8411  -5.7639  -3.6366  -5.8391  -8.9470  -3.5877   7.7259  -6.6988
+#>    7.7574  -9.4756  -1.3226   6.6673  -6.5002  -4.0112  -2.6859  -4.9014
+#>    1.1800   7.3869   3.4971   0.9487  -6.2506  -8.8993   0.6066   1.0809
+#>    0.8496  -1.1507   3.9585  -0.4666  -0.9697  -7.0459   5.3007  14.1793
+#>   -5.4650   1.0423   7.3941   0.2114  -8.2729   6.8540  14.6386   7.4352
+#>   -2.3820  14.5245  -1.4404  -0.4711  -2.0351  -0.3895   1.5225   3.7831
+#>   -7.9096  -3.5263  -2.8515   2.8540  -2.4600  -7.6752  -4.2027   3.7889
+#>   -7.6223 -15.2149   1.1952   8.8253 -16.4614  -2.0181   5.3236   2.5961
+#>   -2.2341  -4.9732  -3.0079  -0.7461  -2.6474   3.5996  -8.9855   2.7606
+#>  -20.7502   4.7540  -0.1366   2.4125 -11.6241  -5.3454  -7.1686  12.4429
+#>    0.6590  -0.3549  -1.2210   1.7432  10.0225   3.8491  -5.5875 -10.6072
 #> 
-#> Columns 33 to 40   2.9796  -5.8591  14.4235   4.4064  -2.7400   0.5498   7.9668  10.1102
-#>   -9.9294  -5.8406  -4.4334   0.4285   3.8579   2.0006  -2.0403   2.3884
-#>   -1.8515  -4.7375  -1.2654   7.3044  15.8168  -2.7423  -2.4067 -11.8371
-#>    4.4462 -14.5330  -4.3808   3.9995   2.6533  -7.2618 -12.5946   0.2572
-#>    3.2368  -4.2141 -13.9740  -8.8299   7.9667   7.1578  -1.1920  -3.9704
-#>   -3.5651  -0.3440 -16.0170   6.9821   0.7315  -7.4706 -11.1905   2.1866
-#>   -1.2230   1.8032   2.2447  13.5615 -13.7201  -7.6674  10.3916  10.3359
-#>    4.5096  -0.2489  -8.7388  -5.6336   6.9253   2.8208 -13.1318   2.1139
-#>   -2.1393   9.3337  -4.5421  -4.7718  11.8707  -8.6940   6.3966  -8.0506
-#>   -2.9345 -15.1383  10.7131  -7.4241  -6.2026   1.8937   6.6104   3.1484
-#>   -4.1044   5.3933  -2.1359   5.8452  10.8731  -3.4783  -7.0924  -6.3855
-#>   -9.3098   2.1850  -4.6465  -7.2282  -2.5180   0.1917  13.3671   9.2172
-#>   -5.2428  -2.8354  -1.1434  12.6908  -9.3882  13.7698  -7.8651  -0.5879
-#>   -7.4181   5.1397 -13.1685  -9.6985  15.4629 -14.1861   7.3946  10.6126
-#>   -4.1513 -13.3208   4.5188   0.1917  -0.8268  11.2592   3.6528  -0.6625
-#>   -0.6403  -6.9440  -4.0975  -3.8169  12.0874   9.4207 -10.7510   3.7073
-#>   10.7357  -5.8957  -1.9186   8.6922  -3.0942   8.8761 -14.8420   1.1825
-#>  -11.9155  -2.5841 -12.9962   5.0492   4.6539  -0.6236  -4.0959  -4.7469
-#>  -11.6471   4.8921   5.0823  -5.0213   9.7070   3.0254 -12.2953   3.9251
-#>   17.0879  -4.0036  -1.0710   0.4942  -2.4409   6.5400  -0.3668   1.1020
-#>   -4.8131   4.7524  -9.4196  -5.5224  -1.1471 -14.8131   9.7049   4.3751
-#>    0.8249 -11.9130  -4.0199   1.9304   6.6916   6.5608  -3.2226  -4.6872
-#>   13.3971   8.0947  -1.4470   8.0229  -7.0477  -9.1122  13.7528   0.1964
-#>   -2.6919  -9.1373   5.1295  -6.3844   0.0863  -2.1316  -0.3428  -0.3389
-#>    0.2724   3.4743   7.8588  -0.7303  -2.4073   1.6905   4.5028  -7.8179
-#>   10.6832  -0.2296   7.1542   9.0711   2.4966  -3.8835  -5.3003   2.0246
-#>   -8.4005  -7.8312 -15.2336   6.9674  -1.8741  -5.7587 -17.6714  15.3893
-#>   -4.9575  -0.1593  -1.1159   3.2139   7.9358  12.5113  -9.8510  -4.5967
-#>  -10.1662  -6.7715   1.6739  -8.9887   3.1111  -5.8976  13.2658  -4.7742
-#>    0.2688   0.8505  10.0891   4.0398  -2.2841  15.1094   4.5957  -6.2957
-#>    0.2787   5.7893   4.1793   1.8786  -1.3759 -13.6348  14.0891  11.3234
-#>    3.5430  14.9865   0.7801  -0.4608  -9.5619  -6.3796   0.0200  -5.6714
-#>   -4.2869   2.2431  11.6142   1.4366   2.0043  -7.9774  14.1313  -5.3636
+#> Columns 33 to 40 -11.4110   9.7205   1.5401  -2.7510   1.2792  15.0599   0.0496  -9.1622
+#>    5.4631   3.3218   2.2288  -6.9674  -9.6491   2.6538  11.1732   5.8183
+#>   -3.0262   4.0388 -10.3257  -8.8676  -8.6690  -0.4287   1.6248   8.4191
+#>    8.0845  -2.8988  11.9952   0.4718  -2.9628   4.2875  13.1174   6.9494
+#>    2.8617  -2.7812   2.3745  -3.8281   7.9429   0.8949   1.9820  -9.1806
+#>   -5.1250  15.9504  -4.2533  -1.3836  -4.5819  -1.7996  -5.7484   5.3758
+#>    6.3032 -10.7816  -9.6452  -1.1218  -9.1026  -4.4460   0.6274  -2.9784
+#>    5.1642 -13.3591  -1.0159  -4.7850  -5.4670  -7.8327   2.5576   1.5834
+#>   -3.9484   2.6348  32.8767   0.9171  21.6369  -3.1694  16.0368  10.6583
+#>   -0.7267  14.1048   0.5024   7.7016   0.3269  -3.7291 -11.3429  -2.6302
+#>    3.6972   2.6802   2.6978  -4.3538  -5.1271  -3.2816   4.0178  12.4351
+#>   -5.8301   0.6046  -3.5828  -8.4483  -0.5102  -0.9339  -0.5123   1.8580
+#>   -0.8860   0.8671   9.9673  11.6676 -11.4563  14.6641   0.1757  -0.6393
+#>   18.4382  18.8040   2.4818  19.5073  -3.9704   7.3254   6.2920  -0.2283
+#>    4.1849  -6.0036  17.8827   8.4112  -8.0271   4.4468   1.9496  -5.6960
+#>   -7.4727 -15.3136  -1.9712  -3.1628   3.9776  -5.6435   2.5168  -1.0798
+#>    3.0072 -10.3900  15.9210  -5.6552  -2.3916   2.4912   3.7345  -4.9033
+#>    2.6913  -7.3348 -10.0893  12.8524 -10.6272  -5.8566  -0.6821  -1.9945
+#>   -5.5521   5.0511   1.8665   6.4624  -9.3969  -3.0982  -8.1768   5.5550
+#>   -5.2313   7.7024   6.8639  -0.2529   1.2518   1.4687   1.6648  11.0534
+#>    3.8489   1.7666   5.6743   8.2437  -6.8864   0.9819  -1.7740   5.7260
+#>  -15.6474  -1.8256  -1.8265  -5.3433   2.2131  -1.4583  -7.3534  -9.2084
+#>   -1.0694   2.0757 -10.0864  -2.4707  -5.4852 -11.5234   2.0255   4.8720
+#>  -12.8576  -6.8095  -3.1034   0.8008   5.0198   8.0740  -3.1447  -2.8974
+#>   -8.0587  -0.2285 -10.8136  -1.8792  -7.9253  -4.7240  11.9411   0.0463
+#>   -1.7676   5.3562  -7.7130  -2.6117  -0.8972   7.5100  -1.8642   6.6625
+#>    1.7393   5.9138   4.0167  -0.1128   2.6578  12.0800   5.0687  -5.0415
+#>    7.4276  15.2452   4.2611  -2.8698  -1.3799  -3.2962 -10.4635  -4.2773
+#>  -14.2017  -7.7666  -0.1308  -2.3338   3.3958   0.2123   3.0525   1.3932
+#>    5.5766   2.5379  15.1168  17.7801   4.6992   2.2616  -2.0001  -4.6684
+#>   -0.1415  -0.1108   7.1028  -4.6800  -1.1069   4.8969   5.6910   7.4224
+#>   -0.6975   2.5799  13.4043  -5.6343  12.2655  -2.8038   0.8236   5.1554
+#>   -7.0196  -2.1910  -5.0328  -3.9712   7.4484   3.2843  -2.1928   2.6612
 #> 
-#> Columns 41 to 48   2.7537  -8.4081   0.8808   1.8540   4.4716   2.6500  -9.3430   9.6118
-#>   -2.9996   6.7433  -0.1671  -6.4098  -9.4328  10.6736  -6.7612  10.3954
-#>   -1.8288   3.1022  -3.4933  -9.7086  -6.0963  12.0057  -4.3052   4.2917
-#>   13.1396  -4.0272   0.2545 -14.0850   5.3521  -8.7964  -0.6994 -10.0611
-#>    5.0306   6.8330  -0.3310  -1.9005 -12.3920   1.2069  -0.4763  -4.8570
-#>    8.2146  13.0620   8.1760 -10.1780 -11.1487  -3.5516   8.0910   1.5439
-#>   -2.7358  -5.7154  -1.5612  -1.4865  -8.1558   0.7511  -5.1962  -2.5678
-#>    7.7914  12.1014   7.8445   9.9661 -15.9547   1.7632  -3.3189   1.1190
-#>   -0.3257  11.3838  13.9759  -1.6907 -12.5652  -9.6671  -2.0921   0.4294
-#>    5.1802  -7.9128   0.9615 -11.0031   3.9570   4.3288  -0.5359  -6.5904
-#>    2.7734   5.8330  -4.6551   2.0356 -21.9550  20.8268  -5.8366   9.2868
-#>   -1.5492  -6.3833   3.1915   5.4757  -1.5982  -6.8798  11.5828  -2.1211
-#>   -2.1020  -1.9674  -2.8494  -1.0265   3.5978   8.0475   4.5782   2.6527
-#>   -9.5135  -1.2447 -12.3409 -11.5975  -0.7767  -3.5692   1.9104   6.9491
-#>   -9.3450 -11.3317   7.6089 -13.9269 -11.1711  11.9842  -3.0715   7.0820
-#>   -5.8388  -2.1508  -2.8305  -5.4169   9.6731   3.7725   0.5182  -6.3051
-#>    2.8293   2.7246   0.6847  -6.7214  -6.0762   5.8649   1.2158  -1.6557
-#>   -0.9985   2.9063  -2.2778  -4.0852  -3.4789   3.1871  -3.4138   5.5535
-#>    4.2588  -2.1259  -6.4136  10.6392  -5.6241   0.1208   1.3676  -4.4714
-#>    9.0142   7.3940   8.1806   1.3387   3.6750  12.4905  -4.2329  11.7774
-#>    1.2015   2.8161  -5.4220   1.0295  -9.5838  -0.5959   3.4263   8.4852
-#>    3.2337   4.1017   9.9291   2.2344   0.6333  -3.3308   3.4500   4.4569
-#>   -3.7095  -3.7454 -13.8820   8.2878   0.2788  11.7937   0.7563   6.4430
-#>    3.9434   0.0988   2.9141  -5.8733   0.7279   1.4817   9.6820  -0.6106
-#>   -3.4528 -12.7778  -4.5525   4.0766   4.5152  -3.7994  -9.8088   0.2060
-#>   -4.0679  -4.0544   4.9276  -0.5949  -0.2665  -0.3859  -5.7662  -0.2687
-#>    0.1229   1.3327  -2.0212 -10.0097 -17.2825  -2.8027   1.7124  -8.1562
-#>  -14.8658  -6.8483  -3.5400   4.9178  -5.4110   2.2206   8.5238   1.9944
-#>   -3.6567  -6.3353  -7.6350   1.1357  -8.3796   2.2761  -6.0554   9.7495
-#>   -6.2436  -2.9375  10.3187   7.0562  -1.3654   1.9643   2.6747   7.7124
-#>    0.9717  -5.3566  -0.0893   9.3926  -4.3974  -7.2100  -5.8098  -6.3129
-#>    3.7503   5.8549   2.2083   3.3030   3.9939   0.3026   0.6920   5.8119
-#>    3.0689  -2.5120   5.5766  -6.1170   1.9618  -0.7384   1.8105   4.9841
+#> Columns 41 to 48  -4.4035  -2.5830   4.7022   8.1924  -2.1343  -9.3300   8.1459  21.9151
+#>    1.8002  -2.3036  -2.1351  -3.0386   3.5527   7.5935  -3.0330  -3.0010
+#>   -0.1520   1.3074  -2.6433   2.3873   6.5859   6.2012   5.0539   7.0840
+#>    8.5743  -3.4502   1.1069   0.7373  13.5990   8.9227   1.5544  -7.8347
+#>   -6.4117   2.6053   5.9950  12.6009  -6.5350   4.6728  10.3349   8.4037
+#>   10.8904   2.5062  -4.5442  15.8562  -8.4158  -3.7200   1.3020 -18.3469
+#>  -17.9926  12.9330 -14.3240  -9.4925  12.8679 -15.0604   1.3404  -7.4257
+#>  -13.3334 -12.3136   2.1616  -3.9185  -2.1787  -1.2888  -2.4949  -2.7250
+#>   13.8176  -0.4837  -0.2999  -1.0246   3.0575  10.9575  -2.9807   0.8904
+#>    6.5266   0.2682   2.7695  -2.0101  -6.3439  -6.1463  -1.7554   0.7309
+#>  -12.4386 -12.0755  -3.3243  17.1322  -7.2759  -5.2054   8.6104  -8.5648
+#>   -4.6738  -0.6317  -3.0386   5.0054   7.9068  -1.5953  -7.0438  -0.2524
+#>   -0.6661   1.2747   4.6270  12.3603  -3.4111  -5.3579 -22.1942   7.7327
+#>  -13.1994  -0.5398   5.8172  -0.9190 -10.5103  -1.4204  -3.9475   3.8975
+#>    3.9828  -2.2820  -2.2537   4.6675  23.5178   2.4094  -1.9476   8.3381
+#>    7.0983  11.5469   8.0891 -10.8906  -4.7653   7.6345  -6.3178  -3.7133
+#>   11.6996   0.2107   9.4498  -2.5221  12.1700  -0.4884   8.7833   1.9933
+#>   -5.9609   7.2075  -1.3952  -5.2639  -5.0297  12.9433   2.6567 -14.6284
+#>    1.4042  -5.2056  -1.2353   0.2738 -13.1495  -4.1853   3.0999  -1.9822
+#>    3.6216   4.0033  -6.8942   1.4035  14.3725 -11.0724  -7.7239  -7.1996
+#>   -3.3201   1.5721  -1.8065  -4.9702  -1.1000  -5.9707  -6.7182   4.7676
+#>   14.8526   5.8902   0.9221  -4.8476   7.3163  -2.9450  13.8894  13.3704
+#>   -9.7871   5.2464  -0.1787   2.1715   7.4210   4.9738  -6.4512  -8.4699
+#>  -14.6873   8.6363  -8.5709   5.3960   5.3547   2.5137   2.2699   4.4890
+#>    1.9063   7.1748   9.6539   3.3108   5.5583  -0.4651 -12.1162  -3.3217
+#>   -0.0195   5.8952   6.7473   3.3462   4.1393  -4.7460  -5.5523  -6.4609
+#>    0.9311  -6.0597   7.9514   5.2771   4.5957 -13.7509  -4.6339   0.5827
+#>   13.3875   2.7049  -3.1045   3.2848  -6.8063  -8.3993   2.3832  -5.3796
+#>   -6.1878  -5.2134  -2.6677  -8.3963   8.3594   5.6325  -3.0220   0.7048
+#>   -8.4356  -0.3373   8.7754   8.3716 -14.5055  -6.0169   0.8381   0.9769
+#>   -6.4941  -5.5264   9.1333  -2.7699  -3.5451   3.5283  -8.2073  -0.4342
+#>   13.9317   2.0593  11.0732   6.9864  -0.4443  -7.3887 -10.1937   1.8091
+#>   -3.3268  -7.3441   1.2955  -6.3151  10.3992  -7.0971   8.5107   7.5068
 #> 
 #> (20,.,.) = 
-#>  Columns 1 to 8   2.6317  -3.2704   6.4893   4.0617  -3.9221 -14.5928  -0.2165   0.3166
-#>   -1.7656  12.3500  -5.9235  10.2710  -5.4963   2.4739   7.8553 -11.0974
-#>    4.2000  -3.5453  -0.8064  -4.0630   6.7606 -12.7523  -2.3810   4.4675
-#>   -2.4432   7.5452   5.9066  -4.3508  14.2043  -4.4407 -11.5158   0.9818
-#>    2.1510  -1.3800   5.0692  -2.6042   4.9786 -15.2360   5.3043  -2.0795
-#>    6.1902  -6.8906  -4.6501   0.0899  -6.4293  -5.1811 -13.5472   0.0140
-#>   -8.3217  -5.8681  -2.1790   6.5348  -0.1611 -11.5489  -0.5299   8.3786
-#>   11.3522   8.5959  -1.9855   0.1147  -7.1166  -0.7803  18.1329 -13.3221
-#>   -0.4951   1.8566 -10.2217   2.6549   1.3131  12.8182 -29.6905   9.1980
-#>   -6.8784 -12.1935  10.4695  -6.8811   5.5331   3.3465   0.9023  12.8057
-#>    7.2101   4.4492  -3.0272   1.0221   2.6727 -11.9136  11.3500 -10.3183
-#>    0.3164   1.4806  -0.3643   4.1276  -0.4381   3.7842   5.1534   8.5298
-#>    1.8226   0.0824  13.5183  -1.3857   6.3781 -17.4850   4.9547  -2.4414
-#>   -6.9603  13.8362 -16.1456  -0.7156 -13.0805   6.3018 -11.9658  -1.1504
-#>   -3.5703   4.7417   6.4240   1.6624 -17.1390  -2.1930   4.6340  -4.7743
-#>   -2.9960  10.0738   5.9415   2.8126   2.9826   1.8867   1.4020   6.8104
-#>    0.6208  -2.6765   3.8367  -0.8087   4.7581 -26.0039  -1.0620  -3.7928
-#>   -3.1788  13.3034   7.1760   9.3869  -2.0361   3.8333  -6.9656   2.2902
-#>    1.5968   2.1896   1.7029  12.7194  -3.5679   7.4415  -0.9195  -7.3017
-#>    8.2864   3.4497   4.3539   4.6864 -14.1377  14.5084  -3.1976  -2.3807
-#>    5.5481  -7.7046   0.3324  -1.6636   5.1512   6.7901 -11.8125  -2.2419
-#>    9.1587   1.7614  -5.1246   1.2268  11.3454  -3.3256  10.4029  -4.0478
-#>    0.3763  -6.8253   7.2491  -6.7257   4.1883  -1.1559   2.6184  12.4293
-#>    2.1107  -7.9201  10.0034  -7.4355  -0.6598  -4.3904 -17.2219   7.2302
-#>   -1.0541  -4.4945   4.3513  -0.1292  -3.6126  -1.0940  -5.8077   3.4546
-#>    5.3242  -8.1556   1.4671  -1.5818 -14.3665  11.7792  -6.1844  -5.0912
-#>   -3.0772  -1.5444  10.0528  -2.3464  -1.0673 -25.1990   1.1664   8.0409
-#>    5.6377   4.6678   6.3061  -6.4699   6.3947  -1.2818   4.1777  -8.6689
-#>   -2.0267   0.4467   0.3525  10.9873  -1.3003   0.2035  -5.4983  -4.9964
-#>    3.6723  -0.4729   0.4000  -5.0117   3.4031  -7.4031  10.6463 -10.4689
-#>   -4.9988  -2.3497   5.1630   6.9212  -2.5057  -1.7708   1.4655   9.3307
-#>   -1.1346  -1.0179   0.6928  -3.7315  -5.7301   5.4592 -11.9378   9.1582
-#>    1.1608  -1.4629  -0.4300   3.2595  -3.4088  12.4622  -8.1716   2.4827
+#>  Columns 1 to 8   0.7204   3.9786   1.9640  -0.2059  -8.7830   9.1705  -1.8978  -0.6552
+#>    0.2677   4.5021  -7.5337  -8.7293   3.7135  -3.2791  -9.3102   7.7991
+#>    2.0238  10.4193  -3.7261   2.6004  -2.2357  -1.1399   0.6076  -1.2643
+#>    2.3528   1.5588  14.7682  -5.5033   4.5602  -7.5587  -4.0420   2.0467
+#>   11.5861   0.3590 -10.4944   9.0292   0.8143  -0.5998   3.8977  -6.2481
+#>    2.7058   5.9257  -4.5206  -6.0080   6.9172   1.0812   2.2904  -5.4772
+#>   -7.9484  -6.3158   5.9529  -0.2769   3.3626   4.0939  -3.7389   6.0566
+#>   -2.8703 -11.0456   3.3494   1.9221   9.4906   0.7238  -9.2836  -1.2321
+#>   18.6367   9.4555   1.5205 -11.1803  -1.6786  -2.4101  13.8343  -3.9238
+#>    6.4481   2.6074  -2.2367  -7.5460   0.5452  10.5616  -1.1428   2.4721
+#>  -14.4207   0.5817   4.2402  10.3480  -1.6565  -8.9320 -11.1446   6.7180
+#>   -5.4396  -4.9739  -1.9238  -0.3277  -2.4354  -6.3329  -2.5660  -4.4941
+#>   -4.1368   0.3714   6.5016   2.5726  -6.0304  -1.2531  15.3232  -1.5994
+#>    5.0992   0.8464  -0.0608 -14.4345   1.3276  13.4636   0.9726   0.4641
+#>   -7.3900   7.8303   2.1472  -8.6425  -6.0820   8.1696  -3.4664 -11.7957
+#>   -3.5533  -9.1910  -1.8995   8.4852  -2.9152  -9.1580   4.6066   3.3084
+#>   -6.5983   0.2991   8.8496  -4.1938  -4.0289   6.0695  -0.0695  -4.3260
+#>    7.7681  -8.5499   3.2238 -10.4777  -0.2759 -10.7467 -14.2476  14.7922
+#>   -6.5367   4.8321  -5.5840  13.0064  -1.3181   4.7354   3.3749   4.3548
+#>  -14.1943  -2.5453  11.3858  -6.9292 -18.6227   6.9544  11.2619   3.6564
+#>  -12.7858  -4.0422  -8.5903   4.4309  -0.4839  -2.5949   1.3119   3.8463
+#>   -7.2035   7.5181   2.8576  10.5868   8.0803   3.8978  12.9722  -7.8365
+#>   -3.1810  -5.2704   8.3277  -7.3357   4.1917   1.4346  -3.3970  -4.6666
+#>    7.6164   2.7580   4.3462  -4.1236 -10.7497   3.7168   5.9468   6.9176
+#>  -10.9850  -9.3629   8.9923  -8.7043  -6.0492   4.1793  11.9841   8.7307
+#>   -1.3313  -1.2323  -6.3410  -6.6105  -1.0481  -5.3265  -3.4231   7.8613
+#>    4.5163  -5.0713  -3.0665  -9.9020   3.3558  -0.8184  15.1954  -4.9414
+#>    2.4900   6.2327  -4.7585   7.4480  20.2261   1.7684   0.4925  -4.7177
+#>   -8.6338  -1.8386   6.8091 -10.7122 -11.2707  -4.4621   4.0206  -3.1576
+#>    3.8452   8.4092   2.1628  -4.3565  -3.2269  -5.2929   7.5236   6.6677
+#>    8.9679  -4.4450  -4.6570  -4.0979  -7.2786  -0.2456   3.9277  -6.8926
+#>   -8.2695   5.8926  -4.0568   2.8527   2.0790 -12.3269   7.8215  -6.1889
+#>    1.9271   2.7948   2.0253   5.2458  -2.3982  -5.5214   1.6662   0.0322
 #> 
-#> Columns 9 to 16  12.3189  -7.4661  -4.2392  11.7499   1.0018  -4.1385   4.2201  -5.1919
-#>   -2.3632   3.5253  -4.6502  -1.3243  -3.5071  -4.1476 -13.1182  -9.8020
-#>   -4.7953   1.5255  -0.6499   4.0517   2.5277  -1.7646  -5.2528   5.2599
-#>   -2.0369  -3.6353  -6.1835   8.7470   9.1838  -5.3791   8.8067  20.0277
-#>   -1.2960   5.6209  -3.3121  -4.8769  -3.9266  -5.8039 -12.2958   4.2274
-#>   10.5936  -6.9445  -0.8961 -12.4010  -6.4007  -5.3509  12.7792  -2.2874
-#>    2.5691 -13.2197   0.6038   8.3391  -5.8939  14.4678  -0.5205  -4.0887
-#>    4.1848  11.3388  -1.9578  -9.1297  -9.4681  -5.6152  -6.0008   0.7851
-#>    4.4057  -1.0972   9.0309   1.3224  -3.7618   0.6563  -8.6499   4.4606
-#>   -5.0686  -4.4629   7.5355  -1.2612 -10.0848   2.7247   7.0528   7.2881
-#>   -4.9709  15.5319  -4.2474  -8.5831   6.5794  12.3218  -7.9114  11.4092
-#>   -2.8564  -7.3116  -5.2396  -1.0795  -0.0653   9.1868  -0.2262  -5.8491
-#>   -0.8342  -1.9837   3.4885 -13.1955   5.6293   5.9005  -0.3317   3.5845
-#>   -6.5592   9.3104 -11.2125   8.8877   8.0341  -5.8824  -1.1890   6.4209
-#>   -0.6094  -4.6323   1.4846  -8.3095   3.2218  11.1752  -8.5526  -3.3383
-#>   -9.9859   0.8720  -2.8285  -1.3160   9.5755   1.7600  -6.3442  -4.7246
-#>   -1.9063  13.7199   1.2022 -13.4989  -8.4142  -0.5966   2.7934 -10.4546
-#>  -11.8290   3.8885   2.5128 -10.7260 -14.9850  10.9759   0.1504   1.9472
-#>    6.3647  -0.5885   8.9794   4.3158 -16.9057  -9.6695  -2.0466   6.2499
-#>   12.7378   9.0779   4.4035 -12.7644  -8.6863  11.0101   2.7448   2.6279
-#>    8.2543  -6.9941   4.6757  13.2053 -15.9934  -2.1725  -3.7339   2.6762
-#>  -15.5257   3.3541  -1.4097  -7.8562   7.5251  -3.9706  10.9223  -5.9378
-#>    4.2843   4.0547   3.5963  -3.8833   3.8719  17.3283  17.0613   1.3608
-#>    1.8062   2.1106   3.0785 -10.7370  -4.8876   5.7470  17.1128  20.2215
-#>   -1.0501   1.8343  10.6267   5.2201  -3.1459  -7.6928   2.0141 -13.0849
-#>    9.1640  -0.2273   2.5444  12.4343  -5.9568  -9.7919   0.5788 -16.2262
-#>   -6.0636  -8.2957   0.5474  -7.2691  -3.9940  -7.2372   3.5615  -0.1772
-#>   -0.0116  18.5618  -0.0501   1.4772 -11.5969  -6.3888  10.5140   6.0256
-#>   -6.4140  -6.3266   8.7698   4.9552  -8.6961   2.5264  -6.4764   1.3719
-#>   -2.1428  13.2581   0.0243  -6.2169   2.2053   5.1598  -8.1174  -8.1981
-#>    6.9769  -3.2637  -1.7238  15.3795  -5.4236   9.5380  11.7204   3.4857
-#>    4.2541  -5.3250   8.2638  -8.9295   5.8716  -3.8351  14.4074  -9.4258
-#>    6.9027  -1.9696  -0.0950   6.5029   3.8962   6.0836   7.7747   8.1169
+#> Columns 9 to 16  -6.5101   0.4217  -5.7984  14.4422  -2.5475   4.9547  -9.7174  -2.6202
+#>    5.0385 -14.2081  -0.6895  -3.3577  -6.5326   8.4067 -14.1657  -2.9425
+#>    4.9895  -3.0387   2.3725   1.2926   2.1103  -5.2542   1.7631  -3.8400
+#>   -1.0840  -8.8766  -9.9298  -7.4363  -5.1668   2.6538   0.6354   3.4885
+#>   -0.9582  -7.3382 -15.7859   8.6350   5.3817   1.9753   1.7504  -7.1652
+#>   -8.2010   6.7050   6.0290   5.1126   7.6760  -8.2627  -3.3050  -0.8235
+#>   -0.0380 -11.6303   9.6557   1.0808   5.9470   0.7350   1.9527  -4.8094
+#>   16.1488   6.5973  -0.7724  -1.1442  -3.1127   4.3683   0.9927  -5.6264
+#>   -5.6930 -10.9362  -5.7351 -18.9769 -10.5021  -4.8395   9.7254  -9.5025
+#>   -0.9750  -0.1790   7.8659 -15.3504   0.8097   4.2399  -7.4569  -1.9779
+#>    5.3159  12.2311  -1.5734 -18.6719  -0.9234   3.3073  -5.4588  12.6989
+#>    2.1220   0.1937  -1.3568 -11.0159   5.9784  -8.6370   2.9642   0.0961
+#>   -6.0101 -10.9997   1.7153 -10.8551   3.5893  -1.6006  -3.9109 -11.8404
+#>    0.6564 -17.8008 -13.0588  -1.9639   2.6150  21.6864   5.9739 -10.7310
+#>   -5.3410   9.3782  -3.5936   0.7654 -18.8627   8.1400  -1.7319  -3.9905
+#>   11.7295  16.9428   2.2334   2.8814   1.9576  -2.6187  11.7411   7.9283
+#>   -3.9663  -1.4653   0.3331   1.9336  -0.7061   7.8008  -5.0434  -2.1069
+#>    1.0235  -4.1951  13.1783   0.8484  -5.8056  -3.0008  -5.0903   3.3962
+#>   11.1815  10.4741  -2.8823   0.8246  -7.2101   1.0224  -8.8046  12.3669
+#>   -1.6690  -0.2361   1.3809  -2.4921  -2.8937   5.5683   6.7189 -16.7126
+#>   -0.0576  -0.5944   2.2741  -7.0879  -8.5062  -4.1132  -6.8532  -0.1614
+#>   -8.9460   4.8719  -7.7315   3.1060   3.4187  -2.7045   3.2624   9.9242
+#>    0.1847  -6.6299   2.1498  -1.2818   9.1559 -10.6412   8.4213 -10.3737
+#>   -5.8952  -6.2402  18.6318   8.4840  -3.2384  -3.6527  -0.7749  -2.9456
+#>    6.7507  -2.8179 -19.7545   5.7751  -6.0175   6.7579  11.4046  -2.5534
+#>    7.7275  -3.8353   3.4662  -8.6468  -0.4626   1.9800  -2.7629  -6.8681
+#>   -2.6012   0.7715  -1.3032   8.3143  -3.8720  -3.3476  -4.2864 -14.2712
+#>   -6.9771   9.1048  -1.1948  -1.5486   4.1960   5.2358   3.3625   6.5611
+#>   11.1025  -3.6381  -1.2623   2.0914  -7.1281   0.5444   1.6081  -7.9267
+#>   -1.7125  -2.6828   7.7532  -9.1398  -1.3714   0.1140  -2.8279 -11.5659
+#>    4.4574  -5.4493  -6.1928   4.5434   1.0821   0.0782   5.2489  -7.7652
+#>   -3.3410   7.2854 -16.1066 -10.1962  -3.5601   0.2287   4.1809   1.1185
+#>   -6.3470   4.3083 -10.7393  -1.5773  -7.4302  -5.6992   1.0806  13.0260
 #> 
-#> Columns 17 to 24   3.1896   7.1136  16.5850  21.3689  14.1253  10.9252  10.6069 -12.9360
-#>    3.1916  -2.3407  10.7862   1.1791  -2.3508  -0.8567  -2.7486  -0.3680
-#>   -8.0882   0.5215  -6.1814 -15.5077  -8.7990   6.2791   3.8403   9.6876
-#>  -17.2918  -3.3978   4.2483  -4.9023   2.4044   0.9558   9.9897   0.8995
-#>   -4.9701   6.9746   3.1969  -1.7714  -6.7167  -6.3982  10.8513   7.5809
-#>   12.1795  22.3433  16.0185   7.4013   8.3001   8.4499  -1.4369  -6.1516
-#>   -8.4451   1.9695  -4.3517  12.2279  -4.1713   1.0889  -4.5477 -12.6213
-#>   -7.2546   1.2788   0.9460   6.0031   3.7693  -3.9883  -3.7087  -3.4668
-#>   -6.9514  10.6885   7.4600   0.1471   4.4466  -0.3061  -0.6925   8.2213
-#>   11.2392   3.6268   8.8963  -4.9476  -0.7547 -13.0547  -8.4332   2.4718
-#>    6.9078   0.5906   1.6236   0.2955 -10.1980   3.3020   3.8726  -2.5545
-#>    5.3922   0.0548  -2.6088   1.4309  -0.8519 -11.7812  -5.1712  -7.9835
-#>   16.3538  -0.2866  -3.9257   4.2461   6.4649   6.9675   0.3583  -0.1692
-#>   -8.3542   3.7870  -5.1359   0.5153   2.7189   7.9143   9.8094   2.3193
-#>   14.3007  -4.8221  10.5206  -3.7007   1.1044  12.1687   2.2237  -6.5298
-#>    6.7927  -0.3696  10.7242   6.0659   2.8485  18.9118  10.8026  -3.7626
-#>   10.4871   3.5258   1.9896  10.0899   2.9323   1.0943  11.1587  -5.3213
-#>    4.1095  11.5633  -3.8133   0.0403  -2.0528  20.9037   1.9324  -0.6095
-#>    0.3955  10.9574  14.2628   6.2530  -1.9365  -3.1222   1.6854  -4.4150
-#>   13.8606   4.9778   6.4612  10.9626   3.8364  14.6639 -11.1712  -4.2862
-#>  -17.4411  15.5994 -10.8096  -7.8068  -2.7533  -5.6480 -10.9167   3.3260
-#>   10.4801   0.7587   1.2777  -0.4488   3.1472   9.4578   1.6560  -1.7689
-#>    9.2851   3.1067  -2.2496   5.5730  -5.7733   7.4467   1.4873 -16.7869
-#>   20.4946   1.6005  10.7375  -8.5792  11.5899  -0.3161  -6.1408   5.1686
-#>    2.6878   7.3988   4.1201  -9.0180   2.5914   1.3089  -4.1484   0.6474
-#>   -1.9797  -2.2724   6.8919   5.8167   0.9384  17.1842  -1.2179  -0.5146
-#>    1.6663   7.3121  14.5475   2.8160   2.4474  -5.1266   8.7430 -11.3458
-#>    5.1560   5.9187  -0.3446   8.4645  -2.7297  -9.8186  14.9239   3.5063
-#>   -1.9313  12.5361   7.1592   1.6826   1.1412  16.7364  -5.3221   0.5639
-#>   15.9264  12.0776  -3.7066  18.7699   9.2437  -3.5412  -1.8549   1.2673
-#>  -12.8781   7.2273   0.9743  14.6515   2.3926   0.9963  -7.2469  -6.9089
-#>   10.2803 -10.6271 -10.5320  -2.2252   6.0630  -1.4732  -3.5993   7.3931
-#>   14.1778   5.7967   7.9909   6.9608   8.6100  -7.6136  -8.2632  -0.3995
+#> Columns 17 to 24  -4.2065   9.2939   3.5892   7.8959  -4.8402  12.8296  -9.3807  11.1112
+#>  -11.2114  -3.0562   5.2946  -2.1114  -2.7639 -12.0290   5.9917  -1.6720
+#>    0.3616   2.4872  -3.7936  13.4046   0.8272  -2.2178  -2.6375   6.0133
+#>    5.5673  -1.4790   4.5947  -7.7102   4.4746   5.0750   2.9876  -7.6257
+#>   -4.3234  10.5083  -3.2607   4.8954  13.0091  -9.5953  -6.3853   3.4346
+#>    1.1920   0.4560  -9.2309  -2.2875  11.4585   0.6218 -10.3348   6.7686
+#>    0.2741  -2.5543   2.9223   3.5489  -6.3500  -9.1605   7.2221  -5.1253
+#>   10.9131  -2.5585  -0.8123  13.5260   9.2868  -5.6773  -5.9908  -9.1926
+#>    3.1580   0.9885  -2.5142 -11.2389  -3.0914   5.3502  12.6157  -6.9490
+#>   -6.9435   0.3712 -16.0947  -2.5742   1.4860  -0.7407  -3.4506  -0.6165
+#>   -5.0914  -5.8298   2.8393 -15.8313  12.4452  18.8915   2.8618 -15.2937
+#>    9.0411  -0.7984 -10.5758  -1.1805   9.5549  -4.1774   4.9280  -6.5197
+#>   -4.3194  -0.6405  -8.7203   0.4725  -9.4539  -5.0604  -4.3800   3.2696
+#>  -11.9754  -4.5121  -1.7260  -6.6479   7.9674  -6.2285  -6.3391  -3.0738
+#>   15.3515   4.3867 -11.9714  -1.5730   7.3768  14.4843 -14.3893  -8.4822
+#>    2.1833   8.3083   4.9578   2.8695  -8.2362   2.0172   4.1385  -0.6822
+#>    7.1028  -2.0772  -0.5397  -4.9920   0.3588  -0.5026   3.9092   2.7471
+#>    8.2071   1.6106 -10.6276   4.8144   1.1925  -2.6815 -13.0539  -3.8571
+#>   -5.9070   4.4068  -5.1588  -3.1788   4.0078   3.1869   2.6544  -7.2323
+#>  -16.9996   3.6218  -0.9238  -5.0546  -6.4982   8.1155  -0.6223  10.5490
+#>  -12.0021  -6.8792   1.1253  -4.1876  -4.9893 -12.1840  13.9612  -3.3745
+#>   13.8119   2.7478   3.3362  -5.4128  -4.2197   3.0150  -4.6457  10.8798
+#>    4.0486  -0.7900  -7.0948   1.7948  -1.7368   8.7998  -0.3975 -11.3148
+#>    2.0332  10.2583  -0.6361   8.6626   5.6039  -1.3762   8.0721  -3.0917
+#>   -2.7974   2.1440   5.0882 -14.1731  -5.8871   6.0872  -6.9159   0.6201
+#>   -9.6967  -2.0377   3.2732   4.7402  -4.2628  -2.1743  -5.7621   1.0514
+#>    3.9071   3.6333   0.4996   6.7331   5.9931  -1.8624 -15.1602  11.3564
+#>    0.9549  -2.2777  -9.9685  -1.9309   8.2334  10.5081 -13.4993  10.0008
+#>   -0.8553  -5.8342   9.6803  -8.2090   3.7659   3.4646   2.6482  -1.8601
+#>    5.2113  -4.0875  -2.1884   8.5691  -0.6622   3.0998  -1.8729  -4.7714
+#>   -6.1489  -0.7365  -2.4212   7.1418   3.8574  -0.3420  -2.1447  -0.3833
+#>    6.6469 -18.9635  -3.2369  -9.7534   1.0872   1.5693  -6.2348   7.0436
+#>   11.0446  -5.3982   5.3690   1.9652   2.7334   7.3781  -4.9787  -2.4262
 #> 
-#> Columns 25 to 32  -4.9114  -6.5843   7.0307  -4.9146  -3.4639   2.0096   8.6154 -10.3412
-#>   -4.9881 -16.5373   0.8815 -14.8672  -3.2208   0.1541   8.4281  -1.7680
-#>    1.6603  -3.2193   0.9856   0.7298   3.6267 -19.4214   7.9226  -6.4101
-#>    0.8370  -3.7783  -5.5143   7.5889  -0.3336  -3.7922   0.4118   7.4096
-#>    9.0980   0.0702  -6.6039   0.2466   3.8681  -4.9208   5.6830   3.2396
-#>   -9.4309   0.0826 -21.4884 -13.7381  -2.1915  -3.1371   6.9177   1.3061
-#>    3.6304   3.2765   4.7168   4.3914   3.3643  -3.9210  -5.0385   9.1815
-#>    4.5755  -4.3560  -1.8081  -0.4241   7.7188  -4.3440   3.7476  -0.3437
-#>    2.5515   4.0793   0.5688 -14.8879   1.5542  -3.5263  -1.1997  -0.4273
-#>    1.3687  -4.1839  -7.1615  -0.2557   6.2164   2.9262  11.1157   1.1417
-#>    3.3176  -6.9254  -1.2322  -7.1140   4.5201 -15.2834  -0.9299  -4.2925
-#>   -4.0570   3.7860  -1.8286  -5.3160   5.1573   9.9160  -7.1879   1.8805
-#>  -10.5045  -1.4338  -2.9479   6.3257  11.2079  -2.9802   1.2451  -5.4419
-#>    8.1824   5.0542   4.5832   2.3177  -7.6769  18.4706   3.9747   1.6845
-#>    4.4435  -1.0758  11.1831  -0.3116   4.0288 -13.3231  11.6478  -3.9394
-#>    0.9642  -0.9493  -6.7294   4.8991 -14.8633  -0.9488   9.4906   4.4281
-#>   -6.7962   0.2925   1.0403   8.6866   0.5212  -5.6361   0.6018   9.0195
-#>    6.7747  -2.7812  -1.8297   2.4707  -7.2375   5.6537   1.1593   5.4003
-#>   -3.9716   3.7339  -5.5811   0.7016  14.3519  -5.9226   2.1348   1.1818
-#>   -3.7538  -4.7981   2.3779   2.0818   4.0368   1.3383  10.4708 -10.9149
-#>   -2.3645   2.2764   7.5331   3.0271   4.3130  -6.0624   3.7986  -1.1034
-#>    6.1696  -3.0635  -5.1319   4.8253   0.4628   2.3354  -4.9309  -5.5517
-#>   -3.8062   0.3722   3.1567  12.7407 -13.9317  -2.3135  -2.0125  -4.3503
-#>  -11.3840   5.4055  -3.2454  -4.5566   6.4311   1.2773  12.4571  -1.6851
-#>   -3.2685   7.4117  20.0026   1.3566  -8.3665  -6.4926  -3.3746   0.9071
-#>    4.8057   2.7966   7.6597  14.1099  -1.2854  -0.8436   0.8242  -3.6776
-#>  -11.2720   1.0225 -14.1237   4.8509   1.6023  -5.1638   4.0037  15.5045
-#>    3.3805  14.0331  -2.4699   1.1550   9.8972   6.2443  -3.5412   3.7678
-#>   11.9026   2.1099  17.6332  -3.5072  -6.8630  -0.3439   9.0685  -6.9890
-#>   -4.7753  -5.2764   4.8345  -8.9518   8.0896   8.2099  -5.8078  -4.4894
-#>    4.6541   9.9653  13.8764   5.6798   0.0742   6.0066  -5.7651   4.0624
-#>  -12.9363  -4.7503  -6.8934 -10.8562  -9.2596   6.5418   2.2233  -3.3519
-#>   -1.8764  -4.3724  -5.7903 -28.4369  -0.7876   9.9454  -1.7579  -5.5863
+#> Columns 25 to 32  -0.4132  -3.0438  -2.5570  10.7310  10.3742   2.7517  -9.0624   5.3794
+#>   -5.0913  -3.7807   1.4220   4.2462   1.2555  -8.1774   1.0138  -4.4225
+#>   -9.0722  -7.4013   0.4344   3.6369   3.0903   0.8511   5.4934  -3.8948
+#>   -0.7730  -2.0138   1.7253   1.1636  -6.0087  -5.1359   0.8172  -3.6832
+#>    1.0456   0.9586   6.2671  12.8951   8.2181  15.7230  -1.0166  -2.2864
+#>   11.6797   1.9767  -7.5161 -16.2880  -6.1565  -2.3005  -6.0404   9.5547
+#>    2.7918  -8.5076   1.0395  -0.0702   6.0480   9.1555   2.8280   7.2094
+#>   -2.3676   1.2288   7.8325  -4.2620  -6.0670  -5.8802   0.1739  -9.9245
+#>  -11.1365   3.1781  -3.0699   2.6420  -5.3172 -11.4722  12.2875  -7.3055
+#>    9.0573   2.6568  -0.5376  -3.5754 -12.3658  -4.9787  -9.3663 -10.2216
+#>  -10.4300  13.0564 -11.5281  -9.5129 -14.5238  -8.5758   5.3888  -8.5080
+#>   -1.9414   8.4734  -1.9602   3.2158   1.4234  -3.2866   5.3142   0.9027
+#>    7.7587   0.4377   0.2226 -12.1352  -7.4375 -10.9733 -10.0341 -10.5191
+#>    7.9724   7.3023   2.8092  -4.9314  -2.2222 -10.6973 -10.8776   3.6404
+#>   15.0376  -0.4391   2.2997  11.7165   7.5794  -2.4340  11.8190   3.9973
+#>   -4.4037   0.2518  -4.4444  -7.6718  -6.4464  -8.3505  10.6614  -2.7018
+#>   12.6746  -9.1315  -5.6649   5.6829  16.9576  -1.7525  -0.2505   9.0063
+#>   13.4992   1.1240   5.1090  -0.6208  -8.2535   8.1563  -7.0806  10.3851
+#>    3.9611  -2.2554  -1.6692 -15.2887  -9.6588  -2.6689  -5.5181  -6.8727
+#>   -7.1649  -7.8517   0.4424   0.5538  -8.3975  -1.6191  -4.9514  -4.5172
+#>    3.1934  -4.5653  -0.7603  -8.0631   1.5094 -16.9029  -8.7604  -5.2588
+#>   -0.0315   3.7042  -3.8648  -1.9195  15.5775   7.2412   9.6295  -0.0544
+#>   -5.8044   6.8197  -0.5106   7.3986  -3.3084   1.3203   2.6686  14.9541
+#>   -1.4753  -4.2442   7.0028  10.8232  13.0157  11.0281  -2.1446  -5.7240
+#>   -6.2500   5.5746  -2.9423  -6.3565  -0.9297  14.4385   2.4830  -0.9576
+#>    1.9308  -2.7205   3.7208   0.0194  -7.1378  -2.8584  -5.6605  -0.5520
+#>    6.1293 -10.4371   8.7476 -11.5443  -2.4726   0.8235 -16.1640  -6.9606
+#>   -2.9057   0.7304  -8.7958   0.9712 -12.5550  -4.7932  -1.8880  -1.8263
+#>    0.9130  -1.5773   7.2767   1.3934  12.3891   8.7260   6.2282  -1.4019
+#>   -5.1485   6.5462   3.3388  -7.5063  -4.3020 -14.0229  -4.9825   3.7668
+#>    2.2414  -3.7179  -3.4286  -1.9659   8.6955  -1.3795   5.7881   1.5206
+#>   -5.4745   4.0108  -0.4451  -7.8754 -15.1264  -9.4654  11.8460 -15.9966
+#>  -10.7536   5.9594   5.6992  -0.8804   7.0413  20.7508   1.7718  -7.0533
 #> 
-#> Columns 33 to 40  -7.9782  -0.1174   9.2081  -0.5138   6.2838   3.0893  -4.8058 -18.1583
-#>   -4.4992   0.1592  -4.9528  -6.1422  -2.1497  11.9840  -6.4663 -16.9782
-#>   14.0196  -7.9512   4.7963  -6.6332  -5.8331   3.4767   2.0578  12.3614
-#>   -3.7671  -4.8483   3.5894   9.3979  -6.9846  -5.1328  16.0085  -1.0437
-#>    8.4310  -3.9356  -4.7848  -3.9968  -1.5736  -8.0616   3.9807  -3.8722
-#>    7.6377   6.1733 -11.0966  -2.6771  -0.5499  -6.9654   1.3978  -3.7241
-#>    1.2850   6.9083   3.2346  -6.3490  -5.6001   3.6724  -7.4045  -5.5348
-#>   -2.1955  -0.1386  -3.9806  -3.2931  -5.8467  -5.0261  -4.8924  -4.6912
-#>    9.8305  12.6788  -7.4050  -5.2257  -9.9318  -1.4535   0.6971   3.5317
-#>    5.0632  -4.2288   2.2580  -1.8263  11.1705   8.9084  -5.8374  -6.0012
-#>    8.1201  -5.6505   0.6060  -6.7071  -0.3699  -6.9170  -7.7779   6.6524
-#>   -4.3955   8.7702  -3.0082   6.5812  -2.8098   0.0620 -10.2726   0.1328
-#>   -0.2795  -6.3769  -0.1139  14.9688  -2.7549  -1.6537  -5.3231   3.2797
-#>   -5.0211  -0.9620   3.7316  -4.8731  -1.6097  -6.1001   9.7407   0.7674
-#>   -2.1829   4.2108  -0.9183   4.6779  -8.4763  -4.7964  -2.1435   1.3281
-#>   -5.8912  -4.4834  -3.0533   2.5049  -3.2299  -9.6439  -1.0027  -6.7984
-#>   -9.0744  -9.8956   9.6744  -9.2164  -2.7103   1.2476   7.4178  -0.1035
-#>   -5.2211  -5.5057  -3.2928  11.1094 -12.1035   4.6906   3.1275   4.5405
-#>    3.7232  -2.3739  -8.5902   0.9798  -7.2881  -9.8486 -16.0251   5.2795
-#>   -9.9477   8.4432  -1.7972   5.5190  -6.1098  -1.9647  -7.1623  -4.2358
-#>    8.4954  12.9459 -10.8776  -2.0965  -1.3022   6.1228  -4.4629  14.5403
-#>   -0.2208  -9.9050   4.5437  -6.8559  -9.4238  -9.3158  -0.3664  -1.7320
-#>    7.6426  -1.2814   7.7386  11.7444   1.8633   2.9149   6.0874   0.1475
-#>    8.4882 -16.9385   8.4649  11.7110   9.7894   4.5368  -6.7920   7.8080
-#>    2.8875   1.8071   2.7394  -8.7541   5.8108   0.4024   7.5638  15.7222
-#>   -6.2697   8.1250   4.2751   1.8647  -5.8885   1.8181   5.5904  -1.6848
-#>   -4.3160   3.0767  -5.3289  -8.2283  -2.4016  -2.6025   4.7989  -1.3792
-#>    0.9026  -8.5282   7.1421   8.1328   1.1274   0.1584   5.5324  -8.1130
-#>    2.6120  -2.0762  -6.0001  -9.1572  -8.5995  -4.7389  -6.9053  10.6624
-#>   -0.1218   1.0712   0.8658  -5.4335  12.2737   6.7308 -10.1385 -27.2439
-#>    2.2173   4.4802   9.8104  -1.4112 -12.2861  10.1242  -5.8051  -2.2389
-#>    2.0607  -7.1254   3.2760  10.5545   4.6498  -2.4794   7.7428   0.6788
-#>    0.9462   1.8375   0.8980   3.4936   8.3570   7.2085  -0.4131 -14.5003
+#> Columns 33 to 40  12.4281 -16.4712   2.3275   7.1651 -14.1225  -6.8937  -0.7977  -7.3150
+#>   -8.1894  -4.2377  -7.8425  13.4008   8.1175  -2.2782  -1.2210  -5.8291
+#>   -4.0410  -6.1736   0.4422  -8.9344   1.6366  -5.9319  -0.1961   6.4251
+#>    2.8202   5.7388  -1.8082  13.6126  -2.7894  -1.2446   1.0118  -3.5126
+#>    9.7174 -13.4668   5.2051 -13.4196  -1.7748  -3.1553   2.4221  -2.7922
+#>   -8.5377 -10.5196  19.9129  -0.6344   5.1032  -2.3426  -6.7487  19.8810
+#>   -1.6694   9.7614   3.0036   2.7760  14.5643  14.3558  -4.5513   7.7336
+#>   -0.0779  -4.0573 -14.0203   3.1155   3.2607   2.8354   1.1164  -0.4351
+#>   -0.3682  -0.1078  -5.3947   1.0590 -11.2919  -4.5850   1.4685  -9.0223
+#>    5.3669  -8.4064   2.9503  -3.3809  -9.6942   1.1302  -3.7532  -0.8993
+#>   15.7925   3.8990   0.5087   6.7966 -16.0462  -4.7659  -3.3954 -18.1975
+#>    4.1802  -5.2493  -5.8310   7.9577   0.9913  -1.0684   0.5915   0.6320
+#>  -15.9878  12.7780 -15.0252   2.7532  -4.3315 -14.0943   6.7232   8.2058
+#>    0.5793  -4.6638 -11.7661  -3.6801  -3.2603 -13.3315   1.2355 -11.0164
+#>   -7.1668   5.4784 -17.4055  -0.4866 -11.8200   1.4205   0.4033   4.5948
+#>   -4.7855  -1.4842  -0.0606   4.0005  -3.1935   2.3276   5.3923  -1.4242
+#>    5.3504   6.0079   3.3508   2.4204  -6.4490   5.5638   0.5387  -2.7749
+#>   -8.6563  -7.1240   6.7491   2.8477  -2.7816   2.5721  -3.5453  -0.1386
+#>    3.1950  -6.7896   2.6337  12.2708   1.5116  -3.2596  -3.9244   4.4117
+#>  -11.3447  -3.6547   5.0978  -8.3254  -5.2496  -1.4632 -11.6462   4.9283
+#>    2.6258  -4.9558  -9.9812   5.4392   6.3579 -11.2515  -3.3764  -7.2731
+#>    1.8118  -0.6791   2.3409   6.1242   0.0062   2.1666   6.3161  18.7852
+#>   -4.1806   5.5259   0.5278  -4.9273  12.1832   7.7378   2.1827   2.4047
+#>   15.1877  -9.5137  -4.8248   4.3539  -4.2380  -4.8231  -5.6607   2.9108
+#>    8.5820   8.7402   4.9946  -1.3527  16.4840   8.5733   7.3263   1.2356
+#>   -5.0214 -14.1126   3.0400   4.4395  -1.4161  -0.6688  -3.3167  -3.9808
+#>   -0.1068  -4.4285   8.3274  -3.3712  -0.3419  -5.2243  -5.1254  10.9061
+#>    3.7933  -5.5490   8.4841   5.1688  -4.2200  10.7959   1.9520   3.0896
+#>    6.0787   0.2255  -3.6709  -4.6714  -3.0751  -7.5436  -0.8438 -11.5510
+#>   -9.3023  -4.0976  -8.9739   9.4679  -6.1308 -15.6199  -4.1747   1.6300
+#>  -11.3287  13.4706 -13.4466 -12.6591  -9.9158  -3.1640   1.2798   2.6026
+#>    1.3376   2.5337   5.7312  -5.4022  -4.4783  -1.6983   0.2834  -2.4832
+#>   22.3789   7.8134 -11.6687   2.9608  -1.2908  10.1047   1.5613   4.2445
 #> 
-#> Columns 41 to 48   2.9347  -6.5413  -2.7144   7.3507  10.2499  19.5012  11.9704   4.0226
-#>    7.6969  -0.3759  -0.9515  14.8465  -0.4770  11.8782   5.7558  -0.1092
-#>    0.5673  10.7646  -7.6328  -8.9557  -3.4129   4.7266  -8.4608  -7.6704
-#>   12.3682   7.7268  -4.8314 -16.3501  -8.0761  13.1647  14.0554  -3.6347
-#>    2.5149   8.1191   7.5053  -0.6208  -6.2784   5.1073  10.8315  -0.1904
-#>   -7.1793   8.9993  -1.1687   5.0640  10.9705  17.1949  10.9811  15.4482
-#>   11.3650 -10.3334  -1.9698  -1.5696   8.0683   0.7480  -4.9720  -0.0693
-#>   12.6532   5.2129   1.0802   4.8707  -3.9001 -10.7755  -1.1094   5.5256
-#>  -14.9249   4.7129  12.2226  -3.0380  -5.4822   3.5384   4.7636  -6.5954
-#>   -2.6631  -4.3724  -6.3074   2.9076   8.8519  -2.7298  14.7076   0.3063
-#>    8.0633  -0.6514   7.6628   2.0957   8.4992  -3.0437  -8.3949   3.6666
-#>   -4.4896   3.1980   7.0277   4.3710  -2.4215  -6.1094  -0.3414   0.4719
-#>    0.2284   4.9868 -11.0636   3.0958   9.8737   9.6359  -6.2769  -3.0681
-#>    6.8440 -10.4315  -2.9086  -9.5762  -5.9412  16.2668   4.3936 -12.5563
-#>   -2.7370  14.5742 -10.9801   4.9551   7.1912   6.3545   7.9185  -2.8689
-#>   -0.6319   5.5352  -1.6038  -6.4819  16.2483   5.5396   7.8797   5.5145
-#>    0.2896   1.5684   4.5287 -11.5754   8.5899   3.2760  10.2598  -2.6193
-#>    3.8999   3.8732  -5.7280  -6.9490   2.7778   4.4373   2.9833   5.8405
-#>   -3.0089   1.5390   0.4579  12.6652  14.0262   4.2430 -16.2028   6.4233
-#>   -9.0877   3.0460   0.8589   5.1432   6.2836  -4.5454   6.0026  -2.2429
-#>    3.9197  -0.0397   0.7961  -1.7077  -6.5249 -14.8881  -7.8815   0.5807
-#>   -0.6347  14.0922   3.1064  -5.3681   0.8794  -1.9129  -5.5581   0.0186
-#>  -17.1356  -7.0935  -0.7364   4.1804  11.8833  -3.0501  -9.5860  -0.3069
-#>  -15.5952  -0.5495 -18.1164   5.3012  11.3696  -3.1717   2.4292  13.4601
-#>   -9.2839  -1.0520   5.2714  -3.2606  -0.7096  -7.9491  -6.9085   2.7991
-#>    6.4440   2.0277  -9.0858  -7.5276  14.1867  -2.4964   5.7862  -4.2194
-#>    6.3172   0.8966  -1.3876 -12.1631  14.6530  12.8441   6.9321   6.2326
-#>    0.7124 -12.4130  -8.9603  -6.4906  19.0114  13.5405  -3.5782  -0.8632
-#>   -1.8861   1.0826  -0.0976   1.6771  -4.5812   5.2838   6.5466   4.5431
-#>    7.3507  -2.6981   5.3992   2.5461  17.2578   5.4251  -1.5992  -7.3955
-#>    7.2382  -4.5121  -0.9355  -7.8539   0.3607   1.1957  -6.9880   0.4112
-#>  -10.8835  -1.8415  -7.2738   5.6169  -6.4167   2.0098   4.2866  -6.7110
-#>   -9.0879  -6.7933  -0.8936  10.1913   3.3567  10.5202  13.4314   6.9530
+#> Columns 41 to 48  -3.6568  -3.3038   4.6883 -13.1722  -3.2869   9.3041   1.2363  -3.9984
+#>    0.8554  -4.4779   7.5493   8.4915   2.5667  -7.9665   3.0118   1.1276
+#>   -0.3631   5.5444  -7.5559  -0.1196   5.7321  -1.9655   1.3066  -4.9991
+#>    7.4106  -0.3171  12.6276  -2.4802  -2.6901   5.2787  -2.9683   6.6626
+#>    8.7507 -10.2061  -8.7086   2.3781  -3.5093 -13.1991  -1.3439  -9.0805
+#>   -2.2748   4.5691   7.7179   0.3833  -2.1670  -4.9527   2.4995   8.1217
+#>    4.9719   0.7007   0.7485  -1.4723  -3.9865   1.8007   4.1444  -3.0241
+#>    2.2218 -12.7739   6.5682   4.2967   6.6679  -5.1592  -0.0165  -1.7690
+#>    5.7675   1.2612  -5.4197   4.4680   5.0405   2.5639  -3.1297   8.7898
+#>    2.4283  -6.6306   0.8696  -1.8889   0.4649   0.0847  -3.0097   5.6809
+#>   -2.3397  13.2972   7.0691  -5.0293  -3.4331   8.3509  -1.2005  14.6979
+#>    2.9298   7.0707   0.1938   5.9990  -3.8105   1.4523   0.1846  -8.9589
+#>   -7.6527  -5.7518  -6.7757  -0.4943   0.1633  10.4889  10.6487  -1.2751
+#>   -6.0931 -13.0421 -10.5523   0.7996  -0.7176   1.9356   3.6764  -1.4679
+#>    8.8261  -4.0749  -3.3512   2.5338   9.6804   5.6512  -3.5942 -12.3289
+#>   -3.1321   7.1272  -3.3435   5.3821  10.7984   1.5269   0.4901  -5.6708
+#>   -0.4924  -1.5028   7.2955  -4.7937  -4.7963   5.2281  -6.2452   2.7743
+#>   16.5464  -8.4592   4.9584  -4.8191   7.7133  -0.3130  -6.9707   5.1337
+#>    6.2497  -2.3872   6.9805   1.3411   6.0068  -4.6236   9.8051   3.4003
+#>   -0.3205   2.0104  -3.0305  -8.0887  -0.4852   2.3644   2.2273   8.0652
+#>  -11.6916  -3.3476   3.0293   0.6824  -1.8505  -0.5615  16.7081  -2.7346
+#>    1.6570  13.6416   0.2709  -2.0993  -8.9250   1.5405  -1.2260 -12.0698
+#>    1.4101  11.9977  -0.9030   1.8829   1.4618   0.2833  -6.6250  -1.9540
+#>   13.8602  -7.0519  -1.3545 -10.9911  -3.3629  -5.0637   8.7662 -10.0588
+#>   14.4918  12.5008  -3.0275   5.4626   1.6309 -11.4781  -1.3635   2.9841
+#>   -2.2965   2.6517  11.1042  -2.1207  -2.0783  -4.8780  -3.0670   4.7831
+#>    0.2110  -6.2690   6.2956  -2.6013   6.8506  -4.1224  10.2412   7.5049
+#>    6.8159   3.1378  -3.7380  -6.0232  -5.6573   1.8460  -8.3177  15.4529
+#>    1.1127   1.1435   1.4419 -12.4564  -1.5157  -5.8976  -0.4586  -5.0206
+#>   -4.2031   1.7970  -0.0877   3.3972   2.1657   9.7306   2.1279   2.2065
+#>  -11.3591 -10.0992  -4.2825  -2.7245  -1.9645  -1.1735   1.4630  -4.6376
+#>   -3.1599  -4.4585  -4.8284  -1.4836   8.4648  -2.7482  -0.4739   3.9991
+#>    6.9959  13.9255   1.2966  -5.6168   0.6488  -8.9664  -0.7662   0.7772
 #> [ CPUFloatType{20,33,48} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,15 +227,15 @@
     
    Conv2d
    
     
 
-    
    torch_conv2d(
-  input,
-  weight,
-  bias = list(),
-  stride = 1L,
-  padding = 0L,
-  dilation = 1L,
-  groups = 1L
-)
    
+    
    torch_conv2d(
+  input,
+  weight,
+  bias = list(),
+  stride = 1L,
+  padding = 0L,
+  dilation = 1L,
+  groups = 1L
+)
    
 
     
    Arguments
    
     
    
 
 
     
    
@@ -248,69 +280,69 @@ planes.
    See nn_conv2d() for details and output shape.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
 # With square kernels and equal stride
-filters = torch_randn(c(8,4,3,3))
-inputs = torch_randn(c(1,4,5,5))
-nnf_conv2d(inputs, filters, padding=1)
-}
+filters = torch_randn(c(8,4,3,3))
+inputs = torch_randn(c(1,4,5,5))
+nnf_conv2d(inputs, filters, padding=1)
+}
 
    #> torch_tensor 
 #> (1,1,.,.) = 
-#>   -3.5486  -7.1217  -3.2540  -5.9799   1.1629
-#>   -4.3378   2.2990 -16.4566   9.0228  -5.5777
-#>   -2.6722  -9.2867   4.0511  -4.4372  10.4134
-#>   -4.7778  -0.7326  -1.6019  12.0401  -3.1893
-#>    0.2123  -0.5914   1.1211   3.0260   6.8859
+#>  -1.2037  2.8533  3.0966  2.7602  7.2992
+#>   6.6099  0.8040  2.1820 -2.2556  1.7042
+#>   8.9937 -2.8627  7.9067  2.2703 -0.2219
+#>  -1.0960 -2.0544  9.6476 -4.1084 -1.7429
+#>   2.2151 -0.6322 -10.7968 -2.1303  0.9502
 #> 
 #> (1,2,.,.) = 
-#>   -3.5645  -0.6611   1.0267   0.0105  -2.2313
-#>   -4.8099  10.2621   0.1669   0.9807  -2.2823
-#>    0.7857   0.4305   0.4022   7.5398   0.2195
-#>   -1.5292   7.5032  10.2237  -4.5421  -3.0606
-#>    4.4675   1.8375   9.2117  -1.7632  -6.9053
+#>   -0.5524 -10.7146  11.9610  -1.8760   2.8073
+#>   -1.4550   5.9693   1.4488   0.5483   0.5193
+#>    4.7319  14.7168  -1.9500   3.4680  -3.1870
+#>    2.3077  -8.0817   5.5639   2.1393  -4.4990
+#>   -0.0674  -0.8898  -4.0815  -8.3260   0.0084
 #> 
 #> (1,3,.,.) = 
-#>   -4.0560  -3.3721  -3.9417   1.2809   1.6676
-#>    3.7261   1.6556  -7.7465  -0.6448   3.6757
-#>   -1.8130  -5.0859  -3.2322   4.3222   5.7618
-#>   -2.3173  -7.4043   2.5139  15.0216   0.5374
-#>   -1.4209  -1.0366   0.8718   4.0616   3.1472
+#>  -6.0152 -1.1616  6.4675 -10.9283 -1.7122
+#>   2.0931 -3.9504 -5.1826 -5.1925 -2.1788
+#>  -1.9279 -5.6841  3.6513 -4.5984  1.9415
+#>  -2.6984  2.3140  5.6889  3.2366 -1.6959
+#>  -1.2875 -5.5225  2.4667  1.2794  0.6366
 #> 
 #> (1,4,.,.) = 
-#>  -9.5668  3.2701  6.5622  9.6684  2.5335
-#>  -4.4349  3.4779 -1.2737  2.9751  1.7985
-#>   2.6595  7.2363  4.4882  0.3747  1.3252
-#>   5.3966 -4.0692 -2.2576  9.7051 -0.5021
-#>  -0.2109  4.6618  0.3269 -6.7229 -2.3171
+#>   -1.3725 -11.9432   6.6814  -2.0626 -12.8413
+#>    2.7790  10.4186  16.5919   3.1043   1.6991
+#>   -2.1525  -0.6591  -7.1645  -2.4902  -2.3716
+#>    4.5753  -0.9017  -5.4827   3.2854   1.9315
+#>    0.5009   4.8452   3.5895  -1.3763  -5.0205
 #> 
 #> (1,5,.,.) = 
-#>  -6.8665 -0.7471  3.0223  5.9422  0.0773
-#>   0.1951  6.6746  4.5183  0.2309 -3.8129
-#>   4.9832  2.5449 -5.4966  3.8797  3.9093
-#>  -1.2040 -1.1130  7.1904  3.6236  3.7172
-#>  -1.3379 -5.7016  6.7872  7.4608  4.0352
+#>   -2.4641  -4.1384   0.0252   5.7390  -3.1903
+#>  -11.9943  -5.2914   1.6707  -5.6204  -5.7324
+#>   11.1295   6.3085  -4.1165   0.6665   6.0104
+#>   -2.1664  -4.9256  -2.1528   6.1870   8.8261
+#>   -2.8591  -4.4202   0.0518   1.5891   0.1274
 #> 
 #> (1,6,.,.) = 
-#>   -3.9338   0.3756  -3.0094  -0.7198   3.5040
-#>    0.3583  -1.8715  -4.4996  11.1578  11.3811
-#>   -7.6832  -2.1231  13.6712   7.9567   4.1675
-#>    7.7207   2.5612   9.3426   5.4903  -9.0039
-#>   -1.2806   1.9558   6.0637  -3.7703   2.7139
+#>    7.7084   7.0540  -6.6758   6.2570   1.1702
+#>    6.1343  11.9692  -0.8624   0.9134   7.8712
+#>   -4.3698 -10.2884  -2.7717  -5.6516   0.0050
+#>    4.3435   2.9247   0.0498 -12.5641  -1.2759
+#>    3.1506   7.0418  11.2416   3.0961   1.4600
 #> 
 #> (1,7,.,.) = 
-#>   -3.5015  -2.8558  -3.0872   2.0101   8.1843
-#>   -2.7114  -4.0357   0.2954   1.3045   2.9680
-#>   -0.0320   0.3198  -2.8986  -0.1796  -4.9480
-#>    6.8524  -9.7840  11.4722   0.6094  -8.8633
-#>   -2.3049  -2.9029  -4.5625  -5.2812  -1.8420
+#>    8.8728 -10.0021  -1.0044   7.5667   2.3943
+#>   -5.8614   9.1914 -11.8036   1.8316   7.5042
+#>   -6.9303  11.0166  -4.5717   3.7854   4.8000
+#>    5.1606  -8.6169   1.7790  10.5232  -1.9409
+#>   -0.5102  -2.8458   3.5851  -7.8170  -1.8686
 #> 
 #> (1,8,.,.) = 
-#>    4.1203  -6.8209  -7.9792   0.5355  -6.1896
-#>    8.1814 -14.2806   2.7375 -18.4326  10.9079
-#>   -0.8891  -8.0756   0.7869  10.5435   0.2842
-#>  -11.1754   5.4648   9.2422   8.0793  -3.8445
-#>    3.1076   1.0749  -4.5827   4.5922  -1.6976
+#>  -1.2384  1.3963 -4.1241  7.3818 -3.9786
+#>  -1.8505  1.5575 -2.4711  1.1911 -7.8127
+#>  -4.5371  2.7213 -7.5125 -5.1210 -6.9012
+#>   3.0100 -9.5409 -0.6372 -9.0458  0.6989
+#>  -9.1543  4.1365 -7.5186  2.5672  7.7889
 #> [ CPUFloatType{1,8,5,5} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,15 +227,15 @@
     
    Conv3d
    
     
 
-    
    torch_conv3d(
-  input,
-  weight,
-  bias = list(),
-  stride = 1L,
-  padding = 0L,
-  dilation = 1L,
-  groups = 1L
-)
    
+    
    torch_conv3d(
+  input,
+  weight,
+  bias = list(),
+  stride = 1L,
+  padding = 0L,
+  dilation = 1L,
+  groups = 1L
+)
    
 
     
    Arguments
    
     
    
 
 
     
    
@@ -248,12 +280,12 @@ planes.
    See nn_conv3d() for details and output shape.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
 # filters = torch_randn(c(33, 16, 3, 3, 3))
 # inputs = torch_randn(c(20, 16, 50, 10, 20))
 # nnf_conv3d(inputs, filters)
-}
+}
 
    #> NULL
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Conv_tbc
    
     
 
-    
    torch_conv_tbc(self, weight, bias, pad = 0L)
    
+    
    torch_conv_tbc(self, weight, bias, pad = 0L)
    
 
     
    Arguments
    
     
    
 
 
     
    
diff --git a/reference/torch_conv_transpose1d.html b/reference/torch_conv_transpose1d.html
index 5c7b195bc..4228c8a57 100644
--- a/reference/torch_conv_transpose1d.html
+++ b/reference/torch_conv_transpose1d.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
         torch
-        0.0.3
+        0.1.0
       
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
  • Datasets
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
-
  • Examples
    • 
+
  • 
+  
+  
+
  • 
   Reference
 
    • 
@@ -195,16 +227,16 @@
     
    Conv_transpose1d
    
 
-    
    torch_conv_transpose1d(
-  input,
-  weight,
-  bias = list(),
-  stride = 1L,
-  padding = 0L,
-  output_padding = 0L,
-  groups = 1L,
-  dilation = 1L
-)
    
+    
    torch_conv_transpose1d(
+  input,
+  weight,
+  bias = list(),
+  stride = 1L,
+  padding = 0L,
+  output_padding = 0L,
+  groups = 1L,
+  dilation = 1L
+)
    Arguments
    
 
       
     
     
     
   
 
 
     
 
     
     
    
@@ -253,5064 +285,4860 @@ composed of several input planes, sometimes also called "deconvolution".
    See nn_conv_transpose1d()  for details and output shape.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-inputs = torch_randn(c(20, 16, 50))
-weights = torch_randn(c(16, 33, 5))
-nnf_conv_transpose1d(inputs, weights)
-}
+inputs = torch_randn(c(20, 16, 50))
+weights = torch_randn(c(16, 33, 5))
+nnf_conv_transpose1d(inputs, weights)
+}
 
    #> torch_tensor 
 #> (1,.,.) = 
-#>  Columns 1 to 8  -0.6004   4.0901   4.1162   0.6192   6.3756  12.6002  -2.0543  -8.9684
-#>    2.5562   5.4577  -6.9559   1.3808   0.9724  -2.1635   0.0468  -9.2402
-#>    2.1054  -3.9812   4.3883 -11.3571  -7.2336  -7.9630  -7.3477  -3.1796
-#>    3.4706  -3.2964  -6.8210  -0.9201  -4.3900  -9.7930  -4.8746   6.7569
-#>   -4.4986  -1.9795   0.7281   3.7279   2.8359  -2.9289   4.9091   0.0196
-#>   -0.1566  -1.7666   6.7628  21.8539  13.1175   3.0355  11.8317 -10.9293
-#>    1.9233  -4.0127   5.4726  -2.8186 -15.2970 -14.9055   9.0864   9.7691
-#>   -6.2536  -1.2273   1.0028   2.6480  -3.2984   0.7050   0.2384  -0.4342
-#>   -0.7137  -1.2991  -1.9438  -0.9533  -0.0313   6.3989  -8.8965   1.5028
-#>    2.8515   1.2079   7.5720  11.2757   4.0208   4.0302 -18.4834   2.6042
-#>   -1.2608  -1.9830   0.4157   5.8398  -8.0424   2.8853   1.9586 -11.1629
-#>    0.9166   3.7020   6.3919   5.6656   8.5167  17.4386   9.7697  11.3048
-#>    3.7391   5.0856   3.8721   1.7100  -1.3962  -9.1984   3.3265  -3.9582
-#>   -1.6088   0.2460   0.5347  -2.3824  -9.6884  -0.5348  -6.0122  -0.1030
-#>   -0.6148  -1.2322   1.0224   6.9799  -1.8193  11.8577 -19.9366   2.6108
-#>    1.4129   6.4468  -1.6165  -3.0123 -14.8057  -1.4289  -9.7638  -1.8674
-#>   -2.9941  -9.5087   2.1096   5.6394  -6.3901  -4.2324  -3.9090 -13.8909
-#>   -0.4531   2.0755  -1.2331  -5.6545   8.4489  -3.9393  -8.1968   3.5906
-#>   -0.5256   3.3574  -1.5862   7.9468   9.7259   0.9548  15.2170  12.6929
-#>   -1.0922   2.8931  -0.3569   1.4262  -5.5169  -1.0022  13.1522   6.1245
-#>    4.8329   0.7218  -0.3335  -5.6755  14.5178  -8.6349   4.7855   4.8308
-#>    1.7461  -3.7079  -1.3584   4.6693  -4.8814   9.9969   0.9081   5.7656
-#>   -0.2636  -0.1443  -7.4224  -3.2104  -8.8714  -8.0212  -6.6852   0.9089
-#>    5.7567  -0.5389   7.3149  -8.6823 -13.8152  18.6386  15.1764   7.8335
-#>    0.3944   4.6797  -2.1249  -4.5136  -3.7679   4.9865  -0.3764   0.3817
-#>   -1.5968   2.2421  -3.5420 -13.0538   7.0071   4.8528   1.0440   4.9850
-#>    1.6772   0.7801   6.6048 -10.8922 -14.4722  11.5427  -2.8412   7.4468
-#>   -5.5516  -3.5058   2.0037  -2.6942   0.7408   0.6017  -1.2814  -1.5558
-#>    3.5480  -1.8899  -5.4336  -3.3593  -4.9892  -6.0104  -5.0009 -13.4594
-#>   -8.6330  -0.3369   9.3590  11.0104  -5.5114   8.3425  -6.3278  -5.8439
-#>    2.4533  -2.4106  -7.8900  11.0399 -10.0707   3.2837  13.9738   1.2788
-#>   -2.3656   5.0306   2.3163  -9.2069   8.0224  -4.3115   8.8182   8.4418
-#>    7.1396  -1.4040   2.8544  16.0946  -2.0559  -9.8389  10.0793  -3.5420
-#> 
-#> Columns 9 to 16  19.6296  -1.0571   2.2479   6.0396  17.4604   5.4292  -5.2999  -3.4805
-#>    4.4689   6.2760  -6.7538  10.4388  -5.3242  -4.4771  19.5997  -9.2312
-#>    7.3714   2.9589   3.8303  -3.8989  -1.2139  -7.2902 -12.3022   5.5554
-#>    1.1485  -3.1741 -14.2423  -4.7028  -7.3247   3.6348   0.5571  11.8129
-#>   -3.9914  -9.8326 -10.1469  11.5117  13.0456  -7.4854   8.4747  -4.2242
-#>   -8.1024  11.8125   9.0316   1.8900  19.6748   6.5998  -6.3706   1.9563
-#>   -4.3288  -4.2767   6.9220  -6.2718 -14.5755   6.9661  11.9982 -11.3933
-#>    3.6337  -0.4349  -2.0598  -2.9402  -2.3109  -3.6216  -9.9334  -6.8303
-#>    3.0969   8.0662  -1.9271   0.9911  11.9432 -13.8506  -0.3506  -8.2148
-#>   -0.3625 -10.1219  -3.2581  12.9066  -2.7811  -1.5516   1.4874   0.0285
-#>    4.2390  11.4472  14.2799  -4.3681  -1.2805  -7.7423  -7.4996  -9.5044
-#>    5.9248  17.1279   8.5384  12.4752   7.6657   4.4720   3.8107  18.1527
-#>   -6.5947  -2.0549   6.2950   1.4028 -10.9088  -7.4368  15.4439  -4.2578
-#>    8.6277   2.5786  -2.0515  -2.8812  -0.1932   2.6490   5.3511   2.3057
-#>   -5.8411  -6.6885   1.8910  -0.9538   5.1784  10.1539 -13.0269  13.5722
-#>  -11.6944   4.1298  -3.8437  -2.5096  -6.4828  -6.6850   7.6715   2.7347
-#>   -8.0584   8.5316   2.6845  -5.3957   2.3331  -4.4324   3.8076  -0.7175
-#>  -18.2143  11.8918  10.6286  11.9950  -1.4405  -5.5318   6.0004   2.2942
-#>    8.1489  -2.0809   8.1918  16.5408  13.5879 -19.5593  -3.7664 -10.7886
-#>   11.0072  -6.6765  -4.8078  11.7145  -4.5950  12.0940   4.1818   0.9954
-#>    3.1661  -1.3012   6.0041  -4.4507   7.4049  -0.6932  13.2061   3.2743
-#>    9.5199   7.2679  -3.3639 -11.7749   8.3600  -3.3641  -5.7926   3.3618
-#>   -6.4810 -12.4436  -1.3938   3.0750 -16.1594   1.9717  -5.5333   4.2702
-#>   11.1419   0.0955  19.3511  -4.1583  -0.4624   3.2259   1.0754  -2.9664
-#>    8.9533  -0.9478  -5.3318   8.1153   6.2505  19.1452   1.0230   8.2152
-#>   -2.8706  -8.6784 -12.3010  -7.2931  -2.0150   5.7070  -7.1829  -2.9493
-#>    5.2020  -1.9136   1.4974   1.2598  -0.3027  -1.9155  -8.9421   2.2337
-#>   -7.7501  -9.0444  -9.3237 -10.6197   3.8131  -5.0688 -24.0951   2.3849
-#>   -7.7414   4.0537 -19.9329   0.5265  10.7112  -3.8184  -6.8621  -5.1801
-#>   -4.2732  10.6180 -12.4161   6.6465  -0.6175   0.5263 -16.3148  11.5405
-#>   -6.2823  -6.9256   3.7962  -9.9520  -9.6631  15.2088   6.3811  -9.0371
-#>  -19.0446   0.8602  14.5508  -8.9178  -2.6391 -13.0266  -3.0735  13.5590
-#>   -4.9628   7.5579  22.1511  -1.2725  -2.9494  -4.4844   2.3112  17.1739
-#> 
-#> Columns 17 to 24  -4.1825  21.5207  11.4520  -4.5708  -1.5438  15.3972   3.2125  17.3042
-#>   -0.4366   5.4250   5.0380   4.0624  -3.6303  -3.8972 -14.9252  -4.3104
-#>   -0.7165  -2.3864   3.1508   8.7325  -1.1502 -21.1438  -1.3657 -13.6428
-#>  -10.3651   5.3801 -12.4743  -2.2434  -5.3823  -7.6754  -6.2727   5.5183
-#>   -0.4864  11.9181 -14.7448  -1.3623 -10.4562   2.1550   1.3195  12.3553
-#>   -1.9183  -2.3853  14.9228  18.9550 -17.5423   7.9292  17.5551  -2.3659
-#>    8.5572 -10.7424   1.5513 -10.6038   8.8510  -9.7853  -6.9093  -1.2731
-#>   -3.3836   3.1122  15.9079  -7.9366   4.0856 -15.1774  15.0615 -10.2502
-#>    2.9098   7.0142   3.7753 -14.3875  -5.1446 -10.9741  -6.6907  -0.5269
-#>   11.1435  -2.2287 -18.3215   5.5178   0.0646   1.9037   8.9206   5.6702
-#>   15.9882   5.7715  -3.0261  12.0738 -12.1655   9.7216  10.7748  -6.2806
-#>    8.5648  10.9936  13.3946   6.0727   4.1547  15.0799  19.8217   5.5906
-#>    9.4260  -1.6321 -10.8740   1.2734   6.4871  -3.9937  -5.1651  -6.5331
-#>  -21.2690   2.0586   1.4541  17.1033 -13.2072   0.4539  -0.3309  -7.4712
-#>   10.4926   4.8057  -0.4733  -2.7449   3.5052 -10.6738  22.6786  -1.7802
-#>    6.1693 -14.3340  -7.6485 -10.2864  -0.2380   6.3123   2.6203   3.5415
-#>   -0.6640  -8.3534  12.0272  10.8964   2.9217   6.1839   2.3538   9.3204
-#>   -5.1073  13.8817   7.7285  11.8317   4.1813 -16.0389  19.8230   5.0212
-#>    0.3832  -2.1313  -3.2197   6.2428  -7.2094  -1.7911   0.1555   0.9442
-#>   -1.6238   9.3784  -3.2428   1.1754   8.0022  -9.8045   0.1608  -5.7132
-#>   -2.2772   4.6684   3.3027   1.0269   7.1341   8.5374   3.5203  12.7100
-#>   10.5552  -6.7427   9.7302  -4.9299  -4.2069 -10.8554  -1.9141   0.6340
-#>    3.6097 -13.6879 -19.2162  11.0106  -3.8462   1.1233  -4.3375   2.9411
-#>   -2.6338 -16.1336   1.1961 -16.3511  -2.5227 -17.6820 -18.0181   9.3209
-#>    4.9945  16.4619  -9.6469  -2.7882   5.7165   5.6490 -14.4887  -7.6353
-#>   -9.1514  -8.5315 -15.5044  -5.3980  -3.1871  -6.8574  -0.2664   4.4388
-#>   -9.5559  -2.3091  14.8938  -4.0996   5.5592 -19.4200   1.9836   6.5751
-#>   -8.0059   5.4177   4.6515  -3.9604 -13.8529 -17.5115   6.4735  -8.0561
-#>  -13.0087   7.8845   4.5359   7.1285  -9.0164  -0.6873 -22.5152  -2.0334
-#>   -9.1964   0.6174  18.8030   6.6753  -1.9048  -5.0334  -0.6214  -9.7074
-#>    2.8011  -1.5303   2.3510   1.6620  -4.6654  -3.1248  -1.7060   6.6866
-#>   -8.1370  -1.5563  -0.5429   4.6920   2.2020  -0.8108   9.9542  -7.6876
-#>    3.8961  21.6270   2.4845  -1.5813   6.1576   2.7277  -5.1375   4.4078
-#> 
-#> Columns 25 to 32  -2.3601  -3.4322   9.3053  -8.1667   4.9778 -13.8512  -0.7769  -1.5251
-#>    3.6972  12.7780   3.2833 -11.2094   4.9365   0.8090  -4.1411  11.1159
-#>    2.1876   7.3518 -12.6594   7.8482  -3.3922   7.2875  11.8075  -1.1018
-#>   -2.9391   6.5277   3.6980  -0.4259  -6.1169   5.0371  14.8063  -8.8458
-#>   11.8637  -1.4726  21.8518  12.0867  -8.8219   4.1038   1.4818  -1.6840
-#>   24.4477  -2.4871  -4.6144  14.6080  -6.4686  -1.9640 -10.3194   0.6770
-#>   -1.7146   9.5057   0.6013  -5.5986   7.1479  -2.1409  -8.9335  13.1021
-#>    4.6454  12.8925  -9.2337  17.1118  -3.7436   1.9554   0.4862   4.6698
-#>   -4.8602 -19.4728  -0.3412  12.0404  -2.2184  12.8902   1.5057   1.0611
-#>    4.5539  -0.5962  -8.7740   5.9415 -14.5654  -1.0543   1.0969  -4.6110
-#>  -13.4540  -1.8438 -27.4494  10.4325  12.9727  -5.3659   4.3214  -9.1497
-#>    8.4932  10.4420  -0.1234  -2.1550  -5.8957   1.4477  -0.5030  -4.5601
-#>   -2.8887  10.2157   4.6852  -2.6711   5.0831  -4.4824  -4.4490  12.0549
-#>   10.8636   3.0547 -14.1468 -11.5786  -4.1872  14.1289   3.3529   3.5294
-#>   -5.0578  19.5757 -10.7164  14.3858 -15.5695 -23.5430   0.6562   0.4816
-#>  -17.2403  10.0789   8.6334 -16.7596   5.0725   1.1319  -1.7669   2.2916
-#>   -1.0254   0.8765   1.0145   7.5937   8.4460 -11.7051  -8.6109  -3.9665
-#>    4.5205   6.8878  -2.7549 -11.9323 -22.9036  11.9790 -22.1282  10.1852
-#>  -14.1557  -5.4662   9.6428  -7.9654   0.4415  16.7461   5.6598  14.2657
-#>   11.7360  15.4267   7.0044  -0.2906   3.8354 -16.8175  11.6116   4.2682
-#>    7.5793  -3.2285  -5.5165  -6.5840   2.1186  12.6062  -6.2278  -9.1656
-#>    2.0305  10.7111   2.9078  11.1756   6.8596  -5.1708  12.8105   1.4350
-#>  -19.5372  -9.6168  -6.2032  -6.6678  -0.1795  -2.0621   0.1891  -0.2887
-#>   -6.1471  -4.1238   5.0940  -0.5406   3.4143   4.3145  17.2420  14.2220
-#>   15.2717 -11.6314   0.6753   4.6649  -8.3632  -4.0830  -2.4835   2.9289
-#>   -7.8195  -8.8384  11.2586  -6.0931  10.6657  -1.6245   5.1102  -8.9102
-#>   17.8543   5.0104 -12.4566   0.1958 -16.1957  -0.1449  12.1718   8.7589
-#>   -1.8529  -4.3217  -6.6898   3.4766   3.4081  14.7981  -5.4841  -4.5221
-#>    1.1381   1.2830  11.2350  -3.1520  -9.8428   4.2556   2.6595   8.2908
-#>    1.7770  -4.4640   4.3794  23.1427  -4.7616  -7.9387   2.9123  -2.6110
-#>   -8.3071   4.1271  -4.8753   1.2700  12.8085  -4.8894  -3.6147   3.9395
-#>   -0.0611   6.6493 -13.0310  -1.8303   1.7900  -0.6020  -5.4802 -13.4728
-#>   26.6121   8.2568  -6.9124 -11.3372  -3.8023   4.2649  -4.5585  14.7970
-#> 
-#> Columns 33 to 40 -13.1377  -0.0706   5.6916   6.5058  -3.0752   4.5630  -7.6258  20.9413
-#>   -0.8843   1.0609   3.0657  -0.0612  -3.1638  -3.3998   3.0816   3.0866
-#>    7.3812  -1.7633   7.9183 -13.8968  14.0850 -20.5245   4.3020   6.5696
-#>    4.5379   5.9704   3.7029   3.3116 -22.2722   2.8277   2.2261  -4.4455
-#>    1.5286   7.1339   3.0548   3.2838   2.5137  -0.4061   8.9326  17.9624
-#>    2.3315  -1.2700   3.9492   2.8422  18.2107  -7.2438  21.5324   3.9252
-#>    9.6432   0.4445  -2.2403   4.9207  -0.7474  -7.9729  -0.5129 -15.4101
-#>  -20.4920 -17.6694  -5.8191  13.5121   3.1379  -5.5635  -1.6764  -9.0776
-#>    3.2790  -1.0290  15.0159   6.6719   5.8982  -3.4839  -0.0748  10.8644
-#>   10.9863   9.2873   3.2989   2.4354   9.8572  -2.2714   7.1368   4.1491
-#>   -7.7011   3.7981  -5.6473  -9.6907  14.5500  -3.3643  -1.8682   0.4615
-#>    4.9033 -13.4153   5.4821   1.2573   5.0443  -2.3632   3.0864   5.1709
-#>   -6.3609  11.7518   9.7436   6.3601   7.2588  -2.8778   2.2560   2.2091
-#>    0.6333   9.5380 -16.4339  -7.4011 -12.0568   0.4711   9.7366  -8.5114
-#>   16.3144   8.5388   0.5593  -8.6230  -5.3358  -1.3064  -3.4502  -4.9662
-#>    5.6114  -3.9022   8.0300   3.4313  -1.6205 -13.8910  -4.1147  -7.6525
-#>   15.3002   6.0428  -9.9738 -10.1090  10.0225  -1.4212   0.5714 -10.6084
-#>    6.0626  -5.3162   7.5097   0.3177   0.6618 -12.0088   5.9090 -13.9977
-#>   -2.9831  -4.7394   1.3440   4.8945  17.2124   4.3742  -1.8140  -3.4268
-#>    3.9576   0.6434 -12.3714 -10.8635   9.2416  -4.4522   5.8705  -3.2069
-#>    3.4398  -2.3467  14.6558  -7.6711  -6.7356  -5.1956   5.4958 -12.6438
-#>   -3.3017  -4.6428  -0.7086 -10.4358   6.3154   0.3267  -4.0852  -0.8664
-#>   10.5626   2.0228 -14.4048   8.2696  -0.0745 -10.8902  -6.6894   5.1885
-#>  -12.5296 -12.9663  -4.4452  -2.9155   5.2775  18.9949   2.4824  13.1430
-#>    0.6722  -7.0047   6.4576  -3.7775  -9.1132   6.3353  -3.5569   4.1473
-#>    6.7089  -7.4115  10.6349   5.1406   2.1408   6.6096  -5.9116   9.5312
-#>    7.9793   9.1878 -15.3085  -6.6413   1.8546  15.5129   1.8919   7.0326
-#>  -10.7967  -1.9923 -12.8950 -10.9955  15.2740   7.0095   8.7453   1.6567
-#>   -3.5113  -4.6141   5.5052  -2.7090  -0.7984   4.3020   0.6053  -0.6996
-#>   -2.2838   2.9193 -10.9042   9.5657  -5.6694  -0.0304   8.0606  17.9457
-#>   -3.7685  -2.6690   0.9492  -7.3359   4.6296  -4.3763  -5.5983  -1.0029
-#>   16.1059  -0.4918  -5.9363   4.5314  -2.9774   9.2013   2.6621  -8.6090
-#>   -1.3673  11.2294   0.8330   0.1796  -1.9448  13.2667  -1.8795 -11.9470
-#> 
-#> Columns 41 to 48   5.4623   6.0557  -2.3547 -10.5777  -8.9394 -17.5632  17.0012  -7.7506
-#>   -3.4502   5.7822 -22.9018 -16.1433   3.3162  -7.2222   4.9175   5.3236
-#>   -6.8991  -4.6364  -2.5290 -20.8305  -5.5299  15.0443  -3.6268  12.7072
-#>    9.9013  -1.1473  -9.4256  -2.5440   0.7606  13.8442  -2.8150   8.2952
-#>    0.2988  -2.1047  12.3417   4.8989  -4.7860   6.8126   0.6023   6.9697
-#>    6.4256 -18.5609  12.9657 -13.4234   3.5923  -3.0290 -19.5210   7.9649
-#>    8.5411   7.2910  -1.3473  12.0052  -4.6327  -5.4855  -9.0772  21.2862
-#>   -8.1589  -3.7239  15.3635  -0.2900  -8.4298  -5.8980   8.3588 -31.6000
-#>    9.1120   3.6091   0.5912  13.4243  -0.9888  -7.5009  12.9340   2.7174
-#>   13.6130  -5.7139   2.6131  -4.1449 -13.8080  12.9982   1.1384  -5.4977
-#>    8.9638   6.3303  -9.3563  11.8058   8.1987   8.6516  -2.2029 -17.6940
-#>   -4.9596   7.8049  -4.9706  -1.9639  -1.2503   4.1113   3.3422  -3.7928
-#>   -6.4181   6.0623  -8.2092   0.7996  -6.1743   0.6363  -7.4182  -8.2864
-#>    1.0932  -2.6376  -9.6817  -8.3699   9.8409  12.7849   4.8182  11.9833
-#>  -14.7259 -18.3420  11.0260   6.2939 -17.9582  10.5616   6.2648  -1.0947
-#>  -10.5056   2.1502  -4.2209  -0.2779  12.3265   4.9331  -2.0322  -2.0515
-#>    8.0115  -9.9201 -15.0605   4.7110   2.5659  -7.8833 -16.4231  15.7626
-#>    7.2102   9.5568 -11.0781  -2.8903  -0.3292   7.6369   1.6897   1.3017
-#>   -4.6736  16.3344  13.0339   2.9969   4.7427  -0.4004  -0.6330  -3.7610
-#>  -12.2635 -13.3897  -0.6776  12.6669 -17.6320  -6.1654   0.0280 -10.9851
-#>   -7.5166  -0.4481  -4.1911 -11.3640   3.6991  14.0500 -13.1051   7.0555
-#>    3.5955  -1.8552   1.2731   0.7391 -11.2164  -4.5338  -7.1506  -1.2045
-#>   -7.6455 -12.3654 -11.3886 -10.4583  -8.9162   8.7736  17.7514  16.4935
-#>    7.3194  24.3887   9.7357  -3.6405   0.1455  -5.7188   6.6516  -4.2392
-#>   15.0456  13.1130  12.7170  -3.8100 -19.4943 -16.5896  17.4840   3.2342
-#>   -7.2503   7.9477  19.6389   4.8204   7.8325  -9.6368   7.2113 -12.7309
-#>    4.2267  -4.0082  -7.8041  -1.5592   9.4208  10.1543  16.0493   4.4434
-#>   -4.3093 -18.7923  -1.8592   0.0332   5.8462  -1.1693   6.3583  -8.1359
-#>   -3.6780  -9.1685  -5.1180 -12.9000   9.9408  -2.7031   4.7989  23.9640
-#>    3.3705  -4.7995   2.9122  10.1147  -6.5252 -11.1011  16.6597 -12.7891
-#>    5.2562   6.5468  -9.5708   1.3940  -0.2515  -9.5245 -12.9337  19.8222
-#>  -13.3648   3.8419   3.6211 -19.4950 -16.4319   5.5172   0.6711   7.0542
-#>   10.5676   3.0193 -13.6869 -17.7664   9.5023  12.4828 -11.6906  13.0204
-#> 
-#> Columns 49 to 54  10.4049  -4.3937  -1.2158   1.0199  -1.4881  -1.6759
-#>   -0.5652  -7.3973   0.0691  -5.2152  -3.7536   1.2808
-#>    7.0183   5.8691  -4.2220   4.2102  11.6324   4.5266
-#>    6.6726  -7.8257  -8.2892   5.9208  -1.7354   7.0313
-#>   -0.5905   4.8962  -0.2178   0.9379   3.7152   1.9126
-#>   -6.6017   8.1995  -0.5955  -5.2730  -2.5379   2.6127
-#>   -5.8200  -4.8252   5.8403  -2.0860  -1.0756   1.2638
-#>   11.4338  11.5370  -5.4214   6.2038  -5.4447   4.8322
-#>  -18.9999   2.5189  10.5246  -0.0110   5.5061  -1.5023
-#>    4.4337  -3.9522 -12.7223   7.7476   1.5671   0.0794
-#>  -18.2992  -1.8082  26.3236   1.9072   4.0479   1.4653
-#>   -0.3543  -1.8854   9.7096  -1.0631  -4.9233   3.6277
-#>    4.8155   0.9072  -1.4398  -4.3319   3.4289  -4.0279
-#>   11.1888 -19.1820  -9.4257   0.9476  -2.8059   2.2915
-#>    0.8938  -8.0130  -3.0255  -4.9254  -6.8895   3.7856
-#>    0.6107  -3.4682  -3.0902   8.6203   5.0539  -7.3057
-#>   -6.0682  12.0741   9.0698   2.2445  -7.1664 -12.1935
-#>   -1.4495   0.8049  -3.1648   9.4595  -1.4624   0.4464
-#>    0.0692  -8.6347 -14.3722  -1.6164   5.0661   0.5902
-#>    9.6713   0.1688  -6.0017   1.9842  -6.2445  -7.3938
-#>   21.8469 -12.3380  -8.1051  -1.8002   0.4829   3.4799
-#>  -12.2342  16.0267   3.8473 -11.1799   0.1126  -5.3088
-#>    0.2123 -21.1952   2.5425   9.4203  -1.4694   0.0316
-#>   -3.6046  -4.2468   5.5372  -5.5574   3.6863   3.7841
-#>    1.9404   5.0886   8.4009 -16.4564   0.9253  11.0061
-#>   -6.1988  -1.1603   8.1998  -3.0862  -3.1927  -1.6622
-#>  -14.0336  -7.8097   7.9282   2.5255   2.7384   2.9216
-#>  -10.8568 -11.8154   6.2506   3.0923   1.0809   1.9786
-#>  -28.8676  -2.6244  11.4707   1.6968   0.9274   6.8802
-#>  -18.4692   4.5212  -2.0207   4.3433  -9.6943  -3.0231
-#>    1.0947  -4.4575  -4.9881  -1.8650  -1.7975  -0.3476
-#>    5.9016  -4.6479  -9.4835   8.6000  -5.9623  -2.5017
-#>   -3.7405  -2.3338   1.5422  -2.0316  -3.3168   5.1828
+#>  Columns 1 to 8  -1.6353   4.4321   6.1203   3.7709  -3.4023  -6.1183  -5.4390  -8.2007
+#>    5.5697  -9.7264   6.0708  -5.7289   0.3792  -7.9255 -14.6813  -3.2065
+#>    2.7488  -3.8040  -3.6862   2.3940  -2.4815  16.9404   5.4111   4.9423
+#>    5.1705  -3.1706   8.2556   8.6014 -17.0782  -2.1720   5.2951 -18.1015
+#>   -3.2398   4.1560  11.1199   5.7068 -16.2701  17.4736  -5.6358   0.3901
+#>   -2.0510  11.1923  -1.5562  -0.7487  10.6704  -3.0011  -7.6444  14.3682
+#>   -1.6671  -1.8266   6.6932   9.1583  11.6255  -5.8125  -2.4459  -8.9862
+#>    1.8152   0.6154  15.3009 -12.0627   8.2672  10.9483 -15.1192 -19.2267
+#>    6.0888   2.3311  -4.2057   1.2558   2.4469   1.8447  -3.5200  -3.4625
+#>   -5.1195 -10.0449 -13.9448   6.3498  -6.7641  -8.2834   8.6267  -4.2428
+#>   -3.6945  -2.8390  11.6784 -17.0383   3.5297  -6.0096  -9.7653  13.4390
+#>   -5.7205   5.9699  14.0259  -4.8045 -23.3689   5.2313   7.3005   6.0076
+#>   -1.1548  -8.7054  -4.9090  11.5328  -7.7649  13.0461   1.4930  -8.2168
+#>    1.6853   2.5822   0.2825   8.1012  -1.2717  -3.7791  -4.3516  -3.0138
+#>    7.4851   1.5490  -0.9358   6.4131  -3.6425   4.7491  -2.7134 -15.5698
+#>   -5.7900  -7.0067   4.0435   5.2198 -10.6764   5.7583  13.1000   1.1120
+#>    1.8349  -3.1394   9.7660   3.0416  -5.8365   5.3293  -3.6430  -3.6948
+#>   -2.8039 -15.9427  -2.1047  32.2348   4.5484  -6.0838  -5.6095   0.2993
+#>   13.2444  -2.2817  -3.4686 -13.9957   0.7390  10.9607  -3.2167   1.8028
+#>    3.4685   1.3891  -4.9012  -1.6123   6.4095   7.3353  -0.7901   3.8872
+#>    0.9645  -3.8791   3.0848  -3.6218 -13.0589  12.2856   0.8339  -5.4423
+#>    2.2557  -7.1083   3.4661   4.3524  -1.8454  -7.2964   0.8217 -10.6039
+#>    1.3620  -2.8747   0.9562   9.8035   8.7008  -8.6258  10.4466  14.0749
+#>   -1.6509  -0.6748   4.2229 -11.8480   0.4392  -2.9401   0.9418 -10.3291
+#>   -7.1141  -0.0717  -6.6829   6.0824   5.2061 -13.2283   1.0957  -6.9558
+#>   -0.9038  -2.2526   1.5140  15.1949  -8.8688 -11.7858  17.6347 -13.8020
+#>   -0.0936   8.9236   7.7583  -3.4908  -7.6502   8.8869  -7.2650 -17.5812
+#>   -4.1580  -1.3969  17.6921  12.3450  -6.7449  -0.4531  -6.4342   9.3969
+#>    1.3312  -1.1501   2.0879   3.7027 -10.5830 -13.1696   4.9839  -1.2594
+#>    0.7320   5.6934  -7.6473  -2.8277   4.6002 -17.2764  12.0295  19.5532
+#>   -7.4079  -3.1421  -3.1011   9.5189   4.4956   8.4683  -4.5437   9.2424
+#>    0.1470  15.2248   2.4783   1.4916  -0.4940   4.4596  18.6246 -11.7759
+#>    8.6609  -0.5493  -6.0460  -3.6809   3.8050  16.3690  -1.5648  -1.7205
+#> 
+#> Columns 9 to 16  -6.2851   3.8360   4.0812  16.2348   8.9454   6.8916  -5.3057   7.0084
+#>   -9.7963  -6.0513   2.1007  -4.4073  -1.2336 -12.0758   4.9370  -7.8475
+#>   10.7937   1.2300   4.0328   5.5895  -1.3036  -9.0790   5.7282  -7.9205
+#>   -9.7344  -2.7516   1.8011   5.4262 -12.7751   5.2865   0.1208  10.6316
+#>   -7.8695   2.1968   3.7347  -3.2290   9.4674   1.4886   5.9791  -9.1102
+#>   -3.9260  11.8193  -0.2911   1.9903  11.0502   4.7821 -13.5610  -2.8117
+#>    7.1338 -15.8626  -6.2505  -5.4815   6.8066   2.4072  -1.2749  10.9718
+#>   -7.0970  16.9016  -6.4371   3.3848  13.3238  13.6089  -7.1793  -5.0847
+#>   -6.3969  10.4879  -1.5486  -4.9103 -11.7403  13.0164  -5.4640  -9.1828
+#>   16.3038  -6.8361   1.4881   7.0337  10.5306   6.0375  -2.5512  12.6395
+#>  -17.6439  -7.1204  -8.0048   7.8022  -5.6107 -12.6919  -1.5475 -11.0052
+#>   -5.6755  -3.5202   7.6174  -8.6198 -10.6437  -9.0619 -14.1580  -2.8026
+#>   11.2652  -5.7832   1.7949  10.2232   1.9751   3.0577  -6.3639   6.9798
+#>  -12.5294   0.2421  -5.5722   5.6743   9.4821  -1.6801   7.3115  -3.6876
+#>    8.9469  -7.7445 -19.2238  -8.9655  -5.9435   5.1982   9.8527  -1.9508
+#>  -10.5100   2.7257  -4.9903  -1.8673   1.9766 -10.2879  -4.9044   2.2399
+#>   15.8220  -3.4858  -7.7946   6.5953   9.5105   3.9760  -9.4776   4.8341
+#>    1.2145 -14.3449 -16.1444   1.9052  -5.3336  -7.6917   9.1494  -4.7977
+#>   -0.1992   6.7144   2.7534  -3.3795  -9.1793   0.7903 -13.2489  -4.9081
+#>   -0.7303   0.8345   8.0622   4.0037   1.3024  14.6448  -0.4291   2.4696
+#>    2.6964   0.6241   2.8331   1.5836   4.1614 -12.3349 -10.7515   3.3005
+#>    5.7378  11.6491  -9.7662  -6.0303   4.0343  13.3529  -7.5251   5.8326
+#>  -10.1247   2.9946   2.0462   7.6774   5.8304   9.0729  -0.5200  -8.0880
+#>   10.2583   6.3646 -17.3697   1.7589  -1.8800 -10.3373   2.6369   1.1038
+#>   -5.8475 -20.4639  10.1173   3.4212   4.2065   6.1156   8.5716   9.1394
+#>    3.5173  -3.9945   3.4049   6.1398   1.9885   4.7099  -6.5305  10.9822
+#>   -5.8909   9.3238  -7.9771  13.8210   4.0316  -1.1631  -2.6270 -12.8470
+#>   -7.5884   0.7262 -11.8103 -18.4281   2.6524   2.5922  -9.9075  -1.9948
+#>   -6.6363   8.0375   9.1075  -3.3916  -5.4109  19.7692  -9.1882  13.5041
+#>   -2.2628  -6.4133   9.2126   6.6218  -3.5964   2.9373 -10.7443   2.5160
+#>    1.7958   0.9604   9.7169  10.9803   7.1440   4.7793   0.5224   3.2285
+#>    6.9006  -7.8793   5.4129   7.4761  10.3766   8.9483  10.8707  -3.1484
+#>   13.0729   0.6346  -4.5002   5.9900  -4.4645 -14.4995  -0.9068   7.1925
+#> 
+#> Columns 17 to 24   6.2779   4.0789   4.6342  -3.4759  19.3502  16.4900  -3.1063  12.2321
+#>   -7.5077 -13.3862 -10.4926  12.8095   3.3936  -4.3838  -5.2340   0.8618
+#>    9.9686 -10.6574   1.3555  -1.4059  -6.0067   4.8209  -2.1228   7.1200
+#>   -4.6190  -1.5177  -4.3027   6.5516  -4.3046   8.7342   6.8208  12.1705
+#>    1.4544  -1.1432  -3.0856  -4.5655  -8.7982  11.7490  18.8031  -7.0710
+#>    0.6605  10.6931  -6.2308  -9.2823   8.9047   3.2196   0.9699   1.5700
+#>   -7.0478  -6.4703  -6.8466   0.2895  -3.2327   5.5190  -3.9232  -4.3466
+#>   -6.9490  -9.7761   2.1006 -11.2086   7.0444  -1.6094 -10.4766   3.2639
+#>   10.9471 -12.1016   1.6891  -5.2735   0.0592  -6.1266   2.8895   2.3791
+#>    0.4054  -1.5346   6.7898  -5.8844 -18.3470  -2.5544  -7.9734  -3.2282
+#>   -8.9070  -5.7965   0.5405  -9.0516   1.9035  -5.9659  -2.0333  -2.8367
+#>    7.4125  -5.1368  -5.3024   4.3004  -0.7027 -18.6231  -0.6111  -9.4889
+#>    9.5441   1.7020  -4.6136  -2.6403   4.6751  -2.7865   6.2623  -0.7319
+#>   15.1835 -13.3803   2.5517   9.3862   4.0670  -0.8356  -1.4139  20.8876
+#>    6.0132  -4.8067  -8.6996   2.5315   0.0879   3.9812  -2.0344   1.3711
+#>   19.0366  -9.8806   3.3997   6.6510   4.8274  -3.8462  -0.8375  -9.1399
+#>   -2.6380  -3.0965  11.8619  -7.6993 -14.2918  15.5233  -8.5303  11.3023
+#>   11.2962 -11.7741  -7.8650  -2.0833  -2.5472  -1.5447 -12.6203   4.6628
+#>   -0.3020  -3.8343  14.7717  -1.7855   8.9758   2.0816 -10.2800  16.3089
+#>    4.2869   0.7756  -6.4838  -1.8731  -2.3366  -2.2847  -1.4424  -0.6003
+#>    0.9071  -8.7299   5.8014  -2.2126  -1.8472  -3.9024   0.4044   1.3947
+#>    3.2037   3.6134  -0.6392   7.8643 -19.8176  11.0584   6.1518 -17.1921
+#>   14.8077   6.8448 -12.1126  -5.0377   2.2326   1.0797  -1.9270   7.2347
+#>   -9.9347  -4.2147  11.3667  10.6318 -12.4513   9.2924  -9.0560  -0.6255
+#>    3.2851   9.6646  -3.1222   5.9368   6.8206  -1.0937  -1.7236  -9.7517
+#>    2.2264   1.0995  -5.7135  11.8533   9.7378  -2.2368   5.8345  -5.5508
+#>   24.7367  -2.7284   5.0194  -3.3506  -0.8626   5.2726   0.8546   4.7908
+#>  -10.1723 -11.6227 -10.1945  -3.2437   6.0436   3.0644 -12.5749  -9.4807
+#>  -11.9441  -7.7931   7.4653  -0.7346   1.8427  -6.7075  -2.2042  -2.2354
+#>  -20.7180  10.4069  -5.9971  -6.1509   8.7020 -11.5701   0.8657  -4.3643
+#>   -1.0755  10.4426  10.0029 -10.2691  11.3524  -0.3177  -2.5653   8.9292
+#>    3.2250 -15.8103   8.8180   1.9467   5.0783   9.4573  -6.3361  22.4272
+#>    3.2350  -5.9312   0.4309  -1.0309 -10.0581   5.4209   0.4992  22.4847
+#> 
+#> Columns 25 to 32  -4.5053  -2.1174  14.2360   4.7665   2.3424  -1.2624  -3.2785  -9.7989
+#>   -1.9127 -11.7792   3.3175   1.9249 -16.6269  12.8381 -11.8423   0.8771
+#>   -4.9612  -0.0928   8.8751  -1.1873  -3.0750  -3.5674   6.7220   9.7173
+#>    5.8238  -3.9061   5.9620  -0.0963  -1.9811  -5.0030  -3.3837  13.3573
+#>    2.6680  -8.8689  15.9369  -2.6194  -9.7763   9.6478   9.1596   2.8069
+#>   -4.1380  -1.9067   4.3337 -11.5888   7.1389   0.0871   4.5074   8.0377
+#>    0.9044  -1.4701  -4.1012   3.8395  -3.9617   8.0290  -2.7151  -4.4134
+#>  -14.8943  -2.8089   3.7858   8.4465   6.1233   1.4652   1.1410  -9.6152
+#>   -4.7238   0.5790  -7.5091  -0.3505  -3.3624 -22.5187  20.4917  19.7872
+#>    4.0737  10.7911   8.4844  -9.8201   1.6337  -2.5235  -7.3762 -10.0326
+#>   -3.9822  14.6734  10.1709   1.8469 -12.6385  11.2676   5.2270  -5.5252
+#>   10.8633   9.7329   1.7909   2.2507  -2.5958 -20.1454  19.3010   8.3581
+#>   -6.7146   2.6502  -1.1800  -5.0328   1.1697   5.0351  23.4061 -15.1636
+#>   -1.3179   1.1593  14.0804   7.1428   0.2216 -24.1185  -7.2368  -7.1155
+#>   -5.5530  -3.7137  -6.2124   6.7344   1.6386  -5.8182  -5.4761  -1.0784
+#>    5.6480  -1.7854 -12.1789  -9.8640  -4.1237  18.6410  -1.5094  10.8923
+#>   -1.6847   4.3116   3.1892  -1.9839  -7.9005  -6.1859  -1.0035  -2.2484
+#>   -8.8033   8.2411  17.0673   0.5185  10.0113  10.6061  -1.7925   0.6375
+#>    2.7209  -3.5135  -6.6464 -27.2545  -6.5440   3.0897  -8.5191  12.1664
+#>    2.6476 -10.4624 -14.4767  16.1376  14.7092  -1.8240  14.6205   0.2579
+#>   16.4958   0.6475  -7.4110  -5.1637   2.5012   1.3436  -8.6728 -17.2104
+#>   -8.1495 -13.4390  -3.5876   4.4540   0.0614  -8.1080  14.6534 -11.2639
+#>   -3.6892  10.6439  -1.3047  16.4691  -7.9526 -10.2501  -3.6996  -1.7498
+#>   -2.8092   0.1179  11.1337  11.5904  -2.4420   3.4980   2.2890   8.6400
+#>    0.1419   6.0515   1.7946  -5.1386  -1.1879   5.6720   2.1412 -14.1735
+#>   12.4793   1.2980  -2.9805   2.9048   4.7302  -9.6842  -1.9983 -10.3513
+#>  -12.7380  -9.5773   3.2642  -7.8737   3.7460  -5.2990  -5.6387  -2.9537
+#>    8.8755 -14.0756  -2.9564  17.2987   2.1981  13.1206   6.9287   0.3181
+#>   -5.0310  -0.6406  -1.5851  -2.7789   2.1290   5.1021  11.6930  -4.4672
+#>   -7.7398  21.4764   3.9897  -0.7631   1.0018   3.7905  -6.6454   8.3266
+#>   -7.4614   6.5273   8.2987   2.0473  15.6732  10.0316 -19.5524  -1.8505
+#>   -5.6506   2.1858   1.0345   5.1929 -11.5952  -3.7969 -11.7629 -18.4464
+#>   -0.1787 -12.8495   5.2724 -11.0030  -3.5747   9.2239 -10.0173   4.9648
+#> 
+#> Columns 33 to 40 -11.9268   7.6184   9.8609  13.3409  -9.7127   3.6975  13.8528  -4.9289
+#>    9.1265   4.4472  -4.3586 -20.8979  -5.4081  -2.3428   7.4659  -2.4016
+#>  -14.5635  -8.6556   7.5654  -5.3610  13.9681   8.1991  -8.0856   2.2180
+#>   -1.0739 -11.3050  -6.3948  -1.3663   6.4392   1.1543  -1.7980   4.4444
+#>  -15.3729  -4.6911  15.3161  -2.1265  26.1166 -15.0314  -1.1702   0.0984
+#>  -17.5054   5.5257  -4.4897   7.9732  -7.9022  -3.5838   8.2722  11.5031
+#>   15.9846  -2.7107  -6.5184   2.3077 -14.9634   9.2020   4.4676  -6.0666
+#>  -26.6501   6.6041  10.1317  16.5637  -4.8222 -16.5782 -10.9113  -0.5785
+#>   -9.0396   4.9282  -5.5893  -5.7280   8.2698  -1.7011  -0.8699   2.8684
+#>    4.3633   0.9977  -4.8213 -20.8736  -1.1724  13.0067  18.3303  -1.6187
+#>    2.2989  10.5885  -6.9858  -0.2267   4.2112  -4.1962  13.2158 -17.2928
+#>   11.0699   2.9381  -4.8933 -38.2932   0.1603  16.0582  -2.9725   7.8612
+#>   -2.0308   0.0037   3.0131  -0.3492  12.0400   6.0014   2.0772  -8.6665
+#>   -3.5162  10.9643 -13.8959  -1.6906  18.3387   4.0186   1.0276   4.2466
+#>    3.7584 -11.4404  10.2994  -5.4826 -12.6490  -4.0644   2.0176  -6.9175
+#>   -3.2310  11.1564   9.4832  11.1806   4.6966   2.6487 -13.8709 -12.8078
+#>    3.7125   4.7114   0.1897  -0.4760   5.1806   2.0457  11.5016 -23.4014
+#>  -21.3517   8.0657   9.8888   7.5029 -16.9792  -8.0239   4.0550  -7.0832
+#>   -4.5678  -2.6708  11.5115   9.7412  14.8756  -7.0594  -8.0661 -14.4081
+#>    2.4442  -2.9840   3.5688   4.4359  10.2149  -5.4918  13.6324   0.8469
+#>    9.0768   4.8015  10.0607  -9.5290  -5.1481  -0.4383  -2.8543  -1.7835
+#>   -8.3108  11.3465  11.2742   0.0542  -7.1745  -2.2104   4.1766 -10.4813
+#>   -2.3820   4.2490   0.7564   2.1989   3.6649  10.0225   2.0802  -2.5912
+#>    1.3367  10.0371   8.8040  -5.2670 -14.0395  -6.0852  20.9049  -0.7500
+#>   -7.8930  -6.1188   0.4769 -22.1620  -7.2019  17.3513  -1.6344  10.9354
+#>   -9.7916   0.0431  20.0900  -7.8611 -10.3414  19.8199   0.6647   2.4200
+#>   -9.6766  18.0063   2.6052  13.7560  -1.9542 -11.7086 -17.1135  -4.3562
+#>  -11.2062   5.4855  -0.6854 -12.7413   3.4211   2.3197  -2.5488  -1.9933
+#>    1.3085  -1.1305  -9.4153   1.9846  -4.8051   8.0312   7.6671   4.6333
+#>    4.5424 -22.8243  13.8807  12.7908   3.7836   5.4811   8.1751 -14.9043
+#>  -15.0608  -2.7074  18.1918   2.7607   4.9620 -11.5461  14.4178   2.9780
+#>   -7.9901  -0.6274  -8.6149  15.8710  19.4579   0.6645 -21.5073   0.2367
+#>    6.2383  -2.7190   0.1438  12.9022  -1.5891  -8.3071  11.5200  -9.9123
+#> 
+#> Columns 41 to 48  20.4647 -10.5523   0.2340   7.0850 -11.2098  -2.3350  -5.0162 -10.3207
+#>   -2.6782  -2.6592  -0.2630   2.4612 -10.6666  -3.4490   5.4198  -0.1389
+#>    9.8990 -14.8340  -6.4583  13.6772  -7.9724   7.2383  -0.3764   5.7707
+#>   10.7997  -1.8674   1.0840  -3.3264   0.4576   4.3198  -8.7924 -10.5595
+#>    3.4498  -1.2010   8.0379  -6.5539  -8.2836   2.1312 -26.0666   4.6215
+#>    0.7633   6.0816   2.7245   2.1741  12.7044  -3.9310  -3.9756   3.0319
+#>   -8.7633   4.9305  15.4024   0.2374  -1.0643  -0.1309  17.7423   0.9355
+#>    7.7370  -5.1074  19.1092 -10.4697   8.9580  -4.5772  -2.8787  14.6211
+#>   10.9599   2.6534  14.1849   7.7106   7.4843   0.0934  10.1777   6.5403
+#>   11.4195   1.3583  -5.1359  -2.0273 -13.9047  -0.7697   5.7659  -3.4000
+#>   -4.5752   0.8380  10.4752  -3.9706  13.7800   4.0281  -4.4736  -4.9348
+#>   -2.1779 -15.2266   1.7633  -9.1774  -2.9366  17.0980   5.9149  -3.0456
+#>   14.2548  -7.3088  13.8884   0.0288 -11.7036   1.9510  -0.0916  -6.3025
+#>   -3.9179 -10.9053  13.9168  -8.3460   8.7899  -0.7906   6.8612   1.4982
+#>   -8.4043 -19.5186   0.4067  21.8964   8.2378  -8.3949 -12.4678   8.2527
+#>   -4.7134  10.3361   7.1094  12.7306  -9.2579  19.6261  -0.4495   7.9905
+#>   -2.7534  -6.5759   2.5518  -1.7753   6.1320  -5.3221   0.3079  10.4943
+#>    0.0245  -2.2497  -3.2277   9.1750 -13.2683 -13.2944  -0.1946   2.3356
+#>   -4.1616   3.4521   1.2067   0.5371  10.9614   2.2384  21.4076  15.6762
+#>    7.0099  -0.8731   6.3360  -3.1024   6.2920 -16.2566  12.2112  -4.0807
+#>   -1.1357  -8.1284  -9.2778  -2.5176   2.5829  12.9534  -2.0807  -3.2123
+#>   -2.0304   9.9117  -5.6539   3.0012 -11.1934  -3.8649 -30.2153  -2.6543
+#>   -2.7060   2.2340 -10.4610   4.1720 -19.0114   8.4332  12.9279  -9.7706
+#>   -5.4916  -8.8655  -6.1061   3.4889   3.6215 -22.6714 -11.8122  -3.6672
+#>    4.6843  -4.2341   9.4974   9.0313 -18.9345   0.5230  12.7945  -9.2693
+#>    1.7356  -8.2352  -6.7436   5.8068 -26.6280  -9.7926   7.3267 -21.7316
+#>   12.2611  -8.2844  12.8783   2.4960  -2.6067  18.6078   7.7587   2.8397
+#>   -7.0828 -10.0814   2.0135   5.3172 -15.1470  10.5911   0.8432   6.6044
+#>    6.0089  -2.5268   2.5346  13.8533   4.5407  -1.5666  -1.0369 -11.2153
+#>    8.7093 -10.0270 -21.6963  -2.8492  10.1861 -19.6865  -6.9108   5.8541
+#>    6.1980  -6.0629  -4.7703  -5.5004  -3.2540  -2.9782  -3.6518   1.2137
+#>   -5.0318   0.4662   3.4348   1.4638  -6.4028  14.7323  -2.1698 -14.5576
+#>    1.8353  -3.1170 -14.5561  13.7529  10.9541   1.7576   1.0423 -10.6220
+#> 
+#> Columns 49 to 54 -17.0780  -6.5815   4.9577   0.7125   1.7213  10.7328
+#>    6.7054   6.2971   0.0432  -9.5257  -4.6027   0.2612
+#>  -30.0003  -2.1935   0.5826  -3.1455  10.4763   2.6974
+#>   -8.7301   7.5076   5.4824  16.5618  13.5231   2.7738
+#>   -0.2364  -3.3846   7.3295   4.4764  -1.7245  -7.5216
+#>    1.3830 -10.6503  -6.3592  -0.1129 -10.5233  -3.6009
+#>   10.6488  -4.8841   8.9133   9.2143   4.2506  -3.3445
+#>  -10.8800  10.1740 -11.0188  -1.2580  -2.2174  -3.9595
+#>   -9.1663   0.6537   6.9500  11.0254   9.0903   3.5276
+#>    9.4244  -3.7664   5.9635 -19.0430  -4.0715  -0.0513
+#>   -2.2399  -6.4013 -15.2913   9.7325  -2.1793   2.1762
+#>   -4.4015   5.8905   2.1533   2.2135   6.1479   4.7397
+#>   -5.8223  -1.9129   0.8422  -4.6915   4.3558  -2.4428
+#>    4.8444   9.1223  -9.5788   2.0387   2.3563   0.2254
+#>    8.4955   9.1437  12.6130   9.4622  11.8852   1.3344
+#>  -12.5300 -10.8933   5.9414  -2.6376   0.0451   1.3944
+#>   -2.5310 -24.5449  -8.2732  -3.2775   3.8950  -2.7564
+#>   -3.7708   1.8577  12.7436  11.9870   0.1528   0.2858
+#>   14.0968   6.7989   4.6220  -0.1335  -7.6953   0.3397
+#>   -3.7870  -5.0250  -2.7751  -1.8903  -7.0356   3.5137
+#>   13.1324  -3.5106   6.6707  -5.1084 -10.2100  -5.8738
+#>   12.0708 -12.8613  -2.4515  -9.8594  -4.1255  -0.2364
+#>  -11.8370   8.1900  -7.6104  -6.6829   1.0282   0.4576
+#>   -3.3568  -8.2177   5.6953   3.6257  -2.4383  -3.6482
+#>  -10.5435   9.2601  15.5485  -1.8051   4.2065   0.5571
+#>  -18.5573  -0.4799  16.9107   0.5709  -6.2331  -6.8055
+#>    6.9494  16.6523 -14.2954  -7.2552  12.9859   2.5998
+#>    3.4777  12.1104  12.9764   2.3842   4.0864   4.6834
+#>    6.1148   6.8673  11.0114   1.9545  -0.6595   5.6429
+#>   -5.4072 -23.6101   9.7078   5.6821 -10.9979   3.5250
+#>   -2.0657   8.6995  -1.6321  -6.7550  -1.4473   0.6146
+#>    4.1655   5.9886   4.5757  -7.0181   6.0293  -3.8261
+#>    7.4021 -10.2871   1.3421  -6.8808   4.4474   2.2112
 #> 
 #> (2,.,.) = 
-#>  Columns 1 to 6 -7.7358e+00  2.7064e+00  4.1159e+00  2.3887e+00  9.3367e+00 -1.4353e+01
-#>   8.5316e-01  3.7687e+00 -3.4629e+00  6.8568e+00 -7.2856e-01 -3.5939e-01
-#>   4.5799e+00 -6.9411e+00  2.7158e+00 -5.9324e-01  6.3918e+00  3.3153e+00
-#>   4.7981e+00  8.9044e+00  5.5984e+00  5.0699e+00 -1.4592e+01 -2.0785e+00
-#>  -4.6695e+00 -2.5667e+00  1.3037e+00 -1.0801e+00 -5.1928e+00 -2.5828e+00
-#>  -3.8563e-02 -2.9945e+00 -2.9541e+00  4.8150e+00  7.5176e+00 -5.7202e+00
-#>   1.0594e+01 -1.3108e+01  6.0147e+00  3.7226e+00  1.9505e+00  1.4969e+00
-#>  -7.6931e+00 -4.9361e+00 -2.7434e+00  4.6197e+00  1.1686e+01  9.4690e+00
-#>  -9.7675e-01  4.7890e+00 -6.4019e-01  1.1604e+01 -5.3119e+00  1.1268e+01
-#>   3.5569e+00 -1.6191e+00  1.7762e+00 -3.0177e+00 -1.7011e+01  9.1518e+00
-#>   1.0447e+00 -6.3763e+00 -4.8049e+00  1.2495e+01  7.7488e+00  1.6410e+01
-#>  -7.6849e-01  6.6844e+00 -3.0198e+00  1.0458e+01 -1.2576e+00  1.5203e+00
-#>   4.3539e+00  8.1895e+00 -8.7703e+00  9.5344e+00 -3.5441e+00 -1.4626e+00
-#>   3.7191e-01  4.0707e+00 -3.7622e+00  1.2110e+00 -1.2392e+01 -3.5298e+00
-#>   6.9584e+00  9.4487e+00  2.3289e+00  1.1768e+01 -2.5903e+01 -1.8819e+01
-#>  -2.9398e-02  1.9986e+00 -1.7783e+00 -1.2250e-01  4.4910e+00  2.3489e+00
-#>   4.1574e+00 -1.2356e+01 -1.4853e+01  1.4956e+01  1.0511e+01 -4.9196e-01
-#>   2.0067e+00 -3.8693e+00 -8.2226e+00  1.2391e+01 -1.7505e+00 -5.3938e+00
-#>   2.2505e+00  7.4454e+00 -3.4723e+00 -1.5694e+01  1.0488e+01  7.3954e+00
-#>   7.9690e-02  4.2664e+00  9.6114e+00 -2.6502e+00 -8.2685e+00  4.5040e+00
-#>   2.7958e+00 -7.8079e+00  9.3878e+00 -1.3784e+01  4.6990e+00 -6.3731e+00
-#>   3.5866e+00 -2.8282e+00 -4.5961e+00  1.0287e+00  2.9950e+00 -7.6990e+00
-#>  -3.3657e-01  1.6978e+00 -9.6235e+00  8.0490e-01  4.4091e-01  1.0902e+01
-#>   2.5087e+00  5.0536e+00 -3.8724e+00  1.0526e+01  1.6037e+01 -2.1520e-01
-#>  -6.3437e-01 -1.1003e+00  8.1602e+00 -4.9335e+00 -1.2635e+01  9.8150e+00
-#>  -2.1249e+00  3.7480e+00  7.7365e+00 -6.1236e+00  6.1852e+00 -1.3069e+01
-#>   7.3109e+00  1.2901e+01 -8.8473e+00 -3.7098e+00 -8.9201e+00 -6.5198e+00
-#>  -5.4844e+00 -2.5238e+00 -4.1557e+00 -1.8064e-01  5.5304e-01  6.7819e+00
-#>  -1.9159e+00  1.0311e+01  3.6737e+00 -1.1483e+01 -7.9527e+00 -6.1219e+00
-#>  -1.0295e+01  3.7689e+00 -1.4383e+01  1.2151e+01 -4.4508e+00 -8.2422e+00
-#>   4.2292e+00  3.6646e+00 -1.0031e+01  2.9930e+00  2.1887e+00 -7.5918e+00
-#>   9.7002e-01 -2.7367e+00  7.7447e+00 -1.0757e+01  6.6942e+00 -8.9944e+00
-#>   7.1615e+00  3.2208e+00 -8.1345e+00 -7.2343e+00  1.8508e+00  7.8587e-01
-#> 
-#> Columns 7 to 12  6.1971e-01 -3.5566e+00 -6.0313e+00 -9.2937e+00 -2.9808e+00  6.3143e-01
-#>   6.1591e+00  3.1784e+00 -1.4260e+01 -4.0602e+00  1.9287e+01  4.2227e+00
-#>   4.9860e+00  1.1289e+01  1.4267e+00 -1.4943e+00 -9.9253e+00  2.7712e+00
-#>  -5.2414e+00  1.2068e+01 -8.3227e+00 -5.1654e+00  4.5057e-02 -6.2535e+00
-#>   4.2992e+00 -5.7681e+00  8.0550e+00  7.8340e+00 -7.1266e+00  1.9123e+00
-#>   1.3649e+01 -8.0137e+00 -4.5874e+00  1.6664e+01 -6.1277e+00 -3.3137e+00
-#>  -7.7574e+00  6.1543e+00  7.9361e+00 -3.3967e+00  4.5374e+00 -1.9442e+00
-#>   1.8258e+00  2.5381e+00  4.9820e+00  6.2882e-01 -2.1422e+00 -1.5931e+01
-#>   2.7219e+00 -8.0431e+00  3.3396e+00 -4.2228e+00 -1.0965e+00  3.3755e+00
-#>  -2.8221e+00 -2.8199e+00 -7.3222e+00  2.3526e+00 -1.6953e+00  1.0169e+01
-#>   1.9237e+01 -7.6633e+00 -1.0494e+01 -5.6553e-01 -1.5653e+01 -1.5750e+01
-#>   1.7894e+00 -5.2464e+00 -5.6364e+00  7.6857e-01 -1.0334e+01 -1.7855e+01
-#>   1.8345e+00 -7.4619e+00  6.2523e+00  1.5827e+01 -7.7351e-01 -1.5328e-01
-#>  -5.8410e+00 -7.1343e+00 -4.4032e+00  4.0195e-01  3.6980e+00  6.1992e+00
-#>  -4.7707e-02  1.5093e+01 -1.3516e+01 -4.6621e-01 -1.1089e+01  3.5860e-01
-#>  -1.9751e+01  1.6217e+01  1.2023e+01  2.6548e+00  2.0095e+01 -1.4241e+01
-#>   6.4834e-01  1.0354e+01 -9.9857e+00 -4.7512e+00 -3.9769e-01 -1.2231e+01
-#>  -1.1477e+01  6.8674e+00 -3.0546e+00 -5.0975e+00  2.7640e+00 -7.8965e+00
-#>  -3.2730e+00  7.8723e-01 -4.4877e-02  7.8095e+00  1.6561e+01  4.8170e-01
-#>   1.2527e+00 -8.8599e+00 -1.6237e+00  3.5457e+00 -5.1429e+00 -4.6922e+00
-#>   1.3649e+00  4.6369e+00 -1.6378e+00 -4.3386e+00  7.2234e+00 -7.3588e+00
-#>   9.8566e+00  8.9358e+00 -1.2413e+01 -5.5862e+00 -1.2195e+01  3.5331e-01
-#>  -6.1922e+00  1.8767e+00 -2.6397e+00 -7.5956e+00  4.6306e+00  5.0578e+00
-#>  -1.8053e+00 -9.5046e-01  2.7005e+00  3.5801e+00  4.6201e+00 -1.5657e+00
-#>  -8.2460e+00  6.8687e+00 -1.0745e+01 -1.4851e+01 -6.3045e+00  1.3622e+00
-#>   9.1886e+00 -1.7410e+00 -4.4931e-01 -2.5491e+00 -4.5174e+00  1.1401e+00
-#>  -2.4219e+00  1.1363e+01 -5.0151e+00 -7.2247e+00  3.0972e+00  1.2977e+01
-#>   8.8036e+00  7.9221e+00 -5.6821e+00  5.5599e+00  6.7689e+00  8.8789e+00
-#>   1.2687e+00  1.1066e+01  8.3913e+00 -5.0425e-01  1.0345e+01  1.3019e+01
-#>  -1.8805e+01  4.4200e+00 -3.2719e+00 -3.9947e+00 -8.4469e+00 -6.8574e+00
-#>   1.2520e+00 -1.8530e+00  6.9782e-01 -6.9486e+00 -1.1507e+01  1.4238e+01
-#>   4.3166e+00 -1.6821e+00 -1.5755e+00 -1.6891e+01 -2.0169e+00 -8.6049e+00
-#>   1.1485e+01 -9.6323e-01 -3.6946e+00 -4.7002e+00 -6.9156e+00 -3.2433e+00
-#> 
-#> Columns 13 to 18  7.1991e+00 -3.1020e+00 -1.0215e+01  7.3400e+00 -3.8720e+00 -3.3105e+00
-#>   7.5273e+00 -1.8029e+00  1.1510e+01 -6.4125e+00 -5.0547e+00 -9.4835e+00
-#>   1.0358e+00  9.7088e+00 -2.3843e+00 -1.5570e+01  2.7400e+00 -2.6007e+00
-#>   1.7425e+01 -1.6332e+01  6.0853e+00  6.0137e+00 -3.3011e+00 -1.4179e+00
-#>   9.4562e+00 -1.8261e+01 -7.5022e+00 -7.5939e+00 -7.0870e+00 -7.4055e-01
-#>  -3.9737e+00  1.8817e+00 -8.4407e+00  8.2835e+00 -4.1121e+00  7.4358e+00
-#>   6.4169e+00  1.1986e+00 -1.8904e+01  1.0954e+01 -6.5748e-01 -7.1496e+00
-#>  -1.8261e+00  5.0867e-01 -1.4623e+01 -3.4692e+00 -4.8791e+00 -1.0374e+00
-#>  -5.0341e+00 -1.4850e+01  2.4858e+00  1.1707e+01  5.5279e+00  5.3110e+00
-#>  -2.7140e+00  9.7383e-01  4.0812e+00 -3.3657e+00  6.4222e+00  1.9005e+00
-#>  -1.6522e+01 -1.1063e+01  6.8178e+00 -2.1295e+01  7.5161e+00  3.4128e+00
-#>  -8.0671e+00 -1.9479e+01 -4.5783e-01  1.4737e+00  1.6592e-01  6.2285e+00
-#>   1.0884e+00 -1.0263e+01  1.7437e+01  2.9594e+00 -2.4843e+00  6.6805e+00
-#>   1.6568e+01  8.4857e-01  1.1532e+00 -3.9707e+00  7.1131e+00 -6.0201e+00
-#>   6.9973e+00 -3.9699e+00  2.1605e+00  7.8246e+00  8.5465e+00 -1.2609e+01
-#>  -1.0380e+01  3.2347e+00 -5.6959e+00 -8.4189e-01  6.5300e+00 -2.4554e+01
-#>  -1.1610e+01  4.5740e+00 -3.2509e+00 -1.4320e+01 -2.0172e+00  3.4778e-01
-#>  -1.8972e+00  7.9535e+00 -1.1391e+01  8.0844e-01  9.2623e-01 -7.4139e-01
-#>   3.2082e+00  1.7688e+00  6.5023e+00  6.8089e+00  5.2405e+00 -4.9714e+00
-#>   5.2694e+00 -1.3602e+00  1.5576e+00  1.1241e+01  4.4270e+00 -1.4899e+00
-#>   6.5272e+00 -6.2469e-01  7.4468e-01 -8.0070e+00 -5.7550e+00 -3.5396e-01
-#>  -4.3746e+00  1.2775e+01  7.9490e+00  3.3861e+00  1.5139e+00 -2.3440e+00
-#>   2.9495e+00 -7.8901e+00  4.4748e+00 -5.6787e+00  2.1329e+00 -1.4135e+01
-#>  -1.9857e+00  4.7930e+00  1.0535e+00  1.0709e+00 -3.0928e+00  6.7112e+00
-#>  -6.3406e+00 -3.5523e+00  1.2149e+01 -6.1518e-01  1.1473e+01 -6.0265e+00
-#>  -8.7913e+00  5.0781e+00 -9.3632e-01  1.7481e+01  6.8463e-01  6.5693e+00
-#>  -1.1081e+01 -3.8875e+00 -1.0769e+01 -2.0818e+01  1.7809e+00 -6.9261e-01
-#>   6.7785e+00  9.7152e-01 -7.7956e+00 -1.0056e+01  6.0275e+00  5.2517e+00
-#>   1.1351e-01 -9.0045e+00  9.5697e+00  2.3714e-01 -9.0294e+00 -2.9469e+00
-#>   1.3735e+01  3.8854e+00 -2.9617e+00  1.0569e+00  1.4450e+01  1.2248e+01
-#>  -2.5803e+00  5.4637e+00  1.1467e+01 -4.1106e+00  5.5802e+00 -3.3711e+00
-#>  -5.2019e+00  4.9950e+00 -1.9193e+01  1.9517e+01 -4.0764e+00  1.0695e+01
-#>  -9.5082e+00  1.0037e+01 -1.1583e+01 -8.1655e+00 -1.2235e+01  1.6803e+01
-#> 
-#> Columns 19 to 24  2.2936e-01 -1.6888e+01  1.3698e+01 -6.3099e+00  9.9215e-02 -2.9322e+00
-#>   1.9769e+00  3.4036e+00 -7.5839e+00 -7.6613e+00  1.7599e-02  1.2105e+00
-#>  -1.8907e+01 -2.0247e+00  3.6977e+00 -5.8704e+00 -6.2005e+00  1.5775e+01
-#>   6.5952e+00  7.7901e+00 -1.2322e+01  7.3395e-01 -9.5554e+00 -4.4870e+00
-#>  -1.4227e+01 -2.1848e-01  2.5902e+00 -5.1566e+00 -2.3201e+01 -2.9847e+00
-#>   8.3254e+00 -2.6919e+00 -7.2369e+00 -2.7206e-01 -3.8869e-01 -1.1716e+00
-#>   4.6815e+00 -1.1390e-01 -3.7367e+00  3.8517e+00 -1.4061e+00 -6.6726e+00
-#>  -7.7048e+00  2.2018e+00  5.9940e+00  6.2055e+00  8.6355e+00  4.7104e+00
-#>  -4.9984e+00  1.8139e+00  8.1921e+00 -6.0335e+00 -5.7494e+00 -1.3691e+00
-#>   3.3706e+00 -1.1477e+01  1.1708e-01 -1.2528e+01 -4.0107e+00  6.8041e+00
-#>   2.3757e+01 -7.6485e+00  7.0026e+00 -1.4405e+01  1.2900e-01  6.8069e+00
-#>   1.7010e+00  1.7767e+01 -9.5315e+00 -3.5809e+00 -8.8127e+00 -5.7658e+00
-#>   6.8095e+00  4.8048e+00  7.5958e+00 -1.7835e+00 -5.2495e+00  1.0999e+01
-#>  -1.2062e+00 -1.4216e+01  1.6161e+00 -4.8818e+00  9.8680e+00 -4.7297e+00
-#>   4.6286e+00 -2.3997e+00  9.1565e+00 -1.8091e+01 -1.8798e+01 -1.7613e+00
-#>  -3.3277e+00 -9.2900e+00  6.0133e+00  3.0082e+00 -2.0833e+00 -7.1763e+00
-#>   4.2549e+00 -8.8263e+00 -7.8801e+00  3.9957e-01 -3.5176e+00 -1.8507e+00
-#>  -4.9498e+00  1.5408e+01 -8.1279e+00 -7.4771e+00  2.3541e+00  5.4949e+00
-#>   4.1214e+00 -1.2128e+00  1.2296e+01  7.6001e+00  1.4415e+01 -1.6055e+00
-#>  -4.9501e+00  1.3041e+01 -4.9621e-01  1.8703e+01 -1.8289e+01  1.0630e+01
-#>   1.0798e+00  1.5102e+00 -2.2588e+01 -1.9352e+00  1.3287e+01 -4.5660e+00
-#>   1.4408e+01 -7.8796e+00 -1.0958e+01  3.5238e+00  1.0787e+00  1.1946e+01
-#>  -2.9766e+00 -3.4401e+00  2.6354e+00 -1.8404e+01 -8.8099e+00  8.9852e+00
-#>   1.6289e+01 -6.3441e+00  1.6091e+01  1.5075e+00  1.1968e+01 -2.3290e+00
-#>   5.2946e+00  6.6794e+00 -3.5291e+00  2.2196e-01 -2.1849e+00 -7.8252e+00
-#>  -3.6348e+00  1.4114e+01 -2.7772e+00  8.4282e+00 -1.0159e+01  1.4503e+01
-#>  -7.9663e+00 -1.0334e+00 -3.8956e+00 -9.7743e+00 -7.9716e+00  2.0617e+00
-#>  -2.1056e+00 -1.1841e+01  4.4612e+00  1.1785e+01  9.6248e+00 -2.4593e+00
-#>  -7.4521e+00 -2.6863e+00  6.6458e+00  8.8731e+00 -2.6243e+00 -7.9622e+00
-#>  -1.4993e+01 -7.1811e+00 -1.8982e+00  1.7245e+01 -1.9409e+01 -7.2242e-01
-#>   7.3318e+00 -1.8824e+00  4.5499e+00 -7.1541e+00  1.9782e+00 -3.5518e+00
-#>  -4.1648e+00  1.9168e+01 -1.1092e+01 -1.5164e+01 -3.6274e+00  1.6950e+00
-#>   2.6833e+01 -1.0341e+01 -1.7529e+01 -1.1678e+01 -3.8251e+00 -1.9653e+01
-#> 
-#> Columns 25 to 30  8.9089e+00 -2.2109e+01  1.4243e+01 -8.6281e+00  4.8957e+00  6.5300e+00
-#>  -1.6790e+01  2.2822e+00  1.2295e+01 -1.3033e+01 -3.7050e+00  1.8875e+01
-#>  -1.0381e+01 -3.2040e-01 -7.3908e+00  1.1772e+01  4.1685e+00 -1.2452e+01
-#>  -1.0310e+01  4.5585e+00 -1.0004e+01  1.9990e+00  5.6939e+00  2.0298e+00
-#>  -1.0004e+01  1.0409e+00 -1.3124e+01 -2.0799e+00 -7.1760e+00  7.3562e+00
-#>  -6.0570e+00 -5.3023e+00 -2.1541e+01  1.9820e+00  7.8665e+00  6.3329e+00
-#>   6.2140e+00  3.1022e+01 -1.2335e+01 -1.1288e+01  1.1501e+01 -4.5731e-01
-#>  -1.8300e+00 -1.7596e+01  4.1389e-01 -1.1390e+01  2.6726e+00  2.1959e+00
-#>  -1.4649e+00 -8.5470e-01  5.2110e+00  1.0870e+01 -5.0161e+00 -5.2889e+00
-#>  -8.2089e+00 -8.4885e+00 -7.0544e+00  1.2611e+00  3.7091e+00  6.7700e+00
-#>   7.2513e+00 -5.8998e+00 -8.3026e+00 -3.4304e+00  1.7832e+01 -1.1176e+01
-#>  -9.1592e+00 -8.7092e-01 -1.0351e+01 -1.0681e+01 -1.2339e+01  6.6825e+00
-#>  -6.7008e-01 -4.8440e+00  2.3226e-01 -2.9946e-02  5.6478e+00  1.3199e+01
-#>  -1.2542e+01 -5.9341e+00  1.2123e+01  1.3029e+01 -1.2662e+01 -4.4325e+00
-#>  -6.6169e+00  1.6597e+00 -3.5043e+00 -1.1193e+01  8.6377e+00 -5.4953e+00
-#>  -5.0659e+00  2.7551e-01  1.3744e+01 -3.0966e+00  1.8892e+00 -4.4114e+00
-#>  -8.6836e+00 -2.4412e+00 -1.4585e+01  4.2378e+00 -6.7566e+00 -8.1642e+00
-#>  -1.2766e+01 -6.4137e+00 -8.1060e+00  4.6149e+00 -9.7195e+00 -7.7202e+00
-#>  -1.4313e+01 -6.9195e+00  1.1693e+01 -1.4572e+00 -3.4242e+00 -1.8018e+00
-#>  -1.0537e+01  1.6098e-01 -1.2272e+01  2.7677e+00 -8.9725e+00 -6.2632e+00
-#>  -6.2350e+00  6.9955e+00  4.7859e+00 -2.2685e+00  6.8022e+00  2.9070e+00
-#>   4.5809e-01 -8.3880e+00 -1.9776e+01  5.2140e+00  3.7813e+00 -6.2155e+00
-#>   3.0042e+00  1.4669e+01  6.6397e+00 -1.2993e+01  6.9356e+00  2.2650e+00
-#>   1.1576e+01 -1.2529e+01  6.9593e+00  1.8562e+01  2.0904e+01  9.1805e+00
-#>   3.0764e+00  1.5342e+00  1.4406e+00  2.7671e-01  8.5106e+00  1.1056e+00
-#>   1.0280e+00  4.0112e+00  3.4976e+00 -3.5274e-02 -5.2699e+00 -3.1552e+00
-#>   1.1858e+01 -4.7703e+00  5.7537e+00  2.1920e+01  5.4029e+00  5.0997e+00
-#>   1.2290e+01  6.4362e+00  4.0488e+00  1.4499e+01 -6.0501e+00 -3.2366e+00
-#>   6.2689e+00  7.3161e-01 -1.2452e+00  1.4861e+01 -8.7672e+00 -1.1689e+01
-#>   1.1985e+01 -1.7174e+01 -1.0914e+01  7.4671e+00 -7.0140e+00 -3.4464e+00
-#>  -7.8820e+00  3.4978e+00  3.0521e+00 -1.5181e+01  1.3071e+01 -1.4274e+01
-#>   1.9783e-01 -1.6555e-01 -2.6493e+00  1.3774e+01  4.8866e-01 -1.0697e+01
-#>   7.7743e+00 -1.2427e+01 -5.4104e+00 -1.0587e+01  1.4412e+01  5.0859e+00
-#> 
-#> Columns 31 to 36 -1.1357e+01  1.1981e+01  9.9716e+00  1.1857e+01 -1.8115e+00 -1.4695e+01
-#>   7.3422e+00  1.5548e+01  3.2082e+00  1.0147e+01  1.4100e+01  1.4162e+01
-#>   3.0844e+00 -7.3096e+00  6.9814e+00  2.2799e+00  5.8196e-01 -4.6313e+00
-#>   7.6082e-02 -9.9290e+00 -1.0213e+01 -8.2199e+00 -3.0799e+00 -8.5401e-01
-#>  -3.4770e+00  1.3473e+00  1.4197e+01 -1.0314e-01  9.3214e+00 -4.5633e-01
-#>   1.0109e+01 -1.0152e+01  5.8044e+00  1.2882e+01 -4.3226e+00  1.0007e+01
-#>   7.5746e-01  2.2464e+00  1.1969e+01 -8.5846e-01  1.3138e+01  9.1428e+00
-#>  -4.1221e+00 -2.3758e+00 -6.9433e+00 -7.5041e+00 -6.2797e+00 -4.0670e+00
-#>  -5.4838e+00  5.2215e+00  1.3257e+01  1.2602e+01  8.5995e+00  1.5516e+01
-#>   1.5377e+01  7.5229e-01  2.2312e+00  3.4551e-01  1.2492e+00  5.7592e+00
-#>   9.3748e+00 -4.4612e+00 -1.1006e+01 -2.0006e+00  1.4334e-01 -1.1820e+01
-#>   1.6497e+01 -8.6081e+00 -6.0907e+00  5.8540e-01 -2.5686e+00  2.6386e+00
-#>  -9.0482e+00  1.9906e+01  2.4323e+00  2.7765e+00  2.0396e+00  8.2006e+00
-#>  -3.8722e+00 -8.3080e+00 -2.0545e+01  3.4558e+00 -2.0635e-01 -3.7895e+00
-#>   9.1895e+00 -2.5371e+00  8.2614e+00  4.9813e-01 -5.2952e+00  3.0628e+00
-#>   1.1221e+01  8.7408e+00  1.2788e+01 -4.7302e+00  2.2632e+00  7.0985e+00
-#>  -7.5462e-03  2.7481e+00 -8.8928e-01  1.5583e+00  1.2498e+00  1.1898e+01
-#>  -2.0618e+00 -1.0152e+00  7.9617e-01 -8.6114e+00 -6.5780e+00  2.5092e+00
-#>  -5.6837e+00  3.7266e-01  1.2264e+01 -5.9856e+00  2.4086e+00  1.5265e+00
-#>   9.8641e-01  1.1195e+01 -9.3773e+00 -6.0877e+00 -2.7005e+00 -9.2722e+00
-#>  -5.5643e+00  2.6269e+00  5.6519e+00  2.0768e+01  1.3134e+01 -4.0507e+00
-#>   6.6763e+00 -1.2067e+01  8.3925e+00  4.6361e+00  4.4979e+00  4.3787e-01
-#>  -4.4566e+00  4.6770e+00  7.7471e+00  5.9833e+00 -3.9309e+00 -1.0741e+01
-#>  -1.5834e+01  3.2567e+00  2.3690e-01  9.5044e+00 -1.1210e+00 -1.2841e-02
-#>  -2.4358e+00 -5.8075e-01 -5.3243e+00  1.2629e+01  8.5652e+00  1.7653e+01
-#>   9.1133e+00 -1.1740e+01 -3.6817e-01 -5.1786e+00 -1.1297e+01  4.8665e+00
-#>   8.5231e-01 -1.4404e+01  2.4547e+00  1.9263e+01  3.3342e+00  9.9445e-01
-#>  -6.6792e-01  3.1188e+00 -9.2717e+00  1.3892e+00 -1.7736e+00  1.7867e+00
-#>  -3.4408e+00  7.4662e+00  1.0113e+01 -1.6295e+00 -3.7887e+00 -6.8620e+00
-#>  -5.6302e+00  8.0703e+00  6.8752e-01  1.4518e+01 -9.6824e-01  1.1248e+01
-#>  -1.8732e+01 -4.1066e+00  1.1552e+01  1.5887e+00 -3.4372e+00 -3.8972e+00
-#>   6.2236e+00 -1.1276e+01 -2.0439e+00 -3.1262e+00  1.2531e+00  2.0769e+00
-#>   1.0418e+01  5.4322e+00  1.2515e+00  9.2656e+00  2.2542e+01  1.0728e+01
-#> 
-#> Columns 37 to 42  6.0988e+00 -8.0439e-01  1.8655e+00 -5.1543e+00 -1.9880e+01 -1.4004e+01
-#>  -1.9940e-01 -6.6575e+00  1.6445e+01 -1.5268e+01 -1.3676e+01  2.8681e+00
-#>  -6.7479e+00 -9.1959e+00 -3.7102e+00  1.6594e+01  5.5031e-01  5.4496e+00
-#>  -6.0228e+00  2.1354e-01  8.0222e+00 -6.6496e-02 -6.0220e+00  1.3493e+01
-#>   2.6637e+01  3.2170e+00  7.4214e+00  2.3912e+01 -1.2703e+01  4.4444e+00
-#>   9.1571e+00  1.4790e+00  9.4493e-01  2.1464e+01 -9.7413e+00  6.8209e+00
-#>  -6.4138e+00 -1.7047e+01 -2.1116e+00 -5.2546e+00 -7.3529e+00 -9.4311e+00
-#>  -7.6436e+00  1.5305e-01 -3.3620e+00  1.1230e+01  3.0282e-01 -9.2001e+00
-#>   6.4844e+00  9.1075e+00  5.0587e+00 -8.0096e+00 -4.7839e+00  9.3991e-01
-#>  -1.8610e+00  1.5192e+01 -8.9527e+00  2.0508e+00  6.9849e-01  1.4954e+01
-#>  -1.0395e+00 -3.6823e+00 -7.6426e-01 -6.1870e+00  1.3009e+01  1.7793e+01
-#>  -1.3253e+01 -1.1113e+01  3.0313e+00  7.1198e+00 -2.9371e+00  8.8453e+00
-#>   8.1860e+00 -1.3867e+01  6.3761e+00 -1.4327e+01 -6.9700e-01  1.3793e+01
-#>  -1.2574e+00 -1.6937e+00 -3.5716e+00 -1.3432e+01  2.9394e+00  4.8733e+00
-#>   1.1381e+01  2.4025e+01 -4.6198e+00  5.5551e+00 -9.6417e+00 -1.0214e+01
-#>  -1.2939e+00  4.9727e+00  7.6788e+00 -3.4079e+00  5.5547e+00 -7.1482e+00
-#>   1.2859e+01  6.2153e+00  4.7319e-01  4.6990e+00 -5.7847e+00 -1.0949e+01
-#>  -2.2701e+01  1.3047e+01  5.7216e+00 -7.4328e+00 -1.2135e+01  3.5048e-01
-#>   1.4917e+01 -8.3515e+00 -1.0262e+01  8.2380e-01  6.2886e+00 -9.5574e-02
-#>  -6.3037e+00 -4.2987e-04 -2.0615e+01  2.1270e+01  1.2580e+00  1.0511e+01
-#>  -6.2005e+00 -3.6623e+00  4.5152e+00 -9.2875e+00 -3.2735e+00  6.2376e+00
-#>  -8.5251e+00  5.6726e+00 -1.0867e+01  3.3117e+00  1.0386e+01 -4.3640e+00
-#>   2.3705e+00 -8.4754e+00  7.1369e-01  1.0543e+01  3.1853e+00  8.7148e+00
-#>  -1.0430e+01 -2.7178e+01  5.6021e+00  6.4076e-01  8.2638e+00  1.2219e-01
-#>   1.0713e+00  7.1091e+00 -6.5412e+00 -1.4832e+00  1.4617e+01  8.5215e+00
-#>  -5.7575e+00  5.8882e+00  6.2337e+00  7.9177e+00 -2.3209e+00 -1.0861e+01
-#>  -7.8439e+00 -3.4696e+00 -4.0875e+00 -9.2443e+00 -4.3091e+00 -7.2878e+00
-#>  -1.6487e+00  6.6453e+00 -6.2274e+00  1.2108e+01  7.4702e+00  3.9402e+00
-#>   1.1521e+01  5.7844e+00  1.4997e+01 -6.6601e+00 -7.1250e+00 -5.4158e-02
-#>   1.6169e+01  1.8308e+01 -9.9830e+00  1.2630e+01 -5.8414e+00 -1.6383e+01
-#>   3.4601e+00  4.3866e+00  3.0461e+00  5.2795e+00 -1.1307e-01 -7.0420e-02
-#>  -6.7810e+00 -9.2056e+00 -6.5165e+00  1.7744e+01  6.2753e+00  1.7646e+00
-#>   2.3100e+00 -7.1944e+00 -9.5489e+00 -7.0821e+00 -4.1903e+00  9.0515e+00
-#> 
-#> Columns 43 to 48  1.8651e+01  5.8580e+00 -2.5540e+00  5.9798e+00  1.3616e+01  5.9374e+00
-#>   1.4042e+00 -7.1437e+00  5.3403e+00  4.8087e+00 -4.3136e+00 -4.4749e+00
-#>   4.2473e+00 -3.3378e+00 -5.9252e+00  8.7385e+00 -2.1737e+00  5.5311e+00
-#>  -7.1610e+00  1.5589e+01 -8.4832e+00 -1.0002e+01  8.0409e+00 -1.4458e+01
-#>   9.0307e+00  7.9130e+00 -1.3612e+01  3.4962e-01  8.6784e+00 -4.7658e+00
-#>  -3.8549e+00  2.0299e-01 -7.7250e+00 -4.7844e+00 -1.3173e+01  2.1174e+01
-#>  -9.5103e+00  6.2254e+00  1.5409e+01 -2.4048e+00  8.4910e+00 -7.8213e+00
-#>   1.4605e+01 -1.4826e+01  4.2888e+00 -1.0561e+01 -5.2321e+00 -1.0529e+00
-#>  -7.9933e-01 -3.1178e-02  5.4608e-01  6.5385e+00  9.7176e+00  2.0845e+01
-#>  -5.3888e+00  2.0419e+01 -2.5235e+01  4.9453e-01  4.0366e-01  1.4562e+01
-#>  -1.5129e+00 -1.3035e+01  5.7489e+00  3.9341e+00 -7.8148e+00  2.0222e+01
-#>   1.7552e+01  3.7504e+00  9.3178e+00 -3.3478e+00 -1.0304e+01  1.3753e+00
-#>   1.0735e+01 -9.2614e+00 -4.0927e+00  1.0137e+01  6.5703e-01  6.3034e+00
-#>  -1.8100e+01 -4.4140e-01 -3.2765e+00  1.1385e+01  8.2657e-01 -9.7269e+00
-#>   7.4825e+00  3.7591e+00 -9.3485e+00 -2.8601e+00 -5.7009e+00 -1.0053e+01
-#>   2.8396e-01 -1.3266e+01 -1.0565e+01  4.4111e+00  1.4520e+01 -1.1182e+01
-#>  -1.6583e+01  1.5507e+00 -1.2863e+01  9.1985e+00  1.0691e+01  2.8953e+00
-#>  -1.2593e+01 -1.3589e-01 -9.3040e+00  1.3394e+01 -1.0354e+01  1.3999e+01
-#>   8.2749e+00  7.5436e+00  1.7821e-01  1.1863e+01  6.5062e+00 -5.8205e+00
-#>  -7.1312e+00  1.1181e+01  3.9970e+00 -1.1016e+01  8.4926e+00 -1.1297e+01
-#>  -1.1315e+01 -2.8348e+00  9.9462e+00 -1.4857e+00 -2.0638e+00  6.0903e+00
-#>   1.4766e+01  1.5735e+01  6.4633e+00 -1.0304e+01  1.8137e+01  1.3001e+01
-#>   2.0535e+00  8.0652e+00 -6.2877e+00  1.0530e+01 -3.1022e+00 -1.1258e+01
-#>   1.7652e+01  1.0152e+01 -2.7717e+00  9.9863e+00  1.9430e+01  1.6897e+00
-#>  -3.1389e+00  1.5090e+01  5.4868e+00 -1.5961e+00 -1.1954e+01  2.6257e+00
-#>   2.6598e+00  4.5923e-02  1.9518e+00  3.6742e+00  4.0554e+00 -4.1224e+00
-#>   5.6864e+00  7.4536e-01  1.2337e+00 -9.2960e+00  6.0701e+00  6.4188e+00
-#>  -3.7751e+00  8.2547e+00 -1.3889e+01  8.6632e+00 -1.6368e+01 -8.1808e+00
-#>  -1.3147e+01  1.0182e+01 -9.7701e+00 -6.5206e+00 -1.8821e+00 -3.4522e+00
-#>   3.7844e+00  1.0395e+01 -9.9069e-01 -8.6665e+00  9.4882e+00 -1.9181e-02
-#>  -2.2905e+00 -4.3181e+00  6.0473e+00  7.4193e+00 -1.3721e+01 -1.1281e+00
-#>   5.2884e+00  1.1093e+00  9.2624e+00 -6.7758e-01  1.8620e+00 -6.4113e+00
-#>   8.1688e+00  9.3747e+00  4.6817e+00 -1.0918e+01  1.2850e+01  2.5548e+01
-#> 
-#> Columns 49 to 54 -7.9121e-02 -9.1412e+00 -1.0781e+00  8.9441e-02 -2.5786e+00 -2.5158e-01
-#>   1.3240e+01 -2.2763e+00  4.5066e+00  3.8285e+00  6.0378e+00  4.7827e+00
-#>  -3.1463e+00  2.6070e+00 -8.4117e+00  1.5027e+00  8.7442e+00  4.2795e+00
-#>   1.3520e+00 -5.2432e+00 -2.7057e+00 -9.7851e-01 -8.7705e-01 -3.2616e-01
-#>   5.5362e+00  1.0861e+01  7.3578e+00  1.9651e+00 -1.9482e+00  3.4265e+00
-#>  -1.0524e+01  5.4771e+00  8.4625e+00 -8.9160e-01  3.9857e+00  3.4231e+00
-#>   1.0566e+01  9.0016e+00 -1.3289e+01 -4.3572e+00  1.4488e+00  2.0633e-01
-#>  -1.1994e+00 -1.0704e+01  3.1709e+00 -1.4812e+00  1.5413e+00  5.3184e-01
-#>   8.8366e+00  9.7180e+00  1.0314e+01  1.0961e+01  3.0913e+00  1.0606e-01
-#>  -1.3399e+01  2.8587e+00 -1.0157e+01 -5.0444e-01 -4.7002e+00 -6.9904e-01
-#>   5.0243e+00  2.1031e+00  2.2536e+00  3.3043e+00  3.4988e+00 -1.4744e+00
-#>  -4.8099e+00 -1.8123e+00  5.6880e+00 -5.0763e-01  5.4416e+00 -2.5526e+00
-#>  -4.8819e+00 -1.3431e+00  6.0133e-01  2.1635e+00 -3.5245e+00  2.3077e+00
-#>  -3.7879e+00 -5.3912e+00 -4.9927e+00  7.2770e-01  6.8276e+00 -2.4188e+00
-#>   1.3457e+01  1.4833e+01  2.3735e+00  7.1478e-01 -1.3741e+00 -1.6697e+00
-#>   3.7287e+00  1.2958e+01 -2.6546e-02  9.0811e+00  1.4764e+00 -5.4536e+00
-#>  -7.3302e+00  1.3150e+00 -7.3113e-01  1.1774e+01  4.5585e+00  2.0654e-01
-#>  -7.4635e+00 -3.2945e+00 -1.6004e+01  1.0148e+00  4.5388e+00 -2.1846e+00
-#>   4.0827e+00  4.6811e+00  1.1506e+00  8.8552e-01 -2.1057e+00  2.4387e+00
-#>  -6.3624e+00 -9.1315e+00 -1.6520e-02  4.3562e-01 -4.4460e+00 -1.1886e+00
-#>   5.0401e+00  5.8519e+00  9.1493e+00 -4.4009e+00  6.4286e+00 -1.5992e+00
-#>   2.8538e-01 -9.3507e-02 -3.7667e+00  4.3281e+00  2.3204e+00 -3.5145e-01
-#>   1.6218e+00  5.5460e+00 -7.3334e+00 -5.5465e+00 -5.7455e+00  3.0386e+00
-#>  -2.5979e+00  4.6217e-01 -3.6948e+00 -9.4825e-01  4.1991e+00  2.1228e+00
-#>   7.4229e+00  3.5157e+00  2.8468e+00 -3.2869e+00  3.3928e+00  1.8340e+00
-#>   8.2374e+00  2.4794e+00 -7.6380e-01 -7.9588e+00 -4.9124e+00 -1.4156e+00
-#>  -1.3288e+01 -2.0089e+00 -6.0241e+00  1.8306e+00  3.4282e+00 -1.2858e+00
-#>  -1.3545e+01  1.6046e+00 -1.0394e+00  2.9090e-01  1.8811e+00  5.5214e-01
-#>   2.4362e+00 -1.8237e+00  1.6450e+00  3.1722e+00  4.5288e+00  5.4734e+00
-#>  -1.0636e+00 -3.5529e+00  3.3539e+00  2.2426e+00 -4.1244e-01  1.8183e+00
-#>   1.4988e+00  2.4386e+00  2.3093e+00  1.1566e+00  4.6186e-01  1.3810e+00
-#>   1.6040e+01 -9.8615e+00 -1.0179e+01 -8.8022e+00 -3.5965e+00 -2.8926e+00
-#>   4.9511e-01  4.6599e+00 -1.6281e+00  3.8184e+00  5.6074e+00 -5.0963e-01
+#>  Columns 1 to 8   4.6859   4.1131 -10.9593  -5.3069  -1.0974  21.6343  13.6091 -17.2558
+#>    2.0795  -3.6493   3.2076  -1.6711   8.9436 -18.0291  -0.7971  -3.2565
+#>   -3.9027  -4.2938  -0.0134  -1.1303 -19.3438  13.8252  -7.1970 -11.2471
+#>   -0.0801   2.3289  -0.7113   0.0903  -5.4598  12.1508  -9.1313  -1.3847
+#>   -6.1731  10.5736  -5.7994  -3.1141  13.6859   1.4036  -2.1168  -5.9932
+#>    2.4950   2.2010 -12.0723   2.8580  -5.8241   1.4615  -4.1857 -12.2922
+#>    4.7572  -3.6669   6.6668  -6.1736   9.5856  -8.1682   1.5696  -4.3483
+#>    1.5150   0.7789  -7.0650  -1.0792   2.6825   2.1119   4.6470   6.2645
+#>  -10.7507  -2.7751   4.3205  -4.4414  -2.8109  -3.4361  -8.7092  -6.7775
+#>  -10.8596   6.5341   7.6802  -2.7770  -4.4282 -11.5187  -4.9560  -3.8449
+#>    1.2454   3.7008  -6.8420   6.1895   4.6964  -1.0605   8.2338  -5.2163
+#>    0.5068  10.6750   2.1842   9.8193   0.8160 -10.6605 -13.9829  -1.9758
+#>   -0.8746   1.6290   2.2230   5.5392  -8.8911   6.6383  -3.6992  -9.8343
+#>   -4.4780  -3.2753   5.7312  -8.8011  20.8802  -3.1306  -7.6031  11.4210
+#>    4.7628  -9.5496  -7.8169  -1.8763  10.0707   3.0489  20.1976   3.3912
+#>    3.5079   5.0603  -4.0514   5.9147  -1.5210  -2.5629   1.6045 -13.0763
+#>    1.0827  -2.3540  10.7913  -0.2960   2.9449   5.5754  17.2647  -7.8718
+#>    2.4678  -0.6623   7.7071  -3.1429  -9.5457   3.3259   2.3223  -5.6405
+#>   -4.9396  12.0043   2.3191   9.3860  -4.9175  -4.0139 -12.1309   2.9604
+#>    1.6151 -11.2512   3.5507  -6.0524  -8.2152   6.1504   6.0037   5.3351
+#>    6.5633   5.6831   0.5622  -6.4501  11.7547  -0.0808  -8.9524  -4.5911
+#>    6.5665  -9.3553  -7.7590  11.1860 -11.7417  -3.3426   5.8870  18.7182
+#>   -5.9680  -4.4924   2.6471  -2.4900  14.9461  -5.4856  -0.3581  -5.6541
+#>    3.8303  -0.8612  -8.0441  11.2660   4.9639  -8.5106  13.2178  15.1816
+#>    0.0305   1.7321   6.8071  -9.4864  -4.2081  18.5272  -4.8900  -6.4309
+#>    2.7676   2.7745 -10.0156   1.3594   1.5707  16.4095  10.2932  -4.5799
+#>    2.4023   1.9727  -1.7814  11.8704   4.0050   7.4812   1.7863   1.4518
+#>    2.2568   8.5514 -10.5295  -8.2845 -12.4669  11.6911   7.9347   0.5729
+#>   -6.5498   7.0906   2.2620  -1.0590 -16.8576   4.4109   1.8163  -4.5055
+#>   -5.3145   2.9363  -9.1143   6.1756  -3.7082  -0.9154  10.7274  -6.5900
+#>    0.4356  11.3939  -9.1932  -5.4917   9.0205   8.5323   6.5337  -3.2082
+#>    1.1042 -15.2302  -6.9172  -2.0502  20.6166  12.2919   9.3639 -11.7269
+#>    1.1434  -8.3028   6.2365   5.7696  -5.1344  -5.3414  -1.9308 -12.6167
+#> 
+#> Columns 9 to 16   9.1021  11.3963   0.0517   2.5797  -1.8836  -6.3062  -1.5125  -0.2164
+#>    6.4254  13.2882   0.8365  -5.4874 -11.7463 -12.9738   1.1775  14.1190
+#>    6.2328  -7.0272  14.2463  18.3032  12.4710   7.4895  -3.9521   0.2015
+#>  -10.1326   0.7080   5.9389   0.8232  -0.9454  -4.4106   9.0112   2.7485
+#>    6.3682  16.9232  13.8757  -0.4842   1.6086   0.6329 -14.1301  -2.6297
+#>  -16.3091   6.9441  -1.7005  -4.7283   7.6129   1.3314  -6.9246  -0.7769
+#>   -6.1779   4.2079   1.6450   2.6515  -6.6223  -7.5085 -12.9131   3.9233
+#>   -6.7064  18.1316  -2.0671   7.9739  -7.0790   8.4678  -7.2208  -1.9009
+#>  -15.1592   1.5098  -2.7493  -5.9910  -5.2947   8.8131   1.3424  -7.1393
+#>    0.8283  -9.2183 -13.2099   5.2295  -9.7209  17.5325  12.1273  18.9702
+#>  -30.8453   3.4478  16.0666   5.7648  12.8819  -6.1747 -12.7869   0.1842
+#>    2.7594 -21.8979  -7.5436  -6.4978  -6.6699   0.0417  -0.7392  10.5569
+#>    9.2321   2.8777  -0.7252   3.2144   4.9839   7.0128  -9.4469  16.0423
+#>   -1.1630  12.0727   4.9134  -3.5467  -6.8740   2.6210   4.4240  -0.5614
+#>    3.7634  -7.6701  -3.2578  -3.3879  -4.0988  -4.7285  -8.8746 -11.3122
+#>    4.8912 -11.2458   4.3606   3.7666   8.8215 -11.0583   1.2629  -3.3356
+#>   -9.4644  15.2032  -2.0640 -12.3908  -4.9165   1.6231  -3.2193  -1.6620
+#>  -15.3968   6.5954  21.0896   4.5996   0.3185 -20.2904 -20.5485  -4.9448
+#>    2.9882  -8.1953  -8.8737 -11.3710  -6.4003  -1.8616   7.0272   8.5134
+#>   -5.4746  -9.2035   0.6705   2.6224  10.0923  14.7217   2.9133  -1.3263
+#>  -14.7742   1.6396  -2.4585  -1.0454  14.1353   6.5585  10.8306  -0.2323
+#>  -12.4131  18.7650  -3.6926  -1.0782  -3.0099  -5.9184   4.8794   4.2946
+#>  -10.3162   4.5767  -4.2125   2.6130  17.0691  -6.6392   5.7069   0.4860
+#>   -7.0846  10.6999  -3.1922  -4.8327  -5.6982  14.4594  -7.5793   1.7193
+#>    6.7061  -5.8098  -1.4005   0.7337  -3.8568  -9.5381   4.9706   5.3874
+#>   22.4558  11.6769   0.2725   0.6936  -2.8309   1.4876  -0.3943   1.7408
+#>    4.5192  -4.5664  -6.3206   2.9128  -5.3906 -11.6255   9.4269   3.7359
+#>    4.0353  -2.0850   2.2309  -8.3801   7.4280  -9.8267  -1.8911  -7.4902
+#>   10.6320 -16.0351 -14.9034  -7.4753  -3.0029   6.5873  12.4090  21.2092
+#>   10.5193   5.5001 -10.0292   4.5498  17.1837   4.0564  -3.3950   2.2623
+#>   -1.7210  -8.2200   5.5402  -5.7478  12.9917  -1.6978   4.4455   2.1951
+#>   20.2861  -0.2725   2.6812  -3.3156  17.0410  -9.6918   3.3910  -6.5238
+#>   -4.9195  -2.3295  -4.6651   7.6490  18.9711   8.2143   1.2420  -5.5669
+#> 
+#> Columns 17 to 24   0.3214  -3.8681  13.0062   0.8861   0.3557   2.9104  18.6002  -0.3745
+#>    7.3534  -1.8112  -0.9140  -1.8187   8.8806   6.4379 -13.0432 -25.5306
+#>   -4.7344   2.6456 -16.2878   9.7185  -6.0796   9.1387   4.4665   1.8055
+#>   -1.3921  -6.8190 -14.9453  -9.6039   1.2278   7.6483  -1.9714  -7.4935
+#>    8.6703  -5.2990  -5.1428 -13.4760  14.8302  14.2600   9.1663  -4.6199
+#>    4.8356   5.6312   9.6040 -18.6807  -3.4531  -3.8706   1.4035   6.0210
+#>    9.2248  13.0940  -6.4734  -7.7884 -10.0523 -13.6050  -9.0217   3.0239
+#>    5.2663  12.9650   7.7468  -3.9608  12.6007 -11.5812   8.4588   4.7509
+#>   -0.5834  -6.3832  -1.7120  -2.8817  18.3699  -0.0287  -2.1830  -6.5680
+#>   -0.6729 -10.1634  12.1604   3.0681   4.9471 -13.4872 -10.0827  -1.1156
+#>   -2.0517  10.4413  -4.9095 -27.6038 -22.6202  10.1480  17.4386  -0.6858
+#>  -14.5994   8.0607  -1.0870  -0.2354 -11.3591  -3.2987 -22.1263 -13.8195
+#>   -5.3956  11.5409   0.4443  -2.8709  -2.4923  -2.0867  -2.0201 -11.6207
+#>   12.5635   8.2138  -7.8983  -9.0094  -5.3698  14.9678   8.8916   4.3412
+#>   -9.5728  16.9806  -0.7743  -2.2287  -4.0398  -0.8629  12.0214  11.4347
+#>   10.7500   0.4572 -11.9303 -13.4179  -5.3441  -3.6433   5.1802   6.1838
+#>   10.5714   9.9106   7.7255 -10.7364  -5.7714 -10.6245   1.6796  17.7730
+#>   14.9344   1.3521  -3.6929 -16.7153 -11.1813  14.2468   1.0509  -0.3189
+#>   10.3767 -11.7552 -14.1134 -11.9555 -13.5678  -9.0238 -23.1570   6.9184
+#>  -12.9087   2.4511  -3.0182   8.3404  -5.3626   1.6748   2.6170  10.9101
+#>  -12.2371   4.2311  -8.0612  -3.1182 -21.5716   0.3783 -14.7003  11.2755
+#>    2.3543   9.2398  10.8943   3.5437   6.2693   4.3286  -1.0154  16.2127
+#>    8.1319  -7.2351   2.9895  -5.8137  -5.2356   0.5123  15.1932  -3.8965
+#>   -3.7209   6.2373   2.1414   7.3283   3.2835  -9.1617  -2.2624  12.6606
+#>    7.7459  10.1475   0.6106  17.8884   6.4427  -7.6953 -13.9603 -12.8029
+#>    3.1120   4.0695  -2.2866  11.3803   9.6135  -7.3300   3.4999  -2.5447
+#>   -0.4269  -0.1814   0.0400  -1.7797  -3.0033  -2.5191  10.9453  22.9289
+#>   -6.3394   1.6993   7.3857  -0.3888 -13.2625 -15.3248  -0.4494   0.9807
+#>   -1.1851   0.0683  -7.0141   0.1083   1.4989  -2.6782 -26.2903 -10.8566
+#>  -12.9193  -4.8826  -5.2862 -23.8343  11.8810   8.4221  -9.7082  -4.5909
+#>    4.8649  -7.2036  10.6279 -11.2238  -7.1414 -19.1860  -4.2480  -6.4341
+#>   12.4443  -4.5601   2.6290   1.9283   0.3679  14.7593  15.5449 -10.6803
+#>   -2.5364  -9.8297 -15.4493  -8.2267 -15.6129  13.9662   2.6087   8.1404
+#> 
+#> Columns 25 to 32   2.7611   2.3272   2.2703  11.8324  20.7194  -0.1435   7.0673  -0.5158
+#>   -8.6295  14.3645   5.4607  -7.1855  -9.7144   0.5462  -3.2703  -3.8569
+#>   -4.7064   6.6156   2.9861   2.6888   2.8411  -0.8180 -11.0941  -0.7763
+#>   -6.2306   0.5112  13.5425   6.5322  23.7617   0.9257  -5.7523  -8.4829
+#>    9.9884  -6.5912  -7.5777  -0.2900   9.9750  -3.8655  -7.3031   0.6106
+#>    9.5733  -2.3083   5.7018   7.8933   7.5142  -0.3586  13.9308  -2.1888
+#>   -0.4551   1.9882   7.8318 -11.4236   0.4450   0.3662   0.6143  -7.2142
+#>    9.8099   6.7530  -5.9614   9.5030 -10.6046   3.6326  -1.6353  -2.6347
+#>  -10.3842  -6.0572   4.4219  12.6185  -6.9640  -3.9483 -16.3170  -3.4683
+#>   -5.8878  -1.6757   0.3368   5.9224   7.3478   3.4951  -4.0444  12.6460
+#>   -2.7145  -0.3078  -1.0998   3.5983   0.3923  10.4494  -0.4025   6.9491
+#>   -5.9985  -2.5409  -0.7230   4.9602  -8.2388   7.0272 -17.3047   4.3018
+#>    4.4925  -2.9386   3.3164   3.4919  -0.1992   9.4277  -6.5478  -0.6053
+#>   -0.5302  10.7326  -3.5301   6.9691   2.1860  -9.1405   2.5006   1.6445
+#>    8.4418  -6.5366   7.9454  14.0289  -3.3132  -2.8501   5.6528  10.7641
+#>   14.3074   1.1101  -6.9249  -4.4225  -2.4290  13.5465  -2.9559  -3.8659
+#>    4.1826  -3.8453  -4.9055  -2.0063   7.8674  11.3759   6.8721   6.1481
+#>   -7.1646   6.4080   2.2303  -9.3383   2.4199  14.0747   4.2509 -11.3046
+#>    4.3393  15.2890 -12.7117  -8.0882 -17.4123   4.5570  -3.2498  13.8391
+#>    1.9676  -5.5145  11.7926  11.1104   7.5781  10.3390   1.0225  -1.9399
+#>   -3.4708  10.5718   4.4411  -3.2468  -6.5553  -5.4458  -4.3555  10.5811
+#>   18.6028  -3.6893 -12.1645 -11.4127   4.7428  -6.2613   7.8210 -13.3803
+#>  -10.1486   7.3556  12.4539  -1.3806   4.7041   6.7583  14.5490  -6.8778
+#>   -0.5740  -1.1135   4.9173  -2.1016  -5.2378  -4.6511  -4.9122   2.0312
+#>  -19.0756   2.8867  14.9448  -3.2497  16.0637  -1.4370  -6.9711  -5.2158
+#>   -6.8037   9.5856  12.5708  -3.5277   3.0072   2.5428 -13.4286  -6.2804
+#>   18.8183  -1.7829 -17.2493  -5.1817   7.1251   9.3990  -1.1581  -2.3858
+#>    0.7691  -5.4008  -1.5371  -7.8966   5.8265 -12.0490  -2.9311  -1.5862
+#>    4.1762   8.3920  -0.9298   8.4403   0.6377  13.1083  -6.5428  12.8787
+#>    4.2664  11.3475  14.5946   7.5401  -5.7044   5.0656   9.9433  16.3912
+#>    2.8144   3.6244  17.4546   2.6555   4.6665   1.0549  14.3992  13.8650
+#>   -3.4108  19.0521  -2.4775  19.8160  13.1611 -12.6593  11.3993  10.5849
+#>   -4.3100   4.4157   6.5085  -0.3309   4.2713  11.3586   4.4289   1.2830
+#> 
+#> Columns 33 to 40  -5.8637   1.2895  13.3441   8.1283 -16.3985  16.8378  12.6993   7.0491
+#>  -10.2695  -6.8925   9.7998  -4.8427   8.8375  15.6287   8.7683 -21.6075
+#>   -4.7919   0.8737  -0.7585   3.7819   0.1388 -12.4514  -0.8057   1.9002
+#>    0.0278   0.2814  -0.4719  19.0383  11.0058  12.6267   9.3832  10.5738
+#>    8.9777  -1.8367 -10.9427  10.3432  -8.6840 -13.8276  -3.1959   8.9293
+#>   -2.0639  -1.6404  -4.6671  18.1058 -13.3077  -1.3236  -8.6058  13.5027
+#>    3.2653  -2.6477  10.0018   4.6693   0.3374  -5.7820  -2.5768   1.0923
+#>    0.8879 -14.3964  -3.5890  -3.3306  -0.1008  -5.0288  -1.4455  13.8029
+#>    4.0307  -0.5976  -8.9527   9.7269   9.0801   9.6730  13.0421  -3.1185
+#>    8.1839   2.7942   0.7323   5.0709   0.2682  -3.2192  -9.1831  -7.2338
+#>   -3.6909   0.4486   5.9406  -6.6713 -11.6067 -14.4492   5.4070   5.5005
+#>   -7.1658   8.7376  -7.5280  -7.1680   1.6818   3.1647 -11.6297 -14.3609
+#>   -5.2513   0.9455  -3.3179   1.4820  -7.2857  -1.3719  -6.8859  -2.6734
+#>   13.0965 -16.1626  -4.2634  -6.9552   7.3453   4.1895  17.4347   5.3152
+#>    4.5326   3.0474   2.4233 -15.4744 -13.9498   2.5395  12.1704  13.9390
+#>    0.1175   5.3433  -3.3282  -6.2194 -10.8869 -11.6978  -1.7299   0.4992
+#>   12.2395   3.5022   1.9265  -5.0205   2.6390 -10.3159  -9.4471 -11.9763
+#>    3.7909  -6.9627   1.3710   1.7278  -5.8415  -9.4329   7.2094  22.5263
+#>    4.5404  11.9810  -9.4707   6.0480   7.1753 -23.3660   2.5013  -8.5898
+#>    1.1192  17.8612  -2.9750   3.6111  -3.5040  -5.2109   6.3410  -6.1146
+#>  -13.3833  -2.1845  -1.0278   3.7156  -4.2655   6.0604  15.2238   1.1985
+#>    5.0865  -6.8844  -8.7953  13.6532  -8.8446  13.4714  -5.4443  22.9488
+#>    7.2044   0.1072   2.9296  -1.8047  -3.2720  10.5534  -2.4302   5.1628
+#>    1.9631  -1.8988  11.3914  -8.8160  -8.1289  -4.9537  -2.1955  -2.3276
+#>   -4.5610  -4.8902   7.3637 -11.9470  -0.7609  15.5464   4.1903  11.5000
+#>   -4.1999   8.8388   9.9628  -5.7092  -4.0545  11.4116  -3.2499  -0.2514
+#>   -1.1221  -0.2230   4.8315 -15.6970  -2.0750  -8.9032   7.9567  -0.7288
+#>   -8.6301   7.5972  -2.4483   5.2722  -9.4625  14.5135   0.3171  -5.1200
+#>  -14.9971  13.2256  -4.7564  18.9661  -0.9293  19.6024  -3.5987 -11.9905
+#>  -10.0076  13.2765  15.5937  10.6344 -24.3300  -5.2166 -12.2141   3.5052
+#>   -3.2359   3.7034 -10.4985   3.6587  -5.6668 -15.2120  11.9552   5.1292
+#>    7.0014  -8.0485   6.6287 -31.3916   8.1112 -10.2537   3.9824   9.1160
+#>   -4.8738   4.4407   7.6256  -4.5846  -2.9823  -0.3574   2.9510  -9.5830
+#> 
+#> Columns 41 to 48  12.3114  -6.5678   3.0162 -12.8669  -5.6505  -5.7403 -19.1519 -10.8163
+#>  -18.1085   0.3156  12.1244  22.7452  -4.3728  13.6746  16.5156  21.0632
+#>   10.6414  -9.2954  12.6458  -8.5825  -3.0427   2.8359   1.1758  -7.0834
+#>  -11.8807  12.3064  -0.1863 -11.1111   6.3309   3.9364  -0.2605 -12.7794
+#>   -0.7343  -6.2175  -4.7950 -12.1935   7.4900  11.3879 -14.2331  13.4536
+#>   -0.9120   7.8151  10.3817 -13.8011  -0.3950  -0.2950 -10.2714   8.8289
+#>   -6.3945 -10.5469  -2.6997  -5.8281  -2.0316   6.7816  -1.5823   6.7282
+#>    9.0767  13.0924  -7.7088   9.5412  -2.9963  -7.2629  -1.2771  -2.7352
+#>   -9.8715  -0.8061  -0.2498 -18.1659   6.0673  -5.2558  12.4421  -2.4405
+#>  -10.0825 -19.7198  -2.6691  -0.1336  -1.7248   6.5902  -3.3641  12.1672
+#>   11.9936   7.0544  -3.7764   5.4963   9.6779 -10.9249   7.0961   5.4853
+#>    8.5235   1.2645   0.5047  -2.7175   4.1771   1.3520  11.4015  -2.5769
+#>   -2.3333  -2.0371   2.5025  -6.4989  12.0019  -2.0206   2.9647  -2.6082
+#>  -11.4482   0.7012   9.9749 -21.0874  -0.0879  -8.9751   4.3885   6.2640
+#>    8.5000  10.8300   8.6350   5.0371   8.7116  -7.6094   1.2183  -2.5200
+#>   11.0376  -1.5345  -2.6689  -5.0768  -1.2628 -16.5428  14.1540 -12.4692
+#>   12.0115  13.4967  -9.9855  -8.3542  13.9205   6.1883  -6.6162 -15.0412
+#>    1.2382  -3.9774  16.8520 -12.3506   3.0238   6.4445  16.4706  -9.5140
+#>  -13.6622  -6.5589   8.1667  -1.2575   2.3490  -2.5885   8.2112   1.6993
+#>    6.5337  -5.6165   2.7663 -13.9573  -4.4401  -7.6872  -1.8505  -8.8208
+#>    6.9599   0.4176   0.5780  -4.3983   4.8720 -18.4348 -12.6010 -14.5679
+#>   13.6287  13.5889 -10.0053   1.3177  -5.7620   1.4773 -10.1316   3.0753
+#>   -0.0373   0.6651 -13.9531  -0.3866  -0.0445 -15.4512  -1.2880 -14.4447
+#>    8.0760   3.0786  -8.4432  -9.3746  -5.1075  -6.4881  -1.7059   0.1589
+#>    2.3820  -1.3718   1.2345   5.9072   2.1069   2.1634   1.2525  14.6490
+#>   12.5619   7.9832  -3.2100   7.5911  -3.3256  -5.8684  -9.5314  -3.5992
+#>   13.8859  -4.2809  -2.3360 -16.9123  -4.8272   2.0367   1.3789 -23.7263
+#>    2.5599   9.3968   3.3757   2.8573  12.0256   4.8581   9.8937   4.4206
+#>   -3.9684  -2.8238  -0.9528  -9.7343  -6.3254  -5.6714   5.2922  -4.4623
+#>    5.6508  11.2602  -1.8538  13.9077  12.0704 -16.0405   0.9848  27.0403
+#>   -0.4521  11.8176  12.5304  -6.4316  11.9286 -10.8989   1.3186 -14.3120
+#>    5.2441   9.2985  12.1178  11.7740  21.3087 -18.1301   7.7393 -15.5094
+#>   14.3697   0.0200   0.4483  -4.1970  -0.3045  -1.1912  -1.2995 -17.4547
+#> 
+#> Columns 49 to 54   5.2051  -2.7292   3.0805  -9.7109  -0.4840  -1.1897
+#>    2.4828   2.5924   4.8842   2.8287   7.8073   3.2042
+#>   10.9652  -0.2877  -0.2782   2.4013   2.4435   0.6913
+#>  -12.1931  -0.2438   0.5390  -1.9409  -7.4068  -2.1269
+#>    3.9070  16.7506  -8.1154  -4.6265  -9.0211   1.5233
+#>   12.6387  11.0710   1.6093  -6.3792  -1.2763   7.0257
+#>   12.7592   6.5146   2.1618   1.4475   3.9531   0.6353
+#>    2.0000  22.4288 -10.9756   2.8924  -4.5897   1.5239
+#>   -2.0396  -9.4105  11.2011  -1.9027   1.7017  -2.0193
+#>    1.1253  -9.3861  -8.1326  12.3274   7.9309  -0.9050
+#>    1.7621  -3.4463  -5.7614  -3.6789  -3.6238  -3.4748
+#>  -17.7608 -11.5497   0.9856  -4.2926   5.1718   1.8400
+#>    5.2010  -2.9470  -0.9289   3.0569   4.0638  -2.9950
+#>   -4.7387 -16.8206 -10.3465   5.5362   5.8112   1.8635
+#>    9.6436   1.0926  -3.9120  -7.2516   7.5891   2.3655
+#>    6.0128  -5.0468   8.3091   0.5521   9.3647  -0.9257
+#>    9.4391  -2.5530  -3.6021  -6.4972   0.4632  -1.1076
+#>    9.0825 -20.0833  -5.6912 -10.3386   3.8220  -5.1085
+#>    8.9449  -9.4812  13.6597   4.3363  11.5952  -1.5450
+#>    2.0255  -6.8440  14.8967  -6.2614  -1.0776  -0.6474
+#>   -3.0937 -11.2576  11.6156  -7.1563  16.4331  -2.2199
+#>    0.6510   8.9429  -6.3483  -6.5441   0.2628  -1.0576
+#>  -15.6888  -9.6134  -0.4099 -11.3571   4.3295   0.2196
+#>    3.1653   8.7047 -18.1418  -2.4446   3.0319   3.8894
+#>  -10.3487  -5.4222 -10.4785   2.9099   4.9732   2.6966
+#>   -3.0941   1.8242 -10.8351   0.4536   6.1304   3.5464
+#>    9.0025  17.8373   0.7618  -3.5603   4.6391  -2.6444
+#>   -3.4393  -2.2774   2.5032  -2.8818   1.0536  -1.4308
+#>   -8.2104   0.5179   9.3516   2.4858   4.6191  -3.5432
+#>   -7.9982  -2.1682  10.3227 -14.7556  -4.6249   5.4687
+#>    1.7113 -17.8578  10.2943  -1.6037   3.3656   3.0119
+#>   17.5817   2.3308  -1.2937   2.7468  -0.4557   2.6153
+#>   17.4440   3.4547   9.0486 -12.7384   4.2286  -3.8492
 #> 
 #> (3,.,.) = 
-#>  Columns 1 to 8   3.0031  -1.1713   1.7735  -9.0906  -7.7523   1.6255  -8.1038   0.4803
-#>   -1.4395   3.3060  -4.9207   0.5426 -26.5450  -9.1573   1.2341   2.6821
-#>   -1.6181  -4.7474   6.6856  -8.8715  -6.7656  -9.3924  18.3535  16.4979
-#>    2.0125  10.5115   0.4946   1.4673   1.5072  12.2754  -7.2831   1.1050
-#>   -1.0779  -1.3903  -5.9078  -2.7975 -10.6282  -1.9355 -28.0926  -8.5147
-#>    1.3459   1.0233   4.4565  -3.9460  11.7021 -22.0011   3.7328   2.8262
-#>   -7.9638  -3.2818  11.9780  -3.6959   1.7928  -2.5773   2.7235   0.0797
-#>   -5.8846   1.2887  -4.5946  -7.9901 -17.1901  -6.6256  16.0995   3.1597
-#>   -0.3332  -5.5103  -9.8488  -3.2059   0.0323  -9.4431 -18.4452   5.7317
-#>    5.8413  -0.3776   3.9603 -10.9460  -8.4634  -3.9871 -12.5948  -3.5386
-#>    4.0921  -6.9083  -7.3737  -5.5127   1.0160 -10.7198  -0.8637  15.9982
-#>    4.4676   6.3974  11.2690  20.4401   8.4547  -0.3921   2.8117   5.6110
-#>   -0.5407   3.5666  -0.3481  -1.5658  -5.2985  -3.3953  -7.0007  15.7980
-#>    3.4430   1.8497 -11.5248   7.5493  -7.5274   3.9757  13.4175   1.6013
-#>    3.4110   3.6490   7.0635  -6.0675   4.6773  -5.6185 -17.0664  -5.3096
-#>   -5.1638  -3.6366   0.1630  -7.2286   0.7935   8.5049  -1.6619  -2.8189
-#>   -2.0293 -11.2180  -3.9819 -13.4532   7.4374 -11.3310 -10.2651   7.9634
-#>    0.4887   9.5984   6.3274   3.2128   7.1249   7.1410  -0.4603   1.0461
-#>    0.6471   0.3731  -1.7175   8.2021   5.7231   3.8515  -1.8011  -4.4598
-#>    1.6338   3.5299   2.0626   4.3411   2.0501  -2.9471  -5.0114   3.5891
-#>   -6.8317   2.1409  -0.3158  -7.3118  -9.3937  14.5959  15.3571  -1.8564
-#>    0.8886   1.4641   0.5028 -12.2130  -5.3902 -10.1844  11.3079  10.3780
-#>   -3.0151  -6.1362   3.1912  -2.0550  -4.8657   0.0678 -20.8676  13.3277
-#>    5.0685  -0.4177   1.6027  -5.8127 -10.5129  -8.3065  -5.8800  -2.2088
-#>    5.2699  -5.7045  -5.1716   6.8226 -15.0316  -5.0530   0.6497 -12.4067
-#>   -1.5064  -1.0875  -1.5125  -5.5702  -3.2429  -2.1734 -12.1101  -1.8075
-#>    2.8814  -4.0411  -7.4772   1.5799  -8.6595  -7.5146   2.7112   4.5064
-#>    1.8012  -6.0426  -9.7752   2.7970  -1.7619  -5.3261  -3.2915   4.0403
-#>    1.3697   6.3530  -4.4796  10.4673   0.5710 -10.7031  -6.3763   1.9706
-#>   -1.2342   1.2121 -13.5034   2.4156 -13.5557 -21.2035 -15.4164  -4.9243
-#>    4.0965  -2.9095   4.9796  -3.4179   8.6316   2.9157 -10.0892  -8.3838
-#>   -5.2568   2.4127  -7.6201 -11.9952  -0.9887 -11.3262   8.5635   5.2253
-#>    7.8430   2.1430   5.2939  -7.9930  -0.9956  -8.6677   3.6763 -15.1486
-#> 
-#> Columns 9 to 16   4.6593   6.7269   6.6702   7.3907  -7.8672  -4.0630  12.8214  -2.5460
-#>   -0.0965  -5.4140   0.5344   5.3575  -7.3039   0.7799   0.9683  -3.1604
-#>    0.3504   0.1819  -1.6127   9.8124  32.5733   3.7362  -8.9751  -4.3400
-#>    2.6242  -8.9156  -0.2250   1.4677   0.5044   3.6535   2.1986   7.7236
-#>   -1.3133  10.9565  -1.7728  17.1786 -19.9343   9.0192   8.5705  -6.0406
-#>    8.5221  -6.1071   0.2244  -8.0260  -2.7828   3.6833   0.7928   7.3192
-#>   15.0199 -16.7423   6.0564  -5.2131   2.0064  14.0828 -14.1567   1.9929
-#>    2.3899  -3.1128  -5.2550   3.8801  17.2259  13.7856   8.9296  -0.2724
-#>   -5.8980  -4.0879  -1.9533   0.1919  -4.3493   6.1849  10.4726   7.2466
-#>   -3.7371   1.9459  -1.1663   1.6162  -2.5395   0.5327  -0.2901  -3.8612
-#>    2.2095   6.9287 -12.3987   7.1235   4.1101   9.2574  -5.3279  -2.2198
-#>   -7.3193   7.8056   3.6111  -6.5194   6.3612  10.7283   4.3855  11.0293
-#>    2.1896  -5.5781 -19.1569   0.7391  -6.5319   9.0854  -7.2169  -9.5070
-#>   -6.4358  -2.3173  -6.2730   4.3687   0.7691  -6.1351   0.5836  -1.7487
-#>  -18.7487   3.9496   5.3738   0.0837   6.9914 -12.1602  -4.9256   6.7643
-#>   -3.4495 -10.0417  12.7282   7.4786   2.5694  -8.6762  -0.5433   0.1506
-#>   12.5367  -4.5606   8.8003   9.8323  -8.9455 -14.3511   0.7297  22.4463
-#>  -13.3844   5.5442   6.8929  19.5213   3.6610  -9.9020  -5.0455  12.4286
-#>   -4.0541  -8.0233  -8.1308  -1.7781  -2.0019   6.3873   8.4276   2.1452
-#>   -5.3457   2.0029  -2.7451   7.9457  -5.2178   2.0413   6.7447 -14.8059
-#>    5.7557   1.3620   1.3234  17.9600  13.0151   2.1660  -8.7398   3.9648
-#>  -10.6699  -8.9068  -6.6008   3.4348  21.5199  -1.6352   5.8334  11.9984
-#>    5.4020   4.2524   9.1617  12.0283  -8.3139   2.7388 -14.3782  -6.9844
-#>   -8.5022 -32.2319  -7.4838 -10.9457   4.3391  -4.9729  -9.7857   8.1494
-#>  -11.2569  -1.4706 -11.1208 -12.0966  11.7453   8.9875   5.0079  -7.2054
-#>  -19.4426  -4.2547  15.5110  -7.6708  -4.6302  -1.4468   5.8384  -4.6624
-#>    0.7718  -2.8727  -7.0337  -3.5872  18.4001 -19.5692 -16.5489   7.8874
-#>   -7.3169   9.3362   1.5757 -19.8433   2.6673  -4.9955   9.7552 -10.4626
-#>   -1.2032  -3.5806   4.6284  -4.4536  -7.2250  -2.1550  11.7586   6.2467
-#>   -0.7258   3.4492 -22.7294 -15.0464   0.7035  -2.5275  18.2421   8.7096
-#>   -9.1511  -5.0277   6.5897  20.7037  -0.6972  -2.7146  -6.0418   8.4277
-#>    8.5743  -4.4994  -6.9698   8.8198  18.6992   0.6420 -12.9564   2.3944
-#>   14.5578  -1.8890  -8.0158   2.9650  10.0440   9.4335   8.7453  -0.1934
-#> 
-#> Columns 17 to 24  -6.6739  -3.3174   4.8387  -0.9273  -7.7149   0.7350   0.7539 -12.7757
-#>    1.4936   1.5664  -0.7254   8.8269   5.0336  -2.1062  11.5775   3.8742
-#>    4.1964   8.1240  -8.9542   9.2426  -4.2874   1.6129  11.9477 -14.8053
-#>  -17.7976  13.5713  10.5377   6.5788   0.5665   7.3900  -3.5301  -8.3864
-#>   -7.7135  24.4112   8.5829   9.0302   5.6441  11.9017 -15.2928   9.8670
-#>    7.8455 -14.1607  24.9822   7.2399  -3.6282 -10.9977  15.6533  24.1705
-#>   -2.7380   3.3217  -5.2483 -17.4094   0.6183  -3.6812  10.6658 -13.4362
-#>   -0.0283 -14.1412   3.7245 -10.9191  -5.2927  11.3169  34.0355  -6.1253
-#>    0.9896   3.7909  15.6755   2.6959   6.6624   5.3276   5.7858   2.5801
-#>   -0.8955   7.1207   5.7362  -3.7618   2.5286  10.6009  -9.3078   1.7687
-#>   16.1548 -19.1678  -5.6831   7.8994   8.6877   4.6436   4.2476  -0.4299
-#>   -8.1035 -10.2413  11.0808  18.7428  -6.1531   1.4083   1.5032  -1.9261
-#>    3.4789  13.5798 -16.1593   0.7770  -2.3820  -0.6284  -3.4782   2.3536
-#>   21.1276   3.7961  14.4141  -6.9490   3.5795  -0.7582   2.4345   7.0882
-#>    0.3352  -0.0920  -0.4495  -0.4418  -4.9307   5.1436 -14.9609   3.9155
-#>    6.8881  -5.7349 -14.4124  -3.8325  12.5811  -4.6771   0.8841  -3.3465
-#>    8.0628  -6.7419  -3.9527   3.5416  -3.5149  -7.7816   0.2831   9.6582
-#>   14.2947 -15.2795  -2.8609   4.6593   1.2110  10.0167   4.2606 -11.5680
-#>    5.3466   0.4190  -8.5543  10.8854  11.1437   4.1192   3.3871  19.8865
-#>   -0.9948  18.3307  -3.0989   1.2748   5.2957   7.5191  -3.6155   5.4813
-#>  -15.4406   7.2360  -3.9158  -8.4858  -4.7730  -5.4790  -4.4418 -26.1587
-#>   -8.9448 -11.9719   2.7618  -3.9798 -12.0544  15.9367   4.1552  -0.8900
-#>   13.3973  -8.4427 -18.6767   6.8483   8.1360 -23.6531 -16.4057   0.3555
-#>    7.2695  19.2310   8.4524   9.4012  13.6135  10.9855  18.6647   4.0355
-#>    0.2357  -2.5369  10.6670  -2.6963   4.3938   9.4022  11.7677   2.9247
-#>    3.1197  -2.7420  -5.7643   4.8510  -6.9641   2.9854 -12.6033  -3.6164
-#>    6.4381   4.2443  -6.4563  -1.7414 -12.8242 -11.2641  -6.9360  -1.4475
-#>   -5.7302  -2.3058  -2.4990  -9.3361  -6.7829  -4.5237  -4.0134  13.4664
-#>   -0.4313   5.6937   0.0022  -5.6486  -7.6801 -14.7791   3.9556  18.4725
-#>    6.2085  -5.9200   8.8480   2.7069 -17.4849 -15.5185  20.1055  15.0809
-#>   16.2159  -3.3914 -14.8833  21.7363  12.9022  -0.7343  -8.1583  12.4803
-#>   11.2343   1.4257  -5.3833 -12.7218  -2.6865  -2.5140  -6.7985  -2.5359
-#>   -4.0532   2.6016   7.4993  -8.4987   9.5665   3.0245   7.0870  -9.0506
-#> 
-#> Columns 25 to 32   1.1221 -15.1468   8.3570   1.9067 -11.5429 -14.4144   9.4440   2.2931
-#>    2.9613  -5.0968  10.2646   1.3281  -7.5577  -0.9216   7.4214   8.1904
-#>   -8.5052 -17.4922 -10.5043  -0.4389   8.9870   1.2418  -0.9949  -5.0835
-#>   12.2524   6.3351  15.4138  12.0733  -1.9435   0.8878  -7.0024   2.1013
-#>    1.0187  -7.7225  25.5166  18.5899  -5.6871  13.7447  -8.9432  20.9760
-#>   -3.3446  12.4658   6.8752   5.7418  -1.4096   2.0620   6.0016   2.0485
-#>  -14.6137  -1.3447   4.2214  11.4609  -1.4652   3.6985 -15.4739  -4.4912
-#>  -14.5247  -6.0115 -16.5187  17.6410   3.8002  10.4642  -4.3999   0.9559
-#>   -1.8676 -14.0983  -5.6664  -3.0940 -13.4328  13.5993  -5.7134   1.2287
-#>   -0.6292  19.5502   6.4174   4.8042   6.3895  -3.5560   2.3204  18.9714
-#>   -2.3795   5.8969   0.7731  -1.4679  -4.4090  -2.2931   3.7329 -12.4271
-#>   -3.1622  11.9378  16.6575  -6.7612   1.4359 -12.4484 -17.6017  -9.2899
-#>   -1.4348   2.1966  15.6815  -7.2354  10.0188  18.4178  15.3872   2.5706
-#>    3.0502   7.7196 -17.1957 -10.0506   6.4401   6.7961 -15.4598  -2.5249
-#>    2.4114   5.5757   7.4796  11.6125  -6.2794 -11.1033  -1.4154   7.4313
-#>  -12.3398   4.9861   6.3509   4.6999  10.8454  -8.6616   4.3145  -0.3505
-#>    5.1506   1.4426  -4.8630  10.4596  -1.3728   4.9981  -0.5317  -2.3504
-#>  -18.7493   9.9155   6.3305  19.4469  -4.2548  -9.9538 -23.5458   1.5363
-#>   -5.7270 -28.5966   1.4610   2.6186   6.9747  19.2051   6.0497   0.9164
-#>   10.9339  -6.6366  12.2284   3.0518   2.6061  11.2897   0.1941  -0.2602
-#>   19.5594 -11.9835  -1.9872 -15.9810   6.5773  -5.2519   2.7786  14.8894
-#>   -1.3409 -11.1748   1.0937  -3.5925  -0.7549  12.0844   4.0608   1.2738
-#>  -11.5565  -7.7279 -11.3881  -1.5980 -15.1130  -6.5238  -1.9475   0.3795
-#>    0.7774  -9.4843   0.2672 -19.9400   0.8753  -2.3304  -3.0680  -8.8586
-#>    7.9566   3.6271  -5.0917  -6.8523  -9.8662  -2.2442 -16.8183   4.5610
-#>    1.2773 -15.6154  14.5905 -14.0544  11.1515  -6.0772  14.6152  -1.7339
-#>   14.9440  12.5391  -9.0561  13.2410   4.0097 -13.9463   2.5720  -6.4587
-#>   -2.3829  -6.7018 -11.9655  -5.8682   5.5277   0.0934  -6.2478  10.2131
-#>   -3.7337 -10.5398  -7.9042  -4.4180   1.0297   4.7407   5.2402   3.7955
-#>    5.4218   8.4790 -25.0305  20.2131   1.5218   1.6669   1.7754   2.5871
-#>   -2.5454   3.7441  10.6950  -0.2284   2.8383  -7.8144   3.1613 -14.6570
-#>   -4.4937  -1.8892 -11.6622  -7.5895   1.2404  -3.1188  -1.9563  12.0491
-#>   -8.3915  34.2823  -6.9476   7.6439  -1.6938  -6.7918   3.5380  -9.0375
-#> 
-#> Columns 33 to 40   3.3995  -0.3210   2.9188   5.1383  -1.3407   7.5646   4.3350  12.8163
-#>   -4.6758   3.8246  -0.4502  -5.3551  -2.4679  -1.6020   6.7734   6.7087
-#>   -1.8866  -7.8625 -11.9390  -9.3608   4.7640 -17.6644  17.5815  -8.8841
-#>   -1.3425  -8.0310  -8.0548  20.8914   3.6614   0.4057   9.1090  -8.9089
-#>   -6.8993  11.7285  -3.0791   9.8707 -14.5684  -1.5579  -4.5657 -11.7019
-#>   17.2565  -3.6128   2.5843 -14.9282  12.2219 -13.2551 -13.5065   6.2647
-#>    6.6300  10.2063  -5.0556  -5.2237  -5.6799  -5.2010  -1.2620  -4.6160
-#>    1.6600   0.4635 -14.1997  -5.3116   5.8385  -3.3701  18.2749   4.3410
-#>   -2.3960   7.0271   7.9698  -5.0902   6.2283   0.9159  -4.6657   3.5176
-#>    3.0836   3.9876  -4.8300   0.3241  -0.3630   5.0639   4.8222  -4.8342
-#>   -4.8762  -0.1001  10.6387  -7.2324  -4.1635   0.1551   7.3297   6.6931
-#>   10.2620  -1.0456  -2.0783   4.0471   5.1181   8.0840   8.2518  10.5336
-#>   -2.2210  18.0167  -1.2456  -8.7770 -17.6564   7.3704 -13.3693  -9.1483
-#>  -14.8682  -7.7006  -2.0882  18.8748   1.6923   9.6779  13.6605 -12.6538
-#>   16.1572 -16.5525  13.6661   5.4012  11.1833  -5.3037   7.9277   2.4325
-#>   -3.3884  -5.0985   4.8811 -17.6648   5.1383  -0.4609   3.3404   1.5507
-#>   -5.6561  -0.2068   7.1859 -16.5431  -5.6844  -0.4892   2.3343   8.0768
-#>   -5.9822  10.8257  -0.3373   0.9161  -4.8604   5.9253  18.3434  -0.3593
-#>  -12.5291  13.5229   8.5409  -9.3303   4.1437   3.7960  -1.9802   5.5924
-#>   -3.9977  -1.4491  -8.0225   2.4355  -5.1887   0.9020  11.6591  -8.5988
-#>  -10.6864   4.7166 -12.1693  15.2190  -7.3645  -0.4941  -1.6018 -19.0628
-#>    5.8257  -6.8657  -9.8551 -12.6238   2.9744  -7.7327   6.4582   7.8213
-#>   -1.5285  12.3356  -3.8897  -9.5486   8.2061   1.1672  -4.6765  -4.8519
-#>   -9.7547   0.5000  -2.3187  13.6487  -4.9364   0.3583   2.2306   5.9768
-#>    6.6336  -7.8624  11.8713   8.1273  20.9814   1.9781   8.6172   1.3056
-#>   16.1903 -10.9793   9.7522   1.6372  -2.8432   2.4354 -15.3075  -1.4748
-#>   -3.4112  -1.5999   9.6158  17.9750   2.0975  -6.0335   9.8225 -14.3505
-#>    2.6330  -1.7996   6.3781  10.6677  -3.4005 -14.3448  -2.8834  -5.9152
-#>  -10.8092  -2.3071   3.1028  11.0733  -1.5603 -22.6859  -2.0244   7.1346
-#>   18.9459   6.7388  -8.3142  14.8399  17.1676 -13.3548   8.5614  -7.7465
-#>    8.6905  11.5966   5.1644  -5.1298   5.1416   5.4848  -8.0632  -0.1872
-#>   -4.2913  -1.5870   7.7001  11.6801  -4.0722   4.0107   8.4250 -17.5451
-#>   -5.4688   7.5811   1.7385  -0.4735  -8.9413   0.2439  17.5193  -2.7576
-#> 
-#> Columns 41 to 48   0.0465  -3.4862 -13.2738   3.8935  -3.2092  10.1039   5.0745   3.7190
-#>    2.0494  -9.1959  -2.2920  -5.7282   3.3374   8.6497  -8.7164  -0.0291
-#>   -9.8911  -0.7584   2.2297  -5.7634  -4.3136   2.0147  20.0442  -3.9719
-#>    5.5081  -8.8566   2.8731 -13.9006  20.8200  -5.5648   7.5882   5.7305
-#>   -3.1711 -12.6775  -3.9172  -8.7452  14.4456 -12.8824  -1.8546   3.4528
-#>    2.4446  -3.4813   6.2757   0.4589  -4.7091   2.1109  -9.6654  -2.8753
-#>   -6.4858   3.2467  10.2775  -8.8833  -7.7731  -9.0477  -1.6386   4.3995
-#>    1.5574  -6.3637   4.2262  -9.4756  -3.0853  -1.0484  17.7309   3.0610
-#>  -11.3941   4.9077   1.1466  -2.9488   7.8308  -3.5794 -14.2437  10.8045
-#>    4.6599   5.4957  -0.6567  -3.0111  -2.0842  -7.0308  -1.1045  -7.3985
-#>    2.8322 -12.0449   5.3779  -0.5008   5.0249  24.7133   4.6630  -1.7614
-#>   -1.8948  -7.6056  -7.6986  10.9410  12.1283  -5.6003  17.2878  16.8752
-#>   -5.6440  -6.6151   8.7042   8.5497  -7.9938  -2.3105   1.3292   0.2753
-#>   13.1700  -2.1738  -1.6815  -2.9282  -2.1487  -7.5467   2.2566  -3.1159
-#>   -6.7321  21.2448  -4.8457   3.3978  -0.0036   4.4460   1.1738 -13.2747
-#>   -9.0073  17.5459  -4.0688  -5.1694  -2.4779  -4.3687  -1.9631   4.2734
-#>    0.6338  17.4965   0.2533 -18.1077   3.7392  13.4510 -15.2351 -10.5991
-#>   13.9620  18.9477  -1.3132 -15.6907   0.9643   0.4753   2.6462   1.9783
-#>    3.1352  -6.3590 -10.6552 -13.6167  -6.0599   1.4963 -10.2603  11.7957
-#>   15.1292  -5.5503   1.0119  -0.6963   3.3856 -11.4307  13.1196  11.5283
-#>  -12.3759  -6.4238  12.8371   2.5045  -1.7021  -2.0936   4.3229   2.1624
-#>  -12.5627  13.9249  -3.5476 -14.4480  -1.9823  13.2438  -2.1036   3.9360
-#>    8.0561  -6.5157  18.4071 -12.5351   6.8261  -7.4138   9.1498  13.4108
-#>  -12.7752  10.7888 -21.2931  -4.3270  -9.9881  14.7584  12.8067   6.9470
-#>   -6.1149   7.5197   1.2564  14.0175   1.7411   3.7951   5.6573   1.6170
-#>   10.2582  -2.0958  -2.3722  -8.6935  -3.2190  -4.3119   4.8846  -6.9076
-#>  -15.0583   0.4817  -4.4535   4.1261  -9.5967  12.2711  -1.1233 -17.4156
-#>   -2.5126  -6.0924 -11.5254  11.3586   6.2273   2.9722  -9.3137   1.5586
-#>   -2.3737  11.0494  -4.2991  -7.8241   3.8737  -0.8572  -3.2632  -3.1751
-#>   19.0771   1.6400   2.4942  -4.3823  -1.9991  -1.5681  -1.9602  -1.9225
-#>   -9.8879  12.0578  -4.9132   4.8810  -0.5849   8.4618   6.0484  -0.8199
-#>    2.6374  -3.6763  16.9745   1.4207   4.6623  -1.5556   8.6995  -6.1866
-#>  -17.2081   3.8919  -6.3286  12.8504   3.4418   0.1833   1.7541   1.8093
-#> 
-#> Columns 49 to 54   4.2277  15.2095  -5.3106  -3.5254  -2.0239   0.5410
-#>    1.0530  -1.5268  -8.3209  -7.5667  -4.3293  -0.3304
-#>   14.5093  15.5059   3.6394  -7.8419  -0.5077  -2.7458
-#>   -4.2606  -1.3002  -6.4896   4.1244  -7.6892   4.5164
-#>   -3.3746  -5.3938   2.3163  -0.2463  -1.7688  -0.0258
-#>    1.9585 -18.0378   7.8877   0.2530   4.8485   0.6589
-#>    3.0419  10.3141  12.3120  -6.6679 -10.8065  -2.5067
-#>   -4.6297   6.5639 -12.5321  -0.1158  -5.2502  -2.2560
-#>   10.4186  -1.9900   9.9362   8.4835   5.7325   0.9782
-#>    1.6694   9.0592 -15.8919   2.0805   1.6827  -2.1085
-#>    5.7053  11.3829  -7.9181   5.3085   7.7903  -0.2385
-#>   22.8388  -0.1096   1.3239  -8.0848  -2.0209   0.9309
-#>  -16.6073   2.2912  -6.8168  -4.4002   6.6341  -2.6954
-#>   -8.1871   8.7314   3.0309   6.2616  -1.7341  -0.1670
-#>   -7.3059  -1.1614 -12.2037   7.7507  -8.1071   1.1435
-#>    2.9345  -2.6649   2.3461  -2.0491  -1.6806  -2.3369
-#>    3.1239   4.0808  13.8076  -2.9526  -1.5092  -7.7307
-#>    3.0788  18.8719   5.5230   1.3509   3.6166  -5.6440
-#>    4.1107  -5.4855   5.2623   6.4164   1.0479   1.9502
-#>    1.7431   5.8753  -4.8010 -18.8203  -9.6599  -5.9157
-#>   -0.0724 -10.8421  -0.9135  -2.0209  -0.0128   1.9955
-#>   11.8888  -1.6736   8.6527  -6.8223  -2.6404  -4.3233
-#>    7.3335   0.9019 -22.4590   1.2885  -7.3577  -2.0053
-#>   -1.2451   2.6736   7.4570  -4.3535  -1.6255   1.7764
-#>   11.3746  -9.8116   5.5320   2.7358  -6.3190   4.3476
-#>    6.3236 -14.9279   2.5646  -0.5103   3.6606   0.7772
-#>    6.1821  16.4744 -10.2067  -5.7784  -0.8729   1.1509
-#>   -0.3717  -5.3564  -3.1124 -13.4197  -5.1752   2.2853
-#>   -3.0716  -4.4458  12.2943  -3.3567   1.3988   1.0051
-#>  -22.5080  -8.9841   5.5813   7.1742   1.6875  -3.7041
-#>   -9.3930  12.2926 -11.7914  -1.4915  -3.2901  -4.0539
-#>    0.4054  -1.7884  -2.7561  -2.5213  -9.4311   1.7463
-#>    2.3147  26.5946   3.4709  -9.9254  -3.4216   1.8680
+#>  Columns 1 to 8  -1.3674  -0.0474   4.3756  -1.9528 -14.5364  -1.0695  14.5505  -1.2320
+#>    4.1463   4.4713   6.1436  -1.0536  15.0170 -11.1981   8.6223   3.2857
+#>   -1.8525  -0.8742  -0.9753  -5.3276  -8.0905  -7.0188  -2.7020  -6.1735
+#>   -0.0057  -1.3533  11.8218   0.0820  -6.8994   1.5879 -20.2024   8.2591
+#>   -5.0701  -0.7449   2.2023   9.5109  -8.5281  13.2540  -3.0778   5.5950
+#>    1.8493   6.1502  -6.0439   8.7651 -18.8250  -6.1873  13.4500   8.8217
+#>    0.6469  -8.8625   3.3046   8.0922  12.7069  -4.4744   1.9985  -0.6127
+#>   -4.5432   1.2356   9.4767 -14.8393  11.2296  -7.7008 -12.9201  23.6001
+#>    0.6837  -5.4234   2.4517   5.8235  13.1936  -2.3797 -13.9642  11.1578
+#>   -3.5765  -4.4489  -7.9510  -7.0725  -1.0782   8.3063 -13.2528 -14.5325
+#>    3.1919   4.2950   3.4313  -4.6576   6.3683  -3.6891  -1.1279   3.1698
+#>   -1.6529  13.3869   1.5862  -0.6866  -6.2219   4.4380  -7.7764  -6.6623
+#>   -5.9887  -3.5318  -0.6315  -6.2202 -15.4760   6.5481  -7.4740   1.4458
+#>   -2.5577   3.9710  -2.8323 -10.4966  12.3644   2.3723  -3.4837   7.0740
+#>    5.5712  10.9409   5.4408   0.0554  16.7365  10.9986   3.1266  -0.2494
+#>    1.7357  -9.7478   4.9140  -4.6112   8.3456   4.9798  19.1581 -15.3838
+#>   -0.7483  -1.1299   3.2862  -0.5158   6.3356   4.0168  -7.2203  -2.2662
+#>    1.1628  -3.1454   5.2235   4.2900  12.1151   2.3068  28.3364 -28.4036
+#>    2.6828   2.8602  -2.8606  -3.1289  -2.8074   0.9105  -2.7195  -5.8197
+#>    3.9371  -3.2949 -11.2866   2.4117  -2.2629   2.9842   1.2953   8.0445
+#>   -1.0550   4.1267  -2.2281 -14.5575 -11.7647  -4.8656  -1.8032  -1.7558
+#>    1.3683  -8.1479   3.6302   2.0502  -7.1031  23.7740   1.5587   6.8549
+#>    0.1720 -10.7989   1.7214  -7.6751  -0.4894 -16.7146   9.4321 -11.1193
+#>    3.5555   1.5577  -6.3123  -4.0664   9.5521   0.0850   6.0485   6.0398
+#>    0.7317  -1.5417  -6.2799  -0.8547  -6.2543   5.0399 -16.8278  -5.3424
+#>    0.7432   1.4527  -2.8910  -0.4864 -10.2852   4.1616   7.0883  12.8953
+#>   -4.5664   2.3052   2.9988  -4.6730  -0.0690  16.5138  11.4428  -1.7565
+#>    0.6696   3.2829  10.9881   7.1078   2.9863   4.3046   1.8981  -4.1067
+#>   -4.9292   4.0736  -3.3587  -4.1053 -25.7239   2.3742  -8.4333  -6.7934
+#>    3.6379   3.6476  -7.2712  10.4917 -13.9796   9.1833   5.1686   8.3373
+#>    5.0251  -6.8629 -10.1861 -10.2394  -2.5654 -16.4522   1.0065  -3.5672
+#>    6.5238  -4.8961   7.9712  -4.1026   2.8123 -15.1049   9.1867 -10.7452
+#>   -0.4319   0.6656  -7.7493  -7.0953  -4.4388  -6.2701  14.8045 -14.8000
+#> 
+#> Columns 9 to 16   7.5561 -18.6763   5.5794   1.1222  -0.8399  21.0040  14.1533   2.9688
+#>  -10.1766   3.4116 -12.6713   0.0116 -14.2647  -2.9001   9.1308  -2.7406
+#>   12.2488 -10.9280   8.9363  -3.5217  -8.8116   8.6491   6.2001  15.4932
+#>    3.4150  -3.5248  -1.5460  -1.2384   0.2763  -0.8866  11.7120   1.9756
+#>    5.7229  -0.8914  -7.4089  -9.6113   6.6046  -9.1741  -5.5532   2.3705
+#>   12.4063 -16.2883  21.3066 -14.7679   5.7952   3.5070   2.0678  -2.3481
+#>   -2.4544   0.6191  -4.8261 -11.5614   2.5510  -1.0357  11.0870 -11.9986
+#>  -15.2627   1.8209  -3.7969   3.9642 -12.1422 -15.5843  12.3632  -2.4090
+#>    2.0089  -1.8554  11.5494  -0.5925   5.3898   0.3639  -3.5549   2.8286
+#>   -8.9665   3.3079  -6.8751  10.2869   2.5814   0.4849  -4.1149  11.0605
+#>   -7.5469  10.9942 -13.4549  -5.6609   8.2273  14.3035   7.9379   1.2737
+#>  -11.3814   6.3335   3.9521  10.5922   3.6210  -5.5640  -1.9494 -11.2489
+#>   -2.1936  -2.7577   2.4907  -9.0372   6.0691   2.6011   5.7338  -4.7557
+#>   -0.4478   0.1406  -2.2274  -6.6588  -0.2405  -6.4989   0.4287   4.5505
+#>    5.4107   6.7985   7.1650   8.4213   1.5030  -8.9866  13.4362   0.7864
+#>   18.2867 -10.3115   4.0058  16.6091  12.5703   2.6496  -7.6738  -1.2900
+#>   -4.9133  -0.4303 -13.1166   5.2650  13.9520  13.0180   8.8784  -2.6578
+#>   19.3509 -25.7979  -1.5014  -0.0419   0.7595   5.6581 -11.3359   2.5229
+#>   -4.7068   1.3142  -7.5965  17.5107 -15.1102  16.4928  -8.7798  -4.3830
+#>   -6.7917   1.3441  13.9030  -1.2224  -1.5963   0.5265   2.2177  11.8577
+#>   11.7615  -0.9957   5.4671   7.2577  -0.9167 -10.2359  -1.9020 -11.2225
+#>    3.6712  -4.7122  -3.3527 -12.0308   9.3876 -12.7993  -0.2782   6.6712
+#>    0.1563 -18.8265   8.8017   8.0897  12.7327   1.7222  13.0710  -7.3235
+#>   -5.3015  15.6229  -0.8487  13.2131  -3.7971  -3.9059   6.7163   4.3495
+#>    1.8756  -1.2401  -8.9469 -10.7884   1.9884   9.2823   6.3844  -7.1373
+#>    1.1368   4.4731  -1.3934   7.7579   4.9732  -8.4724   6.2985  -3.3991
+#>   -5.9330  -3.6940   1.6795  12.9458 -15.9962  16.4554  -2.2548 -10.3433
+#>   14.0752 -16.1698  -6.6347  -1.7063 -13.1069  -3.8874   0.8907 -16.4903
+#>   14.3149   6.6958   4.3645  -3.0488  -0.9147  -9.2828   4.5159   1.2439
+#>    5.6273   0.5974  12.9606  -3.5311  15.5548   6.6464   6.2355  11.5370
+#>   11.5079  -4.4708   6.8192  15.5245  -6.0810  16.6692   9.3721  -3.6822
+#>    0.8252  -0.9475   6.3862  19.8149  -4.2433  -7.0391   9.6773  -3.4908
+#>    4.9312   4.7533  13.4802   8.5619 -14.6161   9.5949  -2.7221   5.1239
+#> 
+#> Columns 17 to 24  -7.1577  -3.6995  -5.2762  -1.7780  -1.6653  -0.6221 -10.4994   3.5850
+#>    5.8044   1.1683  -0.3558   7.6649  -5.1192 -15.8764  13.1516   3.4745
+#>  -13.8479  -5.7453  -1.3611  -3.6277  10.4624  -0.7606  14.8471  -7.7865
+#>   -0.2490  -9.8782  -6.9910  -7.3999  -8.7665   2.1246   2.5054  -5.2556
+#>   -3.8771  12.4232   6.5379  -0.9417  -3.9213  -9.8110  -5.6616   2.4345
+#>   15.1542   3.9985  -3.1631   0.0376 -12.7746  -3.1457  -9.4607  16.6501
+#>   -2.0450   2.1048  -2.2233   8.4675   1.5122   4.1887  -0.2256   6.9480
+#>   -2.2003   2.9629  -2.3984   6.5133  -6.2339  10.0168  -7.0375   5.3545
+#>  -18.3880   1.1027   2.9095   1.1096   1.2324   3.5304   5.5338  18.3571
+#>    3.5390   9.5953  10.7633 -11.8536  -4.7842   5.5026   8.2702   4.0238
+#>   -1.4058  -8.2432   2.4559  14.6921  -5.8694  -5.9541 -13.9616  -5.5515
+#>  -15.2413 -14.8361   7.6717  -6.4663  10.1476 -32.7165   9.7161   6.1056
+#>   -0.8295   4.4860   6.7026 -17.1413   2.0766   1.7990   3.4066  12.6657
+#>   -6.5490  11.2560   2.5612  11.1751  -6.3367   5.8870   0.7805  10.1547
+#>   -0.4421  -0.1175   3.3591  10.0457  22.8698  -5.3771   0.1176 -19.6562
+#>   -0.7159   1.4638  25.8870   2.1177  -3.2012   8.5504   5.0563  14.8134
+#>   -7.4910  16.0077  -8.4419   1.0091   5.2056  -2.8018  -5.1168  -6.2258
+#>   -6.6541 -15.2069   4.4515   4.7033 -13.4411  -1.0817 -14.4979  -1.4283
+#>    2.8184   6.1747   0.9870  13.1235  -2.2721   3.1756  10.0821   2.0893
+#>    3.7717   3.7484  -5.8297   0.9345  11.7022  -2.9186   9.0220   7.4304
+#>   -4.1281  -3.3905   3.0100  -8.4499   5.9637 -12.1129   9.3107  -8.5822
+#>   -4.4446   5.2532  16.8947  -4.0080  -7.0400   0.2281  19.7303   6.2775
+#>   -8.7422  -9.7409  -7.6986  13.7377 -10.5350  -2.9565  -0.3803   0.6314
+#>  -10.0792   3.7201   3.3956  11.4131   2.1909   6.1739   0.4597  -2.6288
+#>  -12.0447 -12.2462   3.2220   4.9946 -13.9562 -21.5143   8.4196   3.6127
+#>   -2.1407  -6.4474  12.4759   4.8093   1.9551 -13.9205   6.6378   6.0908
+#>  -13.7874  25.3899  10.5463  -1.2907  -0.4872  14.4459   5.3111  12.9559
+#>   -9.3350  -1.6281 -11.4549   3.9948   4.7318  -8.9820  -0.2126 -10.9739
+#>    3.5306 -12.9561  14.0357  -1.6310   5.2199  -3.7381  10.7289  -6.8779
+#>   14.1180 -22.5631  -0.5707  19.7512  13.8007 -16.0506  -1.0037 -13.3883
+#>   -3.0834  -2.5929  -9.5906  -3.9285  -8.1015   2.1867 -12.1778  -3.5010
+#>    7.5773  10.8242  -6.6862   5.6016 -17.4869  10.7701  -8.9887 -16.3473
+#>   12.5046  -1.7043  -7.8380  -6.5627   6.9845   5.5593  -5.7123   0.5159
+#> 
+#> Columns 25 to 32  14.8486  15.2420  -4.8102  -1.0207   9.3017   9.0401 -10.2565   3.2499
+#>    4.1092 -11.5263  -2.7902  -7.8758  30.7037 -10.9449   3.2616  -0.6032
+#>    1.9425   3.8594  -1.6268   1.5480   0.2440  -1.5936   2.1300  -8.6573
+#>    0.1057   1.4812 -10.7708   1.5843  -4.8658   8.4246   3.7578   6.5719
+#>    3.7255   4.8072  -1.0537 -10.5567  -5.3966  17.6611  12.8893  -1.7585
+#>   -1.5726  17.7785  -5.9407  -2.2563   1.9415  22.8251   5.7589   9.3990
+#>   -9.4051  -3.4753   2.4506  -0.0697   6.3970  -1.6429   5.9528   1.7476
+#>    9.6894   6.5451   4.7938  11.6005  -5.5751   1.8544   6.0249  -6.9157
+#>   -6.0161  -0.7093  -9.0773  -4.9068  -2.2174   2.2867  -5.4468   9.5960
+#>  -21.1858   0.8711  -0.5401  -2.5011   0.3622   0.3830 -12.1438  10.3562
+#>    6.0248   5.9165  26.5937 -12.1715   0.8186   4.5651   9.1920   2.4046
+#>   -5.6874 -11.2428   0.4476  -4.0312  -7.2714   0.1789 -13.6956   6.4037
+#>   -0.6080 -13.5076 -10.0688  -1.4704  -8.5842   8.1221  -5.2809   4.2640
+#>    2.7920   4.4234   1.8447   1.2050  -1.6868 -14.3365  -2.2684  -3.6796
+#>   11.7954   7.1269   3.2465   4.8046   6.0757  -7.2120  -7.7406  -7.9101
+#>   -0.9181  -1.5657  -4.9042  -2.0582  -6.7856  -3.3091   8.8146  -6.1623
+#>   -2.9264   8.1209   1.7582   3.5584  -7.3829  -8.6926   3.1312   5.5652
+#>  -14.9957   0.5275   7.0772   3.6230  -4.3311   6.8364   3.8225 -11.6278
+#>    7.1336 -14.2441  -8.4764   8.6372  -3.4070 -15.3403  11.9006 -20.8279
+#>   16.5022  -9.0338   5.4943  -2.2632   2.6928   8.8664  -1.2954   6.4135
+#>    2.1301   4.5712  -8.0111 -12.4580   4.7830  -4.3577   7.6809  13.7449
+#>   -8.0539   2.5756  -2.2817  -1.4277  -0.6971   9.8518 -13.8332   5.7477
+#>    6.4345   2.9266  -2.1075  -0.9610 -11.3718   3.1750  -5.1814   3.7080
+#>   -1.8409   8.1347  -4.4548  -2.2635   6.8645   1.4938  -2.5258  -0.5367
+#>    8.0574   6.8297 -12.1605  -6.7478  10.6765  -2.2182  -9.1707   3.9874
+#>    8.9553  10.8921 -23.9975   5.1565   7.3858  -2.8637   8.2508  10.1017
+#>    4.3987   9.3663 -13.0031   1.3200  -2.2764   0.8969   1.3466 -19.3814
+#>   -1.4038   9.1606   1.7880  16.2918   9.2231   2.4753  -5.5580  13.7402
+#>    8.9110   7.4481  -8.0772  -4.7265   0.9895  -0.2133   0.1477   7.5884
+#>   -2.7340  19.7750   8.0242 -14.3747   4.4357  14.7675 -10.0986  16.7849
+#>   -1.3068   0.7934  -5.8461   8.8910  -4.5639  14.7191  -7.9466   5.7123
+#>   11.9354  26.3250  -2.0097   6.0551   1.8219  -8.2836  13.2040  -4.3991
+#>   -0.4803  -0.5662  -5.6762 -13.3313   1.8635   4.5556  16.4861  -9.7490
+#> 
+#> Columns 33 to 40  -4.6601  -1.1740   2.1359   9.9174   9.1745   9.2293  -2.9876  14.9829
+#>   13.7997   2.4711  -4.1644  -4.1183  -5.6839  -0.1679  -3.4685 -13.5419
+#>   -4.3454  -5.8547 -19.2603  -1.6330   3.1751   2.4201   5.7208   0.3049
+#>  -10.1706   3.6377  -6.7274   1.1867  10.1489   9.8295  17.7187  -1.7624
+#>  -19.8190   7.3783   2.3858   9.4029  -3.4392  -5.2516  -1.9954  17.3859
+#>   -8.3851  -9.3860   2.0951  -6.0250  -4.8721 -10.8764 -18.0459   4.7164
+#>   -2.0460  -1.5078   5.2325  -5.9806  -1.2630 -10.8076  -3.5425  -9.8268
+#>    3.2921   8.7789  11.3998   3.3249  -4.9335 -13.2581  -2.0565  -2.1746
+#>    8.0028  -6.6745  -3.8613   3.9192  -2.6947  -5.7549  -2.5220  -1.0072
+#>   11.9393  -2.7838  -7.2824 -22.2400  -9.4644  -5.3764   7.3565   4.9516
+#>    8.6304 -14.5468   5.5615   7.0114  -7.0166  -7.5378   0.4683  -2.9764
+#>   -1.1885 -10.6993  -4.5900  -8.4948  -5.7036   1.0922   1.0335   5.4641
+#>   -8.8373  -1.4932   7.5270   5.7846  -5.6232 -12.2433   9.0230  13.6235
+#>    9.0522   8.0384 -10.0359 -10.8777  -9.8880  10.3123  14.2019  13.5627
+#>   -0.1947  20.8733  -3.8304  13.8305  16.5129  -1.0299 -10.8602  -4.1155
+#>  -10.8069   4.8562   3.7801  11.9037 -12.1172  -6.1474  15.5234   2.0818
+#>    3.1167   5.3243  10.6862   7.3274  -4.6980  -8.3878  -1.7010   4.4320
+#>   -1.3729  -4.2911  -2.9054  15.2937   1.7619  -6.9460   0.1281  11.6048
+#>    6.9564  -4.0573  14.7596   1.2560  -8.0958  -7.2678   1.0946   8.1658
+#>   -7.5156  -2.4651  -1.4295   3.0907  -1.1447   2.1614  -5.1984   3.4099
+#>    5.1281  -1.9168  -2.1913 -14.0885   6.5666  13.3331  16.1212  -4.8225
+#>   -2.8193   4.3137  18.0664  10.1441   8.8621 -20.9819   6.8682  -5.7911
+#>    0.6973  -8.4530 -12.8368   5.3047  -5.6743  -0.2333  17.4566  17.1041
+#>    5.9795  -1.8793  -6.3264  -8.5010  20.6661   0.4262  -0.6816  -7.9042
+#>   -0.2756  -4.7594  -2.5035   0.7690   1.4811 -11.4402   7.8181   0.7491
+#>    3.2958  -5.6547   1.6910   2.2226  14.0465   1.0890  10.6547  17.6703
+#>   -0.1001  13.8662  18.8049   3.7827  -3.9923  -3.3656   0.4385   4.2507
+#>   -9.2873   1.2250   3.4173   9.4951   4.6999  -3.2821  -1.6763  -9.2629
+#>    9.7680  -8.2944  -1.2215  -8.2016   5.7898  13.6520  -0.1752  -0.9474
+#>   -5.9962  -7.4348   0.2910  -6.0173  11.2625  20.2298  -4.1021   5.6394
+#>   -3.4762   9.1295  -8.0767   8.0712   2.2210  10.8200   9.5472   4.8456
+#>    1.5022  11.0013  -0.4574  -3.0831  -2.4817   9.0481   9.0799  -2.1804
+#>    1.1925   1.6844  -7.3798  -7.1684   3.6899  16.5189   9.1875  -3.2399
+#> 
+#> Columns 41 to 48  11.7657   3.1196   2.6665  14.5757   2.9871  -4.5150   0.6151 -10.7336
+#>    3.7077  -9.5561  -4.7596  -3.9707  -8.9381   3.0904 -11.0940   8.2206
+#>   -1.1915   0.4038  -1.1578   1.3649   7.9275  -5.2930  -4.8455   8.0586
+#>    6.1191  13.0811   3.5544   5.0136   3.1571  -0.0679   0.4815  -1.3424
+#>   -5.6082   2.4863   3.6704  -5.5318  16.4831 -12.2830   3.5200   4.5975
+#>   -7.8499 -11.5367 -11.0942   6.3718 -14.5018 -10.6295   2.8046   8.4666
+#>  -10.6047   2.6044   2.9539  -0.7786 -17.7200  -0.1585  -8.4284   3.5572
+#>   -0.2002  -2.8649   8.2642   4.3810  -0.4585 -18.0291  17.7569 -20.8126
+#>   -2.4915  -2.6441  -3.0645  -3.3749  15.8615 -14.1035  -2.5848  -7.3752
+#>  -10.9697   4.0416   6.3817 -15.9578  11.3529   0.0486   3.6263  -3.9821
+#>    2.3428   8.1948   8.4879   0.5575   0.5772  -1.3868   8.3604  -0.7609
+#>   -7.9358  -5.0201   4.4435  -7.1785   4.7986 -13.8062 -11.1606   3.0492
+#>  -12.7244   3.2134   4.3907   3.7924   7.2359   8.0097  13.8718   4.8203
+#>    5.1162  11.4593  -2.6179  -4.6172  -1.6876  -1.9041  12.9672   5.2041
+#>   13.1637  12.5163   7.1326   4.8354  -9.3215   9.6836  -1.2448  -4.7915
+#>  -19.1448  -8.5986  14.4240   8.2182   6.7010   6.1534  13.8244  12.7011
+#>   12.9391  11.5793  -6.4071  -8.2259  -4.4934   0.0400   7.7178  13.8950
+#>  -13.1558   8.2733  14.0063  15.0939  -4.5552   7.8886 -13.8971   3.6136
+#>   -1.5390 -20.4704  -4.7701 -13.0516  -5.4321 -10.1729  19.7763  13.3634
+#>    4.4173  -5.7630   3.1204  17.2562  11.4486   9.3133  -8.0640  -0.9698
+#>   -0.3162   2.7982   3.2823  -3.9588 -11.1833   0.0755 -10.1830   7.1177
+#>    4.4892  14.4317  11.1530   6.3800   2.3002  20.2584   3.9545   9.4784
+#>    7.2510  -1.5897  -7.6888  17.4965  -4.5909   4.2699 -11.1061  -6.1803
+#>    7.4172  12.5421   4.4122   3.9228  -1.4641   8.4155   3.3479   7.8418
+#>  -11.9439  -4.4922   8.0724  10.8412   1.4647  -4.0828 -15.7723  -0.0512
+#>    2.2536  -5.3186   9.7490  12.5554   6.0728   4.9373   1.2492  -4.0672
+#>   -5.8810  -1.2590   8.6954   6.0590   2.2440  -5.5730  16.0551  -6.3462
+#>   -9.5750  -2.3336  -6.8815  16.2932  -2.5359  -8.1689 -10.2422 -12.7971
+#>  -14.8769  -6.4750  -0.9576  -0.7946  -2.9660   5.6949  17.0211   0.1723
+#>   13.1304  -8.2289  -5.7268  12.2450  10.5746   0.6198 -10.9609  -0.8005
+#>   -6.0240   2.5168   3.3276  10.8222   4.2437 -14.0329  10.7717  -9.4397
+#>    5.6491   1.2287 -15.2698  10.8850  -4.4047  -8.7894   2.7462  -3.5512
+#>   -2.5415  -3.3443   0.5059   3.4196 -13.9812  19.8057  -4.6844  11.8615
+#> 
+#> Columns 49 to 54   6.6365  12.0035  -5.6669  -5.1705  -1.0216   4.2934
+#>    3.2771  -4.5684  -0.3776  -1.4875   3.5671   1.1867
+#>   -5.8243  -2.1316  -1.4036   1.0183   3.5670  -0.5805
+#>   -8.9103  -4.3704  -9.4189  -5.4160   0.9377   2.8298
+#>   -5.5767  -3.7738   3.5008  -3.2152   3.0187   4.6025
+#>    6.8752  -8.3211  -3.1953   1.7381  -3.6157   6.2603
+#>  -11.1720  16.4505  -1.2207  -3.2138  -5.4599  -1.0469
+#>    7.0002  -6.5917   5.4852   3.2571  -6.0076  -0.6831
+#>   15.6805  -4.7340  -5.2401  -4.7147  -5.8302   1.9700
+#>  -14.7213  -3.5639   8.3196  -0.7164   4.0685   1.4306
+#>    0.6079   4.2414  -2.1809  -3.8523  -1.7788   2.2253
+#>   27.1695  -2.6988  -3.8703  -0.6199  -2.6460  -2.7430
+#>    3.0292  -5.7494  -2.6844  -9.1045   6.5650   0.4952
+#>    8.1505  -5.3231 -10.5474   2.6840  -3.3425   3.8581
+#>   -1.2053   0.3901   7.8789  -8.6739  -3.8515  -6.6561
+#>   -4.1445   6.5305  -5.5170  -1.2831  -3.1719  -3.3955
+#>    7.1810   7.4807  -4.5605  -6.0726   3.4568   0.7595
+#>  -32.5483 -10.3881  14.4331   9.1012  -5.9864  -4.6985
+#>    5.1197  -6.8232  -1.6448  -0.4090  -0.4590  -1.9440
+#>   -5.3278   8.3632   6.2372  -1.6612 -10.0355  -4.2177
+#>  -17.6023  -2.2498 -10.2863   1.8563  -2.0052   1.0690
+#>   -4.4807  -8.3849  10.5267  -5.4050  -0.1967   2.4815
+#>   -0.5400   3.1266   3.4086   0.3663  -1.9362  -4.0071
+#>   -6.5866  11.5554   4.2221  -3.6277  -4.2902   1.0530
+#>    0.3554   4.7149   0.0127  -8.5945  -2.3719   0.5832
+#>   10.9537  -2.9679   8.0124   3.4808   2.1757   4.3042
+#>    5.8067  12.5087 -10.5138 -10.0168  -1.7698  -1.4850
+#>    6.1171   7.7704  -3.9913  -0.9476  -8.9716  -4.8625
+#>    0.4975   0.1223   1.9876 -11.9856  -2.7984  -5.0787
+#>    2.8590  -5.4372   6.8801   4.9836 -10.0127  -1.2156
+#>   -9.5913   7.9211  -5.7925  -1.4726  -2.8814  -0.8450
+#>   -3.1772   6.0984   0.6627   1.7678   6.2593  -2.6370
+#>  -26.2667   2.7168  -9.9451  -6.4581   2.3221  -1.2038
 #> 
 #> (4,.,.) = 
-#>  Columns 1 to 8   0.2219   0.5287  -7.2462   3.4678   4.6731  -0.1877 -16.9202   9.5219
-#>   -1.9105   3.5858   5.7166  -1.6073  -1.7945  -8.8200  -5.3217  12.1512
-#>    4.7816   1.2403  -0.7889  -2.0563   6.4095  -0.4141   7.0459 -21.4778
-#>    1.6454  -5.1022  -1.6111  -1.3982   2.7441 -22.8674   8.2726   6.3412
-#>    7.9354 -11.1167   3.1809  -0.3604   3.7264 -11.9987  -1.9954  -0.3730
-#>   -3.4294  -3.1767  -4.2183  -4.3765  -1.3808   4.5756  -0.6131  -1.5962
-#>    0.1901   2.4103   8.1420  -5.3695  -5.3668 -10.9919   6.8256 -16.6919
-#>   -1.6335   0.6493  -0.5593   0.5749  -9.4058  25.1887 -13.2565  -1.3850
-#>    6.2220   2.0828   0.7704  24.1788   8.7479  -5.7883  -0.6454   9.4337
-#>    1.0294  -0.5649  -3.3157  -5.5922   8.3732  -3.2402  -8.2360  -0.5550
-#>   -2.9694  -7.1426   3.2555   5.3949   2.0131  15.1476   3.9756  13.2564
-#>   -1.8526 -10.5077   1.2471  -1.1577 -13.0580  -2.4768  -1.2320   1.6977
-#>   -1.5976   2.1847   4.7230  -6.0342  -0.5115   0.7416  -6.4935  -0.2440
-#>   -0.8131  -3.3292  -1.9453  -2.7024 -12.0865   0.0555  -1.3913   1.2639
-#>    0.9961  -5.2428 -11.8401  11.0223 -13.4765 -10.1120  12.0808  13.4634
-#>    2.8489   6.0960  -1.7192   5.1269   1.7591 -13.9983  -7.1799  11.6296
-#>    1.4397 -10.4571  -4.6499   5.7463   1.5327 -22.5954  12.3886   0.7576
-#>    5.2328 -12.1251 -12.2410  13.5180  -7.4857  -8.4028 -13.6096  -9.3512
-#>    5.8540  -5.6182  -1.6361  10.4952  11.7672   4.1303   0.0729   2.5591
-#>    1.0030   4.6119   1.1199 -16.2927  -7.2099   1.8887  -0.6048 -17.4918
-#>    1.1425  -2.4799   9.2321 -15.8380   2.6032 -11.9649   5.2088  -0.5586
-#>    2.0217   3.5377  -2.2017  10.9276   3.7871  10.5646  21.3250  -5.9608
-#>    4.1862   2.7262  -0.0812   6.6039   8.8524 -19.9248  -1.2731  14.4253
-#>    1.9577   6.5865  -5.6471  11.7944  17.1732  -0.8650   5.9225   0.6377
-#>    0.2850   2.7053   4.6109   9.1281   2.9065   3.1857  14.4020  16.7285
-#>    0.3713   8.4739  -6.7031   4.5455   0.3447   0.4787   7.7604 -12.5854
-#>   -1.7503   7.2190  -8.6510  -3.4702   5.6751  13.9228  -5.3114 -15.2229
-#>    3.4015  -3.7903  -3.1152  14.0049   5.0853   7.4088   7.7160 -17.8688
-#>    0.8898   5.4846  -2.9675  -2.9970   0.9324 -12.9664  15.2617   1.2304
-#>   -1.9995  -0.5858  -4.9704  12.2816  -4.0337  16.3481   5.3325  14.1150
-#>   -3.1097   3.8707  -0.5704   6.0035   2.6616  -4.3465  -4.7927  -6.2101
-#>    1.9746  -2.8249   1.1796   4.5028  -2.0795  10.9674   2.1140  -6.5755
-#>   -3.4156   3.5885   1.1321  -6.4363   5.4823  14.2754  -5.5120  -9.4005
-#> 
-#> Columns 9 to 16  13.2124   2.8994  -5.3735   2.2360  25.7976   3.0137 -11.4937   1.8268
-#>   -2.8935   4.7092  -6.5326  -8.3361   3.1229 -11.7171   2.4599  -8.0837
-#>    3.1654  -6.8736  -3.0040  17.3870  -6.3694  -0.7925  16.5050  -2.2160
-#>   -9.2479  15.3839  -4.0923   1.8941 -11.9576  -3.3405  -3.0182  11.0526
-#>   -8.7475  11.4002  -3.1246   9.1074  -8.3205   5.0534  -1.0137  23.3715
-#>   12.7605 -12.0524   4.9179 -14.8454  11.8215  -6.9595  -3.8740  -3.3183
-#>  -22.4466  30.3703  16.0598 -25.0331  -1.7203   8.4954  18.3378 -19.4747
-#>   -5.5124  -7.1481   0.1281   2.4150  11.9876 -12.5131   5.0769 -19.8594
-#>   -9.5846   1.3561  -8.8571  -5.7119   7.8162  -3.4918   1.9622  20.0733
-#>    7.9827 -11.1137   3.3657  29.9334  -8.8698   3.8945  -0.4167   0.9169
-#>   15.6365 -15.8412 -11.0218   3.1657  -6.8065 -12.0324  -1.1061   5.1023
-#>   12.2327  13.6443   0.3516  12.6832  -1.4542   6.8167  -0.6641  -9.1212
-#>   10.2712  -2.0197  -4.6885  14.2755 -10.2557  -6.5737   3.8165   6.1315
-#>   -4.2317   0.6908  12.1939   6.9378  -3.0615  -2.2660  12.1141  -0.1512
-#>    0.8666  10.2340  -6.8133   9.7195   8.6313  -6.9433  -9.8662   2.2463
-#>   -3.7955  -2.5939   2.8869 -10.2202  -0.2583  -2.9208 -11.6945  -8.5631
-#>  -14.7348   3.9317  20.9995   2.2973   2.0594   2.2833  -6.1081   1.9177
-#>    2.8468   8.1607   3.1404  11.0361   3.6421  12.4354   4.0767 -15.4768
-#>  -14.6342   8.3660   5.1886  -8.6252  -4.2892  -5.4175  16.5140  15.3920
-#>    2.0241  12.6425   1.9053  15.0312   1.9797   3.7726   0.5551  20.0474
-#>   -5.4475  -1.1319   5.2913   3.7935  -8.1448   7.8411   4.2528 -14.5803
-#>   -1.9503  -1.1509  -2.4265   4.8543   2.2002   4.1706  -1.2490   8.4582
-#>   -9.7253  15.4198  13.5754 -10.7198  -9.2916  12.8682   6.8419   0.6993
-#>    1.9818  19.2110 -14.5814 -20.0495  -1.6095  -3.7466  19.6918   9.6997
-#>    3.3216  13.9218  -4.2295  -6.2760   4.0693 -21.0303   3.8601   5.3268
-#>    8.4889  -5.6500   7.9181   3.5116  11.6170  14.6077   0.0909  12.0469
-#>    3.3008   9.4569  -8.8514 -13.8592 -17.5208   3.9864   1.5469 -23.5100
-#>    1.5347   7.3003 -11.3367  -1.6975   8.1932  -9.5387  -1.2249   1.8121
-#>   -8.1982   0.8516 -10.5033  -8.6144  16.8025   0.9546  -9.9261   5.5325
-#>    8.1604  14.4861  -6.1633  -7.9642   6.1587   2.8146  -9.4546   5.0518
-#>   -2.6576   6.2556  -0.7938  -7.1543 -17.7354  18.7747   9.7348  -2.2436
-#>    4.5735   3.3980   0.8378  -2.6990  14.0849  14.1037   4.1190   2.0277
-#>   -6.6686   3.3696   8.5207   8.9040   0.9597  -2.7365   3.4979  -2.5273
-#> 
-#> Columns 17 to 24   4.1596 -25.7686 -15.1404  -8.9288  -1.3671   5.0678  -1.9301  16.9985
-#>  -12.4490   6.7502  -3.9174  -4.8482  19.0792 -15.0144   4.8911  -1.4765
-#>   -8.8776   2.3609  -4.1307  -8.2305   5.7766  19.5054   6.7913   8.6220
-#>    3.4641   3.1098  -7.0934   0.3988   4.3806 -22.5267   0.0561 -17.0301
-#>  -12.8308   2.0799  13.8156   1.8082 -13.9609  -2.5745   7.5044  -5.9666
-#>    0.2498  12.5673   7.5503 -16.1104  -3.4187  -7.2543  -4.3433  -7.1847
-#>   -3.1907   2.8129   9.4351 -13.6088   4.9873   2.4302   5.7697   3.3274
-#>    0.9187   1.9439  -1.7335  -4.2249  -4.8246  -5.5245  -5.0744  -8.4851
-#>    4.3851   2.8204 -13.0176  11.6872 -17.4204 -15.5696   2.8239   5.9843
-#>   11.3976   8.9254 -14.8285   2.2775 -20.1134  -1.8695  16.1060  13.2642
-#>    0.3391  -2.1935  -8.9088   7.7289 -12.7010 -16.2638 -16.0576   0.9651
-#>   -6.3201  18.0630 -12.1047  -4.1500  -3.2474   7.1294 -10.6871 -10.4556
-#>   -7.5286   1.0544   6.0757   6.5414 -12.5292  13.6479  10.5169  12.7154
-#>   -9.8171   6.7652   1.5683  -0.0256  13.5938  -4.6878   1.4223  -3.7993
-#>   12.3174   8.9059   4.7810  11.0903  -9.1580   3.7943 -17.4670 -18.2115
-#>    1.8995  18.5803  -6.1339 -18.1816  -7.4241   4.7667  20.6264  -8.6507
-#>  -13.2026   4.1181  -2.7518  15.8183   4.5620   9.4490  16.4680 -21.5272
-#>   14.1773  11.3816 -31.4019  -0.7659  -6.8353   8.2688  10.8511  -1.9778
-#>   -4.7224  15.2913  18.8708 -15.4398 -13.3489 -17.6808   1.6728  -0.1029
-#>  -20.5853  -5.6671  22.0880   3.5830   2.7769   8.0856  14.4633 -13.8408
-#>    1.3969  -6.0445   1.2313   4.9702  18.8178   4.0873   4.1045   5.1362
-#>   10.2653   1.1919  -5.6667  18.5522  -7.2126  -5.5894 -17.3211  -6.0522
-#>   -8.3299   8.8381   4.4417  -3.4878   8.0462  15.5097  18.6968 -12.0810
-#>    5.5836  -1.8382 -13.5796 -16.5298 -22.5308  -2.2137 -13.6045   3.5380
-#>    0.4382   3.1033  -5.0076  -6.2842  -7.6827 -15.5193  -3.6441   3.2390
-#>    9.8403  -0.5997   2.9366  -9.6024   0.6152   1.8874 -15.6021  -0.0891
-#>   -7.9489 -19.4334 -19.7915  12.7997   5.3265  13.1515 -16.4574  18.4732
-#>   -7.7003  -0.6809   5.8432   7.4024   1.7996 -12.3416  -5.2899  -5.3632
-#>   -8.5651  -3.3111  -9.8433   8.6207  22.2462  -7.7504   0.8410   2.2015
-#>    2.5760   2.7540  -8.8211   8.0310  -4.4091  -4.6158 -13.2911 -11.8524
-#>   -4.8951  -2.8482   2.9793   0.8178 -14.6286   2.1986   5.1434  -7.7913
-#>    2.7216   4.1392  20.0401 -19.5082  15.5115  -0.1485  -4.7362  14.6629
-#>   16.3551  -8.6646   5.3361   7.1782  -4.6457 -13.8959  -5.2264  20.3985
-#> 
-#> Columns 25 to 32  19.2265   2.5813   4.2602  -8.6212   8.0270  14.7208  -2.4313  -6.6042
-#>   -8.4114  20.3017   2.0620  -4.8383   3.4691  -1.5760   4.6170   2.7803
-#>    0.6465  -5.7817 -16.7433 -17.2697  -2.9205   9.8512  -2.9238   3.5182
-#>    8.6801  -3.3657 -11.3030   3.9110  -2.6238 -10.8564 -13.4369  11.0938
-#>    9.6046   1.2772   3.9488  -5.2463  12.9554  -0.5244 -21.8934   4.8245
-#>    6.2853  -1.1873  -4.1437   6.8021 -12.8606  -7.8836   7.3618  22.9472
-#>    8.4518  11.2520 -10.6945  10.1761  11.1940 -17.1313  13.7517 -15.8747
-#>   -0.8503   0.0473 -15.0965  -7.3103  -9.4960  12.9389  -3.5424   4.7263
-#>    0.6302   5.3077  -1.5759  12.8717  -0.6424   3.9268   8.6920  10.4053
-#>   19.6230  -6.9193  -3.5887   1.7106  -1.2620   3.1657  -0.0184   8.3143
-#>    2.9247 -12.4803  -2.2822   4.9216   5.1835  -3.6888  10.2193  19.3383
-#>    0.8281 -12.9886 -14.3478   8.3200   3.7656 -11.4106  -8.8675   9.3077
-#>  -14.8788  -0.5899   7.3588   4.3446   6.0987  -8.5687   2.9230  -4.8289
-#>   10.2902  10.2134   4.1634   1.2769  -5.2478  -5.8166  -0.0322   1.0880
-#>   18.3145 -10.1861   2.7633   6.5026   7.1369   9.8278 -10.0902   0.1051
-#>  -20.0572   3.9915 -14.7786  -1.9932   2.8509   2.7118  -5.6367  -7.3608
-#>    0.6559  -3.5271  -4.6592   7.9450   1.1289  -7.8782   0.4414  -4.6317
-#>    9.6705   3.3123 -22.6803  17.0145  21.1617  -5.5084  -2.1212  -4.3812
-#>  -13.4505   9.5107  -3.5649 -16.0908   2.5337   4.6970  -9.4074   0.9188
-#>  -11.0729 -12.7696  13.6597  -2.0072  -5.0806  18.4795  -5.9133  -9.5014
-#>    4.7037   3.6743   5.2145  -4.1880  -2.6656  -5.2377   6.0863  10.4891
-#>   11.7670 -10.4386  -0.2109  -0.2333 -12.2052   6.4594   5.4473   3.6028
-#>    0.8061  -4.8452  -6.2903  -3.1591  -0.0587 -14.1828 -18.9741  -0.2332
-#>   -0.3267  25.3989  19.3063   2.1349   4.2234  -3.1067   2.6432 -11.7914
-#>   -2.3602   2.0220  12.4222   2.7937  -4.9585   0.8077  -6.2972   6.2894
-#>    6.5054  10.4263  11.3934   6.9248  -3.4319   9.5436   9.2800   2.6421
-#>   11.7031  -9.1718   4.6275   7.3577   3.4125  18.0781   7.2450 -10.2295
-#>    3.3806  -6.7652 -12.1229  -5.9293  -3.1413  14.5744   2.6997   2.5371
-#>    3.0301   8.9022  -7.5954  -6.3892 -10.5618   2.1032  10.6880   5.5672
-#>   12.4864  13.4421   6.6533   3.7224 -13.4527  14.6014 -17.2829  22.0472
-#>   -4.2936  -0.0431   9.9653   3.0915  14.5693 -10.5417   2.3160  -8.4353
-#>   -3.0880  -6.1304  -2.0531  -7.1627 -12.8319  -2.8523   4.6373  10.1443
-#>   -0.0269  -6.6783  -4.6217  -4.0809  12.2070  -0.7288  11.2078  -6.6478
-#> 
-#> Columns 33 to 40   6.4185   4.8604   3.7222   4.1845  -6.9717   6.2573  -8.9588  -2.0797
-#>  -10.8805  10.3958   6.5959   0.2492  15.6693  -2.3024  -5.1993  -5.0015
-#>  -11.0611 -18.4653 -17.0961  -2.1793 -15.8091  -4.8495  -4.0930  -7.7277
-#>  -12.6086  -3.9492   8.4564   3.7170   4.2184   0.4347   1.7343   5.0374
-#>   -3.5199  10.0360  18.4011   6.6154   7.6686  10.3938   8.6690  -3.2892
-#>   11.9896  14.9657  17.3100  13.6816   5.7155  -6.2593   2.5469  -7.6239
-#>   -2.0192 -12.7663  -1.8799  -9.2099  24.5372  -6.6509  -7.4240  -7.4372
-#>    3.2858  -6.3573   0.9446  10.3753   2.7172  -6.0686   2.2428  -4.7485
-#>    2.8410  12.0668  -3.0514   1.8473 -11.1713   1.2479   2.0316  -0.6065
-#>    4.6437  15.0374   0.2794  18.3464   4.3572   0.1622  18.0440  10.0460
-#>    7.2547  14.2274  -9.1562   9.7975 -19.9968 -19.8045  -1.2774   7.8726
-#>   -4.9674  16.0535   1.7580  11.0541  -4.9818  -0.5423 -10.2666   5.8336
-#>    5.3223  13.3867  -5.7377  -0.4243  -2.8942   0.4821  -2.8297   4.5070
-#>  -32.6253   0.2349   4.3426  12.1137   1.6440   7.2439   5.9732   7.4394
-#>   11.8648   0.1440  -2.2802  -1.4521   4.3316   2.3332  12.4300  -0.3200
-#>   -6.3452   9.5664   0.8558 -22.2897  -3.8459   7.0089   5.8969  -4.4485
-#>   -8.6390  -1.5985   0.7078  -6.7466   3.5989  12.8649  -1.7226  -2.5022
-#>  -12.6954   5.1820 -15.4668  -0.1136   9.0383  -1.8060  -2.1011   7.6085
-#>    9.8640  12.0282  15.3925   1.3519  11.9642  12.9207  -1.3220  -7.5618
-#>  -13.5007   1.6434   8.9013   9.6739  -0.7869  16.0522   6.1873  -7.6691
-#>  -13.1869 -10.5223   0.5893 -10.4336 -11.4600   2.9302 -11.7560  -9.4367
-#>    1.0218 -12.6724  -3.8524   0.6912  -3.0898  -4.9917 -17.9133   2.3959
-#>  -10.5114  -3.0623 -21.4408 -11.9590  -4.1554   7.4104   2.1276 -18.3623
-#>    6.8585  23.4705  12.6679  11.8100  -3.3717  -1.6442 -19.9979  11.4009
-#>    7.6246   2.4456  -1.0381   5.7176   5.9947  -3.6588  -2.6176  -2.4339
-#>   -2.1380  11.9104   1.4937  -0.9187  11.4557  -1.7577   1.6467   7.3846
-#>    3.8289   8.6479   8.8680   8.2986   2.5710   9.3239  -2.7676   5.4409
-#>    2.0634   5.6217   8.0566   7.1804  -6.9430  -5.0080  -0.1164  -3.7963
-#>  -22.5194  -0.9365   0.0082 -14.0502   2.1485  -1.5372 -13.6636  -8.0557
-#>    4.1657   6.2588   0.7398  27.1365   6.5433  14.9252  -9.3923  10.3682
-#>    4.6615   2.4031  -6.2987  -6.8812   4.4682   8.5512  -1.3710  -3.4787
-#>    3.2988  -2.1579   8.7593 -13.0047  -9.2239  -4.7590   8.9181   5.5893
-#>    3.6827   0.2190   2.4700 -10.8235  -2.6666  -6.8960 -14.1972  14.2358
-#> 
-#> Columns 41 to 48   6.4534  -9.1895  -8.6435 -14.7488  11.7945   7.9069   6.2200   2.6855
-#>    3.7099   6.7005  -6.4447  14.9308  13.9055   3.2064   3.0763  -3.9926
-#>    1.3041  -1.2253  -5.3743  -0.0238 -11.3995  13.4731   0.8305  -0.6777
-#>    2.3425   4.7941   5.5960   4.5050   9.3140   1.8727   0.0980 -16.1363
-#>   15.9776   6.7659   0.1963  -6.5183   9.9605  -3.8728   2.0315  -4.3087
-#>   -0.4646   7.3267   9.4552  -7.7112 -11.6981   5.7818   2.1668   1.2031
-#>    4.4953  -2.8655   0.4263  -9.0706   6.0559   4.9343   3.4586  -1.6319
-#>    9.0060   6.4304  -1.7217 -12.2141   7.1489   3.2940   1.0976  20.3411
-#>   -6.8470  11.7559  -8.2976   8.8400  -4.5367  -5.4740  -9.5925  10.1248
-#>    3.1792  11.0095  -5.4095   8.5358  -0.0891   7.4820   7.9858  -9.3940
-#>   -4.8595  -5.1274  -6.9625  -6.2827 -11.5960  15.2161   9.9113   4.1383
-#>   -2.7703  -4.5346   1.3240  -4.7023   2.2619   1.5294  -0.7932   5.1330
-#>  -13.8488   3.7725  -7.4467  10.0005   2.3924   7.0655  -4.2412 -10.6243
-#>   -0.5703   2.1365   6.5394  15.4758   6.1899   5.1621  -9.3508  -9.5881
-#>   -1.6777  11.2626   2.7623  -8.8503   4.7320  -9.9948  -2.1906  -1.3836
-#>    2.0351   4.1408  -7.0070   4.6746  15.7931  -0.7728 -12.3294  -0.6707
-#>    4.6545   8.8259  -4.0885 -11.8345   1.9865  11.8464   3.1237   7.3947
-#>    1.1175  -3.9012 -12.5408  13.5431  -3.7331  10.7896  -0.2898   4.3303
-#>   11.6799  16.1524   6.9541  -2.2194   0.3828   6.8236   9.9355  12.6040
-#>   -1.0700  -0.2820  13.9910  11.0524  -3.5197  -5.9382  -7.1684   0.8504
-#>   -6.0114  -3.4103  -2.3126  -3.8858 -10.1844  -6.1619  11.4913  -2.3328
-#>   -6.6570   2.1689   0.3162 -20.9597  -7.9286  15.7913   4.5882   6.8197
-#>   17.7398   9.6953   4.2466  -5.7422  -8.0725  12.1194   6.9694 -12.5887
-#>    4.1084   9.9369  -2.8953   4.8401  22.3587   7.2082 -10.6898 -19.8510
-#>    0.9589 -12.2802  -4.2077   4.4325   0.7391 -14.0510  -5.7468   2.9880
-#>    5.0825   1.6433   2.1427   0.0308  -9.4286  -7.0943  -9.0435   8.6887
-#>   -8.7570  -4.5735  -2.5489  15.2376   4.0394   0.9269 -10.9205  -5.9028
-#>    1.9380  -4.2203  -3.1124   4.4167 -10.2325  -5.5920  -8.7279  -4.0147
-#>   -2.2084   2.4891  -9.7241   4.3068   0.6057 -15.8200 -11.3434 -10.7842
-#>    5.4077   1.6802 -15.9240  -6.0652  -2.5681  20.3799  -8.0014   9.7064
-#>   -5.6949   8.5338   5.7999  -3.7980   5.1719   8.5332   2.3118  -9.0947
-#>    3.2450   2.4662   7.0022  -5.3419   5.7970  -2.7587  -6.6008  -4.9079
-#>   -8.3299 -12.9051   4.1120  -0.4969  15.4392   4.8217  14.1742  -7.5018
-#> 
-#> Columns 49 to 54  -4.7991   0.0500  -8.7191   0.4213   3.1326  -0.7528
-#>  -18.3523   3.6540   0.0161  -1.2858   2.6736  -0.7228
-#>   -2.1636  -4.9768   4.3219  -4.3713   4.8987  -2.0903
-#>   -2.0971  -9.9748   3.3338   7.4499  -2.8762   5.8465
-#>    6.8928  -4.1073   5.6118  12.3544   3.8265   7.2727
-#>    2.9705   1.9568   2.5834   0.8506   1.9960  -2.9966
-#>   -2.9388  -2.1280   1.5803  12.5152  -2.4432  -0.8005
-#>  -12.0056  14.8970  -7.3867  -2.2539  -4.7291   0.8013
-#>    2.5186   1.8716   2.1189   1.9451  -2.0923  -5.3365
-#>  -10.2855  -1.1241   3.2962   4.4069   3.3473   2.6102
-#>   -7.0721  -2.5420  -7.3420 -13.0171  -2.2379  -6.7108
-#>   -3.4175  -3.6209   7.7254  -8.8157  -5.4033   0.7490
-#>   -8.5337   9.6271  -0.0745   4.8559  10.0779  -5.1408
-#>    0.0217   1.3013   4.7639  -2.0329  -0.8737  -1.2641
-#>    5.1340   4.6470  -8.7506  -1.6405  -3.4937  10.2746
-#>   11.9159  22.8571   5.2302  -9.1702  -1.6322  -2.3057
-#>   20.3475   1.4480   3.6028  -0.7666  -0.9609  -8.4161
-#>  -10.5663   6.8000   1.0616  -5.9306   5.6407  -3.3033
-#>   -2.6967  -0.7815  -8.6606   1.8991   2.3074  -1.4894
-#>    0.0664  -9.5043  -8.0940   1.5034 -11.0601   2.7597
-#>    5.8905  10.2688   2.1554  -3.0246   4.5676   1.4414
-#>    6.9913  -4.2888  -9.0173  -2.8255  -3.0490  -5.1996
-#>   -4.3810  -6.9911  -6.0053   4.0219   3.4453   1.5885
-#>  -15.1787  -9.3592   0.6733   5.3358   0.3754  -2.1221
-#>   -3.7403  -7.2926   5.3745   1.8880  -3.5720   0.1754
-#>    5.1834  -0.0021  -0.0035  -0.1351   1.0208   4.0843
-#>   -5.8821   0.7086  10.6882   4.8212   3.1791   6.6074
-#>   -9.5721  -9.9010   6.5255   2.3268  -2.0515   2.9862
-#>   15.4546  -1.7387   9.6608   1.5877   6.8544   0.1361
-#>    1.1707  -2.3197   5.2251   4.3118   1.0230   0.6757
-#>    6.7378  -2.8661 -13.6288   2.6468   3.8596  -1.8382
-#>   -3.3849  -7.5928   5.2608   6.9661  -7.8696   2.8806
-#>   -1.0976  -8.2777  -1.6198   6.4236   0.9753  -4.2247
+#>  Columns 1 to 8  -0.4399  -9.8339  -5.7341  14.1821   7.2846 -12.4809  15.3554   8.5535
+#>   -5.9303 -10.1007 -12.6243  -7.5374  -3.0855  -2.6401  -9.1784  -5.8404
+#>    0.2445   5.1412   5.5394   8.5548  16.6175 -20.9391  11.9758   5.5936
+#>    0.6478 -14.3452  -7.8975  -6.9604  -9.5037   1.2038  12.7594 -11.9982
+#>    5.7954   3.6360  -3.9351  -5.2897 -11.6707   4.1323  14.0706   2.7168
+#>   -2.7673  -9.3404  -1.1551   4.1638 -10.0284   7.6380  -6.3285   3.5912
+#>    0.9057  -6.0202  -3.8427  -0.0954 -21.3463 -11.1183  -7.1892  -5.6806
+#>    3.0716  -9.7678   4.7543  12.0256  15.2962   9.7284   1.7495 -18.1781
+#>    6.5256   8.7798   9.8120   2.0260   3.7544  -1.8184  -4.5782 -18.5067
+#>    5.7275  -2.6960  -2.6577   2.1444  -0.6645   0.7423   5.6429  -2.2996
+#>   -7.5429  -2.9588   4.2095  -7.5152  -4.7756  -9.1355   1.8877  -4.5023
+#>    6.5034  -7.0176  -0.3468  -7.6490 -12.5162 -10.2921  -8.5465 -23.4569
+#>    7.1064  -6.8738  -7.1737   4.6043   2.3187  -2.6163  25.5759   0.3414
+#>    3.8790   6.8499   3.0317  -4.5667 -19.5008  -5.1349  -2.6449   0.3416
+#>   -2.5915   2.0918   4.0359   0.8269  -3.3786  -6.7288   4.3243  -4.7770
+#>   -1.7882  -1.1836  -4.7447  -3.5226   3.0817 -10.5895  12.1533   9.3463
+#>   -2.0780   3.1450   2.4594  -2.4749  -8.7454  -6.9414  12.2690   4.2044
+#>   -0.2059  -4.1702 -10.4617  -5.3278   1.5751 -17.1475   8.7886  21.2329
+#>   -1.9195 -13.3358 -10.5479 -11.8983  -0.4503  -7.4361  -1.4711   9.7657
+#>    1.3657   3.9532   5.0426  12.3670  15.9357  -2.8124  -2.6142  -8.2979
+#>   -5.2996  -5.0220  11.0571   7.0752  -4.1674  -4.9632  -7.1966 -13.6628
+#>    9.7301   2.6139   3.8642   9.7427  22.3077  15.1479  23.7175  -5.7975
+#>    4.9020  13.6969   4.3543   1.5525   7.2297  -5.3547   1.8316  -3.7038
+#>    3.0956   7.9357  10.9700 -14.3732  -1.8252 -12.6894 -11.8313   4.5608
+#>  -10.8936  -1.1501   2.5471   2.1257 -11.6878   5.2519   8.0220   5.8740
+#>    4.2600   9.0741   6.7806   9.5234   3.7230  -2.8274   2.1060  15.1678
+#>    1.3539 -11.4143   0.4210  11.2573  -2.2872  -3.2963   7.8345 -12.0063
+#>   -5.4789 -10.7103   4.7610   8.8139  -0.0932  -6.9008   1.8923 -10.9050
+#>    5.9308  -3.2216  -8.8826   4.0317   0.2408   0.0349   0.0710  -0.9200
+#>   -5.4162   6.3745   6.9228   0.9181  -0.0911  -1.9766  -4.8743   6.2025
+#>   -7.4394  -3.0056   6.1070  -3.4087   3.3336   6.2609  16.2879   3.0028
+#>   -8.9153  13.7766   2.6318   8.1106  -2.0410   3.8390   8.8171   7.0878
+#>   -5.4004  -0.9636   1.1346  -2.0752   0.0901  -7.0591  -0.6268  12.9182
+#> 
+#> Columns 9 to 16   5.3651  14.2650   7.5604  -6.7343   8.4843   4.3947  -5.9483   8.7966
+#>   -2.7654  -2.3454   6.9252  -0.0815  12.2935  10.5242   8.8327   6.1936
+#>   11.4527  16.4624  -8.9326 -12.2866  -1.2389 -13.7843   4.9859  -2.1215
+#>    1.8822   0.3879   2.3952   7.1999  11.5789   4.0394   6.9331   1.4807
+#>  -11.3371   0.9226  -0.3788  10.0386 -12.0893  10.4436  -9.3535   7.0422
+#>   -5.5681  13.7311  -4.8841   8.9371   7.5074 -14.6669  -4.8672  15.4527
+#>   -1.3421 -22.3294   2.8623  -2.9001  -0.6917  -3.5467   3.8639   8.0614
+#>    4.9583 -13.9854   0.5634  -5.2125  -4.1940   4.5869  -9.0215   8.4669
+#>    0.3494  -6.7512 -23.1683   3.9889  -2.4973  -4.7381   5.6142   7.4027
+#>   -4.9036   8.7479   4.7585   2.2023   4.9907  -0.7846  -9.8249  -5.8838
+#>    3.0641  -3.2316  11.9413   4.7472  -5.9334   3.9581   7.9338   9.3080
+#>    9.7415   1.4038  -1.3927   3.3384  -8.3999  -2.4913   5.2354  10.8509
+#>   -0.8674   4.5202  -8.4854   5.4010  -2.9413   1.7532  -2.2522   3.2416
+#>   10.9610  -3.2508  -4.7333  -6.3673   4.2876   3.7218  15.9949  16.8563
+#>    2.9891  -2.6980  -4.9086   0.6166   7.1292   6.3679  -0.9282   9.5413
+#>   -8.5565   6.2409  -3.6749   4.3947  -4.2313   4.0329   7.3583   3.4428
+#>    3.5101  -3.8375  -4.2902  -2.3837  -2.1970  -2.6697   2.2321   3.3484
+#>  -21.3187  -8.2250   2.1174   0.2722  14.6967  -4.6395   6.6127   6.3066
+#>   -1.3565   3.5411  -5.6334  -8.1736  10.2623  -4.6321  -4.1303 -10.0440
+#>   11.8812   3.9888   4.6782   7.4353   0.5304 -12.3094  -1.8426  -3.7535
+#>    2.2787   0.4358  -3.1635   6.1347  -2.3667  -2.7445   5.2450  -7.0328
+#>   -7.4824  -2.1243   5.5581   1.3164   1.0700  -3.3431  -0.9782  -7.7898
+#>   -2.0124   3.8889   7.4500  -6.9558 -13.8442  -7.9493  -0.2970  -6.2874
+#>   14.0225 -19.3300  13.2850  -3.4373  14.0544   9.4851   9.0342   0.0487
+#>   -1.7435  -8.5715  -5.0730   0.0395   6.8899  -2.0006  -0.2242   5.7855
+#>   -6.9617  -3.9413   3.1908   9.1024   2.0667   9.9852   0.9369  -1.0024
+#>   -4.9178   4.6218  -1.3753  -9.8213  -3.8856   0.0139  -7.7337   8.8656
+#>   -7.2232  -4.7904  -1.2482   0.5743  -0.0366  -7.8573  -4.1574   3.3545
+#>   -6.3942   9.3756 -14.3798   9.6943  -0.2711   8.9593   6.9881 -10.2577
+#>   24.3169  19.9238  -9.1816  -0.8564   2.9165   4.1707   2.7056  11.6700
+#>    7.4866   8.9667  -3.6551   7.8229   9.5467  -7.9477 -11.2145   6.1564
+#>   -8.2359  17.4440   0.6013 -20.2454   2.8364  -5.5546   9.0681  -2.9457
+#>   -5.6151  14.6247  -2.7100 -15.7017   5.5888  -6.7840   2.6476  -9.4555
+#> 
+#> Columns 17 to 24   5.6183  -2.2153   0.8109 -10.9208  -6.9728  -0.2295   6.7799   5.5874
+#>    2.4850 -14.8900   7.5054   2.9570  -9.5373  -4.2789  -1.5990 -13.0451
+#>   -4.7077 -12.7756  18.7467   8.8846   1.5291   8.3353 -15.4821  13.3129
+#>  -11.3264  -3.0792  10.8050  15.0769   9.2741  -8.4039  -6.9717   3.5838
+#>   -3.6343   1.5097 -10.2390   6.5485  -7.2157   5.6341   5.6290  -7.8932
+#>   -7.3573  -1.9905 -19.9132 -13.8910   1.9008  -8.5817  12.3528  -6.8822
+#>    2.9178 -11.3506  -2.2280   6.5316  -2.1847 -17.1350 -12.5507  -4.8582
+#>   11.4017   1.8128  -8.3513  -6.2435 -22.3032  -0.5120 -15.1850   7.1722
+#>  -14.3802  -3.5207  -5.5234  10.9757   5.1192   0.8222  -2.3774   7.4422
+#>   -7.4805   2.7108 -15.8225  -0.2904  -5.3539  13.1530   9.9738   5.0945
+#>   15.7363  -7.7178  -4.9708  -9.2691  -7.2409  -6.6241  -6.1960  -0.9867
+#>  -10.6237  -9.1690  -5.1146   5.8755   6.6720  22.3087   5.2693  10.9291
+#>    1.4019   8.3625  -2.6825   8.0516  -1.0583   8.2296 -11.6665   1.4366
+#>   -3.3552 -14.9592 -14.8967   2.7919   4.8436   9.3720   4.6877   5.9854
+#>   16.6644  -0.1520   9.0551 -10.4617 -12.0942  -0.9356 -14.0587  -5.2801
+#>   15.3731 -10.3244  -1.0295   9.3016   2.6167  -4.4824   1.2845 -12.7519
+#>   12.8638 -10.6249  -8.9250  -2.9008 -10.5265   9.1539 -18.9211  -3.6155
+#>   11.6078  -4.0365   8.9050   1.3733  -7.9153  -9.3213  14.7705   0.3930
+#>  -15.0470   1.8544   3.5344   9.6559  16.5799 -10.3601  -6.6171  -2.1831
+#>    6.4051   9.0978  -0.0979 -12.9394  13.2711  -9.8917  -1.8478  -0.3260
+#>    8.3673 -10.6700   8.8476  -1.7596  -2.7251   9.1745  18.7763  15.8548
+#>   18.1532  10.2988  -8.2354   4.3982 -18.5632  23.1937   5.4139 -10.2724
+#>   11.5605  -9.1969   1.9404  -5.3495   9.4016  12.6998   5.9488  18.5692
+#>    4.5141  -1.8314  -0.1923  -4.3068 -22.2908  14.4707   1.3276   1.7627
+#>   -3.0217   6.2592   7.4638  -6.1301  -1.9086  -4.5095   3.7591  11.5180
+#>   -6.3339   4.6793   1.6742  -3.5293 -14.0501  -1.1520  20.4809  10.7152
+#>    7.5744  -2.4382 -17.7833 -10.5110  -6.9921  17.4620 -25.7552  -0.7050
+#>   12.8532   5.3170  -3.5521  -4.6499  -2.8613  -3.9584  -5.6844  -8.1551
+#>  -10.0477   7.0236  -0.5387  -2.2920  11.0251 -10.5603   8.7230  -8.2712
+#>   -6.6999   2.9466  23.8148  -1.6187  12.5007 -20.8724  12.3928  -1.9434
+#>   10.5920   8.4415   1.1417 -16.7846   6.8896  -3.3742   5.5085  16.6993
+#>   -5.5027  -3.8251   2.6803 -11.8676  -3.8384   0.5090  -4.3183  -6.4270
+#>    7.5013  -9.0091   6.0672  11.0745   9.3144   9.1787  -3.4240  -7.2819
+#> 
+#> Columns 25 to 32   0.9207   2.0368 -14.7595  -2.3700  -9.4132  -0.5554   2.8267   1.9021
+#>  -10.0508 -16.7542  11.7158  -5.2896  10.7968  10.4491  -0.0389   2.8819
+#>    4.5385  12.4348   1.3891  -9.8461  -0.4487  12.3783  -4.1551   8.8723
+#>   -9.5195   5.4560   5.3158  -4.3291  -9.9187  -1.6283 -10.7847   6.6581
+#>   -1.9136   3.8731  -7.5145 -12.7316  -9.1366   9.3933   5.3921   3.7452
+#>   -0.8538   5.8630 -11.3826  -2.9935 -11.3101  -4.7223   4.4738  10.6260
+#>    1.9936  -8.8177   5.3058  -9.7294  -7.7622  -6.3238   6.4446  -1.3659
+#>    6.0357   2.9529  -7.9592 -12.3859   1.2444 -12.3022   2.6763  14.9436
+#>    5.2183   7.2566  -1.2416  -7.2451   1.4036   1.0254  -9.8252  -1.0778
+#>    6.6504   0.8037  -7.6595  -0.5570   4.0338  14.3104   2.5542  -8.0929
+#>   -0.6015   9.1358   4.5889 -12.5929  -4.2856 -14.6540  13.0719  11.7818
+#>   -1.9586   8.1230   2.3274   7.3378   6.9369  -0.0582  -7.9008   9.1119
+#>    7.8170  -3.8486   1.0594   1.0198 -10.5631   3.9986  -7.4255   0.9664
+#>    3.3753  -9.7303 -14.2008  -5.8655  -1.4843  -1.2457   0.7414  -0.1931
+#>    0.2903  -7.6817  11.7602   7.8886  -5.7281  -1.4388  -6.9232   7.2284
+#>    7.6689  -2.7571   1.0084  -5.8282  -8.9794  -5.1272  -4.6154   4.7556
+#>   -2.3670   8.4990  11.2139  -5.0128  -5.6257   2.4300   2.8358   0.1539
+#>    8.5433 -26.1802  10.0320  -9.0166 -14.9888  16.1721   4.0149  11.9925
+#>  -19.1530   3.3381   9.0346  -1.0924  14.2923   2.4672  -7.5474  -9.1617
+#>   -6.9023   5.2533  -7.8535  -0.1190 -10.4692 -11.7988  -6.9563   0.1468
+#>    0.7852  -5.4089  -2.2657 -14.7595   4.9537  -6.5129 -14.1604   1.7007
+#>   -0.3543   6.7939   9.1758  -7.6594  -0.6388   2.4999   4.5303  16.2191
+#>    5.5623  -6.3744  -1.2816  -4.6006   5.1525  -5.7069 -15.4750   0.9261
+#>    3.3566  -0.7460  -4.1063  -7.2672  -4.1472  -4.7387   6.6353  15.8060
+#>    7.1686   8.8878  -1.2049   0.1900  -1.5401   2.5221  -0.5413 -11.5386
+#>    2.7018  -6.6635  -6.0778  -6.7091  -4.0805   2.3584   3.7314  -2.2992
+#>    3.2233 -10.8133   1.5959   1.2055  -8.1207  -9.8140 -12.6877   8.8985
+#>  -15.3895  -1.1929  -2.5592   5.4308   1.8455   0.1622 -12.5549  11.3373
+#>   -8.3236   3.8338 -10.2195  10.9981  -7.4546  -9.8947 -18.2802   4.1683
+#>  -11.2489  21.4333 -18.1130  -1.8371   1.2404   6.0333   4.2132  -8.1135
+#>   -5.6300  10.1563  -8.9303  -2.1507  -6.3020  -8.1767  -6.7428 -10.7172
+#>    4.8373  -3.1852  -7.8659   1.5173   2.3496   7.1938  -9.8646 -10.5505
+#>   -1.1027  -8.7715  11.7387   1.3545 -13.9336  -4.7665  -6.7524  -3.1597
+#> 
+#> Columns 33 to 40   6.5539  -5.5556   2.7697  -0.5191  -0.9395  16.0489   7.8946  -4.0665
+#>    1.0967   9.0358  -5.7644   3.6538  -9.3637   0.6205 -11.9093   6.7415
+#>   -2.0511  -8.6511  -1.2424   6.0813  -2.3020   3.4821   1.1816   5.3889
+#>   -6.6797  -4.3222  -0.3560  -1.2880  -4.6805  -5.1932  -0.3653   7.9137
+#>    8.4303 -12.4701  20.2444   2.3857  -2.3511  12.6756   9.8967 -15.2806
+#>    5.3830  -4.1600   0.3984  -8.4826  -3.2370   4.4837  -1.3721   3.5716
+#>   -7.6241  -3.5401  -0.1866   0.0584   0.9402   1.8092  -7.4590   8.7904
+#>  -21.6164   9.9771   1.1564  14.1455  -1.9325  14.5105   4.5724  -3.1793
+#>   -5.2144  -6.0559  10.5163   7.0450 -11.0510 -15.2040   3.5164  -3.7926
+#>    8.5668 -10.2205   0.1464  -4.4411  14.4663  -7.1797  -8.6814  -9.0000
+#>  -22.6188   4.1929  -4.9197   6.3402  11.0029  11.3697  -7.4817   9.2828
+#>   15.0105 -19.4201   7.2595  -3.1692 -12.5138  -7.9568  -8.6103 -17.4663
+#>   -9.0719  -8.5978  17.9267  -9.5988  -0.7344   2.6988   1.2121  -5.8299
+#>   -2.4363 -15.8126  11.3802  -0.0943  -0.5220   8.8321  -6.0334  -9.9249
+#>   -4.1756  -7.2381  -7.8929  -4.5356   2.5705  20.5241   6.4802  -7.4891
+#>  -10.1320  -2.7971  -4.5442   4.0991   7.7741  -0.7378  -0.8670   3.6224
+#>   -6.2688   3.5671   3.2446 -13.3061   6.0049   7.3297   4.3155 -16.1011
+#>   -2.8790  -1.0572  -2.0730   5.2036   6.8688   1.6448  -4.6652  -4.9549
+#>  -17.5073  -1.2243   0.6608  -6.2250   0.1022  -8.3891  -0.3210   9.3562
+#>    8.8424  -5.8274   4.4686   5.7836   1.7361   6.5671   9.0251  13.4838
+#>   -5.9233  -4.2397   5.5106  -5.5407  -0.2701  -9.6298 -11.9852  -1.3018
+#>    6.4946   1.7733  -0.7541   4.5311   3.5513  12.8196  12.5087 -10.8967
+#>   -4.2948   2.0791 -15.4223  13.7624  -0.9687  -5.3149  -0.0733 -13.0511
+#>   -2.7266  -0.5527  -5.4479   2.1995   2.8003  14.2527  -1.7781 -14.8952
+#>    4.0721 -11.9535  -5.3805   4.1456  14.0724 -10.7224  -7.3015   5.1099
+#>   15.3555  -7.4927  -5.9483   0.6172   4.8021  -1.5085   4.1855  -0.5813
+#>   -5.5500 -10.3300  17.8180  -1.3665  -0.6823  18.3681   5.4871 -19.1068
+#>    2.1424   2.2024   5.0952  -9.8469  -9.3828   3.2173   9.6302  -1.0092
+#>   -7.5320   4.0745   1.2548 -16.5210  -2.0761  -1.6013   1.0776  13.3129
+#>   19.3149   9.2281 -24.3378   8.1898  26.7277   2.0330   5.7732   7.8458
+#>   -4.0812  -6.1952 -10.2545  -4.4162  12.6505 -13.5147   6.6683  -4.1617
+#>   -6.2417 -20.2140   8.1752 -18.0629  14.1109   7.2749  -0.3871   2.6768
+#>   -4.7742   1.9486   1.1159 -17.6181   7.0251   4.7026  -7.1945   3.8130
+#> 
+#> Columns 41 to 48   3.3232 -13.5924   2.8170   2.3330 -15.8960  -1.8917   2.7798  -7.6116
+#>    3.8347  17.8713  -2.1484  22.3505  -5.9447  24.5912   4.5556  -1.5373
+#>   11.9191 -16.4974  -3.8895  -6.3970  -1.7202   0.6156   4.8091 -10.1829
+#>    5.1317 -14.7918   5.2824 -15.7628  -4.8590  -6.0179  -1.7699  -1.2362
+#>    7.8854  -0.7031  -1.4654 -10.2095 -10.7414  -6.0618  -0.1446  13.8343
+#>   -1.8350   6.9098   7.2743   3.4918   2.6187 -20.0876  -5.8811   1.6351
+#>    5.8766  -4.4788 -12.6568   4.0703  -0.2506  -9.4394   2.6171  -3.7551
+#>    4.2620  -8.3575  14.8513  -7.1902  17.5376   0.1819  -4.9055   5.6810
+#>  -10.8480  -5.5902   3.0382 -11.4017  -2.6344  -8.4439   8.3445   1.9137
+#>   10.2491   3.8944  -0.0634   5.9458  12.0449  -4.2006   3.1583  -8.0179
+#>   -2.4558  -2.3309  -1.1199   6.1755  13.1978  -8.8434 -16.9387   4.4322
+#>   -0.8797   3.1642   4.3431  -8.6493   0.1978   8.2223  -3.5361   1.4634
+#>   12.9270 -10.5220   7.4634  -5.0543   4.8620  -0.5180  12.9420   0.0950
+#>   -4.3370  11.3971  17.9090 -11.3510  -2.3065  -3.1703  -0.0963  -7.4143
+#>    2.0556   3.0468   4.6587  18.2531  -7.3665   6.6207 -11.4257  -7.1988
+#>    9.3272  -1.0225  -8.4347   7.0958  -4.5650  -0.4044  -7.4498   5.0786
+#>    1.6697   2.4790  -0.7083   2.7175  -3.5798  -0.0206   1.3507  -9.2313
+#>   -0.8127  -9.9962  -8.7948  12.4723   7.5816  -5.5933  -8.4468   8.1223
+#>   -8.9043   1.3019   0.5547  -5.8646 -12.5195   6.7520   8.4801   2.8676
+#>   -4.8482  -2.7945  -2.7811  -9.4188  -5.9609  -8.1079   5.5658   0.7395
+#>   -1.9725   8.5564  -4.0857  13.5660  -2.4938  -0.4724  -5.4248  -7.0286
+#>    8.3375   8.9952  -2.7771   9.8040  -8.2494  -0.0066   3.3925  -0.1934
+#>    4.1149   0.1652 -20.0050   0.9962 -16.9388   1.2711 -12.9137  11.3030
+#>    7.5552  -3.2796  -0.4728   2.4928  16.0125  -0.4962  -6.8039 -14.2538
+#>    9.7317  -7.5181   5.3971  10.6695  11.5370   2.9633  -5.9410 -10.9148
+#>   12.8864   5.6636   0.3896  19.3001   2.2492  -2.1819  -4.7484 -20.7994
+#>   24.8203 -12.6014  11.1082 -18.4825 -11.1396  -2.3746   1.8259   2.2090
+#>   -0.5355  -9.7707   6.1803   3.5288 -12.9117  12.0748  -4.3031   1.2460
+#>  -12.0494   8.5140   4.8341  -0.0779  -6.0345  -3.4025   4.1744  -3.7141
+#>   -1.7613   4.1214 -17.0757   2.6091   5.1673   8.0337   5.6331  -1.7692
+#>    1.1393  -0.4348   7.0936   9.1328   9.7192  -0.6600  -7.7376  -2.2071
+#>   15.3554  -0.2694  10.4138   8.9714 -13.8950   5.3369 -14.7004  -2.2660
+#>    2.6429   1.3753 -17.0177  -4.8190  -9.0052  -6.2367   8.9734   1.9156
+#> 
+#> Columns 49 to 54  -3.8169   3.5906  -4.5632  12.4301  13.9804   1.4995
+#>   -9.5793 -16.4893  -4.7312  -8.0008  -3.6178  -8.9891
+#>   15.3577  -2.8947   7.6546   0.0745  -1.9184   4.4233
+#>   11.9067   7.0628   4.8176   5.0115   1.2250   4.5187
+#>   14.0419   2.0488   3.5507  -9.6562   6.0250 -10.7442
+#>  -13.5825  12.7014 -10.9511   3.0237  13.1409  -3.6006
+#>   -4.4251  -6.4177  -0.9253  -0.1735   3.9153   6.9999
+#>   -8.2174 -10.7605  -3.1630  -9.0839   5.0032  -3.0584
+#>    1.8874   8.8065  -2.6591   0.5320   7.9787   2.3050
+#>   -6.3381  10.3886   1.2976   2.0447   0.1569  -2.3262
+#>   15.3167 -13.7464   8.5274  -3.2178   6.9150  -3.5044
+#>    3.8239  -6.0307  14.5465   0.1830   9.4673   2.0756
+#>   12.4815  -9.1989   0.9628   7.6539  -9.7898  -3.9564
+#>  -11.5310   7.6337   6.5070   2.8451   8.1552  -9.6600
+#>    2.0690  -5.9099  -3.8537   5.8093   1.9038   7.2235
+#>    0.7651  -4.2905   9.6545   4.3836  -7.6700   7.6161
+#>    2.7775  -4.7501 -11.2710   3.5349   4.7221   0.4581
+#>    3.7960  16.1732  -7.5054   5.4440  -9.6357   1.4493
+#>   -8.2701  -1.2075  13.1401   2.9992  -1.9170  -0.5563
+#>    6.6165   5.7090   6.5782  -0.5012   6.4436  11.3584
+#>   15.6611 -11.3147   2.4648   3.7152   5.0941  -4.6742
+#>   -2.8079   3.3530 -17.0085  -1.0148   0.3942  -7.2079
+#>   16.2594   1.6486  -5.9208  18.3353  -0.3978   4.8515
+#>   -3.2813  -1.2803  -2.6389   5.2233  10.7540   0.4536
+#>    3.8454  -7.1942  -4.8117   1.3339   2.4427  -3.4145
+#>    0.1433 -11.1748  -9.3389   7.8077  -3.5003  -3.9770
+#>   -2.7575   1.3132  -2.6993   5.9574   0.9215  -0.4006
+#>    9.2865  -0.6235  -6.1245  -6.7864   1.1137  -0.0587
+#>    2.2778   1.1557   4.7738   9.9479   5.5139   3.9762
+#>    6.9869  -2.2712  12.0344   1.5681   5.2382   1.9824
+#>   -0.1761   1.4252   1.4267   2.2728   3.6432   4.0365
+#>   -2.8207  -6.3113  -3.8591   2.0780 -10.3713  -0.3266
+#>   16.1383  10.9544  -4.0993  10.9288  -3.3440   0.0892
 #> 
 #> (5,.,.) = 
-#>  Columns 1 to 8   0.1270   3.9969  -8.8403 -17.5017  18.6949 -11.8889   1.7688  23.0865
-#>   -2.0743  -3.4313  -0.5353  -2.2056  -7.4029  -3.6609 -11.6210   7.4690
-#>   -4.2211   3.8374   1.6643   8.0054 -14.7087   3.0723   3.5819  15.7280
-#>    0.3868   7.4607   2.6972   6.7585   1.5712   0.7332   9.5469   1.5579
-#>    1.3800   9.2863 -11.0584  -3.1627   4.3456  -5.3272   3.4245   1.0607
-#>    0.1366   0.8656   5.4564   4.3627 -13.1777   8.9084 -16.2988  -6.1773
-#>   -5.2455  -6.2299   3.7452   2.0602 -12.6547  -3.8861  -0.2208   0.7433
-#>   -0.9262  -0.8542   4.7811   0.1665 -13.8330  -4.0361  10.8870  10.7425
-#>    1.9948   6.3012 -24.9735  14.5074  13.6684 -16.8739  -3.7360   8.0338
-#>   -2.1510   4.7077   2.5011  -3.1044  -5.4948   8.0186  17.5787   2.0567
-#>    0.3403  -5.0138  -2.3260  -5.7637  -0.8710  22.6176  -5.8266  18.9274
-#>    0.1255  -1.3090   5.7327   4.1940 -10.9863  10.3695   8.2213 -12.3300
-#>   -3.2420  -5.2769 -12.7078  16.8987   8.1143  -4.5887  -6.8306   9.4650
-#>    2.1145  -4.0721  15.0845 -18.5331  -5.0826  12.1581 -11.6930  -4.2580
-#>    1.7775   6.6388   3.0794   2.0254  -6.6043  -8.7520  -7.1985  -0.0161
-#>   -1.2429 -10.8730   0.3081  -3.5270  -1.2679   6.2354  -9.5832  -6.1032
-#>   -3.7292   5.2118  -1.6073  -2.0752  -0.5546 -11.9674 -14.3058  -0.1385
-#>    1.5663  -0.2134   1.1244   8.9575 -21.3111   9.7652  14.7048 -16.1164
-#>    5.0537  -5.4365  -4.2810  -3.5680  -0.4654  -0.6910  -1.4555  16.7210
-#>    2.3733   5.7109  -5.0402  14.3269  -3.7080  -0.5469   1.3461   0.0370
-#>  -10.6768   1.6675  -2.6818  -6.6235 -23.3109   4.4723  -2.9306 -15.1552
-#>    1.3749   5.6761   6.1372   0.0540 -15.2654 -10.2012   9.1777  21.9168
-#>    0.0939   1.4988  -5.8306   2.3862   5.4255  -7.5310   8.3593  22.5439
-#>    0.6239   0.0600 -10.5110 -10.8921  20.7994  -3.7823   9.1831  12.2325
-#>    4.4807   1.2006  11.4099   4.1220   4.0172  12.5550  -5.6524  -5.8280
-#>    2.3734   5.6991  -7.4481   6.7318   2.9518  -3.5768   8.0330  -2.3949
-#>    1.4166   3.1967   4.9052  -8.5509  -0.2633   4.0145  -2.5000  -6.6647
-#>    8.5022   1.2671   4.9056  -3.2028  -5.1207  17.0980  12.4183 -21.3307
-#>    3.3560   0.0335   0.1486  10.0135   4.2337 -11.4092  -7.9652  -3.1330
-#>    3.8520   6.2577   5.6189  -4.1615  -5.3337   5.7469   0.9777   1.5486
-#>    4.1924  -3.3068  -6.0355   8.9054   1.5996 -12.0054  -4.6602  14.9232
-#>   -4.0917   6.6781   3.7390  -4.2841   6.7789  11.4633  13.1591 -20.8491
-#>   -1.2403  -5.8101  11.5358 -20.3429  -9.8043  10.3301   5.6508  -9.9822
-#> 
-#> Columns 9 to 16  -5.0519 -15.6536  -5.5278  10.4839   4.8238  -4.2297  -4.9211  10.4956
-#>   -3.2430  -9.5777  -0.2008  11.8599  -3.4722   2.9282 -10.5189 -11.0866
-#>   -8.1572  -0.2395   8.4587 -11.2127  -0.8930   0.3864  -2.3457  -6.3919
-#>  -12.6023  -9.3652 -15.9834   9.0576  -6.4162   8.6291 -16.4968  -5.3234
-#>   -6.2092 -16.6057  -8.0656   4.7572   0.6566  16.6773  -9.9437  12.1628
-#>    3.4973 -15.5520   8.4753   0.7503  13.3989   7.6743   1.1990  -8.3222
-#>    3.2941   0.5389  -4.4373  -8.0926  10.3525   8.2620  -0.3922 -10.5982
-#>    2.0992   2.8307 -11.3777   5.5632 -18.6747 -11.0641  12.3123  -5.2210
-#>   11.6860  -2.3646   6.6022   9.7806   6.7111 -13.4087  -1.0562   4.7586
-#>  -16.2824 -14.1014  15.6424  -2.8804  -1.8653  16.2567  -1.7422   9.6859
-#>    7.2852  -5.1156  -4.8869  -1.8487   0.9758  -6.1300  -9.8608   4.3139
-#>    8.4879 -16.2979 -22.0021  20.8787   3.8001  -2.2933   2.3055  -6.7258
-#>  -11.6384 -10.7877  23.9544  -0.3630  -4.4196  -5.4371   0.1727   0.2736
-#>    8.2331  -0.9939 -10.9051   6.3860 -10.9876  -2.9687  -0.3035  -6.2201
-#>    1.6205  -7.3983   3.2433   9.5837 -15.1221   1.2189 -11.8776 -10.1880
-#>   -8.5982  -2.8860  -1.5523  12.6660  -6.2845  -4.3429   8.6657   4.9786
-#>   10.7378  -2.4435   6.9686 -13.8020   0.4491  12.7697  -4.6896  -0.3179
-#>   12.3907  -9.3405  -8.8491  -3.5816   5.9158  -2.1643  14.6102 -11.7558
-#>   11.3377   7.6532  -9.1046   0.7160 -15.4803   7.0581   8.9869  -3.3178
-#>   -1.8110   0.7145   0.5814  -6.3573  -9.7225  -9.5813  -7.1352   6.4063
-#>   -4.0320   7.8878   5.9525  -5.4456  -2.6309   3.0996   5.5376  -7.9531
-#>    1.6578   5.8366   5.3673 -11.4888 -13.9038 -17.5789  -1.9719  -0.9318
-#>   -7.2453   1.9663   4.3650  -0.3790  -4.2484  14.1084 -14.0829  -5.9840
-#>   -3.2372   2.3716   7.5951  -1.4341   1.9291  -2.3323 -13.2522   0.9026
-#>   17.4940  -8.7963   1.2609   8.2443  -0.4105   2.7368  -0.9383   5.9460
-#>    0.5119  19.2834  -2.7669  -1.6191  -0.8475 -12.9259   8.1604   5.5678
-#>  -15.2675  -2.3728   4.2026   7.0293   6.2566  -2.7296  -3.3734  -2.0884
-#>   10.2617   5.2372  -4.6707   6.7844   4.9344   5.8431 -10.6913  -6.4078
-#>   -0.4118  -5.9068   8.8041   9.4516   3.0169  -2.9376 -12.5616   7.4301
-#>    5.8621   4.0873 -13.9898   4.6101   8.8701  -0.1944 -10.0589  -2.1204
-#>   -8.7893  -4.7496   3.4217 -19.9301   1.4026   5.6201  -9.7724   3.1006
-#>    3.7177  25.8277  -7.9515  10.2657   1.2519 -10.6447  -4.5983 -19.6696
-#>   -3.3835  -6.4632  15.6626  -6.6609   2.5483   8.2629 -11.0463  -8.7981
-#> 
-#> Columns 17 to 24 -15.1061   1.9276 -13.8666  -3.8967  -5.5002  -7.8204  -5.1597 -13.6479
-#>   -1.0532  -4.3306   5.4546  -4.7892  15.1088   8.0288  12.6441   1.9789
-#>    8.0984   1.0747  -1.2822 -10.0374  10.3140  14.7900 -11.8292  -3.7487
-#>    3.2155  -0.4762   6.4662   4.6925  -4.8926  -7.3207   5.0228  13.0175
-#>   11.5186 -11.5963   9.4002  -5.7629  -9.2373 -13.5491  -1.5002  -1.1277
-#>    8.0051  -9.2534  -0.8459 -10.4178  -5.8362   4.5994   0.2911   5.1845
-#>   18.8482  -9.6356   0.5151  -1.5948   9.9338  15.7383  -7.3023   1.8789
-#>   13.5459   5.4399  -7.1362   2.1980  21.2082   1.5343  -2.2570 -28.6290
-#>    3.2007  -6.6374  17.2961   6.0226  -6.0808   1.0173  10.7136 -12.6136
-#>  -13.5292   2.4464  -7.7585  -0.6386   5.8336 -11.2572   1.8914  10.2651
-#>    9.2183  -7.1085 -16.7239 -14.4238 -13.1062  12.2904  -4.7747 -12.3438
-#>   -5.3513  -1.4276 -10.7133  -1.0104 -14.7948 -11.3771 -10.5380 -14.1806
-#>    2.2582   2.5082   3.1395 -10.3802  -3.4846   9.7888   6.3816   8.8894
-#>   -3.3255   8.7914   3.5600  -2.4824   6.4942   3.9103   0.1821  -1.1500
-#>    4.7220   1.1739  -5.6123  -1.4683   0.5952  -0.7315  -0.1751  -5.2895
-#>  -10.7894   0.0108   3.8297   3.5149   5.1771  -7.0988  13.1429   9.9300
-#>    4.9622  -7.1838 -20.6221  -4.5445  -6.3929  14.4627   5.7682  -5.9828
-#>   -1.6371  -2.6708  -7.7051 -12.2669   3.6014   1.2710   1.0021 -16.3431
-#>  -10.6782   5.9850  32.8903  12.4231  12.3848 -13.1794  11.3659  16.1553
-#>   -8.6677 -10.8305   2.6711   5.8227  -3.2384  12.1780  -8.5230  -3.7069
-#>    9.8047   1.7091  -6.1964   9.3361   2.7999  -3.9781 -13.4320  12.7182
-#>    5.2453  -3.3518 -10.8381  -3.0425  19.1940   6.6235  -9.0769  -0.6954
-#>  -17.2991  -1.8443  13.4762   2.0591  -8.9836   2.1469  12.4268   3.2675
-#>    0.7351  22.4695   6.1724   5.6455  -0.3949   1.2288  10.5278  -0.1145
-#>   -2.4389  -3.5736   0.5515  -5.5292  -1.3270  -3.0887 -18.4237   4.8149
-#>   -4.3389   5.3588   7.6871  10.6304   4.6827  -5.4639  -4.1754   6.1267
-#>   11.6110  -3.9579  -5.9381  -4.1842  -5.2819  14.5803   3.7449  -9.4213
-#>   -1.9401   6.1597   5.4595   6.9357  -1.2076  10.2617   7.7148 -11.8499
-#>    0.7595  -0.6014  15.9818 -10.5274  -5.3796   3.8521  20.9919   0.7645
-#>    1.8653  10.0281   6.2075 -15.7851 -14.1920   9.4755  13.5518 -15.5913
-#>   -1.4591   5.5454  -5.1016  -0.7982   4.8222  -3.2032   7.6409  -8.4062
-#>    6.5436   3.7843  -8.6999  -4.8098   5.4861   4.2819   8.1165   7.3718
-#>    4.6000  -5.8893 -23.4787  -4.9795   3.0594  12.3016   3.2690   3.5217
-#> 
-#> Columns 25 to 32   1.7778  -0.1741  10.0144   0.1100 -14.6647  -5.5856  -0.2637  10.2384
-#>   -1.3931   8.4956   1.8278  -9.6910   9.9097   2.5671 -10.6541   4.6897
-#>  -13.2821   3.1024  -6.2520  -8.1819 -10.8312  15.8243  -6.2202  -5.2134
-#>  -10.4369  10.2615  -5.4063   7.5572  11.9416   7.1635  -1.3847  -0.4782
-#>   -3.4464   9.0079   6.4960   4.4430   5.3117   4.7168   5.1332  11.8318
-#>   -1.6587  -3.9442   1.0908  10.2382  -5.6771  12.8907   9.4950   4.1755
-#>   -5.3847  27.2970  -5.2261 -12.2230  22.8968 -14.2896 -15.2572  -4.1222
-#>   13.7583 -10.0067   0.2167 -21.0369   1.2837   8.8987   5.9922  -3.0999
-#>   -4.2537   4.1788  16.4749  -2.8583  -2.1226  -9.5117   2.1922   1.8162
-#>    8.5287  -2.9122  -2.1248  10.5133   4.8937   7.5389   0.2121  -7.8859
-#>   -4.9846 -12.8030  -1.8882   0.1910   1.2181   6.9836  14.2577  22.4123
-#>   -7.4522 -12.7975  -0.8079  -0.8627   8.6350  11.2845  -2.3146   5.7266
-#>   12.1104  14.6532  19.1453  -9.0659  -7.2229  -6.0188  16.3080  10.1372
-#>    4.5465  -9.0641   0.4322  -7.4698   1.1671  11.2206 -14.3092 -11.6783
-#>   17.7936  -0.8289 -13.6353   6.1814  -2.8911   8.2300   5.4011   4.8218
-#>   -7.9323  17.8275  -0.4554  -3.4589   4.4595   3.7615  -5.4832  -1.0095
-#>  -13.9743  16.0892  -2.6171  -3.5611   4.0792  -2.1144  13.8133   4.5982
-#>   -2.6170  -0.6755 -12.1117 -24.0577   1.1589   6.5868  -0.5188   1.1435
-#>   -8.4654  -2.7579   1.2969  -9.0796 -10.1263  12.4411  -3.7248   2.8195
-#>   17.3934  -8.4432  -2.7909   2.7379  -8.4793 -13.8044  10.1999 -10.4044
-#>   -7.7334  -2.5178   5.2465  -9.3406  10.2474  -0.2369 -22.2037   4.8796
-#>    4.0159   6.8714  -7.3192  -4.6508   1.0704   2.8704   6.4501   5.2904
-#>  -11.5786   5.6984   3.3727   3.5654  -7.6489  -6.9025  -9.9804  -4.0167
-#>    1.5916   4.3510  19.0703   3.0556  -2.2498  -7.1878  -0.2999  11.1407
-#>   10.9710 -12.5355  -3.5661   7.9113  -5.3299  -0.6712  -6.0232  -4.9191
-#>    6.9522   7.4351  -1.8514  17.6501  -4.5240  -8.5315  -0.9537  -1.2942
-#>   11.7416  -2.4716   7.3769   2.1249   2.2503  -5.5305  -3.4552   5.2614
-#>    4.4133 -14.9014   2.6159  18.2596  -0.2846  -0.0281  -4.2213  -7.2945
-#>  -14.1361  20.3555  -7.8349  -0.0203 -15.6937  -0.6371  -3.8028  12.6171
-#>   19.4651  -5.1343  14.9903   0.6087 -10.3531   4.2478  19.4004   3.1429
-#>   -2.1876  17.4168  -5.6756  -0.2676  -2.2310  -0.9550  -5.5168   9.5355
-#>   -5.8493 -14.3634  -6.6581  10.0440  -2.8606  -2.0556 -11.0157  -2.0355
-#>  -14.2199  -5.4934  -0.0826   1.4885  16.0165  12.4117  -7.0615  12.5529
-#> 
-#> Columns 33 to 40  10.1500  -2.7168   5.8167 -12.4654  -1.3378 -12.2143  10.4488 -13.0792
-#>   -2.6546  -1.7643  -5.9071 -11.1366  -4.9924  21.9747  -8.3145 -10.3978
-#>  -11.8286  14.7212  11.9621 -10.4938   7.4112  25.7293  24.8885   3.2072
-#>    6.3994   4.2064 -11.9686   9.1049  -4.7787  18.2113   0.4549   6.3143
-#>    1.8633   5.0401 -10.5132  15.4074  -3.6229  13.2258  -1.8025  10.2815
-#>   -8.0038  -5.8750   5.6899   7.1259  -5.7372   5.9912  -1.7020   7.0553
-#>    0.6529  11.6060   5.7158  -5.1726 -16.5611   5.9302   2.3731   4.5060
-#>    7.9616  11.2526   7.7906  -3.9221 -15.5615   6.2789  18.7343   3.2657
-#>   -9.6967  -2.3485  -3.2977   5.0751  11.0015  -3.8198  -4.6981  13.0108
-#>   -6.1833   0.3852 -13.5263 -10.9368  -5.7111  13.8614   3.1115  -2.4188
-#>  -23.3818  -4.6403   1.6466   5.0277  14.6978  11.7769   1.6959  -0.8511
-#>   18.4458  -0.8086   3.2779   7.4938   2.9144  -8.4173  12.3187   1.5119
-#>  -13.7431  -0.5227  -0.7258  -5.3151 -15.2641   4.0030  -0.8535   3.9266
-#>    5.3062   3.4875  -2.3348   3.8489  -0.5105 -10.3150  -0.6041   9.8378
-#>   -1.0193  -8.3886   9.1420   5.2717 -15.8106 -10.3659 -14.0255  -4.1194
-#>   -2.4097 -10.2394  -4.6149  -4.6161   2.0708   6.0866  15.4649 -12.5058
-#>  -14.6599 -14.0379  13.4541  20.2061  -3.6042  -2.7530 -13.5095  -7.4449
-#>    0.3103  -1.0956  -8.0902   1.3008  -3.4313  11.7765 -11.8600   3.8552
-#>   -2.7998   6.2498  -0.1702   0.4605 -13.8449  -8.1785  18.2829  23.7498
-#>    6.0590  20.4857  10.4082  -2.4494   2.3333  -1.4283   0.1231   7.1851
-#>    7.4581   7.6981 -12.6472  -2.2774  -1.0708   1.0064  -2.8286 -11.5675
-#>   -2.5189  14.7925   3.5630  -9.5981   2.0895 -14.7525  16.8936  -3.0053
-#>    1.2041   3.4081   1.7201   2.0051 -10.9110  22.4472  -5.5768   7.2891
-#>    8.0471  -4.4368  10.0651 -10.3965  15.8845 -15.5329  25.6666   3.4862
-#>   15.5102 -10.4686   4.5244 -19.9958   6.8780  -8.7682  -9.2341  18.1238
-#>   15.5442   3.4110  -5.3646  -3.8514   8.0869  -0.3192   3.4853  -9.9307
-#>    0.9164  -5.3659   4.1568  -6.6659  -5.4278 -18.0584   0.3826 -19.3212
-#>    6.0171  -2.7288   6.8094   5.1870   9.5104   1.4394   1.4395   2.5156
-#>   -3.5162   5.0776  -5.7641  -4.2334  -6.3650   5.7626  -3.5435   1.7901
-#>   -1.3093  -1.8369  -2.8865   0.9459 -20.2915 -32.7982  -7.0939  -1.9332
-#>   -4.3264   4.8188   8.0833  -4.7750  -3.5830   1.9510  -1.8569  11.8234
-#>   14.5494   4.4043 -10.1908   4.0622   0.1435   3.1325  11.6746 -11.3710
-#>   -0.5474 -17.7964  -5.1160  -7.5432  10.3011   2.1522  -2.5519  -6.5769
-#> 
-#> Columns 41 to 48  -7.7728  13.0417  -6.2141   7.5805 -10.5306   2.4039  -2.6211 -10.8351
-#>   -5.7943   1.4155  -0.0184  -6.7851  -2.8573   5.4923  14.2143  -4.9217
-#>  -10.2013  17.2115  -5.5376  -0.1993  -0.0483  -1.8005  27.7821   2.4018
-#>   -0.1497 -10.1295  20.7939  -2.0661   7.0674   4.5289  -5.6561 -18.6444
-#>    2.6511   7.1128 -12.3257   2.0484  12.3790  -4.2943   9.5533 -16.7716
-#>  -12.8534  -8.1058  -1.1198   1.3922   3.3961 -13.1037   8.0641  -0.8374
-#>    2.6301  -4.3767  11.9773 -15.8264   0.5274  13.3549   0.8531 -15.4280
-#>   -0.4952  -4.6740   6.5916  -6.6276  -3.5350  -1.0065   8.1074   3.3948
-#>    6.4617   4.4943   3.4736   9.9566  10.4772  -0.0510   6.4968  18.4616
-#>   -5.7202  -5.0081  -2.1739   9.4466  -5.9768  -1.9245  -4.2107 -12.0566
-#>    6.8396   9.8853  -4.6098   8.1681  12.2168 -11.0984   0.6794   6.4963
-#>   -6.9582 -14.8138   6.2660  14.0955   1.1016   8.0584  -5.1369  -3.8343
-#>    3.1336   2.0385 -10.6221   1.4631  -8.8730 -11.5658  -1.9890  -2.1705
-#>   -3.2012  -3.2624   0.0124   5.5875  -5.9079   3.6307   9.1475  -5.5163
-#>  -16.6238   1.3657  13.1135 -18.5553  19.3835   3.0428 -11.0630  -6.1474
-#>    1.9540 -12.7126  17.6025   9.4565 -13.1545  15.5756   5.4707   2.3039
-#>   -2.2161  -2.3298   4.7162 -23.5897  -1.6494  14.3266   2.2557  10.9696
-#>   -0.9794  -0.4933   2.9129  -6.6436  10.5483  12.2195   3.2601   3.5748
-#>   15.1612  -3.5543  -3.3447  12.1451  -9.2359 -12.5165   8.5579   5.8165
-#>    2.1669  -5.3160 -11.2051  -5.0594   3.0964  -6.5364  -2.7889   0.1063
-#>    1.6183   8.3523   0.4388   3.9910  -6.5673   2.6309   7.4823  -3.9073
-#>    3.9793   9.4371   4.4418  -7.3555  -1.5988   0.2323  -1.8985   1.3787
-#>   -8.1983  -4.2067  11.3807   3.0023  -6.7143   0.3628   8.3436  -5.4336
-#>    2.1477  16.8822  -6.4990   4.1027 -12.6306   5.3138 -21.9907 -14.3988
-#>  -19.9449  14.1068  -1.2428   4.0588  19.1461  -7.3755   1.1968   6.9337
-#>   10.8277   5.7167   1.2530   0.4044  -8.9044  -2.3803 -22.9909 -10.8675
-#>   -8.5395  15.7964 -10.0465 -10.4169   0.0549  -0.2287 -14.4526 -14.1527
-#>   -9.4452   4.3918 -11.0792  -5.8634   6.1970  -3.8583 -18.6164   3.9002
-#>   -9.7997   3.0892   7.0320 -14.7294   4.5604   1.5742  12.2626   3.5791
-#>   -0.0732 -13.5142   6.8419 -11.9071   1.5294   5.4908  -1.9896  11.3291
-#>    1.4363   9.0709   3.7630  -4.3767  -0.1546   1.2374   5.7477  -3.1649
-#>    2.7391  -9.0105   3.2614   4.6591   1.7479  -4.8373 -15.5717   0.2825
-#>    3.0020   6.7660  -1.4641  -1.4705  18.1625   7.9863  -2.3651  -5.9829
-#> 
-#> Columns 49 to 54 -10.3971   8.8077  -4.6116   0.1249  -1.6678   2.0824
-#>   -2.2096  -8.2924  13.3718   6.6673   1.4004  -1.3016
-#>    2.5277   1.7145  -0.9568  -5.3421  -2.9864   1.5047
-#>   -6.6332  -8.7000   7.2478   1.0313   0.7790  -3.3170
-#>   -3.6021  10.2331 -10.4041   4.7000   6.0417  -7.5707
-#>   -1.3524   0.1230   4.3883  -6.7944   4.1890   1.4641
-#>   -2.5252 -11.2596  -0.6901   6.4817   5.9568  -4.7086
-#>   -7.5398   3.9761   2.6150  -2.4091   3.3055   0.5490
-#>   -0.6691   5.7260  -2.5497   1.0488   4.6558  -0.9856
-#>    9.7165  11.1001 -10.1059  -2.6323  -0.2516   5.0828
-#>   -1.5492   4.6172   8.0683  -9.1370 -10.2819   4.1062
-#>  -14.9461  -1.1056   4.2353   2.6166  -2.3585   3.9567
-#>   -2.1073  -2.8609   0.2629  12.1419   0.1297   0.5681
-#>    0.1319  -6.9208   7.5661  -4.5516  -1.4141   0.7718
-#>   -5.0699  17.0210   6.4682   2.1013   5.7954  -3.9657
-#>    8.7692  -9.5887  -5.7504   2.7774  -1.2659   0.4100
-#>   -2.1969   1.8587   0.1379  -7.1127  11.8105   5.3066
-#>   -5.8700  -6.6889  -5.8325   0.0023  -2.7781   9.8854
-#>   11.8987  -5.3737 -11.4409   1.0054   2.0471   0.0693
-#>   -8.3490  -3.2622  -2.3483   9.3945  11.1448  -5.8516
-#>   13.3893   9.0998   6.4305   0.8584  -6.0464  -0.4977
-#>  -10.9009  24.4256  12.4352  -9.8091   3.5869  -3.4174
-#>   12.5356  -5.5965  -0.5642   5.0351   3.2751  -1.5896
-#>  -11.1168  -3.6532  -3.4759   7.6531  -0.7342   0.2704
-#>    7.4699  11.7731   6.1229   3.4599   3.3510  -0.4367
-#>   -4.2270  -2.4345   2.1732   2.9828   0.3095  -2.4458
-#>   -9.8393  14.6494  10.6712  -6.4012  -0.0743   1.9862
-#>   -1.1818  -4.4589   0.8286  -7.5110   1.1673   0.7609
-#>   -1.1280  -5.8980   6.6434   0.7556  -1.9274   5.3015
-#>   -9.2338   3.2889  10.3630   3.2861   9.8004   2.4265
-#>    3.9075   5.8852   3.2487   6.3270  -4.0139   4.3934
-#>   15.9540   1.2406   1.6622   8.0269  -0.0123  -0.9241
-#>    2.7061  11.8639   1.6918   2.4294  -7.4127   6.2571
+#>  Columns 1 to 8  -2.9981   1.9987  -1.0477 -13.6194   6.9298   7.6206  -2.0428   6.8252
+#>    0.8963  -1.2151  -0.7207  -1.4635   8.7503   7.3111   3.6662  -9.0786
+#>   -2.0631  -5.8146   4.6948  -4.8939   3.5372  -1.8821   2.1917  14.4940
+#>   -4.5296   3.5460   2.1648   4.7897   6.7181   0.0067  15.9350  -3.9847
+#>    3.1995   7.2079  -2.7504  11.6689 -15.8049  -1.2787  -9.8361   4.2753
+#>    0.1304  -4.4724  -0.0635  -8.5782   2.4975  -4.0694  -5.5228   9.8667
+#>   -2.9821   2.1418   4.5388   8.5924   5.6464  -5.5546   1.3803 -10.0516
+#>   -3.4923  -1.7521  -8.8606  -4.7262 -25.7114  17.4543  -7.0046   3.4054
+#>   -1.8225  -1.4219  -5.1090   6.8038   5.5271  30.7212  -2.0611   4.6384
+#>   -0.2193   2.7363   3.6337   3.3040 -11.2611  -8.3691  -8.6780  -0.4924
+#>   -8.1473   6.2070   0.9058   6.3800 -11.3422  -8.8116  -3.3142   0.0828
+#>    1.8943  -5.4390 -12.0924  19.0233  -6.6010  -4.4031 -17.7816  -0.5272
+#>   -4.4502   5.9669  -6.9457  -3.5074  -0.9598 -13.4715 -12.9025   3.1840
+#>    1.0585  -4.3245   2.3124   1.1457  -0.9148  21.6873 -10.4921  10.6373
+#>    4.8480  -0.6384  11.2052 -10.2312   7.3468  -2.3318  19.4543  -3.0181
+#>    2.0397   7.5497   7.0159   5.5897  -8.4134 -16.3037   9.6301   8.8296
+#>   -1.5285  -9.1825  10.7200  -3.3999   3.7654  -8.1046 -19.6819  -7.5786
+#>   -0.9638  -2.4457  18.2017  -7.9900  -4.7643 -24.4827   8.0693   9.3977
+#>   -4.0457   7.8084  10.6424   9.7764   1.9130  -2.8986   2.0304   3.5510
+#>    0.8902  -1.2850  -3.8147 -11.0375   3.0638   2.0255  -3.4273 -10.2156
+#>   -3.3306  -0.5066  -4.7918  -4.6034  -3.7404  12.3950  17.5895   9.0359
+#>    0.6129  -3.4534   4.4716  -1.6140 -11.0353   6.7994  12.5446   5.5009
+#>    2.3297  -7.2480  11.6517  -7.3574  16.5833  -7.7865  -7.6982  -0.6897
+#>    4.4613  -8.0269   5.3806  -7.1562  -5.8349   5.1819   5.1881 -14.7557
+#>   -1.3944  -1.4486   4.1030  -3.5756  -2.7655  -7.2241   7.0294  -1.5904
+#>   -1.1111  -1.2950   4.5635  -1.7037   0.2499   3.7121  10.3800   3.2501
+#>    5.2651  -0.4433  -7.4740   3.1947  -4.4924   6.7886  -7.1823  -4.0272
+#>    4.9170  -4.0617   1.0573 -13.0511   0.6103   5.2818   6.9409   9.7578
+#>   -0.7962  -5.8867   1.2623 -17.5376  12.3123 -19.7035  15.3750   8.9767
+#>   -2.4093   2.5900   1.4192  -3.5658  15.1976 -30.3074   7.1319  -0.2922
+#>   -0.0568   5.0923  -6.5125 -14.6115 -13.2712  -3.2822  -3.4963   6.6401
+#>    5.4941  -4.5832  16.0247 -24.6025  15.3211  -4.0191  -2.0110   8.3245
+#>    1.8139  -4.2336   6.5050 -10.5538  16.8541  -2.2905  -0.7633  11.6168
+#> 
+#> Columns 9 to 16 -19.7388  -4.2497  17.7844   7.0490   0.1373 -13.9906  -3.0449  -3.2562
+#>   -9.8894   0.9661  -0.4101 -12.1781  -6.5649 -10.8805  12.6788  11.9859
+#>   14.1947  -4.7796  -6.8920  -2.5855   7.2923  -1.3769  -3.5240  -2.7617
+#>   -4.6408  -9.9279  -9.7712  -8.5201  -8.6075  -0.6716   4.3006  -1.0796
+#>    2.9509  -6.6747   6.2703   3.4515  17.1978 -10.4269   3.8136  -0.3971
+#>   15.2298  11.0304   3.9934   7.4501  21.7057  -2.0523   5.4827 -17.0539
+#>   -6.2983  -9.3994  -6.6639   6.6324  -1.0864  -6.8409  16.5468  13.2975
+#>   -4.5705   1.1321   3.0089   3.2488  -3.9008   6.2613 -11.0166  -1.7206
+#>    1.4281   5.9335  -2.7531 -14.9261  -2.0529   2.6827 -21.9907  12.6534
+#>   -9.6204   2.2846   7.7640  -9.0140   3.0862  -1.3569  10.1182  17.1239
+#>   -6.9389 -10.4684  -4.8376   0.5224  13.2216  -2.0447  -3.1860   0.5203
+#>    2.3373  16.9374  -5.2742   6.0314  18.7102  -5.8949 -11.2776  25.5272
+#>   -6.3283  -0.9445  -7.5971  -9.3140  10.4555  -1.4052  -8.5250   6.4385
+#>   -0.8305   3.4175  10.9144  -8.5010 -10.3696  -3.9154  -7.8015   0.8250
+#>   -3.2788  -0.7424   6.5051   2.7374   0.9923   0.4761   3.6965   1.5076
+#>   -1.7487  -1.9768  -7.6754  15.6914  -5.0728 -10.2906  -2.1927  10.6893
+#>    2.9760   2.3833  12.3606   6.0287  14.1418   0.6970   0.8762  -0.6810
+#>    2.0287 -20.3368  -7.7164  14.0426   5.8805   0.0249 -13.0519  -4.9372
+#>   -4.5226  -9.8750   1.9928  -0.7749 -11.8479   6.5163   0.0261  19.5263
+#>   -1.0261  -8.0168   0.8350  -3.2509   8.3222   4.1742  -8.5501   1.9879
+#>   -6.4162  10.4247   4.9921  -3.5867  -1.9859  -9.2677  -2.4422  -0.0360
+#>    3.7977   0.7369   7.2755  -3.9704  -2.2519 -23.0008  -4.8531 -14.7251
+#>    4.6898   6.8939   6.5778  -5.2031   8.2624   6.6660 -14.6330  -7.6518
+#>    4.5284  -0.9883   8.0322   5.3881   0.9397  -5.1828  19.5255   5.1489
+#>  -11.2951  -4.4947  -6.9464   0.0233  -7.3350  -6.6192  -1.2261  -6.6542
+#>   -1.8227   7.5137   2.7021   6.7293  -5.5610  -8.2128  -6.2575   4.8904
+#>   -8.7624   1.3427   8.2329  12.8056  -9.2667   1.1070   1.3570   0.2240
+#>   -6.6889  -2.9888  -3.8591  -4.8334   3.6868 -16.5301  -1.8390 -10.6246
+#>  -14.8556  -8.5565 -11.4939  -5.0514   6.2092 -15.9428   2.7903   3.3556
+#>   -0.1720  12.1874  -3.2367   1.0141  25.3597   3.4302   4.6980   2.1638
+#>   -6.3482   1.8216   7.7777   9.6635   5.7204  12.9777  -5.7420  -6.2243
+#>    5.4616  10.5734   8.7925   9.1357 -22.7457   7.4112  15.9451  -8.6602
+#>    9.0210   6.7807   1.7586 -12.7231   7.7920   7.5126   3.4841 -12.5947
+#> 
+#> Columns 17 to 24  -2.0086  -3.7429  -3.4922  -5.0073  -1.4823  12.0925  13.1227  -5.9666
+#>    2.5905   7.4030  -1.1400  -8.2132   1.7247   5.3412 -11.4227   2.6169
+#>  -16.5144   3.1496  -4.0716  -4.3978   7.8668   5.3914  -0.9665   5.5490
+#>   14.5195  -6.5885 -10.4912   0.2149  -7.8601   4.1144  -4.2487   0.5029
+#>   -0.7825  -0.1147  -0.6131   1.5169  -1.3588   1.5212   9.0924  -0.1239
+#>    5.7114  -9.1147   6.7794   1.5678  -9.0061   3.4211   2.7157  -8.3202
+#>   18.9572  14.8968  -3.4597  -2.0328 -16.6206   4.3839   7.4423  -2.7560
+#>   -5.6803  -6.4400   8.0516   4.1565  -6.1295  15.9909   1.0870   9.8960
+#>    9.8491   1.4323  -2.4933  -1.2742   3.4939   2.4114  -8.7089  -0.7075
+#>  -11.7952  -1.4127  -9.0026   0.8020  -1.6741  -1.3498  10.3926  -4.6258
+#>   -8.0503   0.2616   4.4266   8.0826   4.4674   2.8782  -0.6460   3.2489
+#>    5.9409   0.7170  -1.1449   9.1042   8.1602  -5.5182  -3.2153 -16.5777
+#>   -3.2937   2.6271 -13.1536   3.9373   3.1741  -2.4886   0.0096  -4.9245
+#>    7.7979  -5.2359  -2.9209  -4.2665   5.6631  14.2786  -9.2505  -0.6026
+#>    6.2603  10.2625   9.1270 -11.1127  -6.7668   3.5576  -7.8623   3.2312
+#>   -1.4024   2.9339   7.2831   1.6765  -6.2406 -14.7317 -16.4826   2.8849
+#>    0.7807   8.7083   3.0579   3.9737 -10.9223   0.7293   6.9710  -2.9484
+#>    8.5802   8.3053   6.2090   5.8093  -3.0959  -7.1872 -15.9926  17.7402
+#>   -0.4575   1.1255   3.4946   8.2534   5.2208  -6.3664 -12.0906  -7.8641
+#>    2.6943   6.7818  -9.0341  -9.4007  -8.5445  -1.6037   8.9691  13.0356
+#>  -14.1278  -3.0522   9.4725   9.2014 -13.1171   6.1672  -0.8044  -4.0928
+#>    6.6042  -6.2427  -3.5026   2.7236  -8.6202 -14.5676  -4.3816   0.7696
+#>   22.2243  -6.9362  -7.0573  -8.7486   2.7153   6.1101 -16.0233   2.1742
+#>  -13.7068   6.6661   1.7033 -11.3240 -11.4527   9.5502   2.0516  12.7820
+#>    3.1524  11.6998 -13.2027  -4.6594   7.7246   6.6598  11.9521 -12.1999
+#>   -6.6184  11.3413 -11.2106 -15.3998   2.6890  -1.4778   5.3495 -10.7252
+#>   -3.6577  11.2616   1.4742   1.5310   4.6568   0.4294   1.0309   2.6902
+#>    8.5144   1.1739  -1.0834  10.9488 -10.1380   5.7704   8.0036   7.0080
+#>    3.8915  -1.2978 -18.9493   4.4777  -2.6526 -11.6430  24.0570   0.0046
+#>  -16.8701 -13.7557   8.7846  -0.6851  -0.2110  13.2500  17.5430  -8.3173
+#>   -8.1549 -14.5187  -2.9298   4.2745  -6.8029  16.8042   1.3567   8.7368
+#>  -12.3244   5.6606  -8.1712  -1.2285   3.8558   1.5351   3.3973  -4.9187
+#>  -17.0428  11.9150   7.8854  -7.6420  -3.5685  -4.1366  -1.1942   4.0374
+#> 
+#> Columns 25 to 32   3.8753  12.1455 -10.3902  21.5935 -18.8873  -4.4217   5.4072 -10.8176
+#>   -3.5803   8.0410   6.5697  11.4057  -6.6880  16.8841 -10.9073   4.8997
+#>   -1.9040 -10.2239   5.8802 -11.0792 -21.3019  -2.0365  -5.9360   9.7430
+#>   -8.0633  -6.1166  11.4493   5.6100   2.9430   2.8234   3.7902   1.3998
+#>   -7.4949  -2.5945  10.6476  -0.1444  -2.0789  -4.2223  12.1262 -13.0240
+#>   -1.9563   7.9041  -8.2371   4.3589  -1.7796 -12.8283  -4.6996 -10.9314
+#>    5.3424  -7.0446  -9.1074  13.1848   9.3316   6.7332  -0.4355  -0.4085
+#>   13.1640   6.5910   6.6802   8.2557   5.8556   8.8972 -29.5956 -18.1542
+#>  -13.0963   1.9939 -14.2596 -15.5464 -13.6829  15.0417 -16.4008  -2.9759
+#>   15.3115   1.7133  -1.4397  -5.3574 -11.3403 -11.9070   3.8297  14.8635
+#>  -14.1224  12.8473   5.8808  24.0365   1.0657  -0.5976  -9.2761  17.4670
+#>    0.3925 -13.7639  15.7773 -14.0898  -3.5661   9.7524  14.9191  14.1870
+#>   17.9036  -8.2760   2.8901   5.3520  -3.4983   5.3481   4.8200   0.8641
+#>   -8.7172  -5.9535   9.4824  -8.0497  -9.2682  11.7967   0.9077  -8.5644
+#>   -5.9413  -1.3933   8.7317  -2.0789  -7.9804   3.9378  -1.1050   3.8051
+#>   -7.1543  14.8597 -15.1809  13.5658   7.1848   3.1301   4.2739 -12.0077
+#>  -12.0043  -4.9602   3.2887  11.7474   0.8738   4.3405   0.6988  14.6923
+#>  -10.9789   9.9063  -4.3363  16.6096  10.6429  -8.7303  11.5028   1.0445
+#>  -14.3490  -8.1508 -12.7705   3.2201  10.8448  17.9310   6.7438  -3.3404
+#>    9.3696  10.0469  -4.5626   0.0225   2.7414   0.0235  -9.0254 -15.0893
+#>    2.2884  -1.1768   6.5486  -4.6490   5.8459   4.5072  -3.3865   9.3412
+#>    4.5308   9.0664  -2.7439   7.3581  -4.1265  -5.9932 -16.8750   5.9705
+#>  -21.3790  19.8427 -15.4205 -11.0711   5.5214   0.8218  -0.6010   2.8180
+#>    1.2954   5.8568  -0.8064  -0.7497  -7.1671  -3.2086  -5.9610  24.4766
+#>    8.3796   1.1624  20.2413 -13.5125   5.7963   8.6417  -0.6447   5.5746
+#>   12.1926  -2.8949  14.3854 -16.4621  -2.0113  -2.2683  -4.5900   3.5811
+#>    5.1163  -5.6551   7.6786   9.6312 -10.8648  15.4038  -9.3148 -28.2952
+#>   -3.9097   8.7084  -6.6107   8.1558  14.1674   5.0755   1.6191  -4.8684
+#>   13.0704  -1.4697   2.6999   0.3325  21.9355 -14.7359   4.8868  20.5376
+#>    0.7001   2.7432  -7.9853  -1.1106   2.8453  -3.1376   9.9429   7.0484
+#>   -4.3222  18.9010 -11.8631   6.8128   5.8591  -9.9242  11.8052  -0.1487
+#>   -7.2910   5.3112  -2.1243 -17.4465   8.2097   2.4354 -10.0280 -10.1925
+#>   -5.5466 -12.9839 -16.9296  10.1625  -0.3858   7.9458  -6.4372   6.2500
+#> 
+#> Columns 33 to 40  -1.3404  -4.6652   0.2426   8.0424   0.6240 -16.2287 -14.0641 -19.2772
+#>   -7.0186   3.6977   7.4803  10.7408  -8.8736 -14.8762 -16.7146   3.4571
+#>    8.6031   7.3687 -11.6173   0.4644  -4.1822  -5.7826   2.2955 -10.0396
+#>   20.5584  -2.4726  -0.2091  -7.1217 -10.4729 -21.7939  -5.5822 -14.3465
+#>   -3.9753  -7.7535  -5.9620   5.0671  -6.6821  22.7495  -1.0369 -18.5720
+#>   23.5852  -5.0578   1.8712  -0.2534  11.4244  -9.9855 -11.5512  -9.1573
+#>   -7.3007   1.1173 -13.2500   2.9722   3.0153  -1.2784  -1.3786   9.7177
+#>  -21.0926  -2.6614   2.1680   4.4065   2.3558  18.4460  -4.7796  13.7280
+#>    6.1411  -6.1539   0.5368   7.9554  -0.5887  -0.8597  -9.4036  10.7069
+#>   -5.3225   6.9767   7.7706  12.5271  -8.3516  -6.7179   6.0514 -12.4826
+#>  -16.8952 -17.3380  11.5551   7.8882  -1.1232  13.2687   0.0060  -7.5381
+#>    9.1225  -4.0228  21.3088  14.6427 -15.0176  12.6761  -2.8529  19.8361
+#>    6.3292  -6.2752   5.2451  18.2435 -14.3518  -4.0304  -9.1245  -9.3231
+#>    4.2020  -8.5146 -10.9979  11.6502   3.6841   3.6257  -7.7977  -5.8441
+#>    6.8744 -12.7409 -35.1760 -13.4471   0.2886   5.2108  -3.0605   2.9356
+#>   -8.3027   4.0940  -3.8117   5.8515  -5.6651  -4.1635   7.6188  -0.6298
+#>   -5.0789  -7.8177  13.5700   7.7381 -10.9249   4.0041  -6.8821 -10.9187
+#>   14.9364 -14.7832  -6.2318  25.2370  11.7722 -10.2568  25.8816  -6.2300
+#>  -10.9443   5.2411   5.4978 -13.5603 -14.5684 -22.4316   0.6638   5.9151
+#>    5.8436  -4.9731   5.6021   0.3048   6.9230   1.0252  -5.4153  11.8626
+#>   -6.0750  -4.4299   8.1145  -6.4224  -7.7818  17.3477   0.4304   1.8351
+#>    0.4143 -20.8468   3.2939   0.2564   4.4796   9.6334  12.4289  -2.4255
+#>    2.4209  -0.2858   7.2184   8.9520  18.5336 -14.5567  -2.3773  -0.9898
+#>    5.8089  -3.6283 -11.5245  -6.8369   8.2689   8.0623   5.3862  -0.2187
+#>  -13.1077   1.8365  -6.6235  -3.1059   1.2266  -0.3518  -1.3653  -1.2756
+#>   -2.6019   9.8177  -9.1612  -5.3509  -6.6606 -13.1829   5.1352   4.0275
+#>  -11.0478  -6.3089  -4.6812   0.9207  -2.8915   3.5621  -7.4784   2.9841
+#>    0.3917 -22.1856  -3.4268  -4.2744  10.9030   6.2728   8.8917   8.5960
+#>   -6.0017 -12.6934   3.1050  -6.7541   2.0820 -10.3219   1.6366  -3.0606
+#>   -6.1262  -2.7377   0.3554 -18.5050   1.3357   4.7694  -4.1198  -5.4279
+#>    9.6639  -6.7754  -9.4867  -7.6361  -1.1628  -1.1184  -9.1079 -10.8073
+#>  -13.6114  13.6032 -18.6887  -5.2429 -17.2754  -3.4884  -7.1409 -19.8385
+#>   19.4462  -1.8338  -5.5235 -12.7856  -7.5409 -12.7443  -0.8764 -14.7751
+#> 
+#> Columns 41 to 48  -0.7075  -3.6871   1.0846   3.5491   3.4595  11.7627   3.4947  -3.7417
+#>   -5.6032   6.1089 -17.6123   8.4267 -11.8817 -16.1448  -5.5878  15.7792
+#>   -2.1483  -3.1593  -3.4288  17.5301 -15.8145  -1.0703  15.5746   0.5555
+#>  -15.7804  -7.4713   0.1368   4.8883  -0.9655  -6.1302   9.3916  -3.7362
+#>    5.3681   2.9075  12.5258   9.1905   8.1007  -1.0967  17.8497  -4.7358
+#>    2.8150 -17.4774   8.8003  -7.3641  -0.1207  -8.1908   0.8019   3.8171
+#>  -13.9557  -1.9243  -4.9146   5.1251   1.8861  -6.6843 -11.1276  10.3518
+#>    4.8836  -7.3427   7.6724  11.3192   1.1050   2.1295  13.2262  -5.3453
+#>    8.2690  -2.3365  19.8755   5.2859  -3.4894   6.5555   1.4601 -13.8344
+#>  -10.1978  -0.7198   1.0085 -11.5294 -18.9771   2.2056  -0.8485 -12.1431
+#>   -2.2154   4.2609  -9.3026   2.6947   0.2253  -5.8004  -6.6905  15.8501
+#>   10.0000  13.3104   5.9539 -15.7541  -8.8126   1.6318 -16.1992 -13.3627
+#>   -3.4504  -2.6034   5.4614  -3.6517  -9.4828   1.6330   5.7189 -13.0835
+#>   -3.8162 -13.3095  -0.0463   5.3641  -7.2300   4.0203  14.4388  -9.4494
+#>   -5.5874 -14.9909 -18.4548   4.6491  11.6345  14.4683  -0.9663   9.3870
+#>    6.4413 -12.1750 -15.1652   7.4948  -8.9703  -9.7146  -3.1714   6.5990
+#>   -3.7498 -12.8697  -1.7002   3.1643  -0.0843   1.2336  -4.3673  -2.5000
+#>    3.9990 -20.6433  -2.5107   8.6411 -16.4188 -15.1635  14.5629  15.7645
+#>    5.3677  -1.3633  -5.7406   2.8192   2.1830 -17.3066 -10.0983  -5.0137
+#>   -5.7851  -0.4517  -3.3496  -8.0487   6.5608   0.9516   5.0757  -6.3950
+#>    0.6421  15.2076  -0.0578   1.5717  -6.6474   4.1189 -18.8823  11.6054
+#>   -1.4062  -5.0325  -2.6647   4.6340   1.9222   4.8882  -0.7630 -12.8169
+#>    0.0601  -5.9999   4.0442   7.2946  -4.8901   4.5484  -2.1316  -3.1636
+#>   -1.7716   6.2030  -5.7828  -0.3988   5.3320  -6.8372  -4.4370   1.1744
+#>   -4.5766   6.5112   5.6447  -4.3751 -10.3169  -0.3645   3.5060  10.3789
+#>   -9.9366   8.3807   4.1887  -1.2544   2.1130   1.5043  12.1316  17.7038
+#>    4.1311  -9.2013   8.8707  13.5254   4.5054   3.1386  13.2180 -20.1743
+#>    5.2612   6.4132  -3.2005  -5.6742  10.9046   6.4287  -0.1744   1.8012
+#>   -6.1653   0.3093  -0.3940 -10.2995 -13.1186  17.8623 -14.0711 -13.1964
+#>   10.3253  -3.8395  10.5909 -13.7830   7.2059  -2.9572 -19.1352  31.3352
+#>    4.7028 -10.9875   3.3549  -8.2194   7.2142   3.7944   1.4418   4.4237
+#>    1.0914 -18.5742  -3.5826  19.5459   0.2027  18.6070  19.6489  10.6596
+#>   -1.6508   0.1999  -7.0168  19.1316 -12.3294  -3.5433   1.1942  -9.3961
+#> 
+#> Columns 49 to 54   9.9891  -8.3729  -6.8773   0.4956  -0.5590   5.0643
+#>   -8.0108   8.4069   7.9129   4.0763   4.8907   5.1555
+#>    0.3710   3.4169  -8.5330  -5.0090  -6.0567  -2.3113
+#>   -4.2001  -7.1632   0.0594   0.0326   9.0647   2.3119
+#>   -9.2387 -12.1768  -0.6337  11.8236   2.7781  -8.6904
+#>    3.4671  -9.5394   8.2766  -2.7984  -5.0650  -3.5717
+#>    3.3864  -2.4448  -2.4917  -2.8890   1.6809   2.4046
+#>   18.7811  13.6295  -0.8040  -2.9517   0.2973  -1.5790
+#>    5.1043  -4.6780  -0.0868  -4.3414  -3.8778   2.0134
+#>   13.8499   4.1464  -1.7200   2.3408   0.7762  -4.0872
+#>   23.4962 -10.3445   2.5922  -2.8299   0.4665   3.2759
+#>    4.5790   3.4190  -2.6821  -8.1166  -4.6453   5.1660
+#>    6.4918  -3.8520   9.4369   6.8390   0.1168   1.2807
+#>    9.5057  -1.4327 -14.1694  -1.9196   0.5504  -1.6491
+#>   -1.2961   5.8970  -5.8560   7.8415   2.6092   2.5312
+#>  -10.8947  11.9945  -0.7405  -7.7695  -5.9003   3.6948
+#>    6.9811  -5.4123  -7.1316   8.5120   4.9062  -3.5583
+#>   -4.5261  -0.6889 -10.3639   5.1176  -6.1424   4.9872
+#>  -12.3113  -1.7938   6.5298  -1.8617  -3.0636   5.8704
+#>   -0.8052   3.7009  10.1956  -8.2405  -4.2964   8.8030
+#>   18.5907  -6.3344  -1.4395  -6.6994  -4.3130  -5.4660
+#>    9.5798   0.9034  -0.3554   1.5547   5.9826  -8.7406
+#>  -13.1542   0.9034  -8.4751  -6.0076 -11.4328   1.2385
+#>    8.7175  -3.3861  -5.9570  -2.1920   3.3056  -4.5674
+#>   -5.2959   6.7782  13.3159   1.1097   7.3374   0.2493
+#>  -11.3829  -3.8999   4.7460  -0.7568   1.2069  -4.0650
+#>   11.6582   7.8920 -15.4628   3.6439   3.7283  -1.1876
+#>    1.7519   8.5374   9.2063  -5.8952   0.1843   3.2878
+#>    2.1885  10.6823   5.4717  -7.8739  13.3070  12.0191
+#>   -8.3412  -8.3748  28.7164  -6.4650  -0.8949   9.5608
+#>    5.7447   8.8891  11.6408   3.4265  -5.8180   3.1247
+#>  -20.6969  13.7208  -7.9311   9.4007  -0.7257  -3.4730
+#>    0.9533  -1.9686  -4.0524  -3.8427   4.0237   0.0430
 #> 
 #> (6,.,.) = 
-#>  Columns 1 to 8  -2.3933  -2.8231 -14.5649   1.2265   6.9484  16.7138   2.5152  -3.8714
-#>   -2.3410   0.0383  -1.8482  -1.2086  21.3870   2.8062  -8.6456   0.0021
-#>   -1.8909   3.2607  -0.2578   0.7585   1.2088   4.6651 -14.7558  -7.0397
-#>    1.6005   3.0437   2.7254   0.4729   0.4200   2.9602  -5.7026   7.4910
-#>    7.7097   5.1390  -4.2629  -5.3246 -13.7337  25.3440  -7.3598   4.1057
-#>    8.0588  -5.3984   7.8002  -8.1057  -0.1618  24.6554 -12.0122   3.7527
-#>   -2.1816   7.3111 -13.8940 -13.4551  19.3834  -5.2697   6.9472  -6.4791
-#>    4.0208  -3.4722 -17.0254   7.7089  -7.8556   7.9077   1.4991 -18.5755
-#>    4.4408  -3.0581   2.1746   0.8543  -2.8558   7.9674   8.2400  19.3803
-#>    2.6803  -8.3320  -0.8502  18.1305   3.0918  -4.5918  -7.5745   2.3753
-#>   -1.4478 -10.6514  -0.9545  -0.7664   1.4152  -5.6242  19.3267  14.1202
-#>    9.0945  -8.8592  -4.3406   0.4397  -4.6462   6.7036  -5.7391  24.0809
-#>    1.9166  -6.8628  -7.8494   3.6546  17.2094   3.9769  -4.0957   3.9199
-#>    1.7243  -9.9460   6.4939   4.1563  10.2148 -24.9290  -1.3239  -3.4306
-#>   -0.1722 -10.1212  -3.0323   5.8537 -12.9172   1.2957   9.8134  -4.4496
-#>   -0.8825   1.3276   5.5673   7.1959  13.0362 -25.3318 -13.5864   6.1727
-#>    1.5064  -4.7732   4.0040 -23.9419   3.9104  -2.8041   3.1461   1.7080
-#>    7.4478   0.0707 -10.1757   2.4835  -1.7016  -3.2533   1.0278   6.0833
-#>    1.6632  14.5217   1.0237  -7.3828   0.9577  13.4193  -0.7450 -11.0810
-#>   -0.3358  -0.7283  -2.9503  -3.8002  -5.4520   9.4111  -3.8151   6.5682
-#>  -12.6043   3.4720   4.7416  -0.9385  -0.6316   5.4884  -2.0362  -7.3382
-#>   -8.0593   7.6517  -8.7342  -5.1107  20.6218   9.9912  16.3979  -8.4791
-#>    1.8494  -0.2744  10.7613  -6.2050 -19.5197  -5.7177  -5.4184  -5.1411
-#>   -3.0168   6.6093  -3.3874  -2.2436  18.0242  -1.7924   1.4984   6.4660
-#>  -12.2303 -14.3616  16.1126   2.0701  11.4154   0.3789   4.9664  -0.8457
-#>   -4.4937   8.3347 -13.6119   7.7609 -20.5116   0.1745  -0.5136  15.9122
-#>   -7.5482  -0.6359  -0.0841  -5.4823   0.1912   0.8122  20.5052  -2.4496
-#>    6.8388   0.3139   7.5516  -9.9683 -10.5476  -2.7888  -1.1222  -9.8913
-#>   -3.1357   9.2803   7.8254 -11.2691  -0.8377   2.4385  -8.8452   4.2665
-#>    0.1097  -9.1326  -7.8229 -12.0185   2.6693  23.2933  20.2058  -1.6425
-#>    0.5991   3.7670   0.2063  -1.4881  -1.1862  13.1080  -3.0750  10.8134
-#>   -8.0925   2.0068  -6.0206  21.5375 -20.4440   3.3259  -3.7572 -20.7331
-#>    0.4287   2.6760   2.0979  -4.7913  11.6869  12.6154   1.7529   7.2859
-#> 
-#> Columns 9 to 16   1.5429 -16.2952  -4.6099   4.4046  -3.0855   6.6220   1.6809 -15.7730
-#>    4.9859 -10.9777  -9.2018  -1.2260  11.0141  17.4103 -11.0088 -14.0926
-#>   -5.6609   1.2191  -2.1708  12.3986  10.5315   3.7087  14.3801   0.3327
-#>    0.4658  15.3275   5.7982   1.7107  -2.0811  -0.2691  -4.6511 -22.7007
-#>   12.6620 -10.0261  15.5672 -11.6777  -9.5031 -12.7180   1.2024 -19.7086
-#>   -6.3520  -7.2483  -7.2158  -5.1041  -6.8909  -7.9554  -0.5690   7.9753
-#>    2.5586   1.4266  -5.7646 -22.6115  14.7957   6.7313 -12.9693   6.5472
-#>    2.1179  -6.2491   6.2176   3.6738  -3.9064   6.0592  17.8334  -1.8069
-#>    5.5736 -10.2409   9.7896   9.8987   5.8255   9.5428   3.6325   2.3045
-#>  -11.4319   7.3671  -8.7400 -20.3050 -16.0279 -13.2162  -0.9830 -18.5106
-#>    1.1721   0.5274 -10.7990  15.6938   5.4564   0.7109  -6.9359  22.1753
-#>   -8.8085   9.1855   9.5483   1.9305 -12.1039   1.4839  -9.9639 -15.0440
-#>   -2.3187 -12.9011  -8.9538  11.1161  -0.4570  -9.3071 -13.1429   4.4905
-#>    2.0216   8.9996   8.6279  19.9109  -6.0012  11.1547   5.3846 -15.7855
-#>   -5.7400  11.2579  -7.9448  -0.6473 -16.4727 -14.3253 -12.2542  -4.0223
-#>    9.3313  22.7818 -15.1444   2.1813   6.9884  -2.3573   2.4577   7.7630
-#>   -7.4960   5.6692 -14.7068  11.3074  -1.1772   1.8731  -1.3718  19.7326
-#>    3.1983  -1.7400   0.2044   0.1885   5.6679  -7.9948   6.3472 -15.2114
-#>    7.0347  10.1483  11.5145  -8.7661   0.6672  15.2777   8.6046  -5.1687
-#>  -15.9128  -1.2608  -3.7305  -6.6174  -0.8022 -12.1907   0.5581  -6.8652
-#>   -2.6660  -8.1604  13.6352 -12.5818   4.3933   0.8350  -0.8670   6.9186
-#>  -14.4307   4.6077   1.2247   8.5692   7.3176  -7.7655  11.5433  26.7380
-#>    0.2708   1.2055  -9.8883  -8.4861   0.2955   1.5498  -8.4476  -3.7971
-#>    4.1670   4.8443   3.1621  19.7748  17.0420  10.3664  -0.9168   3.7278
-#>   -2.1689   7.2192   8.4080  -3.2654  -4.2774  14.1565  -3.8137   7.5986
-#>   -1.7891  -7.4096  -4.3061 -13.8280  -5.9388 -12.7266  11.5695 -13.9269
-#>  -12.7099  -0.0428 -14.1989  -5.1375  -8.5218  -9.1595 -16.8514  -6.4152
-#>   -7.9016   4.6911 -10.4946 -12.6796  -4.2166  -1.3278   1.8187   1.4088
-#>    0.2709   1.4040  -2.1233   0.3287  17.2023 -10.9112   5.0172  -3.8812
-#>   -9.1995   8.2871 -14.2229  15.7726 -10.5637 -11.2286  15.9094   2.9421
-#>    2.8056  -0.9664   6.7691   7.8808  10.5534  -9.2041  -1.0348   3.5583
-#>    7.8407 -16.9434  -1.9920 -17.0384  -4.8415  -8.9256  11.2713 -15.5950
-#>   11.6981   0.1152   0.9315   1.8805   8.5721 -14.7573   0.4268   9.2058
-#> 
-#> Columns 17 to 24   1.6778  -6.4415  -0.1352  -8.3186   2.3479  -2.9124  -3.0235   5.4116
-#>    1.7527   1.8941  -7.2753  -6.6662  -0.6948   5.5409   7.5639 -11.6145
-#>  -19.2897   2.8418   7.8411   9.0814  12.8986   2.8308  10.1728   5.7908
-#>   -1.6884  14.0491  19.4663 -11.5493   1.3748   4.0061  10.2249  -6.3584
-#>    1.6027  -2.9045   7.8118  -5.7253  -2.4294   1.6924   8.8773   2.7202
-#>   -6.7086 -17.3329   7.9259  -2.4976  -7.2582  13.8021   1.8096   8.7034
-#>  -12.4561   6.8747  -3.2511   4.0770   1.9898  13.9476   0.8878 -12.9841
-#>    4.3855  -2.7669  -3.9468  -3.1256 -11.9641 -10.6414  -4.2919  -4.2538
-#>   -3.9837   0.9678 -11.9469  -3.0423   3.9294  16.7164  -1.3809   6.7290
-#>   -2.8724  -9.0192   2.6321   0.5427  17.7220  -3.3728   0.8575  10.2964
-#>   22.2418  -9.5127  -9.0471 -19.1665  12.1969  -8.3111   6.4907   3.1734
-#>   -0.2076  -2.4305   5.1785   2.1002  -0.7725   1.4751   1.8629  -5.4309
-#>   -5.0746  -0.7222   4.2633   6.0595  -1.4383   6.2370   2.5049   6.4484
-#>   -2.7657   6.0351  -9.5651 -15.2493   6.4888  -9.2095   4.7027  -5.8996
-#>    9.5221   4.1215  11.3843   1.7492  -3.3621 -11.1883  -7.6068   1.9288
-#>   -0.0211   8.0418   4.4088   7.3769  15.1228 -22.4525 -11.0030   4.5516
-#>   -7.2525   5.1525  -8.8931  -5.2451   4.2944  -7.1268  -4.7372 -10.3392
-#>    1.4353   4.9326  -9.9241   6.8887  -8.8230   5.2892  -2.2666   2.2917
-#>   -1.3109 -19.8390 -10.8671  -9.9451  -1.5207 -11.8419   2.0018  12.9587
-#>    0.4461 -10.1880   3.2137   2.4221   5.3363   0.4954 -15.3489   6.2938
-#>   -4.6830   3.2804  -1.1812  -8.2964   3.7031   2.6834   4.3869 -19.4382
-#>   -9.0388  12.4784  -3.2806  -7.6163   1.6500  -1.9572 -12.4062  -7.6018
-#>    7.8623 -13.5411  18.3099  14.9309   9.6770 -14.5300   0.7799  -1.9263
-#>  -18.3736   3.3309 -10.0669 -12.7049  -3.2633   1.2947   3.9347   9.7269
-#>   11.3697   3.8972   6.4741  -3.7142 -12.9091   6.1751  -3.9448  -7.4640
-#>    8.2126  -0.8895   4.4201   7.0476 -13.7706   1.3601  -4.8520   6.2506
-#>    8.5005  18.1041 -16.0235  -2.0557   6.6932  10.8115  -3.1100 -13.5942
-#>    7.9085  -2.9537   4.6969  13.3141   6.4515  -1.0102  -2.8777   5.5790
-#>   -7.1045  15.9441  13.3113   4.8020   8.2366   4.4381   8.0431  -7.1640
-#>   -8.8677  -2.9996   9.1404   2.6122 -21.6991 -16.3302   2.8424   6.2128
-#>   10.3883  -5.7031  -1.9179 -10.6424  -7.1544   2.9624  -6.7363   8.1942
-#>   -0.9672  -1.5058  -4.0205   6.8180   1.1208   9.9072   3.4915 -13.4807
-#>   -7.6882   1.6917  -7.1852 -13.2910   8.2148  22.9102  -9.2222  -9.8807
-#> 
-#> Columns 25 to 32  -5.2330  -1.3815   2.1893 -13.3959 -20.0664  -1.1699   2.4693   0.6031
-#>    3.2243  -1.5599   8.9246  -5.4088  -5.8966 -15.8264   3.9623  -7.8535
-#>  -12.6833   2.4453  10.2644   6.5495  -2.7492   3.6596   1.9944   7.6586
-#>   -3.8781 -10.5531  -3.0427  -4.5520   4.2960   9.7992  -3.2453   6.4634
-#>    0.0360   1.1191  -0.1727   6.7454  -9.5639   5.6739   2.7641   2.8032
-#>    2.8271  -7.2727  -0.0424   9.8217  -3.7367 -21.3563   0.2294  11.2744
-#>   10.2004   0.9974  -3.1559  -1.7444  11.6059  -4.6158  12.4775  -5.4706
-#>    4.2351   3.3644   4.5505 -12.7553 -10.2433  -4.5546  -1.5327  21.5162
-#>    4.4139  13.4896   1.2470  -2.8440   0.5755   4.0851  16.5470   3.8477
-#>   -4.0246  -4.0716 -12.7473   3.2761   4.2678  -6.4974 -14.6987   2.1921
-#>    3.4868  12.5677  -2.0901   2.2527   5.1330  -0.4987   4.6309   2.8002
-#>   -7.3434   1.1411 -22.2636  -4.9744  -1.3532   1.0651 -18.1702   9.8706
-#>    4.9933  -8.0530   6.8392  -4.6851   2.0099  -0.3594  16.5478   1.6143
-#>   -6.7089   1.2296  -3.3766 -22.8374  -6.2673  -0.4287  -0.7818  -2.8487
-#>  -20.3780 -10.1946 -14.1414  -1.8622   0.5909  -3.7518   1.6533  -5.9114
-#>   -7.5912  -1.6410 -11.5662  13.6936   8.9424  15.1482   8.7913 -13.7951
-#>   -0.9836  -3.2381 -11.7469  -7.2732  -3.1640 -25.6097  -7.5313  15.5874
-#>   -4.0414   9.6809 -10.4774  -4.9385   1.1550  -7.6655 -16.8005  -0.4686
-#>   11.9079  12.2473   6.1715  -0.1022   0.0843  11.3742   3.1708 -18.4931
-#>   -6.2798  -6.1154  17.0070 -10.1745 -13.4848  -1.4222  -6.4125 -15.9544
-#>    4.5880  -1.8172   1.3404  -0.7641   9.0390  -0.8292   8.8150  -0.0080
-#>   -8.6819  13.4015  -8.5101   2.4812  10.9497   0.8663   9.6707   3.3289
-#>    0.1464  -8.4763   0.8412   2.7965   2.5752  -0.5562 -12.5688  -0.9830
-#>   15.1069   4.9995  16.4681  -0.5231  -7.8215 -13.6747   6.4876   0.6850
-#>   -8.4894  -3.5768  -6.4240  -3.0812  15.4772  12.1769  10.4209  -3.3678
-#>    3.8084   1.9189  -1.7310   8.7086  -3.6002   2.5440  -0.2780   4.0019
-#>   -1.3956   2.6794  -9.5926  -6.8309   6.3613  -9.5152  -0.6415   8.0382
-#>   -2.3512  -6.4113  17.4262   1.2770   2.9487   0.0967  -1.5846   5.8918
-#>   -7.3301   2.1242   0.9905   2.8800  -5.8558  10.1734  -3.6074   3.0594
-#>  -13.2812 -16.7233  -9.6330 -11.3087  -9.2737   1.5179  -0.8227  28.9040
-#>   -2.7239   6.9531  -0.7233   4.6741   4.4453   3.3907   6.6621  -4.8867
-#>   -5.7248  -7.8697  -9.8186  -2.3132  -7.1376  -3.2057 -12.4708   8.3674
-#>   -4.3927   1.4272  -8.7889   3.6932  10.4733  -8.7547 -10.5809  13.0088
-#> 
-#> Columns 33 to 40  -0.1658  -4.2741 -13.0167   2.8125  19.4319  -5.4442   8.7345  11.3106
-#>   -5.4128   4.5412 -14.4241  -6.9806  20.9891   1.9883  -8.5468 -11.6336
-#>    3.9230  -5.9303   1.0094  -2.0760 -18.3764   2.7971  -9.8910 -17.1911
-#>   -0.7150  -1.6136   5.7454   4.3717 -10.7253   1.1466   0.7195  -8.8246
-#>    4.0325  -3.4147   6.5115   6.8854 -28.3982   9.8396  -4.9240   6.0618
-#>   -0.7968   7.2536  -8.6915  -4.6818  -6.5899  11.5288   8.7270  26.6901
-#>   -4.0894 -10.4742  -2.8013   3.5833   2.8203   5.2279  -3.2011 -19.0499
-#>   -7.7248  -1.2338  -5.4393  13.5333   1.4814   6.0945   0.7191  -8.0978
-#>    7.6185  -0.8284  -4.9946  13.8686  -2.9363  -3.2145   6.9183  -2.4251
-#>    5.0350 -11.5492  14.6761   9.1763  -7.2025  -0.9546   6.7639   9.4197
-#>   16.2627   1.5523 -14.3638  -2.5518  -6.0636  -0.8085   1.3869  -2.2191
-#>    2.2576  -7.7883  -0.2024   3.2101   2.8854  11.1889  -3.9979   2.4221
-#>   -5.9915   4.2164  -8.7803   4.5399  19.8924  -5.1557  -5.6777  -5.8437
-#>    7.3394   6.0241  -1.8508  -1.9891  10.3307   4.8951 -12.2203  -3.0790
-#>  -22.0107  -2.2640   7.6870   9.3464   0.2916  -5.7537   7.5604   3.1492
-#>  -10.7479  -3.7193  16.7831 -15.1875  13.6446  -8.0983 -22.4715  -2.1607
-#>   11.9150  -5.6264  -2.5958 -14.5199  -7.6892   2.8243   6.1238  13.7542
-#>  -12.1732   1.5765  -3.3213  -6.7741   1.4289  15.2627  -7.1139  -3.3994
-#>   14.9497  12.3898  13.8701  10.1220  -3.4746  14.4940  23.5101   4.9444
-#>   -7.4806   3.8434 -10.1420  10.8480  -3.5622  -4.8281  -7.2726  -2.8693
-#>   -4.7255  -2.2326   5.0139   0.5783  -5.6597   5.1953 -15.6661  -0.2395
-#>    8.3898 -11.8495   7.7439  12.7849  -1.7928  -6.8798  10.0592  -1.0226
-#>   -4.4024  -0.1014   9.6252  -3.3212 -20.1394  -5.8556   4.4151   3.6770
-#>    8.4042   9.1342   9.2365  -7.5792  24.0859   0.5709  12.2008 -14.5676
-#>    0.0516  -2.5967  -7.9509  20.5446  -4.4098  -3.6914   0.0778   4.5274
-#>   -7.2622  18.3369  13.0771   0.7538   5.7499  -6.8232   7.2616   9.0808
-#>  -12.1754  -9.0076  11.1918   1.9653  11.7068  -2.9894   4.2650 -11.6029
-#>   13.0535   1.4470  -8.0668 -17.0397  -5.0373  12.8577   6.7251  -4.5107
-#>   -3.0594   2.9478   3.9375 -13.9612   1.8279  -0.7990  -6.5206  -1.8909
-#>    4.2958   2.5674  -5.0706   3.6540   7.2688  10.8935  35.1408   6.1412
-#>   -7.2251  -0.6509   7.0742  -7.2755   6.0282  -2.5625  -7.4393   1.4161
-#>    5.6039  12.7617  -3.2069  -7.9473   7.4579  20.2680   0.9503  -9.4715
-#>    1.8726  -0.4505  -8.4528  -3.1456  22.2555   8.8612 -14.3986   1.5937
-#> 
-#> Columns 41 to 48  -7.0365  -7.1911  -6.9832  -6.6034   1.8335   7.5551   3.6796   8.7989
-#>   -3.6209  -3.4856  -6.2510   2.3313   1.1667  -8.5825   9.9867   1.9490
-#>  -12.8983  -1.8999   2.1977   4.7087  24.5768   8.9532   3.6447 -12.3067
-#>   -0.1780   2.2829  -1.9048  13.2251   2.4473  -1.6018 -13.6998  -0.8499
-#>   -4.5863   8.7913  -1.2143  -1.1927 -10.1472   7.0486   7.8177  -2.5055
-#>  -10.8818 -10.3920  -4.0015 -27.1391   4.7582  -6.4043  12.1196   2.8603
-#>   -3.2827   6.1637   3.9089  -1.5718  -0.1650   2.7290   5.3034  -7.1740
-#>   12.8172  -3.8016   1.2016  -2.3019  -5.9471  -3.1962   3.1356  -7.3677
-#>   -2.7111  -1.8917  -2.2459 -13.3483   7.5332   7.3395  15.2006   3.9319
-#>   -4.5157 -11.7697  -5.2488   5.5137   2.6628  10.3019  -2.9456   2.4325
-#>   -8.0180 -11.1056  -4.5557  -1.5262   9.2000  -6.9966   7.3665 -15.5740
-#>   16.5981   2.1450 -13.4648   6.9906  -3.8754  -6.5307  -2.1259  -2.9880
-#>  -14.1285  -9.4364  -1.0108   7.9575  13.1723  -3.3897   3.5740  -9.5083
-#>    4.1621  -1.5070  -2.2087  21.1738  -1.1345 -12.7376  11.6858  12.5840
-#>   10.2081  -7.1385  -0.2708  -9.7277  -2.5300   8.6155   1.8450   1.3057
-#>   13.8926   7.6847  -7.3537  -0.5823   9.2490  -7.8143 -11.8197   2.1618
-#>   -7.0815  -0.0924   1.7060   0.2990   3.1005  -2.8214   0.9185  18.8770
-#>   10.3080  -2.8859 -18.1575   0.8361  14.2335 -10.9023 -10.3132   6.2573
-#>   -2.3226  10.1153  -9.8714 -11.1489   8.7382  11.8088   3.5335  -8.6061
-#>    0.9529   6.2439  13.7589   2.4170 -15.8774  -0.7123   7.9430  -5.7493
-#>    3.0389  -8.2618   0.8278  -5.5897   2.5569 -11.8408   5.3491   3.7439
-#>    0.4842   9.9147  -1.0470  -2.6416  20.6687  -2.1343 -11.0162  -4.9360
-#>  -12.7470   9.6998   6.0791   5.8153  15.2451  23.3747  -4.8672 -11.0663
-#>  -21.7379   2.9156   6.4470   3.1172   8.1243   8.6528 -10.7619   4.5328
-#>   -1.5000  -0.2806  16.6390   0.5521 -15.4529  11.5029  -1.5393   1.3152
-#>    1.4420  10.6928   4.3994 -12.0668  -0.2689  -1.5219  -2.0803   8.6118
-#>   -1.3304   5.7595   8.6643  -0.6670  -0.9824   5.3312  -9.3878  -6.1657
-#>   -2.6928   5.0323   4.3580  -2.5496  -8.2724   4.0027  -8.0189   9.2038
-#>  -14.3821   7.1752   1.4434  -7.9288   3.9231   6.6579  -7.0609   2.5534
-#>    0.6453  -4.4102  -4.5158   4.6314   9.5885   2.2223   2.2485  -1.6284
-#>   -3.5814  -5.0103   3.0189  -1.3147  -7.1761  -6.2938  -8.0212  -0.3543
-#>    7.2415   6.0512   3.7243  10.1745   9.9101   0.0936   9.8109   6.2233
-#>   -8.6651  -7.9083  -5.1292   5.5781   5.1312  -1.0708  -4.8641  11.3148
-#> 
-#> Columns 49 to 54 -10.8975  11.6077  -6.2775  -1.0497 -13.6208  -1.8142
-#>   13.1456  -9.1939   0.3886  -4.1949  -1.6206  -4.2013
-#>    6.0395  -7.3898   7.8786  14.0081  -3.9137   0.4371
-#>    2.2743  -9.5347   1.2135 -10.0645  11.8385   2.1302
-#>   -0.4494  16.9023  -1.9057  -6.1568   3.9840  -3.3445
-#>    0.9947   2.2809  -8.2821   9.3761  -6.5058  -5.3371
-#>    0.0837  -7.2570  10.4998  -5.4291   2.0629  -1.2072
-#>    2.0734  -4.8856  22.5101   0.4016   2.8442  -2.9386
-#>   12.2798  -4.8926   1.5630  -0.9470   4.9285   0.0782
-#>  -10.5178  16.6957   1.3643  -8.1838  -7.0226  -4.0855
-#>    8.0503  -0.6926  -5.9212  16.0828   1.3463   1.9198
-#>    4.4199  -8.7152  -7.8884   2.5225  -3.3634   1.3823
-#>    7.1209  -6.7880 -12.6276 -14.1402   7.5011   3.6135
-#>    2.4809 -16.6612   3.1206   1.3862   8.9676   1.8443
-#>  -17.2778  -1.3190   5.9414  -9.6632  -3.6013   2.3111
-#>   -1.8095   1.7647   5.5546   9.7994  -1.9943  -0.8855
-#>    4.7578  -7.2273   2.8811   0.5475  -9.1510  -3.1228
-#>    3.7045  -0.2151  17.4081  -3.1675  -5.3228  -5.7981
-#>    9.6104  13.4691  -1.4277   1.9960   6.9995  -3.7394
-#>  -13.1755   1.6854   9.4084  -9.5875  -0.8298   4.6526
-#>   -0.5333 -10.8859   2.6966  -0.2496   5.6243  -0.3994
-#>  -11.5627 -13.2616  10.7873   4.5478   2.5188   5.3648
-#>   14.5499   9.6646 -10.2373   1.7955  -1.3717  -3.4287
-#>   13.2639   0.8773   1.1604   1.2519   3.5976   8.2300
-#>  -17.1914   0.5858  -4.3062   1.3593   6.9147   7.4865
-#>   -4.8861  20.8843  -4.2372   3.0550  -6.0051   1.4374
-#>   -3.9068 -33.6806   1.2267   0.0336  -3.5000   3.5497
-#>  -10.3744  -0.5291   7.1012   4.2670  -4.3717  -0.9353
-#>   -1.4874 -10.1441  -6.8394  12.4491  -1.1372  -2.3226
-#>   -0.8102 -29.3352   5.3444  -8.5302   3.6247   1.2677
-#>   10.1890   5.5535  -6.2732  13.2793   6.5547  -1.0918
-#>   -2.8983  -3.8226   1.3922  -5.4163  -2.0137   0.7241
-#>  -13.1667  -1.6609  -2.3496  -2.3967  -2.1740   3.1770
+#>  Columns 1 to 8   2.2135   2.2744  -2.4032   4.7625  -0.6137   3.1394  10.0055  -2.8687
+#>   -1.3064  -2.2801  -1.3256   2.4759   2.6833  -7.2205  -7.7346  -9.7641
+#>    2.4956  -6.3248  -6.2271  -3.9590 -18.0408  16.4091  -1.5628   9.9763
+#>   -2.4925  -2.3138 -11.9997  -6.1532  -6.3317  -5.0805  -1.6468  17.1797
+#>    0.0629   0.1995   5.9242   8.3304   9.0459   7.6142   4.8036 -12.0736
+#>    3.4413   4.0913   0.9712   6.2869  11.0677  -3.4056  -7.8102  -2.1270
+#>   -4.4579   4.1702   0.6375  -0.0477   5.1982 -11.2275   8.9460   1.5677
+#>    2.6904   3.4541  -1.6124  16.3042   3.2777   4.2698  -1.1955  11.9641
+#>   -2.4262  -3.9030  -3.6114  -8.2710  -1.4164   8.7284   2.1595  -0.4117
+#>    1.0814   0.8000   3.0875   4.7548  -5.3547 -17.6405  10.6495  -2.7958
+#>    5.8510  10.6322  -6.3643  -3.5246 -12.9604  -2.0855   2.7855  11.7784
+#>   -0.3453   6.5470  10.6913  -1.5554 -16.3289 -11.5359   5.2700  -3.9608
+#>    1.2436  -4.5511  -9.1398   4.2730  11.6361   1.9257   7.8826  -6.6093
+#>    0.1510  -6.1002   1.6126   3.4322   8.5997  -6.4239   1.0188   4.2273
+#>   -1.7900 -13.3173  -1.4404  -8.3024  -4.3626  -4.5511   8.2458  -1.4294
+#>   -2.5784   1.5192  -6.5783   1.6499 -12.4026  -4.8758  -3.4585   1.5789
+#>    3.8580  -4.9930   0.7346   6.2328   8.4082  -6.5991   4.4083  -1.0046
+#>    0.6743  -4.3152  -5.8452   5.0080   3.7384 -21.9220 -12.2148  11.2428
+#>   -0.3790  -2.2588  -6.7439  -4.6867   3.7880   1.1640 -26.5236  -0.0054
+#>   -0.0567  -0.3626   6.5940 -10.9867   8.1805  -7.1203   1.7278   7.2201
+#>    4.6416   2.0586  -3.6743   0.3590  -4.3049   0.4161  11.3404  -1.8059
+#>   -5.2165   0.2337  -4.2656   3.6181  -5.0486 -11.6908 -11.6442  -6.0226
+#>   -3.0021   3.2002   2.1940  -3.9442   1.2898  -5.6723  -2.8187  -2.1008
+#>   -3.2257   6.2033   6.8204   4.7004  -6.8386   1.4361  11.5841  -1.2795
+#>    1.0397   5.0716   9.2202  -6.6404 -11.1956   1.3397   2.8808   7.5543
+#>   -1.6870   0.4534  14.1308   1.8232  -3.4654  -1.8852  -5.2898  -4.2924
+#>   -1.9876   0.6338  -1.9068   8.8323  -0.4345   8.5468   7.0683   2.0120
+#>   -3.1540   7.7343  -1.5833   5.7757  -4.1702 -10.7095  -4.9754   5.7679
+#>    1.6136  -3.3095   2.3101   4.8368   8.0606 -14.4697   1.9953   0.8581
+#>    5.2108  -3.1173  11.8045  -1.9914  -8.0544  -5.4813  -1.7920  -3.6895
+#>    4.2425   4.3510   3.0851   5.5588   1.3848   1.8118   6.7842   4.0513
+#>    0.0644   0.8374   6.2682  -5.2885   6.7913  -0.9329  10.0503  -7.4671
+#>    3.6094  -6.5895 -10.7463   5.7850   7.3585  -5.4725   5.5822 -16.1018
+#> 
+#> Columns 9 to 16   2.5039 -16.2922  -6.7044  11.8824   2.4025   0.4172  -9.0057  -2.2229
+#>   10.7156   3.2957  -8.1330  -0.3797  10.5093 -15.2512   0.5237  -0.6342
+#>    3.5383 -10.4639  -0.4879  -6.5776  -1.2709   9.3121  -1.1333   2.7572
+#>   -3.7386 -17.1051  -7.2766 -11.3527  -7.5308  13.9741  -6.1271  -1.9946
+#>   -9.7924  -4.5332   9.0743  -1.0211   5.2158   0.8992   7.3770  -6.9080
+#>   -9.4799  -0.7012  -2.4332   1.7575 -18.9986   2.0167   6.3571   0.4500
+#>   15.5448   5.1408  -7.5658  -4.0225 -11.9862  -2.9355   4.7610 -15.3453
+#>    1.0483  12.8483   9.6863  -7.8222   5.2626 -11.0110  -9.7563  -7.3414
+#>  -12.3625 -18.6203   1.6335  -4.7872  -2.5059  -4.4079 -10.5989  30.9205
+#>    7.5354  -7.8220   3.7788  -4.7764   6.7773  14.5389  -4.9261   1.6773
+#>    5.5107   9.9199 -16.7830  -3.2340   5.0264  -7.7856  -4.4200  -2.0426
+#>   10.2898  -0.6562  13.8536  -7.5734  -3.7500  -8.0234  -7.8404  12.8900
+#>    7.7300  -8.4238  12.9362   4.6471  -3.7397   2.9992   6.6356   1.1758
+#>   -1.9281  12.2917  -7.8830   1.6001 -13.4961  -2.0411 -13.3746   8.9633
+#>    7.3628  -4.3547   5.3241   2.6318  12.0781   3.5330  -6.0297 -11.6909
+#>   19.1012  -1.9865   3.0640  -3.6507 -16.9498   9.8562   6.5746   6.8137
+#>    0.4844  -3.9859   9.2041  -1.4860  -3.0415  -3.6633  -2.2930  -3.6276
+#>   17.7334   6.0643 -20.0312   7.5501  -6.6048  20.6153  -8.1628 -16.0870
+#>   -2.8171   5.4657   3.7815 -22.8397 -20.5106   5.6540 -23.2079   3.1174
+#>    0.3893  -3.5663   7.9546   7.6300   2.5896   1.0693   4.0117   6.4425
+#>    4.9693  -6.6747   2.4427 -12.0373 -14.6726  -3.0411 -15.4597  15.5744
+#>    8.4786   0.4865 -13.4001   8.0536   5.8684   8.9350  13.7514   0.1885
+#>   -1.3111  -0.0431  -5.9225  12.4518  -6.1468 -14.7867   5.1663  15.2329
+#>    2.2690  17.6904  -5.1850  -1.2463  15.9314   3.8881   7.6665  -0.2140
+#>    4.5795   6.1591   7.3600   0.5866   7.5447 -11.4432  -4.2585   0.7696
+#>    1.5206  -4.7729  -2.0287  10.6773   9.3859  -8.6040   6.8237  18.7661
+#>    3.6645   5.7808  -5.1768   2.2680  -8.8899   1.0830 -12.0403  -4.9888
+#>    3.9320  -6.8440   6.8704   3.8760  -3.5369 -11.5604  -5.6720 -13.4513
+#>    7.2588   3.0454   9.8922 -18.0485  -4.1625  10.1040   6.0447   1.3142
+#>   -3.6985  -9.5494  33.4702  -8.9046   0.8596  -3.5953   2.8533   8.5468
+#>    1.5409   4.8864  16.5131 -14.2863  -2.6458  12.7455 -15.9059  -2.5760
+#>  -13.7460  -3.1347  14.8093  26.8160 -16.3366  -9.4262  -0.3195   2.1399
+#>   -6.3656  -4.6834   3.4649  -9.4979 -23.5982   7.6751   1.6219  10.9824
+#> 
+#> Columns 17 to 24   9.3324   6.3304   1.7720 -15.0587 -12.5964   9.3043  12.9548  -7.7601
+#>   -2.7592  -7.0683 -12.8870  -2.1885  -4.5443   8.0038  -3.7140  -3.1095
+#>    0.0334  -5.2943  -0.1540   5.6037   0.8544  16.0952  -0.0605  -5.9662
+#>    6.0038 -13.9490   2.1470  -8.1573  -6.1015  -2.2869  -0.5762  -2.5651
+#>   -4.2905 -10.4983   1.6719  -4.4364  -5.8537   6.5986   8.9249  -8.1591
+#>   -4.2759 -16.2675   6.1078 -16.7187  -3.4638  -0.2850  13.6264  -5.7115
+#>    5.9395 -13.2931  -4.3535  -0.6677  -6.1867 -17.5077 -12.4860  -2.8227
+#>    4.8826   4.2179   1.1313   0.9272   1.3374   5.9875 -11.4002   6.4245
+#>   -8.7054  -4.9952   2.0889   4.9943  -6.1132  -0.7138  -7.9137  -4.9143
+#>    4.4980  13.3026  -8.7420   5.3964  -1.8704   3.4872  -2.0934   2.9711
+#>    0.8424  11.5982   8.0608  -5.2656   1.2197  -9.4929   1.8903  21.7185
+#>   -6.3589   3.1115  -4.2720  -0.3339   6.0476  -4.0408   8.2888   8.5415
+#>   -2.0864  -0.1170  -1.4761   2.8857   8.8764   5.5274 -13.0928  -1.6003
+#>   10.2591 -11.8200  10.0686  -3.7683  13.3431  -0.2714  -6.4799 -15.4492
+#>   -1.4053  -5.2044  -8.1386  -1.1091  -0.6233   2.0652   1.0445  -1.0414
+#>   -1.4189  18.2151  -1.8874   2.3994  -6.4932   3.5080 -14.4957  -4.7396
+#>    0.0770   8.8373   8.3253   2.9777 -14.2051  -2.8648   2.4273   1.7811
+#>   -5.4495  -1.0354   6.6137   2.8131  -7.6769  -0.3869 -14.4008  12.3147
+#>   -5.8493  17.7427  -2.8514   9.4776  -4.1274  12.2208 -26.4459   1.3627
+#>    9.2113  -3.5756  -2.1142  -7.9503   3.3314 -12.5494  -0.1233   7.0149
+#>    9.4371  12.6448   6.0816  -6.4636   4.4609   4.8990   3.3260  -2.3265
+#>   -8.1406  12.9907  16.7523   5.0516   3.8437   1.0699  11.7544  -9.8462
+#>    6.2008   7.4172  13.1104   3.6327 -10.7350  -7.3427   1.3633  10.8861
+#>    5.4840  -6.2143  -3.4092  -5.3348  10.6867   1.3953  -5.5659  -7.5357
+#>    1.6014 -11.1681  -4.4866   6.6944   4.7726  -1.3633   8.6329 -10.3593
+#>    2.0079   6.3870  -9.1691   1.1962  -0.2855  15.4217   6.2776 -10.5935
+#>   11.3961   0.1276   4.1711  -0.8448  -5.3681 -17.8231   0.1996  -1.4930
+#>  -18.3679 -12.0041   5.6460   2.0658  -0.7714  -3.8083   1.1132  -6.1140
+#>  -16.6530   7.9177  -7.8466   2.6111   2.3612  -0.4131  -7.2756  -5.7625
+#>  -13.5851  10.7676  -5.7843  -5.9604   0.4106  11.0133   7.5143  -2.6937
+#>    1.4349  -4.3626   6.1910   5.5450  -7.7830   8.3621  -2.4486   4.5995
+#>   -8.3759   1.9431 -11.5438   2.6202 -16.4537  21.4471   8.1414 -14.0446
+#>    3.1302  -7.5561  -4.9328   3.0413  -0.5228  -4.5005  -1.1739   5.5412
+#> 
+#> Columns 25 to 32  -4.9921  11.8749   6.8052  -1.6223  -5.0417 -11.3130 -12.3659 -15.4293
+#>  -20.0101  18.2990  -4.3814 -12.4221  -3.5064  -5.1753  -7.5437   2.0174
+#>   -9.1740  -9.9307  -4.6429   6.4327  -6.8483  -1.8640  10.6255  -1.7954
+#>    5.9874  -3.2789  15.2537   9.3382   0.4256  -4.3901   1.0370  -6.2250
+#>    8.0407   2.5339   5.1116   4.2795  -8.4246   4.0116 -10.0233   4.4015
+#>   -9.1687   1.7920   0.4000  -7.4694  -1.9409  -7.4935 -17.8723   9.2113
+#>  -12.8515   2.6711   9.6064   1.3426  -3.8090  -4.3189  11.7298  18.1138
+#>   -8.4509   1.6229   1.3712  -4.4523  -9.1117   5.8101  -7.4262   1.7048
+#>   -9.1652   2.7710   8.5037  -7.8006   3.0689  12.0864  -1.2945  -2.3185
+#>    8.3726  -1.5290  -2.3691 -10.1070   3.3510  -0.9877  -0.1463   7.1754
+#>  -23.7770  16.7063   0.6362   0.3466   1.4782   2.2913  -3.0341  -4.8129
+#>   16.5063   4.6042   6.6903   0.4337  -3.0114  15.9774  -0.2011  -2.6899
+#>   -3.3346  -2.6632   1.6846  -0.4937  -1.4823  -5.8397   2.9372   9.4176
+#>   -4.9656   6.2064   4.4998 -14.1976   1.2556  18.1513 -18.6776   3.5407
+#>    1.6853 -10.8171  -1.8259   4.8707   6.6832  -3.4421   4.1701  -4.3055
+#>   -3.2763  -3.8923 -19.9942  11.1664  -9.5751  -9.5473  19.7872   7.4483
+#>    5.0459   6.1259   5.0875  -0.8051  -1.8123  -2.4846   1.1983   5.1986
+#>   -7.8122   5.9089  13.8430  17.0934  -3.2713  -0.4226   1.4303  13.0954
+#>    1.5944   6.8495  -6.6204  -1.3772  -6.0934   4.3985   9.0246   7.3300
+#>    0.2200 -12.9269  10.1386  -0.1954   1.0603  -4.0962  -3.2273  -3.2283
+#>    2.6499   9.6159 -10.8460  10.3052   7.3553   2.6659  -0.1121 -13.7584
+#>    4.8797  -0.5332  -9.9482  -7.0226   0.5603  -7.3748   9.5130   5.3608
+#>    0.5389   6.1693   2.5994   2.7665   9.5016  -3.2650  -5.3591 -14.3288
+#>   -4.5699  -8.0362   0.2745  -5.0735  -2.3603  -7.5081  -0.3132   9.0614
+#>   -1.8522   6.8134   7.5966  -1.7252  -5.4286   0.1030  -1.4991   6.4211
+#>   -0.5422  11.9454  -3.4362   4.7169  -5.9638 -10.3555  -2.4434  -2.9211
+#>   10.6703 -16.0884  -0.9812   3.4574  -2.8120  -0.5155   0.2814  10.9890
+#>    6.6505   2.1370   1.5319  -0.5443  -1.3172  12.2760  12.4180  -7.8838
+#>    5.4640   1.9931  -9.7384  -0.3721   1.3064  -0.6287   4.1623   0.5917
+#>    2.8879  23.5030 -12.1704   5.0036  11.4511  -5.2605 -13.5595   5.2515
+#>    1.8030   2.4854   6.9497   8.4886  -2.1977 -11.0915   5.8441   0.6094
+#>    2.5570   4.1091 -19.2412  -2.0765   3.8786 -12.6116  -5.7205 -11.3402
+#>   -7.5732  -4.0357  -3.7934  11.4792  13.5739 -15.8708  -3.9369   9.7511
+#> 
+#> Columns 33 to 40  -9.1498   1.0224   2.5557  -3.9999  -8.7879 -15.8448  -8.8763   4.5561
+#>    7.9687  -6.1811  13.6454 -13.7227  -4.0827  12.9292  13.3715  -4.3120
+#>  -10.2845  -0.1378  -3.0810  -1.4613  13.9452  -3.7052  -6.3972  -4.3207
+#>  -18.8757  -9.8238  -0.3281  10.7633   9.3164   5.6381 -10.5065  -5.7902
+#>    1.0771   1.5498 -12.3555  -0.4910 -15.2706 -24.6012   8.2595   1.6147
+#>   13.2954  -1.7923   5.6691  14.0812 -21.1235  -9.5387   1.6765   4.6125
+#>   -2.7839 -16.8119   1.9217   8.7875   5.1847  -0.6776   7.1346   5.8532
+#>    2.7831  -6.8621 -14.7492   0.8036  -8.6342   0.8280  -6.0761   9.9090
+#>    4.6954  -1.1437   2.4594   7.7631  12.6277  14.1254  -1.9389 -11.5542
+#>  -12.4512   5.5937   7.8544 -14.2836   2.4115   1.6958   9.4456   0.7077
+#>  -13.2471  -6.6811  13.6936  26.3369  -2.9248  -8.9903   4.7060   5.2106
+#>    5.5752  13.4318  -4.7569  16.1942  11.2705 -14.4103  -0.3421 -18.0020
+#>    7.0430  10.8793  -5.9126  -2.4798   3.1072 -13.2734  10.1392   0.3825
+#>    0.0120  -9.2309 -12.4875  -5.4033  -7.6199   8.9924   3.8505  -6.9480
+#>   -1.5667  -1.6259  13.7165  -4.1303  15.1214  -6.4428  10.0190   0.8410
+#>   17.0503  -1.6365   5.3205  10.6327   4.6021   4.1293  14.2263   6.0942
+#>  -14.7770  -2.9867   0.1682  -0.2817   6.8840  -1.8693  -8.6032   9.6126
+#>   -2.2571 -20.5931   5.9232  -3.2959  -5.3999  -8.9706   2.4636   2.8744
+#>    5.3935  18.8268   0.4861   4.5469  -3.2428  12.6261  20.5986  11.6719
+#>   15.1916   8.1120  -6.9524  -3.3624   2.8661  -2.5539   7.2161  -6.4069
+#>  -10.2325   1.3713  15.6838   3.6210   3.9250 -12.5961 -16.3249  -5.0422
+#>    6.4411 -13.6013   2.6299 -16.0739 -14.0839  -2.5225  -4.4807  13.4892
+#>    2.6592 -13.8785   2.6847  -5.3585   7.4292  -3.3500 -14.3786   2.4257
+#>   -9.8452  -0.2178   8.4658  -2.9448   7.7259  -5.2272  -7.5180   9.4841
+#>   -1.5067  -5.7126  -3.0854  -9.3424   1.0947   2.1711  -2.3600 -12.6407
+#>   -9.3124   7.7902   3.9494 -16.1575   9.2356 -11.4665 -12.6826   7.4724
+#>    7.3826  13.3859  -8.7221   2.6186   1.3185   0.2336  -3.6932  12.3689
+#>    7.7037 -11.6459 -20.1851  11.4569   4.8962   5.0734   4.9487 -16.9875
+#>   -7.1961   1.3238   0.7320   9.3074  11.5284   6.7604   6.4831  -6.3404
+#>  -11.3669  -6.5104   4.0795  -9.7762  -4.1450   2.9030   3.7602 -11.1470
+#>    1.7323  -0.9422  -9.4310   6.6960   0.2927  -5.3295  -3.4203 -11.4168
+#>   -6.8595   5.5981  -6.3271  -6.0393   3.0477  -0.7424  11.9399  -0.2345
+#>    1.6577  -6.0810   6.6819  -8.9084   0.4325   6.0519  -7.9133   7.4273
+#> 
+#> Columns 41 to 48   1.8353   5.0528   3.4951  -9.0679  11.0028   6.1925  -0.5418 -16.5812
+#>   -0.5433  10.0272  -1.1334   0.6936 -17.7075  -5.5724   4.6281  12.8386
+#>  -11.7197   3.4715  10.7780  -9.7709  -1.8842   1.7804  -6.9247  -7.3299
+#>   -5.3639 -19.2410  -1.4794 -10.3681  -3.9660   2.1006  -1.9470  -5.5627
+#>    7.5097  -7.9963  -3.1425  -1.0279  -2.1544  -5.5581 -13.8334  10.3771
+#>   11.4252  15.3848  13.0953   8.2470   2.2717   1.5647   4.2417   4.9367
+#>    1.3768  14.3687  -7.2375  -5.0713 -11.7499 -11.5194  17.1535   0.5708
+#>  -10.1209  -8.5866   3.6746  -5.3425   8.3581   2.2553  10.8325  13.2715
+#>   -8.2578  15.1133   8.9757 -11.0993   2.9291  -3.2638  -3.3501  -6.2311
+#>    6.3174  14.0842   1.9107   7.1787   1.0850   7.0974  11.5582 -16.5498
+#>   -8.6835   3.2800  -1.3284   4.3463  19.7286  -4.2625   3.3034  -5.5352
+#>    3.2935  18.2161  -8.0461  -7.0316   4.0370   3.8959 -14.0842  -4.0833
+#>   -7.9546   8.4912  -2.7412 -19.9193   2.4274   5.6734  -1.8485  10.6074
+#>    6.8169   4.2674  -4.0421  -1.0973   6.0960 -23.0291  -8.7688   3.5648
+#>  -12.7413 -28.5586  -1.3460   4.4852  -8.8658   3.3518 -10.8100  -3.7896
+#>   11.1173   4.7237  -8.5040  -5.4392  -8.6458   6.3683 -10.8678   3.8805
+#>   16.9131  -9.5203   4.9735  -3.2252  14.8521   5.3122  13.3178   3.8548
+#>   -4.5715 -16.7926   9.0824 -22.1700 -26.8291  16.6743  -2.2120   9.8949
+#>    6.9240  -3.8696  -2.9557 -13.2527 -12.8807   0.2085 -10.8347   4.7033
+#>   -6.0386   8.5350   4.0996 -14.7628  11.7446  -3.6745   4.9260  -1.6664
+#>    4.6730   1.0010   4.4995  27.9075   5.8951   4.2947 -16.0618 -15.9623
+#>    4.7371 -17.9729  11.8856  -7.3971  -8.8382  20.9224  12.7971   8.6029
+#>    2.2808   1.5251  13.5189  -2.8363  -5.6756  13.5164  -1.0008  -7.6924
+#>   12.7574 -10.1915  20.8699  14.2555   0.8298  -9.3327  -0.4088  -8.4686
+#>   -4.0076   6.0963  -9.8001  -7.9556  -2.6791   6.5401   3.9974  -5.1087
+#>    0.3525   5.4211   9.4951  16.6767 -11.3788  21.0782   0.5490  -9.1744
+#>   17.9557 -16.0236  -4.8447 -22.0739  13.0146  -1.4386  -8.9642   4.9656
+#>    7.4886  -4.7218 -11.7758 -17.3871  -1.9418  14.2106  -6.5096 -10.2552
+#>    5.9031  17.9611 -10.6873   2.5068  -0.9294   8.2299   1.4218   0.7348
+#>   -3.3478   0.3052  -7.2493  10.8731 -13.4435   4.8601  15.2912 -15.7411
+#>    1.8852  -6.9789   3.5930   7.2022   9.4417   9.3241  -0.8272  -3.1054
+#>   -3.2483 -14.6299  -3.9589  10.7887   2.1387   3.6395  -8.1679  -1.0316
+#>    8.4340 -13.7136   3.2912   5.7727  -3.5039 -14.3083  -7.9429   7.0883
+#> 
+#> Columns 49 to 54   2.7577   8.8217  -0.2796  -8.7620  -5.5553   1.0933
+#>   -4.2471   1.8396   3.7060  -1.3979   6.7423   1.0845
+#>   -8.9284  -1.8396   1.2401  -2.4745   5.2761  -3.5389
+#>   -6.7518  -7.7967 -13.8865  -0.5287   7.6688  -3.0929
+#>   18.2269 -20.6937  -3.4987  -4.5275   5.0985   3.1071
+#>    7.9901   8.3455  -7.7927  -9.5336  -6.4837   6.5534
+#>   -8.3591 -10.3706  -3.5707   0.7897   4.8532   1.9355
+#>   12.7343  -3.2890   1.0176   4.5947  -5.5457   4.0487
+#>  -11.7678 -11.1459   5.1397   3.8807  -3.6064   0.1270
+#>   -3.7639  11.1989  -0.8373   0.7988  -0.4052   0.5196
+#>    5.1139   4.7246 -11.9855   4.7655  -1.4255   3.5579
+#>   -4.4424  -4.7940  11.2954   1.1835  -5.0625  -4.0952
+#>   17.4430  -3.5959  -1.6289  -1.0479   2.3471   1.1935
+#>    3.2738 -14.8813  -3.0527  -6.4569  -4.9056   6.5913
+#>   -2.6169  -2.9127  11.4907  -8.4904   2.7326  -3.8852
+#>   -6.4685  -4.4735   3.3633  -9.3217   3.8744   0.6654
+#>    6.3299   1.4495 -13.2675  -1.1175   0.7794   5.0476
+#>  -18.9639  11.4914  -9.6418  -9.9962   3.9545   1.2274
+#>  -13.2205   9.5347 -19.4671   9.4712   0.4172  -0.8135
+#>   -3.8090   3.6536   3.6461   0.9179  -0.0971  -2.7749
+#>    5.3839   9.3851  -6.3293  -4.7842   2.4043   3.9667
+#>   -5.7411  -0.1300  -8.2024  -6.3546  -5.5695   5.0834
+#>  -15.1551  -6.5435   4.6504   2.4342  -0.4790  -0.9245
+#>   -3.3217   7.6986   0.6394  -2.9978   0.9986   0.9748
+#>   15.0740   8.9770   0.3050  -2.1275   3.9056  -3.8107
+#>   -0.2836   8.7942  -0.9631  -9.8259   0.8564   1.2944
+#>   11.2496 -11.1414   2.7440  -4.4352  -0.4223  -0.1063
+#>   -5.9215   1.6966   6.6517  -6.6146   2.2959  -0.8255
+#>   -0.4415  12.8665  -2.4494   3.0863   0.6487  -5.0703
+#>   -2.5567  16.7094  -3.6701   1.3881  -6.7850   1.9591
+#>   27.4791   6.6245  -4.3833   4.1281   1.2326  -2.1893
+#>   13.5952   5.3210   5.1178  -2.3340   1.2214   2.3725
+#>   -1.0774   5.8084 -17.8315  -6.4149   8.1494   2.8319
 #> 
 #> (7,.,.) = 
-#>  Columns 1 to 8  -1.1510   3.3446  -4.6907  -4.0923  -9.0119  -1.8544 -11.5465  10.2266
-#>    2.1530 -10.3229 -13.4920 -10.5353  -7.1878   4.3803  -1.0222   1.2178
-#>   -0.0279   2.5698 -14.4998   0.8784  10.8162  -5.7057   7.7050  -9.5477
-#>   -4.2213  -5.9699  -0.4114  -2.1312   6.4255   7.8751  -3.3435   0.9462
-#>   -1.5995   8.4146  -2.0530   4.2347   3.0521  -4.4703  -8.0160  -9.5067
-#>    5.6889  -3.7806  -3.1521  -3.1988 -12.7691  -0.8307   9.4835 -12.2073
-#>    4.7189   1.5370  -4.2539   1.2660   7.0476  11.2019 -15.6511  -4.4649
-#>    5.5624   3.4705  -2.2476 -13.6931   9.5659  -6.6446  -3.9418  -8.1081
-#>   -7.8403  -4.5030   0.9928 -18.9725  -0.6898  -3.9355   6.6413 -12.1244
-#>   -2.3357   0.3881  -5.6700  -0.9241   3.7925 -16.9245   4.3452   0.9876
-#>   -0.0763   3.1268  -3.8460  -4.8019 -14.2220 -17.3226   7.2250  -0.6345
-#>   -2.4367   6.0777   4.7913 -12.2375  -6.7339   0.4671   9.0299   3.9063
-#>    0.6186  -2.9219  -2.7057  -0.7764  -2.6864  -7.8872   4.4441  10.2005
-#>   -0.5241  -2.6075  -1.9883  -0.3281   4.3225   6.6077   7.0158  -7.8009
-#>   -3.4496 -11.0826 -11.6018  -4.6082  25.9934 -16.3178 -11.9316  -8.0340
-#>    1.5328  -4.1684  -6.1294   4.3586   5.1483   0.1453  -6.2353  -2.7218
-#>    3.5766   6.2953   1.1756  -2.9681 -14.6940  -2.2812  -2.3563  -2.7273
-#>   -2.8772   0.3871   2.0151  -0.8548  10.4352 -24.0016   3.5732   3.9069
-#>   -4.8925  -2.5335   5.6550   5.4676 -15.1168 -12.5531   0.4016   9.9141
-#>   -1.1719  -0.4005  -3.1729   2.5641  -3.1845 -10.5227 -14.5495  -7.9963
-#>    0.0419   2.4212 -12.3028  10.4835  -0.1816   6.0758  -1.5183   7.0821
-#>   -0.2427  -4.8821   1.2099   2.4364   0.6832 -11.2929 -12.2063  -6.9524
-#>   -1.1091  -2.1504  -1.6837   5.4961   1.8894  -3.6969   7.8218  -4.1573
-#>   -2.8983  -1.7186   6.0291  -6.4366  -7.9518   0.8214 -14.5556   7.1323
-#>   -6.7137  -2.2434  -6.1717 -17.8276  -1.7784   9.0319   3.7935 -14.0787
-#>    2.4205  -0.9703   4.8595   9.9742   0.5819  -7.0176 -16.9618  -4.5669
-#>    0.8702  -2.8651   8.0752   1.4832  19.5654  -0.9157  -2.8576  -7.4101
-#>   -0.2374   6.4131   4.4602  -5.2020   4.4795  -0.2797   7.7103  -2.0048
-#>    0.3967  -5.9701   1.5705   4.2041   3.2667  12.0663  -2.2622 -15.7162
-#>    2.2880  -3.6944   5.7249 -23.6550   3.7084   8.1872   3.2680 -27.8372
-#>   -4.2591  -4.9057   4.0272   1.2348  -9.4757  -9.8123   1.3069   5.6520
-#>    1.8418  -9.5065  -8.6097   3.2440  -5.6186  -1.7234   4.7165   5.6540
-#>   -5.0394  -3.8266   1.3010  -4.1619 -13.8733  -5.5402   8.6290   6.4715
-#> 
-#> Columns 9 to 16   7.9968   3.1824   1.6528  -5.3501   2.8699  -6.5961   5.7740 -12.2310
-#>    0.7302  -4.2759   1.8779  -4.9163  -3.0430  -7.2339  11.5894  -6.2907
-#>   10.8629  17.9219  -1.4833  19.6404  -5.1833   3.2871  -3.9377   2.2159
-#>    3.5275  -4.0493   0.6487 -13.8285  -3.1606 -10.5714   8.2817  -5.4528
-#>    2.0357  -2.3497  -8.4855  -2.4340  -0.7470   0.5274   6.4336  11.4348
-#>    0.1513   0.0801   2.9735   9.0915   5.5422   6.9197   1.6482   4.7752
-#>    2.4132   6.8459  -3.9542  12.0085  -9.6651  -3.9433   7.8995   5.2293
-#>    5.2293   2.7419  20.1011  -7.3417   7.4816   2.0838  -1.1586   5.7965
-#>   -9.5940   0.7334   5.8397  11.7022   9.7054   9.1951  -2.6624  -2.2839
-#>   -5.4442  -7.4897   5.2764  -9.4087   3.7028  -4.2671  -6.9070  -0.4682
-#>   -5.3623  19.0221   7.5789 -11.4834  -3.0362  10.1072   9.7035 -12.8669
-#>   -6.9003   8.0125   1.4583  -3.7150   1.7329  17.1508  -2.5087  -7.2431
-#>   -9.6946  -9.9120  -0.9855  12.4596  -5.2041  -3.8580  -1.7943   0.2785
-#>   -5.7165  -4.5681   8.4780 -17.5609  -6.8587   0.9480  -0.0522   0.0123
-#>   -1.7467  11.0275  -7.7045 -11.1939  -0.5519  -0.3445  -1.7720 -18.8731
-#>   -3.1978  -5.6833   8.4781  12.3317   2.4258  -2.4807   7.0808  -4.3378
-#>  -13.6104   7.2438   4.5246   8.7591 -20.6504   1.5312   0.3517  12.4284
-#>   12.5804  -1.3469  16.2424  -1.5184 -10.0994   5.1005  -9.0594  11.6394
-#>   11.3501  -2.1988   2.7424  12.4550  15.6389   3.3059   6.8598   2.2757
-#>   11.3390  -3.5430  -2.9019  -0.1729  -2.5298  -6.6461   6.2712  -2.0054
-#>   -1.1417   9.1742  -3.3956  15.1536  15.2132   5.6723  -0.3860   9.3219
-#>    9.2356   9.3205   0.0930   5.9758 -12.2520   3.8998 -10.7852   8.9788
-#>    2.4628   3.8309  -6.3892  -0.0402  16.0499 -17.4290   4.0783 -12.4398
-#>   -4.7559  -7.5739 -16.7031  -4.0543 -18.5902  -1.6052  16.7595   1.5219
-#>   -4.2533  -5.4680  -2.1122  -4.1514   6.1736   0.0462   6.5320 -11.1835
-#>   -0.7842  -4.2493   7.3948  -7.3262   7.4715  -8.2523   4.5708  -4.9115
-#>   10.5087   5.8179 -25.7325 -25.3737  -9.0901   2.1340  -2.1222   1.4530
-#>   -2.2761  -6.2733  -7.1621  -6.9965  -1.7172   3.2972  10.9736  -4.0085
-#>   12.1618  -6.2517  -6.5980   2.9502   4.2635  -4.8189  -5.1003  -4.1420
-#>    3.9728 -11.8250  15.6391 -13.9557 -14.7237 -11.7873  -5.1508   4.1413
-#>    9.6275  -2.4659 -10.3977  11.2501   3.9619   1.8273   8.9549  -1.4237
-#>    1.5820  -7.8791   2.5443   8.2473   9.9800   4.2415  -4.5965  10.8618
-#>   10.0454 -13.3391  -6.2946  12.4930 -10.3633  -1.6016   2.9378  10.6521
-#> 
-#> Columns 17 to 24  -0.2017 -14.2036  10.9189  -6.3093  13.3421  -5.2021 -10.8571   5.6784
-#>   -3.7236   4.2107  11.5695   4.1625  12.5397   3.5341  -1.0868   3.1356
-#>   12.3779 -15.8945 -11.7514   6.5719   7.3708  12.7398  -4.8171 -10.1031
-#>   -6.3732  -3.1501  -0.9881   9.8138   7.9670  -6.8364   2.6885   6.0887
-#>  -17.8622  13.3396  -1.6779  -2.7707   9.6506  -6.1295 -11.8552  12.9470
-#>    5.1236   5.7030  -4.1452  -5.1171  -0.0168   8.9045   9.7367  14.3302
-#>   -9.8137   4.5833 -17.7446   3.9888   4.8591  21.3616  -9.9362  -9.1715
-#>   15.0867  -4.7968  -8.5767 -16.3302   0.7973  23.9033  -5.7737   1.1635
-#>   -8.7811  -3.0575   2.9939 -10.3879   9.7657   9.5343   3.0429  20.9961
-#>   10.0502  -8.2041   2.8456  11.3417  -2.4953   6.4162  16.7315  -4.6014
-#>   22.0820 -21.9904 -11.3016 -22.8116 -23.9028   3.8842  22.1187  -0.2680
-#>   -1.6472   1.6026  -7.8115  -3.6156  -3.2747  -7.2421   5.1116  -1.7513
-#>    5.3359   2.4528   0.3550   1.3230   7.9741  -1.6141   2.0854  12.3013
-#>   13.7491  -6.5297  11.0942  -6.7257  -3.1055 -16.7902   9.7864  -7.6694
-#>    8.0004   0.4933  32.4629   4.3232  -5.2497  -1.7230 -17.6472   2.9151
-#>    6.4136  -0.2973  -0.9971  -1.3520   3.8680  13.8084  -2.5657 -16.9238
-#>   -7.1824  -2.1916   4.0267   4.1113 -13.9599   9.5921   5.3595   3.1066
-#>   -5.2619  -8.1361  -7.5799  -2.7969  -0.9957 -13.9166   4.1316  -8.2160
-#>   11.3372  16.3689   8.8371  -7.3994  -2.0990   3.0968  10.8980  15.3794
-#>   -3.8021  10.2220  -1.2202  14.9014   3.2765   3.3371 -11.9771  -2.4916
-#>   -6.8762   4.6891   2.5929  -0.4768   9.1211  -9.8519  -9.6648 -11.6314
-#>    9.7460  -6.4685  -4.7942  21.3611  -3.0056  17.9657   1.6230   1.0961
-#>   11.9743  -0.9533  11.4663  -6.1106   2.7248   3.4914  -5.2735 -14.6978
-#>   -1.5166  -8.1734  -6.6487  -5.7655   2.7949  -8.5387  -3.7835  29.8814
-#>   -4.0876   4.7392   1.2242 -15.8987   7.1728  -9.1029  11.9292   8.2534
-#>   -8.9881  -4.8746  -9.5954  21.2173  10.0572  -1.6628   1.2289   5.8971
-#>    7.0142  -4.1670   5.6467  15.3771 -12.8819 -14.6869 -11.2740  -0.9921
-#>   -0.2972  -2.1018   6.0177   0.5221   1.4981  -6.0632  11.9915  -3.5423
-#>   -3.3258  10.5987  -5.7706  -3.9644  12.1994  -9.6323  -1.2799   8.8518
-#>   11.6141  -4.6315  -6.4642  -3.2025  -0.1698  17.3934   3.8328  13.8618
-#>   -2.5127  -4.7518   6.7672  -2.5339  -1.2967  -2.0569  -1.7243   6.5882
-#>  -16.7856   4.9965  -0.3747   5.9526   2.7530   9.1097   7.1798   2.2725
-#>   -5.8005  -2.7035  -3.8047  -0.9284   5.5126   2.2023   8.9701  -2.5880
-#> 
-#> Columns 25 to 32  -3.3465 -12.1665  -4.7266   3.3576  -3.1950  -2.8802  17.6598   4.7404
-#>  -10.6723 -15.0681   1.1665   5.6881  -4.7752  14.3467   4.8009   5.4295
-#>  -17.6142  -1.5378  -2.4484  -7.1934 -10.2677   1.5343  12.1779  -3.2596
-#>    6.8188   7.3524  -2.2905   4.4959   1.7009  -2.8864 -18.7653  -4.7065
-#>   16.6051   6.1646  -1.4842   0.5813   1.0516  11.0768 -12.5964  14.5830
-#>   16.7302  -7.0112   3.0949  -5.1746  22.0134  -4.1310   8.7279  24.2889
-#>  -10.7381  -3.4775   6.6372   6.4803  -3.1785  -5.5057  -2.6962   9.3441
-#>   -1.8757 -11.1710   9.7184  -5.2805  -8.8909  -5.4596   0.0158 -14.2830
-#>    1.7568  -7.5622  -1.3491   2.7702 -10.3292  -1.1636   2.6564   4.2344
-#>    0.7000   1.7821  14.5931  -9.0773   5.2029  14.6774   7.8485   2.8135
-#>    1.6623   1.7492   1.3100   2.6948 -13.5191  -4.6533  10.4787  15.1358
-#>    7.3247   4.5906  -4.4544   2.1359   4.3850  -3.7317  -1.3749  13.7357
-#>    0.8120  14.9744   2.7189  10.4039  -2.4289  12.5968   2.3494   2.9934
-#>    2.5820  -5.7150  -8.7422   5.8390  -3.7284  -3.0733   0.3706  -9.7306
-#>    4.4213 -16.0351  -3.1989   7.5213  -1.6001   0.9968  -4.5627   0.9700
-#>   -2.7907  -7.1278  -2.0649   4.2140   4.5857   5.0167  -4.1840 -13.4642
-#>   10.4783  -7.9112 -17.1973 -10.2099  -3.1797 -10.2205   1.3935   8.0176
-#>   -0.7982   2.0720  -6.4560  -8.0288  -6.7375   3.6489  -0.0936  -5.3324
-#>    4.8929   8.4467   5.4012   3.9065   0.2363  16.5182  -2.2986  -0.8222
-#>   -4.1151   8.6142   1.8951   6.1967  -1.7194  -2.3320   5.2856  -4.6069
-#>  -19.3944  -1.7963  -4.2656  -8.2972   2.7705   5.6570   6.9059   4.4057
-#>   -0.4050   3.9011  -2.3972  -3.1239 -17.2196  -5.5653  12.2193  -0.9968
-#>   -5.3592  -3.0987  -7.6986  -7.4511  -4.2069  -3.4384  -4.8321  12.0722
-#>   20.0403  19.5729  15.6391   6.2954   0.4169  -3.8861  12.2240 -12.1667
-#>    3.5800 -11.0232  14.0919  -0.2808  -7.4968 -12.0439  -4.0667  -9.9398
-#>   10.0156  11.4773   6.1229  -1.3124  -8.8166  -8.6302   1.0571 -11.2056
-#>    1.6166   6.8320   0.6153   6.1363  -8.2500 -11.0222  11.3847   1.4746
-#>   -0.7793  -0.4208  -0.2259  -6.4614   9.4015  -4.7347   2.8738  -6.9280
-#>   -1.6318 -12.4125   3.9271   7.4695  -5.8701 -11.2740  -3.0332   1.2802
-#>   28.0953   2.1393 -24.2158  -4.0061   7.0950  -8.6099   2.1225  -2.6146
-#>    3.7211   9.1366 -11.3096  -4.2825  -2.1420  -2.1255 -12.4218   9.6057
-#>   -1.3382  -5.4725  -1.8357   0.5935  -9.3416  -3.5055  -1.9799 -12.6972
-#>  -11.4266   8.7223   2.7088  -8.5663  -2.2837  -0.0327   6.6363   6.6428
-#> 
-#> Columns 33 to 40   6.7635   0.2676  -0.4159   9.0724  -7.2259  -4.9980  -5.8763   8.4493
-#>    4.4937   2.5311   1.9138   0.3078   1.9588  -3.5497  -1.1907   0.5561
-#>  -10.2568  -3.1322  11.5117  -4.3087  -4.6816  17.6844   8.7753   3.3748
-#>   -5.3756  -7.6747   2.2086   0.9428   3.6217   1.3725   7.7266   3.1351
-#>   14.0019  -4.5984  14.8477   0.3603  -5.1469   1.7272  16.7233 -14.4129
-#>   -5.7418 -11.3068   6.4387   4.5919  -6.4510  -4.8739   1.7584  13.3482
-#>   11.4781   1.1217   3.0755   7.8919   6.0445  -1.6499   9.7943 -12.8667
-#>    0.0311 -16.3330   5.5842   9.4925   2.7538  -3.7333  10.3775  -1.5636
-#>   -0.8140   3.1831   5.1449  -2.5414   6.0609  -1.0483  10.0245  -6.0115
-#>  -10.3292  -5.1663   4.7419  -2.7025 -10.1061   1.4991  -2.2201   3.2466
-#>   -7.9465 -14.7259   9.1298  -5.0068 -12.2494  12.7274   7.7615  -3.5029
-#>   10.7479  -8.9968   6.5722   3.0814  -1.4771  -5.4336   1.0488   6.0063
-#>   -6.4348   0.7132   4.3070  -8.7904   8.6975   1.1075  -2.0351  -5.8650
-#>   -4.7090  10.8575   2.0938  -4.5925  13.6765   5.7960 -10.5568  10.8240
-#>    4.3676   6.4377  -5.5873  -2.7393  -1.0691 -10.3165   7.6410  -2.0931
-#>   -7.1456   5.1314   9.0001  -1.0220   1.6024   7.0429 -20.3988  11.5544
-#>   -9.4779   8.7472   4.8977  -4.8484   1.3657  -1.3088  -2.8118  -0.2669
-#>   -1.8931  -0.8126   5.4710  -1.1423  11.9743   2.1789  -1.0370   2.7930
-#>   12.8539  -7.2875  -3.3640   4.9971   9.4708   5.3407   0.1027   3.0712
-#>    5.3685  13.3009  -0.5570  -1.6243  12.8898  -6.8893  -4.9286   5.7364
-#>   21.6231   7.1604   9.1344  -1.7378  -3.5171   1.3859  -0.7098  -8.5414
-#>   -8.8463  -5.5049  -3.2001  -1.2804   4.5352   1.3429   7.7656 -17.9811
-#>   -3.8347  -4.9080  -4.4590 -21.8495  -7.2955   6.4522  -7.3021  11.5973
-#>   -4.6035   6.9409   1.1151   7.4165   9.8849   3.9993  -3.3113  -7.1636
-#>    0.8386   3.7905 -14.2038  14.4263 -17.5220 -10.7280   8.3832 -17.0463
-#>    4.4654   5.3002   3.3907  -0.2470   3.5849  13.1716 -12.6129   0.5816
-#>   -0.3431   5.6900  -5.8313  -2.5538   3.6540   3.3511  -0.2439 -17.6064
-#>   -6.1437  -9.6946  -9.5116  -0.1163  -5.2854   0.0935   9.3468  10.3278
-#>  -19.4557  -1.8373   2.0779  -1.2904  -2.0416  -1.6558  11.6877  -1.9486
-#>  -12.2806 -12.7014 -19.7658  -2.3969   5.6329  -8.3815   2.2392   0.5735
-#>    1.0984  12.8486  -2.2627  -1.3134  12.2106   5.7082  -9.0247  -9.3327
-#>    7.9066  -3.8783 -10.4517 -15.9973  -3.3150  -6.4859 -11.3980  13.3674
-#>    3.4618   3.1344  -6.4312   3.0740  -4.2376  -2.6805  12.8825  -7.3342
-#> 
-#> Columns 41 to 48  -8.2446 -14.1066   7.4180   5.9619  -4.1717  -2.6857  -4.5057  -2.5795
-#>   -0.3471  14.8749   0.6670   5.6553 -10.9413   2.5896  -3.8698   4.2730
-#>    8.1345  -4.1757   3.7532   6.9645 -14.8808   1.3076  -9.9735  -9.2789
-#>   -5.8473  13.6811  -7.2703  -5.9008  -4.0263  -8.5845   3.9876   3.7570
-#>   11.3226   6.9115   4.8852  15.7906  -0.8682   8.0937   8.1273   5.7852
-#>   17.3444  -6.6595   6.7296   5.8521   9.9824  -1.5831   3.1717  -4.8209
-#>  -16.4776  10.5050   2.9079  -8.5284   2.8132 -16.2037   8.6382   0.7495
-#>  -23.1697  -3.9381 -13.5591   5.6852  -0.6891  -3.2555   8.2294  -8.8434
-#>    3.8975  10.2780 -12.6152   5.3798 -15.7025  -3.5821  10.9657  -7.8737
-#>   15.2915  -0.5255   1.8512  13.8459   2.2610   3.5411  -1.5479  -1.5811
-#>   -8.6926  -1.1466  -7.9095  -5.5129   3.6154  -5.7410 -18.9909   3.5314
-#>    0.0673   7.6417  -4.5786  -8.5491   2.4879  -8.6047 -16.9503   3.3925
-#>    6.1347  -0.2703   0.6698   2.9947 -18.5610   6.9394  14.4577   5.3886
-#>    6.0268  12.8496   4.6240 -11.4068   6.4780   1.9538 -23.4217   2.5520
-#>   -2.0623  -2.9128 -12.5985  18.0719  -3.6771   9.9461  14.1066  -9.3032
-#>   10.3474   0.1668  -9.4097   4.7920  -0.7543   0.7548   9.4571   7.3780
-#>    8.5396  -8.9624 -13.7220   6.6342  26.1063  -2.2718  12.9850 -12.8724
-#>    4.9769  -0.4265   1.8554   6.0727  -5.0484   3.0449 -11.8768 -16.2155
-#>    3.4199  11.6511  10.9404   5.5802   2.0642  -3.1614 -32.3605  -4.1784
-#>   -4.5305  -7.2991   9.0261   1.8573  -2.2770  17.8930  -1.7181  -2.7132
-#>   -5.0601   7.4311   6.1313   0.2610  -3.9911   9.5264  -9.0407   1.1036
-#>  -14.7860  -6.6657 -13.7383   0.9878  12.0193   9.0834  -0.4269  -0.0988
-#>    5.7226   1.2954   4.7022  12.1605  -8.3241 -11.2832   3.1006  -1.2237
-#>   -2.4029   4.1849  -4.9961  -8.9011 -17.0678 -17.9874  -4.4997  -1.9155
-#>  -12.5505  12.1856  -3.0765  -2.0942   0.7412  16.8770  -4.1587  -3.8859
-#>   -2.1647 -20.1726  -5.2616  -6.9617 -11.7598  13.4308   4.2533   1.5311
-#>   -5.6661  -1.5919 -12.8315   6.9896   2.5813  -7.4786  -2.9170  -0.3050
-#>   -6.6409  -7.6123  -2.1276  -1.4862  -4.7415  -1.6506 -15.4718   8.7143
-#>   -1.0364   8.5024 -13.1561  -5.9855  -8.4445  -3.3230  10.1388   2.4691
-#>  -13.9208  -7.7000 -11.4377   3.8244  -0.5969  -7.5562   7.3189   1.4867
-#>    9.7942  -4.7978   3.3158   9.0595  -9.0613   1.8362   3.8362   1.1675
-#>    1.0413   0.4598  13.2523 -17.2823   3.7930  -3.0105   2.8211  -2.3156
-#>   -2.8977   7.6209   9.4072  -5.2823  11.0450 -18.1353  -5.5680 -11.8955
-#> 
-#> Columns 49 to 54  -2.5928  -7.1004   6.6081   1.7156   5.4287   0.3410
-#>   13.2924   3.9968  -1.9209   8.0944   2.1424   3.7392
-#>    6.7899 -10.5478  -0.7002  -1.6988  -6.1060  -0.1987
-#>    7.4364  10.9952   3.2161   2.4305  -4.5592  -6.1626
-#>   -9.6062  -3.1601   0.2585   3.1764  -6.7485   1.2784
-#>   -3.3731   1.0436   4.3733  -4.6247   4.0325   2.0421
-#>   -4.4729  -0.6966   4.3825   8.6256  -3.5688  -8.9680
-#>    5.6425 -15.1321  -2.3662   0.1096   2.5494  -4.4745
-#>    1.9892  -0.5806  -9.2635   2.6563   5.5949   3.1520
-#>   10.0269   8.4024   7.9782  -0.0012  -3.3929  -4.4011
-#>   -3.4735   0.3273   1.0878   9.8868  12.0104   5.5299
-#>   -5.3334  -3.3646   2.0360   2.3895   6.0884  -1.9941
-#>   -8.2128   9.0170   6.6366   1.8423  -5.0797   2.4894
-#>   -1.3292  -2.9701   2.3141  -7.0520   0.3502  -4.1201
-#>    2.2892   0.8809   8.7419   8.5965  -1.6637   2.2177
-#>    0.5825  -2.6708  -1.1449   0.7359 -10.7928  -3.5240
-#>  -19.0753  -6.4115  -2.3333   0.4257   1.2642   9.3979
-#>    6.4100  -9.9433   0.5144  -0.9337   6.1790  -7.8487
-#>    7.3200  -6.5671  -8.7311   1.1663  -4.1018   1.6795
-#>   -4.5269   5.5922  10.9591  -1.6388   1.5133   5.6238
-#>   -3.2997  -5.9542  -4.4557  -3.3275   1.7602  -5.1058
-#>   -7.5134  -0.7009   5.4777  15.1911  -0.0899   3.9860
-#>    4.1985   1.1996   0.2030  -4.6156  -3.7712   0.6755
-#>    8.4728  19.2328   3.1302 -10.3603  -1.4621  -0.7851
-#>   13.4330  -4.9662   7.7158  -7.3931  -4.1295  -6.4962
-#>   -8.0304   8.5032  -9.3079  -5.2790   2.8347   2.4643
-#>    7.0726  22.4254   3.3912   5.3902   8.3798   1.6767
-#>   21.0437   3.2168  -6.0675  -4.4073   3.2828   0.1357
-#>   12.8162   6.2139 -10.2245  -6.9193  -3.1154   1.8901
-#>   -2.3231   0.6678   9.1898   6.6339   4.3099   5.7301
-#>   -5.9029  -2.0468  13.5363  -1.7687  -7.4800   2.0132
-#>    0.1635   7.0316  -4.7992 -14.0503   4.2662  -0.6726
-#>    4.5819   2.0853  11.7331   0.3567   9.5702  -6.5673
+#>  Columns 1 to 8  -2.2730   7.8593   0.9908  -3.0444   6.1337 -25.2705 -14.2599  -6.6618
+#>   -0.3577  -0.4505   5.1022   5.3875  -2.1536   0.6500  13.6969  -6.1476
+#>   -7.7850   4.0331  -1.2691   1.4275   6.7814   5.9597  -0.2232   1.6182
+#>    0.9961  -0.6523   5.4388  -8.5267  -2.7399   5.6863   5.8690  -4.3801
+#>   -0.1006   1.3807  -3.2668  -1.3749  -4.8580  18.8711   3.0766   6.0173
+#>   -2.9582  -6.0321  -9.7915  -4.0201 -13.1074   6.6917   0.7936  13.7708
+#>    0.8232   4.9478   5.4542  -0.5045  -1.0632   0.0558  13.6762 -16.8812
+#>   -2.4208  12.6910   7.1453   1.5003 -17.3859   0.7820 -11.5887  -9.7050
+#>   -3.2414   2.2264  -8.0102  -5.2580  -5.7959  -1.7368 -14.4322   0.4818
+#>   -2.2727   2.5185 -16.4346  -3.1027   0.0889   2.2516   6.2611   7.8665
+#>   -2.4878  -4.5203   1.3185  11.1101 -10.0181  -9.2895   0.5022   1.1197
+#>    6.2122  -7.3675 -11.0835   7.7296   3.0535  -2.0279   0.5060  18.6268
+#>   -4.0568   5.6577  -0.0712   2.9243  -0.0427   0.4657  -3.0712  11.9293
+#>    0.1447   0.9984   8.0982  -0.2298  -8.8112   7.6361   7.1027   2.4461
+#>    0.2856  -9.6297  -1.5844  -1.1736   2.9140   9.2969  -6.1753   3.1525
+#>   -4.7177  -4.4425  16.4893   3.4271  -2.3453  -4.3842   4.4446  13.8547
+#>   -7.1218   4.3438   7.7292  -7.1477  -4.4932  -7.2707  -1.5090   1.0484
+#>   -2.9824  -0.9287  17.3670  -8.1004  -5.2840   0.1963   8.2633  -8.9787
+#>   -8.7298  -0.9489  12.0986  11.0660   3.8593   2.7286  -4.6051   9.9778
+#>   -2.9336  -6.1185  -2.0629  -9.7719   3.1541 -11.3900  -8.7379  -4.4613
+#>    3.6517   2.1274   5.8082  -1.4068  -2.3069  -6.5642   7.3878   6.1230
+#>    4.1871  -0.9537  -1.0768  -3.6213  12.9236   0.1057  -5.3870   0.3658
+#>    4.5481  -6.4953  -5.5291  -8.7735   8.0858  -5.4139  -7.9247   3.1546
+#>    2.0853  -1.0929  -3.0532   1.5453  -6.4799   5.8163   8.7538  -0.7050
+#>    3.5095   9.1956  -0.5975   9.8420   0.4532  -7.5270   0.7032  -7.9548
+#>    5.6316   5.3247  -6.0735  -5.5060  -7.4822   8.9425   4.1544  11.2958
+#>   -1.8783   3.5574  10.7198   3.4882   0.4321 -10.3987  -8.3182   5.9989
+#>    5.0677  -4.5184   1.3690   8.1197  12.3121  -9.0753 -15.5622  -7.8918
+#>   -0.8347   4.5280 -14.8661  -1.0116  -1.9119   1.5700  -6.7108   7.0353
+#>   -3.6526  -0.5379 -16.4044   8.4148  -3.1234  -4.6541  -4.3963  -7.4973
+#>   -0.3159  -6.2344   9.0694  -3.7032  -1.9176  -7.5660  -9.6904   4.4349
+#>   -0.3463  -3.8474  -2.8252  -8.5979  -4.6760  12.8513   2.3690   6.9538
+#>   -9.8493   2.6900   7.4000 -20.6824   7.3917  -0.8028  16.2666   7.9293
+#> 
+#> Columns 9 to 16 -13.5713  -2.5760  -4.9393  -7.2169   8.6358  -8.5392  12.0442  -0.1647
+#>    0.4966  -0.1420  -0.1760  -1.6560  -6.8205  15.1826 -11.7602 -10.7671
+#>  -11.0952  -0.2855  -3.2001  -0.6889  -5.2732 -10.2177  -1.3504  -5.1594
+#>  -14.3325 -14.6651  -9.9135   4.6613  -6.1976 -12.1132   5.4452 -12.5988
+#>   -4.4341 -14.7879  -9.4919   6.7474 -14.7154  -4.2532  11.7998   5.9935
+#>   -4.5361  -3.1370  -1.7791  -5.8429  -2.1636  -0.1224   0.8331   7.3680
+#>    1.5726   1.0104   1.2858   6.1455 -13.8538  -4.9884   8.7709  -6.3910
+#>   -4.9205 -17.7037   4.4343  -9.0261  -9.5053  -2.1501   5.3031 -12.7681
+#>   -9.9058  -5.0338   3.1186   2.0879  -9.1277 -13.4672  -7.4933  -0.6072
+#>   18.3112  21.5954   0.5695  11.5204  10.4862  -2.5303  -6.6446 -15.3077
+#>  -11.1269   1.3703  -9.0922   1.0853   6.2027  -1.4406   4.4617  -9.4731
+#>   18.0525  12.2814  11.0963  12.1213  10.1177   1.2374  -4.2953   4.1410
+#>  -10.2644  16.0067  -5.7257  15.5210  13.6253  -3.8203  20.4329   1.3354
+#>   11.3955 -23.7706  -2.3947  -5.2285 -10.5981  12.1519  -5.9301 -20.3726
+#>    4.5903   0.4530   2.6283  -1.4012 -10.2537   2.5154 -11.9201  -6.2894
+#>   -5.3331   6.5027   4.1514  18.0472   4.4327  -1.1695  10.5339 -10.8364
+#>    8.5975   8.1958   6.0396   7.9658   2.8937  15.3728  14.1661   7.5359
+#>   -7.1388   1.4318   1.4532  -4.9481   8.0767  -7.1541  -8.7552  -0.0985
+#>    4.4449 -10.1757   7.1359  -7.1204   3.6736  19.4050   6.1920  13.4474
+#>  -14.4783  -4.1935  -4.1255  -2.2721  13.3225  -8.7610   0.0321   2.7674
+#>   -2.1757   4.6784  -8.5011   5.8105  -9.0521   5.8155  -7.1599 -18.4400
+#>  -11.1479   6.7380  -4.4160  -5.5327  26.1272 -17.7498   3.7817   2.6385
+#>    0.7380  -0.5406  10.3081  -1.0304  21.3875  -2.1121  -4.7990   6.0009
+#>   11.5466   9.0894  20.6480  -2.7185   1.3281   2.2790  -9.7400  -8.6737
+#>   -2.7586   2.9799 -11.2673  11.4694  -2.5877   1.8151   3.8847  -8.8739
+#>    4.1564   6.3242  -2.1553  -1.0679   4.1584   7.5325 -11.1966   1.7027
+#>    1.3052 -16.6512  13.6294   5.7100  -1.7764   1.3304   3.0484  -5.2398
+#>  -19.3760 -11.8877 -12.7904 -19.4183 -10.4183 -16.2829   3.9666   6.2953
+#>  -17.5562  21.2359 -22.1550   0.6797   0.5508  -2.0378 -11.4867  16.3799
+#>   -7.7626  10.7128 -20.0223  -4.6639  -8.0793   4.6555   0.3998   4.4482
+#>    3.0209  -8.8355  12.1543   8.1113   2.8692   2.4867   4.0020  -8.4796
+#>    3.4418 -10.1315  -0.7398 -12.2104 -16.3583  11.1954  -5.6027   5.5067
+#>  -21.0277   5.4547  -7.7433  -3.2208  -7.6423   7.8742  -5.3315  -6.3196
+#> 
+#> Columns 17 to 24  -0.8044   7.6940   0.5729  -0.8598 -14.8655   3.8065  11.0061   3.9517
+#>   -1.7344   5.4716   4.0392  -2.0350   6.4923  -2.8588  -7.0289  -7.6552
+#>   11.0347   6.9663   5.8463  -4.7432 -12.3731  -0.9869  17.3195  -4.6528
+#>    6.6301   5.3814  -7.7161  -6.6339 -10.3006  10.7296   4.2550  14.8271
+#>    3.1266 -10.4885  -4.2312   4.1358  16.9532  -7.5228  30.3083   5.1970
+#>  -16.4844  -1.4614   1.7639   9.5224  -8.6649  -5.9088  15.3286   1.8804
+#>   -9.3211  -3.9786  -4.1051  -5.3606  -4.2863  11.4523   2.0951 -10.8718
+#>   -1.0368  -7.5980  12.3542   2.6582  -8.6567  -3.8804   5.7039   9.5411
+#>   13.9589 -11.9419 -13.2947  -5.3372   4.0980  -4.4041   3.9258   2.8270
+#>   -5.5901   2.7887  -1.1067  -6.4275   8.2780   3.8845  -1.4423  -9.6241
+#>    7.1248  -4.9265  -2.4889  -2.0815   6.4172   2.7767  -0.7365   6.0730
+#>   14.3584  -4.9104  -6.0361  -0.3906   9.0165  -9.9866   0.2489  -9.3144
+#>   -9.4347  -9.8951  -7.2084  -1.0173  -0.3145   3.2613   3.6082   2.6699
+#>    9.7243   1.5669   7.1077  -5.8768  -7.0043   1.6861   8.2713  -1.4772
+#>   -2.0790   4.6980  -3.7801  -2.6672 -13.7883   9.6221   0.6044  10.3643
+#>  -20.7165 -11.5271   9.3761  11.6901 -12.1154  -9.9378   6.3912  -1.7335
+#>  -12.8631  -6.2434 -11.0467   3.9998   1.3249  -8.0850  -2.0281   9.0038
+#>  -16.9440  23.3617   5.7348   1.3088  -4.4689  12.6930   3.7079  -2.5876
+#>  -34.8685 -14.0574  12.3507  12.0336  -0.2335  -9.0516  -4.3334   3.1520
+#>   -6.0417   2.8619  -6.1685   4.0157  -7.7805   8.3510   7.5021   5.3347
+#>   -5.8736   6.3922   4.5812   0.4039   7.5982  -5.8035   2.2279  -7.2749
+#>  -14.7741  -1.2870 -10.9958   6.9673  13.8836 -17.1309  -9.8639   5.0669
+#>   -1.6298  -1.3950 -24.2018   4.4567  14.0468  12.1605 -18.0359 -14.0211
+#>    7.4055   7.1378   0.3266  -9.8586 -11.9663   7.1298  17.7259   0.9935
+#>   14.1076   3.3928 -13.1731 -17.8079   5.5772  12.3619  -4.5905  -3.9774
+#>   -1.0345   6.6008  -1.3905 -16.2688  -2.8131   4.9475   3.6227   4.9430
+#>  -16.0886 -17.8450  13.2029  -4.3504  -3.7232   3.4661   2.1836  -6.4093
+#>   -0.0434   6.5221   0.8334   6.0033   1.4263  -3.7186   0.0274  -9.0248
+#>  -12.5186   7.1847 -11.6790  -8.2131 -13.9474   3.4847  -3.2464  10.7246
+#>    6.4342   4.0180 -16.8509  15.4589   0.5730  15.0659  10.7185  17.9494
+#>   -0.8620   2.6107  -2.2791   3.9882  -2.0982   8.4218   5.7368  -4.1497
+#>    6.5447  -7.6214  -4.3677  -3.7800 -12.6336  18.7059   3.8062  -4.3111
+#>  -15.3726   7.4275  -0.0249 -11.3225  -0.0146  13.2671   9.2744  -1.1364
+#> 
+#> Columns 25 to 32  16.7140  12.5390  -3.8460  -5.1873  -8.2286  -4.2000  -2.4032   1.3773
+#>    4.5517  15.4829   3.5227   9.2409 -16.0337   2.1957 -16.4219  -0.5252
+#>  -14.1090   0.1770  -9.4900  -1.2289   7.8689  -1.1464  10.1450  -1.6497
+#>   11.8066  -5.3717   3.8524   6.5927   1.7388 -12.0818  -0.5250  -0.2491
+#>   -8.7325   3.3834 -13.9050  12.1629  -5.4468   1.6252 -12.4929   2.9964
+#>   12.0322  -8.7257   3.1479   8.0130 -11.4440  -0.8196 -12.3785  -7.9827
+#>    4.0670  27.0924   8.9623   8.0875  -9.4515  -9.7726 -14.8642  -0.9397
+#>    0.6661  -0.0387   6.8619  -3.6456  -4.6241  -8.6900  -6.5807 -12.8149
+#>   -1.0908 -13.1932   0.5457  -6.3462  -6.4273   2.3103   6.1080  -6.3447
+#>   -8.2056   8.6274 -15.0308 -14.4721   2.0131  -3.5753   2.0280   7.4207
+#>   11.3355   2.6975 -15.5221  -8.9064  -5.1234  -4.9808  -5.6037  -4.9280
+#>   -1.0172   2.7507   4.1338  -3.2062  10.9898  -2.0258  10.8534   2.2687
+#>   -6.5802  -0.1283  -7.2080  -0.7063  14.2631  -7.1419  -0.9013  -2.5769
+#>    9.6233 -17.0679  -2.8874  21.5422 -12.9105   6.9918  -8.3253 -17.6162
+#>    3.1537   8.3606   6.5124   6.2343  -4.0419  19.4723   6.3941  -3.6153
+#>   -2.4588  -2.0989  -0.8255  -8.3086   9.0995  -0.5779 -16.9909  10.4875
+#>   -0.5940   8.9188   0.5501  -8.9874   0.9578   7.5963  -0.4968  -6.8239
+#>  -21.5109   2.0689  10.2137   3.5746  10.4391  -1.1877 -10.5458  -0.5157
+#>    9.3930 -10.2973  10.3239 -15.1966   5.5297  -6.0198   4.9566  -7.3151
+#>   -2.8751   5.4660  -0.7704  -2.8695 -18.3324  -6.0660   6.8699  -5.0511
+#>  -14.2165 -13.0150   5.1183  11.2955  19.5342   1.3691  -2.7344  -7.9917
+#>   -0.8479  -2.5890   4.3555 -17.1836  -1.9581   1.7468  -5.1517  -4.0209
+#>  -10.1379   9.0568  -0.1629  -3.3383   4.6196   8.1972   0.5770   9.1230
+#>  -15.2504   7.6431   1.3029  -3.4058  -3.1297   4.1920   4.2794  -0.6498
+#>    7.7819  13.0803 -10.7424   3.5486   7.3103  -0.8610  -5.8997   1.0007
+#>   -7.1900  -0.7159  10.6423  -2.0924  13.7340   2.4895  -9.7194   0.9214
+#>   24.0935   3.6397  -5.3558  -9.3220   1.7999 -14.4170  -2.9897  -5.7092
+#>    7.5935   9.3421   3.6951   6.6828  -5.5483  -2.1949  -4.2317  -0.7691
+#>   12.5178   0.1022  18.3640  -7.1903  -3.9141 -10.1061 -17.7475  -2.2567
+#>   -7.2339   2.7263  -5.4803  -5.0212 -12.6820  10.8720  12.0039   3.1567
+#>   -7.2658   7.6804   1.9481  12.0319  14.1453   0.5688  12.4939   7.6836
+#>   15.0504  -1.9957   0.6219  14.2578  -1.5269  20.5701  -4.8401  18.0711
+#>   -6.8896 -10.9830   7.8463   0.5201   7.1333   1.6838  -2.4744  -1.2777
+#> 
+#> Columns 33 to 40  11.1891   2.3612   4.9869   7.1164  -6.1635   7.9671  -8.8226 -20.0022
+#>   -3.7496   4.1229  -7.1114  -6.0235  -7.9529  -5.6305 -11.9795   5.7476
+#>   16.2346  -9.1481  -5.1870  -0.7856  -3.0979  10.1354  18.5026  -2.2562
+#>   14.7152  -6.8124  -1.4971  -3.8059   5.1113  -4.4989  -2.1806   0.1147
+#>    9.5490   8.4203   7.4613   3.4967   1.4412 -10.0005 -10.3874   0.0541
+#>   -1.1241   8.5744  13.9264  -1.6042  -6.0417   7.2974   4.2899  10.3889
+#>   -2.1472  -3.6327  -2.5070  -5.4610   7.5473   4.8237 -16.9401   9.5811
+#>   -3.5434 -10.1649 -11.0285  -1.5163   2.9661   0.8941   0.6125 -11.9428
+#>    4.4512  -1.4903   2.2263   3.6980  -4.8034   9.9793  15.5765  -5.5659
+#>   11.7977  -6.4713   3.8184  -1.4604  -5.3925  -2.9009   2.5318   7.9008
+#>   -7.1032 -12.1406  -1.3852   0.5985  15.2772   4.0568  -1.4362  -6.7224
+#>    2.0491  -8.9185  11.5803  -8.8514   9.4828   6.5328   5.7315  -3.7928
+#>   18.5637  -6.3672   8.6959  -8.4857  -6.9228   2.5588  -3.8137   5.3303
+#>    3.1835   7.2718   4.8441  -5.8965   4.7756   2.0748   5.2194 -11.6351
+#>   -8.4733  -6.0291  -3.4255  -0.8344   3.8360   5.8734   1.9755  -0.3546
+#>   -2.7398  18.5369 -13.5673   3.6614   9.9130   5.3302  -1.6828  -0.9912
+#>    6.1713   1.7696   6.0380   8.1149   3.7931  -3.6109   3.0379   3.2310
+#>    7.9317  10.1544 -24.9884  -2.1906   8.9362   4.2951  -0.3964 -12.8506
+#>   -4.7451  -1.6990   2.8409 -10.0277  -0.0674  -6.8846   2.5878 -14.7227
+#>   14.1526  -1.7900  11.8586 -13.1311  -4.5975   6.3489  -1.4287   1.1265
+#>   -7.1040 -16.4280   5.8764   1.3607   9.6846  -5.4847  -8.9554  -9.4251
+#>   17.3347   4.6266   6.6357  11.3441  -2.3738  -5.8814  -7.4835  10.5533
+#>    3.7688   7.3121  -7.5062  19.9391  -3.7574   2.1544   6.8715   2.5047
+#>   14.9763   4.6681  -1.9971  -2.2574  14.3317  -1.9973  -9.5711   2.0809
+#>    1.7701 -15.1203 -11.3706  -1.7282   1.2281   1.6709   1.1860   0.4642
+#>   21.2303   3.5344   0.1070   8.0554   6.4689   9.8879  -7.5437  -2.9940
+#>   -1.7553  -0.0390  -3.6977  12.9958  -1.8626 -16.0140  -6.0981  -2.0215
+#>  -11.4318  -7.7619 -12.3137 -10.7971  -5.4202  -0.5294  -4.9391 -11.0302
+#>    5.9739 -13.7469   8.6302 -14.3386   1.0901   2.0474  -2.7627  -6.5631
+#>    9.6283  -2.5697   5.4815 -16.6955   0.4443   9.1330   7.0235  -2.3131
+#>   -7.8267  -0.7191 -11.4275  -1.7554   3.9102   1.4224   2.6154  -4.3396
+#>  -15.6128  -1.4557 -13.5651  13.0218   2.2950  -3.2419   9.3395   3.5658
+#>   11.1861   2.0958  -4.3153   1.2679  -4.5878  -8.0363  -0.5200  14.5533
+#> 
+#> Columns 41 to 48  -0.9108   2.5736  23.6068  17.8971  -9.8998  -0.3723   8.5228  -6.2444
+#>   -4.7507   1.0621 -21.3736  -2.5079  -8.7703  12.8073  -2.5485  -2.0069
+#>    7.6028 -17.9349   9.7133  17.6869  -8.2228   6.3778   1.9821   1.6661
+#>  -27.2213  -5.6466  -2.0318  -1.8861   3.6299 -13.7714  -0.1735  -5.2628
+#>   -7.9302 -17.4987  -4.8797   3.4711  -3.8896 -12.7326   5.5984  26.1975
+#>   16.2949   0.5148   6.2633  -7.7494  -5.0770   3.9893   7.6127   5.1836
+#>  -16.1897 -13.8911 -14.8253   2.9741  -2.3398 -12.4067   0.7181  -8.8012
+#>   -9.0352  -2.2348  -0.0923   8.4101  12.7918  -3.3611 -13.4531   5.9796
+#>   -2.1294   1.2724   7.9003  -1.9198  -2.3139  -0.0962  -4.9995  -8.6472
+#>    8.0456   6.0421  -2.1854  21.4954  -7.7784   3.7823   9.5822  -9.6594
+#>  -16.1417  -3.0719 -14.7597   2.9943   5.5766  -3.6289   1.7907  -0.9100
+#>   -7.0368 -10.0777   4.1014  -3.7436 -11.2970  -4.4734   4.0854  13.2371
+#>    3.4379  -0.3793  -0.5829   1.4768   8.8789  -9.8077  -1.7780  -0.9594
+#>  -11.0606 -12.8748  -6.1057 -17.3899   8.0851   8.2202  -5.3422   2.0588
+#>    3.0444  -0.6357  -2.7121 -24.9212 -10.1625  -9.9997  -1.3370  -6.9828
+#>    4.3923  -9.9107  -2.8040   2.8179  10.3227 -11.9491   3.3004  -9.7261
+#>    1.1471  -4.0315  -6.8643  12.3258  12.8620  -6.0243   5.0360   2.7953
+#>   -7.1764 -18.0498 -14.8368  -7.4712  19.0543 -21.6376   1.9780 -11.5958
+#>   -2.2876  -2.9053 -10.4628   9.8194   1.4327   9.7972  -5.3808  -7.2988
+#>    9.2306   6.3700   1.8751   3.0940   9.2425   0.1972  -7.2674  -3.6516
+#>   -8.5610  30.9970   6.5833  -3.4096  -8.0928   3.4764  -0.8839 -13.3513
+#>    3.1135   6.0214   4.6994   3.2300  11.5337   9.8493  -7.4466  12.6573
+#>   -1.3111   4.1462  18.8419 -13.5653  15.4664  -8.1628   8.1597  -5.4016
+#>   -0.2862   8.9065 -14.0087   6.7767   1.4279  -1.3798   1.2166   3.1805
+#>  -21.7577   2.1208  21.7048   3.0792 -22.3270   9.4551   2.1030 -13.4714
+#>   -2.2481   7.2565  20.6121   2.2482 -12.0235  14.7799  -3.3109  -0.1584
+#>   -5.9734 -14.8000   9.7602   9.3335 -19.9157 -12.0126   3.0379   7.8461
+#>  -15.8157  -6.9058   1.7340 -16.3998  -8.3330   1.1420   0.3805   4.5508
+#>    5.9457  -9.3403   9.2522   7.0673   5.0233  -2.6928 -13.2402   9.3040
+#>    4.5461   5.8450  -3.5006  17.0313   0.8917   4.4630   7.0531 -12.1679
+#>   11.5264  20.0394   6.1846   9.4172   9.2527 -16.1174  12.3933 -29.4922
+#>   15.4788  -2.2228  17.2747 -28.9027   3.3319  -0.0649   3.0690  -2.0674
+#>   12.0413   8.4730 -28.0324   8.4682   2.3961  -2.8019   2.9341 -10.2863
+#> 
+#> Columns 49 to 54   8.7118  -5.3373  -0.8911  -2.9775  17.7174   7.0686
+#>    1.4402  -1.5954  13.7874  -5.3311   5.6940 -10.3293
+#>   -9.5794  -8.2956   1.0855  -0.2443   4.2143  -4.9750
+#>    2.4641   0.5950  -0.5537   5.2224   4.7215   6.1185
+#>    3.8407   6.2760  -1.4256  -5.4305   8.3171   0.1903
+#>    6.4141  -0.9386  -9.7720   4.4181   2.0147   0.6292
+#>   -0.1498   3.9477  11.1233  -0.2984  -0.6797 -13.7208
+#>  -14.4884  11.3525 -15.1903   0.3645  -3.9955  -1.9401
+#>    3.8658  -4.2446  -2.0747   5.3005   2.9922  -1.7759
+#>   -7.8709  16.7344  17.4355  -7.7345  -0.2476  -8.2806
+#>   -8.7224  -6.3422   1.5312   1.6095   3.0019   6.1128
+#>  -11.5291  -4.8024  -3.1520  14.2362   3.9954  -2.6066
+#>   -6.4523  -3.2470  -0.7550  -0.7630   1.7560   0.8667
+#>   -4.1342   3.8586  -7.7788  -3.0716   0.5799  -1.4901
+#>   -1.6157   4.0802  -2.3738  -5.2966   4.4962  -6.2342
+#>   -7.6457  -8.5709   2.2574   3.0248   3.0652  -6.4177
+#>   -0.7423  -4.3019   9.1465  -5.4589  10.4766  -3.6808
+#>   -7.9415  11.0033  17.3416   1.2805  -5.0901  -0.0253
+#>   -1.5102  -1.4065  -6.3656   0.5411  -3.6698  -3.2818
+#>    9.0264 -14.8046  -5.9661  -3.1890  -4.5762   4.8290
+#>  -12.7225   2.6138   0.5408 -13.2594   5.0941  -7.5691
+#>    6.1799  -0.3532  15.1285 -10.7989   6.8885   6.0073
+#>    0.1308 -10.8585   3.4953  -1.3139  -3.1275  -0.0590
+#>    3.6928   0.1863   6.3759  -1.3974   4.2864  -5.7240
+#>   -7.3965  -0.4803   4.7945  -0.2172   1.3750  -3.5740
+#>    6.2396   7.8088   6.8077   2.2023  -2.3868  -0.2787
+#>   -7.1989 -12.9299 -10.0476   0.0304   6.3930   0.3320
+#>   16.1494  -8.7735 -11.4019 -15.4966  -2.4481   0.2085
+#>   -6.2417   4.2229 -14.9676 -10.2305  -5.3899   7.4819
+#>    6.4306  -6.8505  -8.0652   1.1807   6.5301   3.2707
+#>    9.9707  -6.9622 -16.6376  -3.0652   1.6486  -5.8388
+#>   -1.9141  -6.4129   1.6620 -16.3314   2.3941  -7.8276
+#>    0.3518 -19.5827   7.9812 -11.8222   8.8929   0.0334
 #> 
 #> (8,.,.) = 
-#>  Columns 1 to 8  -2.7529   0.8192  -7.1341  -6.5282  11.1871   4.4511 -21.7879  -1.4713
-#>   -3.1048  -7.8195  -0.2788  -6.0910   1.3162  -9.9899 -16.0470  -0.5912
-#>   -2.9547  -5.6337   4.0594   6.9994   6.0116   4.2885  12.9769   0.2763
-#>   11.0393  -3.4285  -3.4719   3.9043   6.3892  -3.4250  14.3331  -7.9237
-#>    9.0916   1.6536  -3.8375  16.7278   3.2863   8.3293   5.2747   5.0463
-#>   13.7368  -2.6661   1.8664   3.0571   4.3028   0.9034 -16.2985   1.4940
-#>   -8.0692  -9.9216   5.6677  -6.9962 -15.2937  -7.3063  15.3760   3.5604
-#>   -9.0523   8.6461  -0.4937   3.0509   5.6696   2.0694  -5.2931   3.3585
-#>    2.3426  -9.7887 -13.0725  -3.5629  -4.1340  -4.8556  -3.6626   4.4876
-#>    8.1377  -3.9488  -4.9067  -6.2834   3.1234  -3.5747   4.6790  -8.5427
-#>   -3.0277   0.6315   8.4442   7.6313   4.6818  -7.2317 -26.7969  -3.1051
-#>    9.9994   4.5279   2.0965  10.3779   7.8810  -0.4459  16.5216  11.3792
-#>   -2.8980  -9.2737  -1.2585   1.7333   6.8224 -17.7776 -10.4174   3.9152
-#>   -3.9673  -0.4021  -2.0732  12.1576 -10.3663   1.5133   4.0214   4.7282
-#>    7.0511   0.4957   4.1995  -5.9918   0.0764 -12.3378  14.4978  -5.6393
-#>    0.2674  -7.7723   6.0367  -7.7422  -0.1882  -5.4300 -14.0436  12.4318
-#>    0.9067 -13.9798  15.4094   6.1903   8.0087 -11.8253  -4.7356  12.0953
-#>    1.1894   4.2628  -1.1142   2.2573  -7.1488 -11.9867  -3.1376  -4.5180
-#>   -1.3142  -6.1627 -11.0111 -10.1632   5.6541  -1.5477 -22.9148  -1.1882
-#>   -3.0296   4.3970   1.8265  -2.7886   0.0352  16.6297  15.4871   1.1961
-#>   -5.0692   1.5222  -4.8863   6.1280  -4.2142   4.1118  10.0311  -7.1992
-#>   -2.7119   0.2040  -0.3274  -2.1352  10.6831   2.7113   2.9998   2.2773
-#>    4.0870  -8.3630   1.3345   5.7558   8.6777   6.6120   4.7221  12.0395
-#>   -3.0057 -11.6187  -1.4478  -0.9246   3.5884  -3.1255   4.0088  -2.2778
-#>    0.9240   4.9592   7.4613   6.5776  -2.6666  14.3401  17.3487  -2.4898
-#>   -2.3642  -2.1005  -9.3355  -9.3243 -10.9523   6.9057   7.6943  -7.1152
-#>   -3.1120  -1.7855  -4.4008 -13.6525  -6.0453  -0.5619   8.5483 -13.9939
-#>    2.8317   2.0840   1.2822  -0.4408  -2.4619  14.0995  -2.7047 -13.0475
-#>    7.0575  -5.8074  -4.1218   1.6133  -3.2632  13.8478  -4.2430  -8.0296
-#>   -0.8498   5.2949   6.6301  -0.3180  12.0754  -5.2642 -18.0740  -1.4874
-#>    1.4737  -7.5670   5.6318   2.6489  -0.7847  -5.5680   3.2172  -1.7407
-#>   -5.1628   8.3471  -3.3556   6.5424  -9.1413  10.8724  14.4525  -8.6271
-#>    6.2300   0.4581  -3.5530   2.6457  -8.0018   3.6802  -4.7906  -3.5306
-#> 
-#> Columns 9 to 16   2.6059   0.2068  18.7862   2.4858   5.5476   0.0574   8.6758  10.5389
-#>   -5.0830  10.2386  11.1307   0.6531   6.4362  -3.3191   0.2904  -6.1147
-#>   15.8550  17.1665  -3.4921   3.4039 -17.2131   1.2187  -0.2304  -0.9924
-#>   -9.5623   2.8004  -8.9082   2.4214  -2.3780 -14.2012  -8.7973   4.5452
-#>   -2.8062  -6.0448 -13.6052   3.1826 -10.9059 -17.3714   4.2268   1.8483
-#>  -27.0679  -5.9070   5.8950   0.8538  -6.8051   7.9535  -3.5834  13.9089
-#>   -9.0877   3.0419   6.0590   5.6717  -0.5998  -3.2637  -8.2851   9.2411
-#>  -14.4284   7.5758  -1.0864 -11.7635   2.7582 -10.3796 -12.3267   6.0934
-#>   -7.5945  12.7614  12.6524  -0.1052   4.6192   5.3419   6.2143   7.6067
-#>   -6.7993   5.3109 -10.7815  15.6736  -7.0126   1.1654 -10.1354   1.9374
-#>    2.6210  18.7662   2.4249 -17.9167  -8.3412   1.4214  10.6310   1.9863
-#>   -2.9500   3.1937   7.4129   3.2962  14.4996   0.4553  -0.1157   5.2943
-#>    5.7296  -1.6762   2.8099  -2.4858  -2.4683  -5.6878  -6.6655   3.5194
-#>   -8.0123   1.1176  -3.9421   1.1844   5.9917  -4.3421  -8.6695  -1.2122
-#>  -16.6057   5.8500  -2.5749  -3.0693  14.9980  -1.7176   6.8648  -9.9549
-#>    4.9229 -12.4192  -3.1418 -18.3803   3.1458  -5.5795  -6.2845   2.4021
-#>   -9.7384  -9.2445 -14.0000  -6.0651   1.8076   7.7087   3.2962  13.9044
-#>   -5.8195  12.8527  -4.2801  -4.9660   2.3438  18.3197  -7.5903   2.4078
-#>   -3.8759  -1.0662  10.1111   2.1849   3.8999   0.2588  -8.7367 -11.6584
-#>    9.5571 -12.2928  -2.4735  10.2014 -12.1671   1.6716  -0.6683  -6.9441
-#>    5.0652  10.4032 -11.4672   3.9434   0.9181  -3.1731  -6.7213   3.8030
-#>    5.0064   4.5650   7.9435  -7.7492   5.5196   8.4759  -6.5715   2.1932
-#>   -6.2972   2.7103  10.7781   7.4434 -12.7144  -2.8269   2.7386  -7.9616
-#>   12.6700  25.3529  21.4739  -0.1103  -7.4641   3.7377   0.1204  10.8745
-#>   -6.6823  19.7743  13.1868  15.4460   1.7699   1.6169  14.1696 -10.2873
-#>   -5.2075 -23.1905   4.7842  -5.2366  -8.8474   9.9627   0.4570  -7.2879
-#>   10.5321  15.3019  10.4495  12.4939 -11.4734  -2.0136  16.9252  -5.5005
-#>   -3.2051   0.4821  -8.7248   2.0482 -11.4909   8.4767  -4.1454  -6.0090
-#>   -2.6472   7.5251   8.0112  -1.4287  -3.5853   0.8019   6.6144  -6.8130
-#>  -16.1993  -7.3859  14.0124   8.9923   5.5679  -0.9503   8.3413   3.4190
-#>   -0.9727 -14.9218  -5.1464   0.0045   0.5696  -5.9744   2.7713  -0.7118
-#>    4.2479   6.4606  -0.9434  -4.8116  -8.2512   6.2986   3.6989  -2.9903
-#>   12.5828  15.4578  -2.0097   7.4678  18.1141   2.4997  -9.8066   7.8747
-#> 
-#> Columns 17 to 24  -9.6185  -5.2363  -6.4971   2.9259   5.3478  -1.3745 -11.1241   5.3987
-#>    5.0377 -10.5867 -14.1546   1.7548 -25.1766  -3.7241  14.0057   1.3400
-#>    7.3620  -7.2763  -4.2834  -3.2599 -12.6782  -1.8999   0.6878   0.6934
-#>    3.7993  -3.0477  -6.4918  -2.9106  12.9166 -10.5429  19.3072  -3.5060
-#>    1.1725  -6.3711   2.1429   5.8187   0.5333   1.2548  12.6958  -7.0119
-#>    7.1364   1.7381  -5.0821   2.3576  -1.2286  11.1923 -10.4377 -13.2910
-#>  -12.4025   1.6296   7.5249 -17.2821 -11.1699   6.4293 -10.1163  -0.0761
-#>   -1.7264   8.4885   4.0593   4.8025  11.4839  -7.3611   1.4249  -6.4636
-#>    3.5115   6.1677  -3.1524   3.9877  -9.3612   0.2320  -6.9287   0.9210
-#>    1.7669  -1.4957  -1.1088  -5.6035  12.9229  -4.6847  -8.2598  -2.7438
-#>    4.8460  -4.1944   5.4778  14.8436  -2.7482   1.0736  -4.6544   6.0900
-#>   16.3952  -1.7126  -2.9432  -0.8184   6.0616   2.6004   3.9056 -10.1400
-#>   -4.8629  -2.0349  -4.2152  11.5647 -15.5042  -6.9312   5.8201   7.9488
-#>    2.3042 -10.7469  -1.8377  -7.0127 -10.5825  -5.2462   5.5303   1.1535
-#>  -14.1828  -6.2652 -12.6412   7.8766   3.5697  -0.5576  -9.0542   6.4479
-#>   -1.8594 -12.0191   1.1954  -3.4492   2.6362  -1.5502   1.0217  11.0366
-#>   -2.3127 -12.8279   1.9349   5.3822  -0.3430  -8.6638  -9.1417  -1.8167
-#>    0.4679  -2.3985  -4.0016   7.9150   3.4129  -0.2558  -6.5079  -0.5395
-#>   13.7021  14.7185  -0.9215  -2.1865  -5.6755   3.5487  -1.3128   0.8928
-#>   -5.6018   0.6378   5.0673 -22.4938 -12.6288  -2.9217   6.7909  -6.8253
-#>   -5.8383  -3.5588  -1.0980  -5.1938  -6.0518   2.1598   3.9049  -2.2670
-#>    8.3973  -2.8062   3.6439  -7.4556   7.8676   0.9450  -9.9529  19.5965
-#>   -6.7550  -6.3885  -4.9750  -1.8393   1.2241   6.1629  10.4999  -8.8812
-#>    1.9692  -6.6736  -4.2791  -6.9886  -0.1644  -3.6476  -1.0289  19.9012
-#>   14.5276   6.4475   3.3525  12.8286  -7.6074  20.2843  -3.5258   6.6068
-#>    1.7833  13.3814   0.1390  -5.2190   8.0605  -5.4690   0.5839 -14.1667
-#>  -10.1046   3.2568   4.2568  -0.8551   9.1603  -7.0637   8.3551  19.1710
-#>    5.4195   3.1397   0.0737   2.5032   8.6055   6.7515  -1.5177  -0.5559
-#>   -7.0993   0.6425   1.4658  -0.1229  -0.3991  -2.2585   6.4073   8.8977
-#>    5.6671   3.0705   3.2044   3.6582  31.1265 -14.4046   2.2149  -6.1952
-#>   -5.4701 -14.7604   8.3713   9.8865 -24.9880  11.3028   1.5293   4.6248
-#>    0.1451   5.1354  -3.6193  -6.1743  28.6728  -9.9950  10.5469  -1.0057
-#>   -0.6977  -5.1401   1.7695   1.4553  -2.8253  -2.7118   2.4154  24.9040
-#> 
-#> Columns 25 to 32  -7.1700  11.6366   7.2286   8.8610  -3.0368  -3.5862   7.6909  15.8158
-#>    7.5102  10.8181   2.6158  19.5021  -4.5559  -1.4966  -5.9985  -7.0599
-#>    3.9965  -4.6997  -5.6602 -11.8554  18.1068  -2.0255 -15.3478  -3.8725
-#>   15.7267  -8.1247   1.8175  -6.2360  -1.4675  -4.8595   5.2477 -14.5281
-#>   -8.9276   5.8084  10.6656 -21.9621   7.2696  -9.8578  10.2391  -8.6043
-#>   -5.5958   4.3886   3.6833  -3.8290   1.8972   0.2591  -2.6312   9.3042
-#>   16.8863   6.3746 -14.4350   5.9461  -7.5033   5.9999 -13.7993  -2.1955
-#>    4.5430  -1.1801  -5.7656   3.3432   0.2830  31.4724 -10.8935   5.0580
-#>   -2.5888  -4.2127  28.1604   6.3434 -21.9281  -1.4052  10.1537   5.8521
-#>   11.1758  -4.5972  -3.2592  -2.2157  11.2609  -4.3342   8.0417   4.7088
-#>  -10.4686  -9.2667  -5.1960   7.8169  14.3069  -7.7104  19.9064  16.9445
-#>   -1.9276 -11.7381  -1.7933 -17.9961   5.6561  -0.1927  21.0128   0.4839
-#>   -0.4672   8.2286  -2.6962  -1.3400  -7.7344  -9.1882  10.3880  -0.9670
-#>    8.2809   9.7850   1.1968   8.3133  16.6780  -8.3523  -2.4638   0.1139
-#>   11.1578   3.0020   6.4177  -8.3652  -7.9631  17.1988   8.0300   8.5071
-#>   -0.0551   5.9983   5.7762   2.2087   9.6836  -9.9556   6.5206  -2.1632
-#>   -3.6828   6.6844  19.4924  15.1757   4.4342  -9.5140  17.7458  17.2699
-#>    0.4415 -11.3196   3.8629   2.5370  15.5115  -5.7184   6.3163  13.6840
-#>   -1.1127   1.6238  -3.6081  -1.9011   3.7526  -3.0333  -2.1946 -10.1399
-#>   10.7830  -5.8261  -6.1259 -10.7859  -0.4588  11.1663  -6.9900  -4.2215
-#>   -1.4840   4.4005  -0.6102  -6.3680  17.9664   8.3212 -18.3523   4.9031
-#>    3.2974 -10.7969  -0.3268  -9.9017   7.4522   6.7771  -3.5755   3.8225
-#>    7.3126  -3.5130  -7.6412  -2.8489  -1.1771  -8.6129  22.5971 -16.1773
-#>   -0.3023 -13.9086 -10.5268   5.9208 -12.7641  -0.8612 -13.7925   4.4451
-#>   13.9948  -7.1070   4.2837 -14.2782 -14.0873  22.4218  -6.5989  -5.5982
-#>   -3.4005  -8.0452  -0.1647   3.6283   8.3792  -6.1744   3.5500   0.9821
-#>    5.4092 -11.6146 -20.8671   3.7039   5.2048  -0.4829  -6.9013  13.6288
-#>   -6.7347 -13.6019   2.6732 -11.2069  -1.1424   5.3381 -22.5442  -1.3596
-#>    4.3021  -3.2848   6.6782 -12.2661   1.3848  -8.1107  -0.6805 -17.9858
-#>   17.8665   6.8305  15.7989  -0.8882   0.4430  14.3679   9.7999  -4.2770
-#>  -13.5004   3.0550   9.7904  12.1239  -6.2687   0.7774   8.3725   3.0753
-#>   10.9506  -5.6546 -27.5954   5.8827   9.0555  -2.9388  -2.4872  10.3958
-#>   -1.1340  -7.0988 -14.1755   1.7169  14.1141   0.9794  -3.7148  12.2845
-#> 
-#> Columns 33 to 40   1.4384  -4.6102   7.9683   2.7660   7.9938  -1.0803  -8.9664  -1.3981
-#>   19.5743   0.8177   3.9418   5.5161   9.0790  10.3763   1.0911  -3.6978
-#>   -5.6234   7.9174  -1.2428  13.7931  -5.4066  -0.4459  15.9044  -4.9218
-#>    7.1198  -5.6939  -1.3782  -2.8288  -7.2263  -1.5326  -8.7972  -7.8809
-#>    2.0581   5.2294  10.4372   1.7771  17.5576  -2.2725 -11.5410 -10.8612
-#>  -14.4360  16.1779  -8.2027   8.4059  -3.5090  -2.6810  -7.2530   5.8010
-#>   11.7753   1.6925   0.0918  15.1680 -12.1380   0.0158 -10.3996  -7.2048
-#>   -7.0974  10.7667 -10.2725   7.3891 -17.3839  -2.9920  -2.4067  -1.1251
-#>  -18.5467   4.7870   9.5273  13.4591  19.5929   1.9708  -6.4323   3.8563
-#>  -13.7966  10.4357   2.8547   0.2278  -6.0680  -6.4969   7.0030  -6.4925
-#>  -15.3292  -8.6361  -4.0396   5.9759  -8.9914 -28.1967  -2.3788  21.7079
-#>    4.3733  -4.7229  -4.6953   8.4878   0.0928  -9.9990  -1.3342  -1.0123
-#>    1.7047  -0.8910  16.3733 -11.1787  -1.2181 -17.9266  -9.3722  12.1319
-#>    3.6155  -6.4393 -10.4984  14.2492   0.6283  19.7343  17.0469  -1.6854
-#>   -3.7397  -4.4730  10.8904   1.0943  -2.3979  -6.4401  -2.0084   0.4883
-#>    2.3665   9.4115   3.8417  16.7768   1.0740  -2.6458   1.6131  -7.3026
-#>  -10.7354  -7.7811   5.5553  -1.1074  -8.8395  -2.0963   6.2890  -2.4415
-#>    3.8500   1.9622  -7.5663  12.1152  -9.1200  -2.7134   2.1331  -7.6049
-#>    6.3665   7.5608  -5.2248   0.8428  15.7291  16.6072  10.2988  -1.4232
-#>   -7.8167  19.1128  -2.7300  -3.5795  -5.0596   5.3191   7.9266   3.7644
-#>   10.8489   8.5284  -1.8161  10.5279  -3.5506   3.3824   3.4306  -8.5454
-#>  -13.2690  -0.5293  11.1187  -4.5714 -14.9835   4.9575  21.8038  17.2836
-#>   -4.4791   7.3667  -4.0882   9.4668   4.9189  -6.9242  -1.7868  -3.7555
-#>   12.5676  -4.2350  14.2625  -5.9949  -1.6661  15.3602   5.4294  10.8306
-#>    0.6979 -12.6459   2.3800   8.3869   0.8838   5.4597  12.7520   6.8345
-#>   -4.8699  13.0221  -1.8689 -20.3537  -4.7671   1.1076  -5.6562  -1.5576
-#>   -5.1534 -12.7627   7.0405  -8.1219 -15.5294  -5.6776   3.9444   0.8268
-#>   -2.5128   1.5007 -11.5941  -0.8027  11.0840   2.9976  -9.7512  -9.2726
-#>   21.1139 -10.3481  10.7656   5.1777  -2.5764   2.7313  -1.9686  -6.8200
-#>   -7.9482  -3.3359  -0.4480   9.9027  -4.0077  -1.9207  -1.9934   9.1539
-#>    0.7318 -13.0766  10.2638   0.4785  11.2678   8.6835   3.3261   4.6726
-#>   -2.1868   6.1275  -9.7815   2.8962 -16.2180  -6.2915  -4.5034   3.0573
-#>   15.5121  -6.8184  10.7959  12.5375 -13.7759   4.4653  -0.9482  -0.9945
-#> 
-#> Columns 41 to 48  15.1234 -10.3035  18.9469  -9.3543  -7.7956 -18.9362  -0.6373 -16.9786
-#>  -19.6301  -9.1963   0.4785  15.1186  -9.2118   6.7499   7.7051  -9.3235
-#>  -11.8282 -12.6039 -13.7831   3.7439 -23.2749   4.1191  -1.8422  22.8505
-#>   -6.4394   3.4854 -12.3427  10.9700  -5.3929   0.1003  -1.4660  15.0701
-#>   -0.5888  -1.2878  12.4392  16.2797   4.4355  10.1608  -3.6143   9.7532
-#>  -11.4246  18.0569  21.2602   4.3748   2.1658 -14.8113 -24.5796   2.4979
-#>  -14.2909   4.8997 -17.6585   0.7266  16.5683   3.5429   1.4633 -10.7058
-#>  -13.0987  -7.3477  -3.6767 -15.0189 -17.3225  -2.4886  -1.2141   6.8781
-#>   -4.2019  -7.1041   6.8834   3.1884 -12.3768  -3.9347 -16.8851  -4.9041
-#>    1.1614  15.5545   6.9877  -7.2775  -7.2149  -2.0622  -5.2004  15.0375
-#>   17.3684   5.4120  -3.9649 -26.9709 -22.3064  -3.7620  -0.4840  -7.7502
-#>    8.2134  16.4462   9.2899   4.5362  -3.4135 -17.8783 -23.2531  -8.3197
-#>    4.2102  -8.7115   5.7801  -1.2040 -16.3119  16.9851  -4.2366   3.0893
-#>   -7.9770  -1.9856  -1.2772   4.4906  -0.2718   3.9672  12.5172  -9.9648
-#>    0.6586   9.1921  12.7662   8.2750 -10.7659  -4.8818   4.7952   3.0726
-#>  -13.0078  -7.4784 -15.6910   2.9226   3.6097  -7.4876  23.6680   2.0568
-#>   -7.8754   5.0710   4.8404   2.5667   3.2970  19.4876   2.7635   2.3988
-#>   -7.3073  -5.4642   2.1707   0.9878 -13.8835  -4.3143  -0.7010   0.1638
-#>   -6.1368   8.6022  -2.3794  -4.9842  -2.6207 -16.6344  -0.5803 -23.0859
-#>   -1.4737  -6.8409   9.4013   3.6841  -2.5767  10.2542 -13.6277  14.4757
-#>  -12.7047  -0.9505  -7.3066  -5.3821  -0.2884  -0.8365  15.3252   2.6350
-#>    8.7984  19.0534   3.6122 -15.4152 -12.9271  -0.1066  -6.9598   8.6080
-#>    2.0097   2.7893  -0.8227   4.1339  -0.6433   9.9833   1.5379  22.7720
-#>   22.9658   2.7287   0.0924  -7.3743  -8.6120 -10.5792  -8.3092 -34.4353
-#>    8.3216   0.8046  -2.7417  -2.8410  12.2403 -14.6068   2.8725 -18.7368
-#>    5.1215  -0.3085   6.0280 -10.1927  -2.0305  -1.7860 -14.4816   7.4972
-#>    3.4380   4.4268  -8.7352   4.2027  -5.2833   9.8693 -19.4086  -7.5258
-#>    2.5557   0.1169  -2.1515   7.2013   3.8568  -4.1783  -1.7116   2.7032
-#>  -16.7468 -16.8727  -1.2258  18.1821  -4.3218  -5.5793   4.0499  -5.7701
-#>   15.1284  14.2322  -1.3899  17.1513  -5.2165  -4.9669 -25.0727  20.5660
-#>   -0.5518 -23.9666 -10.6307 -12.3171  -6.0767   7.3313   4.0299 -18.5027
-#>    6.5236   7.7597   2.4812  -4.6051   0.3597  -4.3789   6.9711   9.4499
-#>   13.7426   8.1034  -3.4679 -12.3309   2.7366  -7.2719  14.5037 -21.5581
-#> 
-#> Columns 49 to 54  28.2266 -24.0744  -3.0613  -9.8396  -4.7171   8.8575
-#>   11.6383   2.1390  -7.0953   1.6003  -7.2498   0.8229
-#>   20.9584   2.1395   5.4265  12.1389  -4.7847   8.8648
-#>  -17.1530  16.8176 -12.7773   3.5272   1.3383  -2.3195
-#>   15.4277   2.0832   5.0761   4.7348 -12.5190   2.1667
-#>    9.4932   3.9052 -16.7069   0.1884 -13.9748   6.2520
-#>  -20.4911  21.7655  -0.5098  -0.9561  -2.9878  -0.4902
-#>    5.1038   6.4373 -13.7096 -10.1529  -1.5253  -2.9657
-#>   -0.1040 -13.4354   5.6028 -10.4792  -0.9341  -0.8847
-#>    6.3219  -4.8316  -6.8600  -8.3210   3.8692   3.7136
-#>   13.8827 -26.3527  -8.5919  -7.6520   3.4166  -2.0100
-#>   -6.1717  11.1735  -1.6515 -11.3219  -0.4028  -5.4764
-#>    4.0893 -19.1908   6.6321   6.2797   5.8809  -0.7183
-#>   -4.3332   4.8074   8.4485  13.3534   1.9774  -8.3769
-#>   13.6668   7.4613 -10.1284   6.3912  -4.4749  -7.1188
-#>    0.8212   4.6318 -10.7527 -10.8872   2.0984 -10.7213
-#>   -4.6114   8.9248   7.3142   2.4720   3.0747  -8.1015
-#>  -15.3217  19.8732 -13.4930  -7.6570   6.2652  -3.6485
-#>   -0.9852  -9.7691  -1.8700  -3.5571 -12.5527   0.9310
-#>  -12.0288  20.0358  15.8906   4.8544  -3.7699  -5.6404
-#>    1.3941  -0.1866  11.3678  -6.3311  10.9359  -7.0841
-#>   12.0489   3.4691   5.6454  -3.1639   3.1077  -4.2794
-#>   11.4797  13.1703   8.9269   9.0371  -7.2471  10.1417
-#>    6.3791 -32.7896   6.8775  -5.3002   2.7534   2.1391
-#>   -7.3106   7.0380 -16.5145   8.6710 -12.6582   3.1825
-#>  -17.3828   5.1435  -6.8716   3.5323  -5.5878  -1.0485
-#>   -6.2563  -2.6207  10.9603  -5.9135   3.8629   1.1014
-#>   10.1823   7.6165   0.5025  -4.5793  -2.8579   0.7155
-#>   18.8798  10.5151  -3.2269  10.4497 -10.7137   3.3351
-#>  -12.9150  14.6824  -9.3680 -12.7423   3.8019  -5.0127
-#>   18.3241 -10.5744  21.1866   7.0562 -11.9339  -3.2777
-#>  -18.0871  16.1602   5.9427   1.3406   6.7964   3.2303
-#>    9.2389   4.0168  -4.3945  -2.0490   0.0984   1.5683
+#>  Columns 1 to 8   1.8260   8.1591   4.6035  -6.4535  -7.6263   0.3832 -19.6011 -12.2446
+#>   -1.5352   6.6871  15.1543   1.0247  -1.3265   5.2470  -2.6448  15.8465
+#>   -0.5355  -4.9958   5.0326  -2.5350  -2.4010  10.1999   9.6205  -4.1794
+#>   -1.4542   2.3526   4.0340   5.7586  -3.7329   8.7590  -2.1620  -2.4711
+#>   -4.5747   1.3335  -2.8664  -0.6998   5.3227  -4.3237  -5.6851  -2.9924
+#>    2.4556   0.6984   0.3695  -4.9028   0.2950 -11.8196  -4.6375  16.2335
+#>    1.2967   0.8378   5.2698  14.6375   7.4172  11.6153   0.2900   2.3175
+#>    0.1551  13.5187   3.9923  -0.1120   8.6104  -5.6072 -12.3877 -14.0587
+#>    0.7140   3.6238  -2.5277 -11.0936   0.8576  -6.2835  -4.0109 -13.8049
+#>    1.7948  -1.5655  -5.2298   0.0798  -8.0445   2.9370  22.9451   4.5566
+#>    2.8522   0.2362   2.7696  -1.4341  11.2378  -7.5862   5.8331  11.2259
+#>   -1.2209   9.4171  -6.8260 -17.8173   2.4927 -14.2671  11.8725  -1.5519
+#>    0.1673   0.2711   3.2382   2.2383  13.0791  -1.0681   8.9259   2.2542
+#>   -1.7408   2.7892   4.4890  -4.5976   6.4182   3.0700   0.9142   4.4284
+#>   -5.4744  -6.6379  -6.0099   3.4705  -4.1193   2.7548  -4.5794  -3.8785
+#>   -4.0447  -1.4522   4.8310   6.9625   9.3192  17.6498  18.7523  10.0904
+#>   -0.3412   2.1651  -6.6297   1.7064  -1.1193  -4.4957  -2.3076  -2.0743
+#>   -2.2017  -6.4894  12.5846   9.7681  -6.6289   3.3749   7.7310   0.2515
+#>    0.1496 -11.5796  12.4953  -5.5924   6.9807  12.4693  22.2900  10.4280
+#>    0.5502   0.3681   0.1878 -11.4241  -3.2833   5.7589   4.8359  -8.5480
+#>   -0.2198  -1.0807  -2.4916  -0.2052  12.6240 -13.0816   1.3949 -13.1501
+#>   -9.1901   3.7052  -5.4623   0.4961   3.9664 -13.9748 -13.2404  -9.6157
+#>   -6.5239  -1.2500 -14.0527  -0.6232  -6.6544   6.4823  -7.0401   4.9028
+#>    1.6598   3.4389  -4.0135   3.1290  -3.1109  -2.9381   3.0452  -7.1035
+#>    5.8949   4.3308  -2.5224   4.5669  -0.7112  12.5232   6.6898  -9.7690
+#>   -1.2793  -1.6489  -2.6060 -12.9363 -14.7577  -2.7659  -4.2459 -15.9953
+#>   -0.8876  10.4545   6.1134  -1.4375  17.9921   3.2297   0.4034  -8.9399
+#>   -3.4494   9.9486   0.8924   1.7481  -0.6058   2.7960 -12.6821 -10.6016
+#>   -1.3018   1.7970   4.9323   1.5759   2.0413  -8.5546   8.2645   2.4378
+#>    3.2835  -8.9576 -11.7936   0.7575 -16.7894  -9.6921   5.6635   0.3417
+#>   -1.3662  -5.6534  -6.4740   3.6154  -5.7681   9.4283  -6.2955 -14.4455
+#>   -2.9512  -6.1373 -12.6356  -8.3252  -5.0713  16.3846 -14.4110   7.8157
+#>    0.3033  -9.7302  -0.1077  -0.0731  10.7779   0.9662   5.9024   4.8023
+#> 
+#> Columns 9 to 16  -0.1047 -19.1578  -5.5226   8.9494  -9.7815   6.2850   6.2250  -3.6184
+#>   13.4626  20.6692   1.7037  -1.9196   0.1335   0.5153   6.5421   0.0458
+#>    7.6406  -8.4481   7.4565  -5.5883  -6.3770   0.9975 -10.3960   3.3317
+#>    5.5526 -12.5955  -4.6110 -19.9294  11.1434   8.1111  13.4437   9.7602
+#>   -5.7701 -14.4509  -9.9148  -3.3918  17.3232  -1.3501   3.5308  10.2287
+#>    0.7887   6.1411  -6.6744  22.2058  -2.0214   1.5489  10.9984  -6.2188
+#>    6.8477   0.5003   4.8697 -18.7130  -7.5220  -1.4153   1.2948   2.6685
+#>   -8.0237  -5.8528  -0.7898 -15.4213   9.1824 -19.4928  -1.9316  -9.3313
+#>   -2.7807  -3.1351 -12.9898 -10.0935  -3.4731   3.1313  14.3338   9.1565
+#>   12.5202   5.4359   8.5619  -1.1187  -6.3920   7.4425  -8.1584  -2.5314
+#>    0.6057   0.0600   3.8801   0.6867  15.4532 -14.8271  -7.0832  -2.0445
+#>    1.5331 -11.5073  15.0946   6.1962  18.0587   7.1307   7.5428   2.6637
+#>   10.9646 -27.9754   8.5566  -5.6839 -11.2368   5.6492  -4.9974   7.9753
+#>    5.1423  16.6858 -11.1429 -20.4088  -3.7578 -11.3367   4.2205   1.3338
+#>    1.0923  -3.0073   5.6252   2.0922  -8.9872  -6.8790 -14.4178  -7.0144
+#>   -6.6900   7.0685   9.4401   3.3826 -20.2304 -12.6441  -2.6827  -3.5986
+#>   -0.5642  -7.2309   3.9038  -1.4890   4.5334   6.6294   0.7921  -5.6311
+#>  -16.0923   1.8307 -11.3612   9.5744  -9.1781  14.5992  -3.8145   2.3548
+#>   -0.8599  24.2808  10.7316  11.9193  -1.1999   0.2188  -5.6878  -7.7666
+#>   -3.6908  -9.7783   5.4089  -5.6423  -9.1959  -3.0122  -0.3511  -7.6429
+#>    9.6349  11.3599  22.9755   2.4793   6.1315   3.9302   4.0085   7.4404
+#>   12.3148 -21.0538  -4.0262   9.8068  -2.7723   8.6776 -10.1430 -12.6838
+#>    3.1660   0.0423  -8.8353  18.5997   4.9787  -8.4963  -4.6031   8.2449
+#>   16.4236   5.8065  -3.0484 -11.8502   6.6388   1.7884   4.9407  -1.4769
+#>   -3.8862  -4.6335   7.3494 -14.5256 -11.2026  -5.8785  -7.1175  17.1685
+#>    8.8301  -2.4920  -1.1694   6.6143 -10.2259  11.6213   2.7629   5.8684
+#>   -3.7236 -21.2345 -10.5814   6.4557 -15.1036 -17.7614  -2.8330   7.2787
+#>   -6.1088 -10.3529   7.3196   6.1368   5.7111   7.4768  10.7212  -4.7709
+#>   12.0777  -6.1155  -0.6965  -3.0857 -15.9443  16.6812   3.3802  -0.4840
+#>  -13.8430 -11.1731   5.2937  14.3018  10.1421  -2.0058   0.1930   3.3149
+#>  -11.8601  13.8479  -0.6223  -2.0810  12.6734  -7.9995  12.0444  -7.5359
+#>   -2.6741   7.5763  -0.9344  13.4706  -9.5540 -17.5891 -12.8672  -1.0086
+#>    5.6310   2.4510   2.4199   9.9744 -11.7516   9.4566   4.8498   3.5874
+#> 
+#> Columns 17 to 24   9.6117   2.4139   7.5414  -5.4717  10.6299  11.6584  -1.7004  11.8702
+#>    6.7951   2.9962   2.4875  -7.5958   0.7660 -14.6585 -12.2632 -11.9941
+#>   -3.8407  -0.6697  -6.9220   0.0953   1.4455  -1.8162   4.2619  16.1030
+#>    0.0833   4.5801  12.3592  17.6265   5.1370  14.8573  17.8954  10.2154
+#>   -6.3291  -5.9660  -0.5349   7.2442  -3.1581  -7.9011   5.5732   5.6579
+#>   -4.1975   8.1636 -10.5229   7.3959  -1.4640  -6.7787   8.5577   2.5785
+#>   -3.2045   4.7122   7.3140   0.8511  -9.0000 -13.1235  -7.8732  -5.9827
+#>    9.7054   6.6750  -2.9342   1.2708  -6.1004  -5.7021 -20.9160  -6.6215
+#>    7.4206  -8.0444  -1.5943  16.2185  -2.3163  19.1996   1.9113  13.7482
+#>    2.9260   2.4947  -7.3885  -8.8559  -9.0710  -4.7847  -5.0627  11.4911
+#>   12.5281  12.4077  -2.7609 -15.7855 -11.6894   4.7595   8.0279  20.6484
+#>   -1.4544   4.8802   1.4279 -19.9919   0.5369  11.4888  13.1405  21.1828
+#>   -7.1931  -8.4896   3.5959   3.5184   0.3361  -1.3475 -10.0686   5.0022
+#>    5.0852 -15.3644  -8.2730  14.5316  -4.5948  -4.5328  -3.0399   2.6500
+#>   -3.0184  -1.1849  13.3050   9.4191  11.6314   9.5854  -7.0939  -1.2023
+#>  -14.1128   2.3235   2.0549  -5.8081  -0.9814   4.2295  -2.2474  -1.8073
+#>   12.0891  -1.0395 -10.5660  -0.1711   8.6656  10.9097  -4.5949  14.3297
+#>   -2.1854  14.0908   6.5025  13.1104  -7.9046 -11.7269  12.3389  16.1556
+#>    1.0126   0.1390 -21.8147  -7.0993  12.6624  10.4887  -1.4911  -3.0295
+#>    4.6010  -5.8652   2.4004   4.7835   8.2194   8.9928  -0.9659  -1.6229
+#>    3.0149   1.3793  -2.8025  -2.6144  16.0011   9.7169  -9.5069  -2.4568
+#>   -1.9979   9.8677   1.6009  17.3100 -10.6102   4.7812 -11.5583  -9.6018
+#>   -2.9051 -10.8274  14.8063  13.6623  -3.1345  13.3263  13.9614  25.6438
+#>   -3.4692  13.5901  -2.4435   0.5632  -9.5823  -4.3950 -16.7901  -3.5702
+#>   -1.4436  -8.1252   1.6708   1.9321  -7.2231 -12.8222   5.2369  -9.0412
+#>  -11.3765 -10.9554   6.7334   6.0455  -1.2395  -5.1354  -4.2885  -5.2551
+#>   -0.2414  -8.7988  -2.0368  -0.1567  -0.2542   0.0353 -10.0441   0.7034
+#>   -4.2507  16.4335   3.1021 -13.5747   5.7078  -3.6657  -9.8541   2.0731
+#>   -5.1024   9.4153  10.7014 -14.6222  11.9865  -2.2591   3.8923 -11.3250
+#>    6.6266  -4.2345  -4.3679  -4.6797   2.0929   0.1601  23.6827  14.3724
+#>    0.3226   3.2979   3.2266   0.7749  -1.2714   1.9274  -9.0633  11.0556
+#>  -16.3087  -0.7284   3.7947   5.4353   4.1057  12.1630  -1.2969   6.4635
+#>   -1.9961  -8.7054   3.5599   7.4334  25.2743   9.4001   7.6899  11.7341
+#> 
+#> Columns 25 to 32  13.8565  15.0534 -18.1175 -18.8683 -12.1822  -8.3684   3.5843  21.8066
+#>  -16.0126  -6.0157  10.7098   2.4732   9.8452  -2.1667  18.1304  -0.4926
+#>   -4.7170  10.2174  -7.5685  -3.7832 -11.6528   5.0592  -3.5901  -5.9223
+#>    5.7385   7.0945  -4.2476 -13.9630 -20.2407  -0.9073  -0.4925 -13.6198
+#>   -7.6288  -4.7604 -14.5075  -3.0168  -1.6811  10.3598  -8.5905  10.3897
+#>   20.3282   8.7429  -4.9332   9.1588   8.3854  -3.1852   4.2343  10.8840
+#>    9.3045  -4.3913  17.8795   1.6561  14.2900  -6.1013   1.2602   3.0010
+#>   -1.4932  16.7030   6.4388  24.3908  16.7804   8.2126   2.6756   6.8680
+#>    8.0064  11.4419  -7.4075 -12.1296   1.4976  14.1038 -11.0142  -2.7533
+#>   -2.3584   5.1514  13.3293   9.5870  -3.3005  -8.7325  -3.8137   8.7257
+#>   10.7233   5.8765   1.9809   5.5919  -2.3698  -3.4344 -11.4501  -3.1339
+#>   -3.9927  -1.0936 -11.1450 -20.3392  -8.2775  13.7119  -6.6823   2.5255
+#>  -10.6514  -8.4597  -3.4908   8.2157   2.4306   3.8472 -12.5723   8.2174
+#>    5.5668   0.6475   3.6847   0.0888 -10.0817   7.1276  -5.1332  -0.7860
+#>   -3.9734  -2.5397  -4.5659  -1.4551   4.2701   9.2423   3.8241  -5.8377
+#>  -16.2858   0.6074   1.8516   3.4390  -1.7665 -14.4418  -6.4367 -16.9911
+#>   11.5260   0.7023  -3.2096   8.5777  15.9507   2.7943  -9.4643  -8.8775
+#>    7.5726  -4.5065   4.3539   7.8640 -22.8240 -16.5590   3.1033 -15.8813
+#>  -23.2959  -5.4135  -0.5304  15.2863  22.5813  -0.5325  -2.0420 -13.5478
+#>   13.3480  10.0768   7.1202 -12.0740   0.2621   3.9127  -2.8234  23.3141
+#>   -1.1018   7.5446  13.4782  -7.2760  -2.4564 -21.3437  -5.9590   1.2723
+#>   -7.9235   3.9276  -1.2627   6.3375   9.3817  -8.5482  -7.4119  -9.3964
+#>   12.0387   2.8146  -1.9433  -6.0720 -15.1555 -13.0470   8.5779  -0.2309
+#>    8.5396   1.5353  -9.4736 -12.1676  -6.1710   3.6298  -7.5355 -14.2486
+#>   -4.3749  -5.9070  -0.6163   9.9351  -4.5684   7.1634  13.6035  -0.2342
+#>   -1.7573   1.0551  -3.1468 -13.1839 -12.6333  -8.2721   1.8924   3.8757
+#>   -0.1967   9.2809   1.7006   7.2561  -3.1687  19.4680  -2.1642   8.5549
+#>   -2.6086  16.0496  -6.1886   0.1446  12.8329  -1.6468  13.3924  12.2389
+#>   -2.8380  11.9721   8.7617  11.0669   8.4057   3.5837  13.2194   2.3375
+#>    4.2631  -3.9737  -9.2538   5.4973  -6.0531  -8.0730   9.9880  11.7268
+#>   -2.6810   7.9992   2.1081   4.4030  -6.6208  -1.7038   3.4373   8.8476
+#>   -2.6851  -8.7269   0.3655   2.0906  12.1190  11.2801  13.8482  19.2171
+#>   11.2260  -6.7638  -1.5984   1.1283   1.1331 -13.0027   8.7570  -7.0304
+#> 
+#> Columns 33 to 40   7.1918 -11.0123  -6.4861  -0.1183   0.3635  -8.0039  -2.6474  13.6560
+#>   -9.7596  -8.8562 -14.8694  -8.2309 -16.1653  -1.5027   5.3128  -5.9354
+#>    3.4086   7.0576  -0.3896   2.7934   5.2402  -4.5817  -3.0550   5.1175
+#>   -4.6062   2.8740   4.0586 -12.2719  -8.1342  13.1166   1.1305   4.9577
+#>    7.1038  18.1793  -7.9760  -3.6311   5.5416 -12.3066  -2.9630  -8.3412
+#>   14.5975 -14.0890  -8.2750   0.4054  -0.8280 -15.8696  -8.5135  -7.9173
+#>    4.5631  -8.4553   9.5980   6.2341  -7.0497  -3.8439  -3.8311   3.9239
+#>   12.9110  13.5431  -2.7905   2.7211  22.8222  -6.5699  -9.7038   0.1738
+#>   -1.9580  28.5856  10.2951  10.0750  14.9270  -2.3017   8.5844   6.9776
+#>    0.3076  11.4036   1.4542   7.1880  -0.9892   5.9726   6.2072  -7.7617
+#>   -9.2929 -14.6650  -8.3304   0.8636 -12.6975  -2.0798   6.9953   3.4992
+#>  -30.5649  11.2118 -12.6991 -11.0419  -7.8663  -2.7282  15.5507  -5.5073
+#>   -6.9478  -6.3732  -1.6843  15.1864  -3.4811 -10.0513   7.6497   3.0546
+#>  -10.2720  11.0129  15.4102 -13.7251   3.6784 -13.4604  15.8577   9.6818
+#>   -6.7153   6.3852   3.7858   0.3862   9.6262  -0.0152  -2.2117   4.3051
+#>  -15.0817 -22.0049   3.9758  13.2642  -4.3705  13.5733   0.3564   1.6646
+#>  -12.8667   4.2065   5.8434  -3.0097  -2.0800  -6.0556  -7.0629  -3.4629
+#>   20.7134 -19.3672   6.3106  -5.7618  -1.0921   8.2990   2.3327  -0.6665
+#>  -20.9373 -10.6720 -24.5484   3.8842  -1.6623   3.9842  14.9838  10.4721
+#>   -0.6862   7.3007  -1.0001   9.2735  10.9631  -5.7798   2.6837  -3.3766
+#>   -1.7613  12.7982  -5.2639  -3.2515  -4.7278   1.7007  -8.7418   0.0157
+#>    4.5151  -1.8616   4.5131  12.7287  12.2601  14.0306 -16.0782  -5.0002
+#>   26.9779  -7.7813   3.0088   3.7676   1.9643  -2.2549  -1.6592   9.9631
+#>    3.4674   5.6946 -10.3938  -6.5255  -2.4956  -4.2236 -13.3714  -6.7357
+#>    0.2363  -3.1763  -1.0663  -4.4249   1.9171  10.9242  -0.5985   5.7317
+#>   14.4323   7.1065  -7.7282  -7.7047  -2.6848   2.4342 -14.8455  -7.9755
+#>   -9.4945 -10.4411   8.3310  18.4509  16.0904  -6.4055   6.5227  13.2564
+#>    0.1447   0.9421  -4.3497   6.8124  -8.7435   1.6754  -3.0355  -1.9535
+#>   -3.2269  15.3326  -7.1119   3.1387 -22.4404  15.9652  17.1364  -6.8747
+#>  -13.7395  -5.0539  -5.1533 -16.5728 -13.1708   6.5803  -1.3005  -8.1917
+#>    9.2851  -9.1253 -13.2328   4.4441   2.4160  -7.9322 -11.6210  -4.9168
+#>   14.6798  -0.8670   0.9353  -4.6907   3.0096 -10.7709 -10.0609  -1.9744
+#>   -8.0928  -2.8592   0.6087   9.3705 -15.5771  -0.5251   0.9091   8.0747
+#> 
+#> Columns 41 to 48   8.1006  16.8604  17.1993   1.5062   2.8623  13.6276   0.0033 -23.9927
+#>   -7.3958  -2.5589  -1.5578  -7.3520   9.4930   9.6351 -11.2366 -16.3185
+#>    1.1102   1.6622   5.3786 -12.9149   0.3962  -0.3849   1.8834  12.0826
+#>    7.0652  17.6994  19.7010   8.8079   6.7431   9.6183   8.8504  -4.0046
+#>   -4.8416   4.3447  -6.4734  16.7248  -4.6533 -11.7857   0.5011  -2.2642
+#>   -2.4893   6.1604  -5.8977  14.0653  -3.7521 -11.7748  -5.4343 -11.8478
+#>    4.6204  -0.3881   4.9293 -16.6730   1.9469  -1.7783  23.9308  -8.2245
+#>    3.3164  -0.3111  -2.3785  -3.8382 -19.0539  -1.0947   7.6452   4.1497
+#>    4.7905   3.3564 -13.3232   3.2799   3.5246  -1.4202   9.6656  10.6428
+#>   -4.7419   8.5789 -16.5311  -2.7748  -0.2826  -6.6775   1.0639  -2.6836
+#>    6.5005   0.2734   5.3065 -12.6514   8.6547   8.0053  -4.7755  -0.1551
+#>   -6.9675   5.6982 -15.0773  -1.5211  -5.9475 -14.1388  -7.0005  -4.6153
+#>  -10.4425  -0.5933  18.7262   1.3891  -0.8319   1.9496  -1.7930  -4.6701
+#>   -7.6568  -3.0406  -1.1290   9.4498  15.3772   5.6354   2.0975  10.4207
+#>   12.1873  -1.1623   5.2466  -3.5829  -3.3226  17.0875  -2.0909 -11.0734
+#>    1.0031  -0.9128  19.3637  -2.9508   0.3005 -10.7548   4.1302  20.3919
+#>   -0.9462   3.1503  -3.7012   5.8681   6.4743  -2.4770  -6.1728   0.1677
+#>   -7.3106   0.4775  24.6495  -4.7428  -6.3881  14.7008  -6.6530   6.4338
+#>    8.4131   6.7445  -5.6644  15.6822 -12.5014   4.1015   1.4066  13.4287
+#>    3.9977   3.2108   7.6805   4.9462  -6.3765   1.1978   1.8262  10.2912
+#>    7.0599   2.8675 -16.8190  -8.7010   8.2732 -10.6164  15.4398 -17.0620
+#>   11.5717   5.8657   7.2089  -1.8162  -3.3848  -1.1113  12.3739  -4.0730
+#>    0.1380  -9.2253   7.2103  -2.3036   4.3707  -4.9597 -12.2279   5.7701
+#>   14.5090 -14.2638  -6.5145  -3.8222 -17.9963   1.6697   7.6186   0.6131
+#>    2.2813   0.2900   3.0762  -7.8753   8.5035   4.4506  -6.6869   1.8409
+#>   -0.3724  -5.0969   4.3368   9.2297  -7.7656   5.4747   0.2278 -12.1794
+#>    2.4222   2.3833  17.3921   0.6806  -7.6551   1.2875  -4.3404   4.1111
+#>    5.8935   2.9006   2.8999  -2.3060   6.5036  19.3929   0.1177  -8.8524
+#>   -1.9444   7.0333   4.3451   2.3663  -2.1195  10.7117  14.2797   5.6832
+#>    4.2026   7.9457 -31.7168  18.5949  -2.7400  -7.9383  -4.8747  -2.3428
+#>   -1.6635  13.3650  -9.0384  10.2653  -1.6372  -9.8862  -7.2558   5.3548
+#>    0.3649  -8.9365  10.4085  -9.7159  17.5403  -2.7090 -29.7048  12.1964
+#>   -5.4408   4.2499  18.7786 -10.5886  -1.0374  -6.2835   0.9434   7.4485
+#> 
+#> Columns 49 to 54  -2.4635  26.4433  -5.2289  -1.8283  -3.3177  -1.4009
+#>   11.3204 -14.5709   7.7352  -1.9641   9.5851   0.8920
+#>    2.0535   2.5428  -6.3752   3.3288  -5.9639  -3.5136
+#>   -2.9311  -8.7297   0.3804   7.1837   7.8500  -3.9282
+#>    1.2784 -10.1014   1.3071   2.1363   3.3534   5.2828
+#>   11.6092  -1.9299  -0.2373   3.3368   0.0811  -1.0494
+#>  -10.9732  -7.6794  -7.6382   2.4690  11.3893   2.7271
+#>   -9.6305  -3.6221  11.8598  -6.5606   3.1518   1.5069
+#>    2.8755 -15.5314 -16.0310  10.8255  -8.7148   1.8761
+#>   -1.7920   5.8465  10.3092  -3.1931  -8.2935  -3.4691
+#>   -6.2296   1.4329  13.1407  -3.5149  -0.9253   4.1405
+#>    9.6564  -6.0261  -4.9751  14.3209 -15.2343   3.2894
+#>    1.0771   2.2845 -15.0481   3.1343  -0.8870   1.3347
+#>   -5.9095  11.7338 -12.1575   6.2385   0.0487   8.9511
+#>   -1.3080   7.8309  -8.7697  -3.5777  10.9397  -3.8944
+#>   -3.2248   1.4651  -5.4231  -2.0645   3.4962   3.2184
+#>   -0.4314   1.2685   2.4387   4.9287  -4.2598   0.0481
+#>  -22.3894   8.4356  -7.0722  17.6679   8.3444   0.5431
+#>   12.5965  -1.1868  -1.5539   8.0991 -10.5500   6.2496
+#>    4.9151   3.7412 -14.8398   8.7518  -8.8861   5.8264
+#>   10.7290  -0.3469   3.3986  -5.0775 -18.4906  15.0917
+#>    7.8041 -10.3125  21.9555  -1.8409   1.2616  -1.9164
+#>    1.8639   0.9491   8.0367  -4.9478  -2.2863  -2.0672
+#>   -9.4279  -1.1549   7.8288 -12.9975   4.1920   3.9919
+#>    7.3442 -22.9798  -6.0588  -1.2220   3.1272  -1.7624
+#>   11.8644   0.6579   4.0728  -3.1797   4.3028  -0.5456
+#>   -6.1925  25.4354 -21.3934   1.9897   5.2866   0.1201
+#>    7.6163 -13.0325   4.5060  10.8893   0.7714   9.2330
+#>    0.6249 -14.2223   9.0848  -1.4057  -4.6715   5.6285
+#>   25.9531  -5.6675   2.4861 -15.5022  -7.3610  11.2828
+#>   -9.5851  -0.6091  -7.3243   7.7205 -11.3502  -2.4365
+#>   -4.0420  21.3837 -11.6227  -8.4066   0.6026  -2.8530
+#>   17.9246   2.4747 -21.6623  -7.9381   0.5815   6.4550
 #> 
 #> (9,.,.) = 
-#>  Columns 1 to 8   4.0875 -13.1660   6.1413   1.6866  -7.9687  11.6420   0.5888  21.6606
-#>   -2.9234  -7.8627   3.5386   6.7541   8.4469  -0.6668  10.9694   5.8437
-#>   -2.8451   9.0504  -2.0656  -3.7975  15.4361 -10.9013   9.2741  13.7776
-#>   -1.3023  -4.6213   1.9568  -8.2512  13.9255  -7.4534  11.3668   7.4748
-#>    1.5525  -2.3667  -5.8136  -7.4960   5.6232  -1.6982   0.8556  11.8351
-#>   -5.5165   1.7084  -7.7292   5.4986   3.2420   7.6540  -0.2872   9.7683
-#>   -2.9122  13.6950 -12.6256   7.8668  13.7894   1.3318  -6.7552  -0.6951
-#>    2.8501  -1.6932   3.2410   0.0887  -4.4175   3.7085  -3.0652  11.5549
-#>   -1.7622  -6.1368   2.4826   7.4410   9.7392   3.6960  15.6597   6.1769
-#>   -7.6209   1.6972  -3.0448   1.9954  -8.1176   2.7204  13.5791  -2.6416
-#>    5.6467   2.8106   4.7776  -9.6193   2.1398   6.4787  13.7958  19.0501
-#>   -0.1869  -5.5733   4.4560  -7.0290  11.0339  -1.5122  17.4960  10.0469
-#>   -0.5857 -10.2198  14.7713  16.3169  -1.5068   1.1987   4.6137 -15.1676
-#>   -1.8894 -15.2043   7.0605 -11.0119 -11.8196   1.7829  -0.6664  -5.7275
-#>   -7.3169   6.6425   3.8902   3.2093   6.3221  -1.0500  -2.6227  -9.2949
-#>   -0.2854  -6.3065  10.3913   8.1978  -4.6598  -6.8949   7.0462  -0.3631
-#>   -2.6381  13.2912  -1.1430 -12.5023   8.5150 -10.3241   5.7787  -1.6207
-#>   -3.0306   0.1186 -11.0713  11.8305 -11.7191  -8.0379  20.6490  -3.5747
-#>   -3.7223  -4.9261  -7.8515  -3.3555  -0.5600   4.2985  -4.3782   6.1615
-#>   -0.7275  -6.7427   6.0927  -0.9234  -3.6294  -4.5123  -4.4903 -12.0694
-#>    1.7222   6.4943   8.4215  -5.9419  12.6630   7.3062   5.2832  -1.2977
-#>    0.4108  15.7560  -8.1166   1.0658   1.7338 -23.5575   3.8808  -4.2467
-#>    2.3169  -0.3698   6.6242 -16.5452   5.7667   2.0703  -6.2268   8.1523
-#>   -0.0676  -4.9680  -3.6224   0.9647   3.0948  -3.4271   1.3068   5.9702
-#>   -3.0543  12.9846   3.5692  -9.7936   6.0392   1.1089  -6.0544  -0.5150
-#>    0.9283   4.9139  -8.5166   8.3903  -1.7509  -5.2080   7.8338  -6.4984
-#>   -3.3726   5.7619  -6.9525  -0.1470  19.7022  -3.8281   0.6373  10.6404
-#>   -0.5416   1.2048 -10.0297  -3.0471   4.5664  10.2175   0.1318  -5.1289
-#>   -2.1869   0.0989  -1.0279   7.7973  11.0315  -9.5611  -5.9728   8.5446
-#>    3.6401  -5.9616  -2.8115 -12.7283  13.7908  -3.5134  -8.3821  -3.8347
-#>   -1.2762  -0.9875  -2.2588   5.1875  -8.8331   6.0745   3.8728  -0.2223
-#>   -1.7755   2.7929  -3.2278   3.5319   2.0656   9.9338  -0.1135  -2.6921
-#>   -0.7828   2.1875  -2.6695   1.7830  -1.5953  19.4810   6.1483  15.0575
-#> 
-#> Columns 9 to 16   4.2088 -23.9762  -6.8043   8.8212   8.7422  -2.3903   3.2339  -5.2097
-#>   -7.2594   4.7735 -12.3461  -4.8847 -10.4494   7.4665   8.4385 -12.5377
-#>  -11.8828   4.3953  11.8320  21.5989  11.6513   6.9019  -6.6922  -2.8721
-#>    6.7395  10.5906  -5.7747   7.1411 -16.0808  -6.9254  -5.4521 -10.9185
-#>    8.2466   1.1895  -0.1548  -2.3253 -28.0649   1.2423  -9.5628  -0.1640
-#>    3.5897 -17.9191 -17.7579 -27.1033  14.4983  -2.0833   1.3654  15.6925
-#>   10.1009   8.5532  -0.2808  -0.2258 -16.1121   9.4849   3.8841  -3.2685
-#>    1.0302   2.9111  -1.6666  23.5359  -4.0120   0.3524 -16.0500   0.5214
-#>   -1.5642  -4.4428  -2.7665   1.2616   7.2632   7.8903  -8.8398  -5.6303
-#>   -6.0943 -19.7397  -0.9850  -0.2281 -13.3369   1.8731   2.2916   9.9167
-#>   -6.0780  -1.6448   7.8172   2.0459   7.7387   2.5029 -23.2533 -12.3586
-#>   13.2757  11.1168  -9.2176  -5.5516   0.8257  -8.8642 -13.2948  -7.4019
-#>   -9.3961 -10.9207   8.7869   1.3486 -11.6727   6.0785  -0.1468   1.0374
-#>    4.1470   8.7967   9.1593   8.6602  15.7938  -2.6806  10.9040  -1.8152
-#>    6.2388  -2.0511   0.3299 -10.8950 -11.9330   2.8885   0.4726   9.5734
-#>   -4.9775  19.8541  15.4489  -0.7692  -9.5588 -11.9333  12.7164 -12.2642
-#>   -5.1962   9.7119  14.1155  -5.6186  -3.1858  15.5836  12.0592   2.3122
-#>   14.0698  19.7998   3.3043   3.4178   0.8533  -0.0373   5.3411   2.4784
-#>    5.4941   5.4324   4.7054  10.5663  -9.1261  -4.5124   7.2460  -6.0025
-#>   13.6831   8.8033  -6.7323  11.9500 -30.1266   4.7476 -18.0578  10.4861
-#>   -6.9986  -4.8844  -8.5996   7.4400  -0.4392   3.8846   5.0058   3.8753
-#>   -3.3200  -5.5672   1.5313  17.5748   3.4141  -7.4009 -13.9479   3.4515
-#>  -10.8194   6.7758  -1.8326  -5.6283   1.0390  20.6476   1.7635 -15.6967
-#>    9.8613  -2.6945   4.2391  -7.6302  -0.9557  -6.1139  -7.9384  -6.0483
-#>   11.0115   4.5217  -7.0642  -9.2148   7.0387  -8.5065   0.6995  -6.8530
-#>   -5.7435   4.7510  -8.7562  -3.7314  -0.9758  -3.0986  -6.8076   8.7105
-#>   -8.1038  -2.4940  -4.7496 -17.7449   6.4212  -9.0613 -17.9790   7.3966
-#>   -6.8502 -18.9183  -7.4141  -2.1069   7.5214  -5.9213  -2.8063   7.5368
-#>    1.5855  -9.5122  -0.6550  -0.7084  24.4028   1.6516   0.6475 -11.2705
-#>   -9.6477  -0.4903 -17.6532   5.7543  18.2415  -4.9971  -3.8286  -3.2842
-#>   14.1147   9.0895   6.6478 -18.3653 -13.5341  10.5265  -6.7646 -13.0001
-#>  -16.9131  -3.4685 -19.9777  -4.9386  -1.7271   4.9551  12.4286   7.8517
-#>    4.1537 -11.3263   3.8526  -6.3522   3.6299 -13.6234  12.4613  -4.8751
-#> 
-#> Columns 17 to 24  -4.2706   9.6318 -14.4805   6.3895  -3.7929   8.0710   2.0545   1.8292
-#>    7.8162   6.6203  -5.8975  -1.1224   1.9272  -0.8045   6.8998  -3.2840
-#>   -6.8472 -17.6737   7.2496  -3.0438   8.6990   3.2873  -2.7906   2.0976
-#>   -2.9556  -0.9176  17.6425  -7.5155  -2.5665 -10.1610   3.8583  -6.7622
-#>    1.1791  13.9903   6.0277   3.7011   9.8324  -2.8115  -1.3943   7.6187
-#>    0.9433  16.6836  17.6236 -18.7736  -6.3167  -4.2612   8.3085  -3.4196
-#>   -3.4771  -1.7288  19.2656  10.8092   3.1097   2.7039   6.5544  13.4471
-#>   -2.5811  -7.8217 -10.2289   7.0625  -8.1599  -0.8722 -11.0821  -5.3660
-#>   -0.3557   0.1654 -27.0798   7.3281   9.1698   4.4319   7.1142  11.8328
-#>    2.6711  12.0621  26.9639   3.9798  -1.4104   7.9621   2.4684  -6.6909
-#>    4.1163 -10.5928 -14.7403 -12.4781 -14.6933  10.3249 -18.3088   4.6522
-#>   -0.0078  -4.2932   4.7690   0.9550  -3.7099  -4.5541  -7.9861  -6.3120
-#>    4.3695  13.4931   2.4103  11.5745   7.9793   8.5670  -1.6531   1.4744
-#>   -5.4559   5.6987 -10.2102  -7.2425   0.9817  -2.2712   6.4125   3.5513
-#>   -1.8864   2.3376   6.6529 -13.3615 -10.9942  -2.3597   0.7256  -9.1127
-#>    0.1496   1.5402  -2.3539   3.0123   9.9166   5.6713  -8.0756   3.9683
-#>   -0.4101   9.1029   2.3711 -10.7000 -15.2477 -17.3745  -7.0343  -9.0581
-#>    1.8222  -8.6733   6.3450  13.1310   1.8973  -3.9703   9.2409   0.7033
-#>   -6.0650  -3.3313 -14.9866  13.3368  21.1127  25.5643  12.0376  15.6202
-#>   -6.1982   0.8577   7.0105  -3.4303   5.7572  -0.5078  -4.1691 -14.3487
-#>   -4.9384  -2.2306  12.4091   9.5809  -7.9701  15.5971  -6.1461   3.4497
-#>  -14.6010 -19.7534  -3.0943 -15.2183 -10.4810   3.3483  -6.5617 -10.0276
-#>   -1.8306   1.1335   2.9998  -4.5367   0.8741  -3.0733  -2.7563   4.2682
-#>   -5.0458   1.9771 -33.9085  -3.2428   1.2707  16.8337  14.7150  10.6823
-#>    2.4681  -7.3226 -22.9366  -3.9452 -18.8797 -18.5527  -2.8076  -8.6607
-#>    9.9208   3.2776  10.6686  -4.7430  14.8613  -3.8122   4.8039  -4.9235
-#>    9.9328  -2.9447  -0.5335  -0.8264   0.1861   9.5124   5.4681 -14.0606
-#>   -3.1846   8.4049  -1.4289  -6.1339  -9.3869  -2.7514   0.4952 -11.9531
-#>   18.0310   2.2193 -14.0216 -13.0492  22.2719  -4.2757   4.5596 -10.6983
-#>    7.9559   1.0702  -5.5950 -17.2573 -13.7766 -13.7111   0.9190  -4.5991
-#>   -0.3905  -3.3458   1.7576   6.3945   5.7497  -2.7466   2.4251  13.0997
-#>   -4.5891   0.2517   7.7112  -8.0639  -1.7789  -8.1066   0.9122 -13.0374
-#>   -5.3413  -4.9465   5.5711   6.2203  -0.3533   2.2089  -8.5250   1.5109
-#> 
-#> Columns 25 to 32  -2.5417   0.8453   4.7376  -2.9612   1.8473   1.4810   4.5415   7.2610
-#>   -4.2711   2.9126  -7.8663  -0.3670  -9.6876 -15.3951   3.4643  -1.2463
-#>  -15.1669  -2.1372   9.1019  -1.4552   4.6120   4.7544  -4.4678   2.5946
-#>   -8.8096  -2.5299   0.3544   3.5773   1.0737  -6.6828  -2.0851   1.3127
-#>   -7.1648  -0.3307  -9.3747   4.5719   3.2879   7.4797  11.3753   8.1376
-#>    4.2475   2.2863   4.1592   2.2073  18.8083   1.7558   5.6452 -11.2770
-#>    1.8582 -17.5141  -9.8492   6.6420   0.0380  -7.6875  -7.2550   6.8817
-#>  -12.4302  -8.5575  13.1428   6.6443   5.4586   4.6629  -0.2422   5.3181
-#>   11.7403   1.0674  -9.5835   1.3690  -7.6413  -6.8457   7.1888 -11.0833
-#>   -8.1675 -11.4043  -6.3836  -7.3862  -8.4281  -5.4854   7.3219  -0.0165
-#>   -0.8766  21.4948  12.8014  14.8860  26.1611   7.7665 -12.3399 -11.0969
-#>   -6.5066  -8.8206   3.9323   7.7160   7.8918   5.5574  10.4558   9.2808
-#>   -6.7053  -1.6588   1.5652  -2.6664  -4.8871   1.6638   4.5137  -3.4380
-#>    0.3392  14.3931  -0.6118   0.2134  -6.2186  -5.7362  -4.2839   4.3567
-#>  -13.5350  -6.2299   0.3904 -16.4546  -7.2557  -0.6942   9.1569  -9.9394
-#>   -4.5590  -2.8492   0.1492  -0.4079  -4.7893  -0.5491  17.3962   3.4775
-#>    2.0336   5.1708 -12.4232   0.2248   8.3460   1.0982  -0.4847  -4.7426
-#>   -5.0570  -7.8702   3.1474  -8.8002  -2.3591   1.0060   1.5078  -5.2314
-#>   16.9447  12.7382 -13.2697  -0.0683   5.2825  -3.0754  -7.3953  -5.5983
-#>   -7.7573  -5.0490  -4.0720   1.8675  -6.4763  -2.1763   3.5788  14.7622
-#>   -4.0019   6.1244   5.4763   0.6726   1.2970   4.2389   2.8034   4.1622
-#>   -0.9995   6.5293   2.1186  10.6913   2.2119   4.5758  -7.5272  -8.9917
-#>   -8.7199  -8.4839 -18.1779  -9.1905  -4.0585   1.9482   3.9999  -7.3518
-#>   12.7110   9.5307  15.4975  13.9518  -9.2565   2.2380  -5.5922  -5.2402
-#>    4.5704 -11.6175  -7.3932   6.6459   4.7025  -2.2518   8.3495  -2.0091
-#>   10.7717   2.8472   6.3731  -5.9258   0.8502  -2.0102   1.9226  -6.5894
-#>  -14.1405  -2.1212   6.4797 -14.1395 -16.3247   7.2465  -6.4626  -4.0388
-#>    4.1977  -2.3055  -2.6956   0.9332   2.7282   2.3741  -8.7264   8.0648
-#>  -12.1615  -6.7460  -9.2023  -3.4595 -13.0378  -0.4108  11.2732  -8.4836
-#>   -9.0775 -24.4088   0.1750   5.2929  -2.1573   3.3092 -13.1942  -8.2572
-#>    3.6687  12.6269   0.1908  -6.8676  -1.9316   6.3263  -9.2124 -11.4998
-#>  -17.3930  -2.7537  -1.4206  -6.4436  -3.9444 -11.5823  -4.6937   7.5235
-#>  -11.4818  -5.7549   5.9569  -1.7157  -7.7764   6.1034 -10.4827   3.0300
-#> 
-#> Columns 33 to 40  -4.0546   5.7033  -1.9717   4.9453  -4.0232  21.8872  -3.3472   2.4886
-#>  -10.6006  -6.1495   1.9815   4.9314   1.9719   6.6286   6.7427   2.7484
-#>    5.8264 -10.3392  18.2553  -9.3866 -14.3963 -13.8339   0.8560 -11.0095
-#>   -7.8975  11.0141 -15.8547   3.1282  -5.9673   8.7539  -0.4567  -4.7988
-#>    7.7529  -1.0745 -16.7453 -11.1365  -7.0175   0.1077 -11.2151  -0.8434
-#>   -0.7133  -7.7374  -0.7937 -10.8897  16.1370 -13.8290  -0.1160   3.4583
-#>   -1.5516   8.6448  -4.6002   1.4309  -3.2235   2.6007  12.0963 -13.2540
-#>   -7.9362  -4.9958  26.8412 -13.2264  -6.6898 -11.1547  -2.7267  -0.6085
-#>    4.1175  -7.4804  -3.3964 -10.2496  22.5975 -22.6124  -4.8042 -10.3592
-#>   -8.3552  -2.6763  -0.5044 -19.3676  10.0768   3.1593  15.9853  -3.9306
-#>   -4.9406  -2.3733  22.2437 -21.2701   4.0356 -19.9860 -16.7914   8.4032
-#>    9.8218 -10.7498  10.6639   1.5719   0.2460  -3.3337  -1.8820  14.1830
-#>  -11.1259  -1.1850   0.7274  -3.5176   4.5723  -1.3078   3.5844   4.4614
-#>   -9.0342   1.3072   0.6087   5.9559  14.0140   2.3856  11.1323  -6.3673
-#>   -0.9597   6.0675   3.4484 -16.5896  14.6504   2.3404  13.2214   3.1287
-#>  -15.1154 -12.5263   6.2127  -7.7274  -0.1448   3.1026  -2.2499 -11.8948
-#>  -10.4774  -1.3816  -4.5550 -23.3595  11.2419  -2.9943   2.6173   5.4653
-#>   -1.0691   5.1118   5.7607 -16.5816  14.0878  -3.2260  17.9101  -9.2509
-#>    4.7743  -1.2142  -1.9710  12.9453  -5.1697  -6.0383 -12.7658  -3.3180
-#>   -4.7639  -9.1992   5.1599   2.0450  11.3600  -2.9904  -4.9618   1.1367
-#>   -0.2035   7.4995   6.7237   9.6282 -15.1450   0.0953  -1.5054  -3.1165
-#>    9.3922   6.3088  10.0187  -6.8202   0.7742  -5.7718   9.5027  -9.4457
-#>   -7.3586   9.0690  -9.2550 -11.7036   6.6068  -4.6989  -0.0246  -0.4834
-#>    8.0758  11.4354  -0.4742  16.5706  -1.5196  -2.9838  -5.4655 -11.9395
-#>   16.8395  -8.0241  -7.3224  20.5876  14.2268 -10.0591  16.2582  -3.5242
-#>    6.7963  10.5324 -16.6560   5.6012  -9.8153   2.5755 -13.8786   3.6098
-#>   -4.7407   3.8492  14.7185  -4.0867  17.5908  16.4011  11.1086 -21.1926
-#>   13.4565 -10.2485  -0.9491   5.4584   7.1630 -14.4297  -6.8817   1.7544
-#>    4.9086   3.5352 -14.2149  16.3630   4.2086   3.2492  -4.4649  -5.6819
-#>   -2.4664  -7.4547   7.8175 -12.1694  21.7147  10.8425  13.5847   6.1566
-#>   14.5458   1.8772  -4.1889 -15.3383  -8.2980   6.9765   3.1014 -15.1226
-#>   -1.3925   3.0163 -11.4281  19.1352 -12.2901  -1.0417 -13.7466  13.6779
-#>   -0.7463   1.0728  -0.6218   9.9406  -9.6542   3.4510  20.0130  -7.8743
-#> 
-#> Columns 41 to 48   7.7196   0.3849  -8.3918  -7.7087   9.4628   6.3112  -4.8614   0.7898
-#>  -10.5400   6.7290  -3.3070  11.4925  17.5507   2.6617  10.3062  -3.3887
-#>    2.3790   1.5194   5.8885  -2.5441   5.1343  -5.0835 -19.2448  -6.2814
-#>  -13.8772  -7.0727   5.0106  -2.6973  10.7691   4.7806   1.1937   1.7533
-#>   -0.1879  11.4678   7.5956  16.2828   2.8012   5.0949   5.4573   7.6978
-#>   18.0733  16.1860  10.2843  -3.6556   7.2638   8.1729  11.3880 -15.7216
-#>   -9.6233  -3.4684  -1.5559   2.1601 -13.1356   7.2361  16.7344 -10.8216
-#>  -16.5723  -7.2646 -11.1144  -3.9776  -0.2065  13.8502  -8.2284  -9.6323
-#>    1.5238  12.3028   8.9714  17.9027  -4.7627  -8.3126  16.8880   7.9462
-#>   10.9103  -5.3810  -5.5318   5.7035  21.4334   2.3475  -2.6262 -12.9918
-#>    8.0442   8.9567 -11.8374  -5.4994   7.8793  -1.9821   3.3344   9.2903
-#>  -12.1435   6.2812  10.7557  -8.9740  -0.2044  10.1391   0.1696 -13.2730
-#>    1.4029   9.6779  -5.9944  21.5273  12.4168  -2.9909  -7.2323  -0.2837
-#>  -15.4937 -15.8609  -4.5449 -11.5226  10.0661 -19.1849 -13.9259   4.5161
-#>   11.5241   4.0590 -15.7215  -2.1419  -1.3034   5.4464   3.6719  15.3133
-#>   -4.4282  -7.1290  -0.5408  10.7173   9.4064 -10.2900  -1.1956  -0.3817
-#>   13.4626  14.8415 -15.4962  -9.9346 -15.0116 -11.9595  17.3005   7.6668
-#>   -0.6313  -3.6673  -7.9908 -18.4216  -0.2426  -4.6290  -3.6879  -7.4714
-#>  -10.8215  -7.7815   6.3899  14.1215  -1.5517  -4.1354  17.5691  -0.2966
-#>   11.1281  -7.7009   6.5719  10.8587  -6.1595  -8.7532  -4.3086  15.5671
-#>   -9.7915   8.7561   1.0023  -2.0281  -1.7689  -4.6823 -12.4498  -6.9113
-#>    7.7028   4.9294  -7.8488  -6.5750  -9.0153  11.6397   4.5690  -0.1016
-#>    7.1106  -1.6929  -1.8737   2.1624 -13.8151  -5.3098  15.0948   0.1795
-#>   -1.3814   9.2974   5.5842  13.2400  12.6897  12.6315   3.4940  -2.6392
-#>  -11.3209  -8.2444  16.8397  17.8638   5.5211  16.4997  15.2528   0.0082
-#>    3.0446  -4.4829   4.3880 -10.7411  -0.3064 -13.2129   2.5039   9.2481
-#>   -6.7606  10.0870   5.7216  -5.6511  -6.1712  -2.0900 -17.3603   8.3145
-#>    6.8814  -0.1777   5.1503   0.9947   5.6574   2.5916   0.6318  -2.3832
-#>   -0.4918   0.3861   4.7932  -4.0897   4.0422  -1.6061   2.8181  -8.4493
-#>    5.8768 -11.2762   5.9376 -21.8132 -10.8796  26.4854   3.2273  -6.3154
-#>    2.7235  -7.0789  -3.0443   2.1171  -7.4423   2.0577   9.3470  26.1017
-#>   12.9806  -5.9052   5.8160  -3.9925   4.2319 -11.9046 -10.8885  -0.4372
-#>    7.3300  -9.5418   7.7864   2.6138  10.5347  17.3636 -21.5352   0.4739
-#> 
-#> Columns 49 to 54 -14.8691  -9.1324  10.1655   0.7449   2.1667   5.3363
-#>   -4.4574  -0.1098  -3.1628  -9.3421   1.4818   0.8749
-#>    4.6459   9.2058  -0.2434   0.4508  -7.6855   3.1409
-#>    4.7861  28.0300  -0.7577   3.9693  -3.3897  -2.4329
-#>  -21.8895   4.7810  -9.7748 -11.4574   1.2092  -4.2950
-#>   -4.4833   5.0633   4.8649   8.0878   0.2127   2.9271
-#>   -5.7122   0.5988  -9.7677   0.9075  10.6970   0.2351
-#>   -1.1009  15.3324   9.8893 -11.4473   4.1047   0.4827
-#>  -11.1070  -3.0292   2.0740  -3.7679   4.7414  -1.2884
-#>   -8.6583  15.6834  -9.2855   8.0586   0.4443  -0.7787
-#>    0.2194   4.4984  11.6936   7.5706  -2.2380  -5.1833
-#>    4.4206  17.4562  11.1093  14.2875   3.0627  -1.2111
-#>    0.6527   2.9872  14.8202  -1.0611  -1.8122   1.5132
-#>   14.1045  -4.4124  -9.2904  -4.5478  -2.2182  -0.7207
-#>   -1.4828   3.6784   3.2015   2.2996 -12.7189  -7.8033
-#>    5.9095   8.7084  -5.1051   0.3851  -1.0407  -5.2680
-#>   -6.3020  -4.3540  -4.5427  -1.1850  -3.6061   2.5747
-#>   -1.0570   4.7170   2.8429   7.9308   2.2215  -0.9558
-#>  -12.0819   4.4918   7.6590   2.5101   9.8169   4.9874
-#>    0.1378   8.4419  -1.8811   0.1350  -1.8110   1.3977
-#>    7.6449  -6.2310 -13.1248  -9.3925   5.5972   0.2361
-#>    9.6474   5.3826   7.5009  -9.5686  -8.0677   1.1340
-#>  -32.7488  -2.4500  -8.0628  12.2802  -0.2749  -3.8348
-#>    8.0790  -6.9282   3.2445  -1.6245  -2.1593   2.9972
-#>   -0.7125   0.4441  -7.0608  -6.4584  -8.6841   0.2525
-#>    3.5664  -0.2363  -2.8050   7.3713  -2.8613  -0.4915
-#>   17.1820 -11.7188  -3.8804  -3.7392  -4.7100  -1.1502
-#>    8.2372  -7.1018  -3.2752   3.9507  -2.3532   1.3612
-#>    0.1603   4.1866   2.4981   3.8943  -6.4633  -2.6680
-#>    1.7882  -4.5337   6.1890  -0.3154 -10.4658  -5.8276
-#>   -7.1726  -8.9011   0.1020  -3.4402  -8.8854  -3.4632
-#>  -10.3952  -2.3793  -0.3542   3.8725   4.9056   2.3551
-#>    7.1893   7.1117   1.6026  -7.5178  -0.4649   2.7714
+#>  Columns 1 to 8   2.7891   5.6461  11.2726   1.9882   3.1389  -7.7933   4.4229  17.3522
+#>   -4.3693   7.6364  -1.5425  -3.8255  -4.4841  -2.9940  -6.4672 -16.4546
+#>    0.0327   0.0078   3.5946  -4.1353  -4.3344  -6.5809  -0.6687  -8.7116
+#>   -2.7286  -3.9566   3.5076 -11.6647  -4.0101  10.4190  -2.4942   0.9421
+#>   -2.9444   1.1068  11.7706   6.0877  14.4685  14.3227   7.0167  -0.0975
+#>    2.0883   0.5287   5.0323   5.7100   4.4980  -0.1426 -13.9365   0.9413
+#>   -2.0620  -6.3690   3.5664  -6.0346  11.5299  10.3415  14.0979  -1.5701
+#>    4.1305   4.2571   1.9339   1.0847  15.3376  -9.6260   1.9086  12.2972
+#>   -0.4064   1.8530   3.1236  -2.5911  -4.6137  -4.8262  -1.5832   7.0104
+#>   -5.8752 -15.0220  -4.4444  -6.3587  -6.1152   3.2593 -11.4632   1.6653
+#>   -5.8421   5.4665   1.5583  -5.9300   5.9576 -10.8424  10.3203  -3.0753
+#>   -2.8996   2.3752   3.6025  -8.0325  -5.1392 -16.5147  16.3896   4.0846
+#>   -1.5363 -12.6707   4.9563 -12.6877  -4.8858 -20.6553   5.1575   5.8557
+#>   -1.5121   2.8635  10.2283   4.1047  -4.7667   0.5365  13.8085   7.6162
+#>   -0.3384   2.2910   5.9901   8.0042   3.7401   8.7724  15.4495   0.7961
+#>   -3.6876   0.6770  -0.0762  -7.1910  -8.5069   8.1194   1.8044   4.6803
+#>    3.5639   4.3075   8.1912  -3.7056   4.8064   3.7267   2.1989  13.8254
+#>   -0.4432 -12.2472   7.0346  -3.1157 -10.0137  30.1598  -7.0072   6.6565
+#>    0.3731   3.8682  -2.2841  -1.9968 -18.7218  10.1465 -17.4892  11.2863
+#>    3.2327  -2.9070   4.2799   7.6125  10.6098  -8.1591   5.8310   1.0487
+#>   -3.3853  -2.4264  -6.6538 -15.7584   6.6708  -2.3824   2.4382  -6.3140
+#>   -0.4255  -6.4060  -1.4134   2.3033  10.4701  -1.0757  -6.2709  10.4229
+#>   -1.4168  -6.1772  -6.9050   8.0146   4.7789   1.4485 -10.0194   7.0168
+#>    5.3006   3.2196   1.9223  -2.3551   8.1670   8.1758   6.6591 -11.6577
+#>   -4.0528  -3.2018  -9.0353 -14.9391 -14.1932  -2.4723   1.4463 -10.5708
+#>    2.7737  -2.4013  -6.7505  -2.2005   1.7287  19.6719   3.5308   5.2615
+#>    1.2905   2.5430  10.9490  11.6910   0.3056  -2.3869   7.7539  18.0730
+#>   -4.6555   7.8963   9.1508   4.2204   6.5066   1.2389   3.1907   4.6268
+#>   -1.2872  -5.9298   1.2936  -4.3192 -14.3200   0.1818  -5.3164   2.6740
+#>    1.5379   6.7997   0.7273   5.0672   6.1576   4.8976   6.5064  -2.6637
+#>   -6.5611  -3.4853  -4.7243 -13.0694  -2.1603   4.8007 -18.2887  -0.3439
+#>    3.6181   5.9698  -7.8726   9.0949   4.4630  19.2695 -16.6097  -3.5550
+#>    4.6032  -6.2528  -1.4542  -1.4308  -3.4709   1.5289  -6.0862  -7.9197
+#> 
+#> Columns 9 to 16 -13.3502 -10.9006  15.3224  17.3260  -8.3167  11.1412  -2.7912  -5.2268
+#>    4.2691  11.9275  -4.2768  -0.3375 -25.0954   9.3447  -4.0346   5.2689
+#>    9.1157 -19.2557   0.4819   3.0041   0.8986  -0.5568 -12.6354 -13.4393
+#>   -1.9457  -5.6830  18.5670  20.8668   7.6358  -1.9210  -1.3930  -3.1887
+#>   -3.1992 -10.8519  10.6605   5.7853   0.0061  -5.3224  -0.7501  -2.2627
+#>   -6.6866  -9.3200   1.9750 -15.0306   2.2915  -3.1248   4.0134  -8.8367
+#>    7.2783  -3.3737  -0.4615  13.4524  -9.7191  -1.0670  -5.9242  12.4941
+#>   -1.6839   3.4820 -18.7390  -3.8986  -2.3229   6.1428   4.9823  13.3527
+#>   -3.9977  -2.0847   0.8875   7.1335   1.6811 -13.0423 -10.3999  -6.6988
+#>    9.0746   3.6729  -3.3916 -11.3161  -9.9358  10.6504   5.9969  -0.3506
+#>    2.7198   7.5492   1.5438   3.8592  -4.6483   5.0131   1.4573 -13.3437
+#>    3.6485   5.0274   4.9936 -11.2782   0.2270  10.0538  -2.0702  -6.0261
+#>   -6.6231 -11.2644   7.1774   9.7326  -0.2837   4.7962   4.5807  -3.0669
+#>   -1.6483 -23.7315   4.3302   3.3571  13.7165  -0.6550   8.5681 -16.5317
+#>   -1.8355   0.5975  -0.3203   0.6874 -10.1596   5.2492  -1.2169 -11.3643
+#>   -4.6443   2.4558  -4.2523   8.1814 -10.1897  -4.2530 -12.1652   8.3655
+#>    5.3690  -3.9224  -3.1992  -5.3694  -3.2890  14.9747  17.4545  -5.3308
+#>    9.1016   4.0328   6.0780  15.6832  -5.6727 -20.5686  10.7428   3.9753
+#>   13.0496  -2.5721  -2.5217 -11.5500  15.4258  -1.8422   9.2925   6.0233
+#>    8.5035  -4.1750  -8.9295   5.7701   9.1853  -6.9553  -8.4313 -19.3226
+#>    2.9166 -11.0147  10.1236  -4.2951   7.6608  12.9772  -3.4486 -20.8659
+#>  -11.1108   4.4085  -3.7069   6.7342  -7.9812   4.2891  12.0735   2.7704
+#>   -5.0800   5.6861  -9.9988  19.4505   7.6321  -4.0207 -17.1867 -13.2480
+#>    5.4979   0.7310  -5.1859  -1.3413 -12.0993  -9.1632   4.7480  -1.7103
+#>    3.6900  -6.0383   4.4401   4.4768  14.5726   8.9373  -4.4441  15.4543
+#>   -2.0458  -5.3701  -0.7855   9.2217   6.3305  -0.1386  -1.3346  -0.7774
+#>    4.2399 -16.3759   7.3887  -1.5502   9.8142  -2.0231   2.8552  -9.4405
+#>  -15.3747  -0.1222  20.4641   9.7776  -9.1597   3.6420  -2.2226  10.6921
+#>  -15.7407 -12.6524  20.7552   1.8564   9.5878   6.5725   1.0907  -4.1684
+#>   -5.9031  16.2492   3.8512 -14.0848   2.3558  12.0028 -12.6915 -14.5405
+#>   -3.0352  -2.2557   1.2893   6.8459   8.3554   5.8113   1.5446   9.2033
+#>  -14.0303  -3.6214   3.6899  -4.3906 -14.9126  14.7248   0.4015 -11.0723
+#>    4.9811 -13.0231   6.2604   3.8745   4.4099  -0.2151  -3.7590 -29.0314
+#> 
+#> Columns 17 to 24   0.5903   1.2292   4.4785  13.8327   5.3019   5.0653 -15.5849   2.4288
+#>   17.6620   9.9821   0.2180 -11.9256   0.3329  -6.3259  -2.0938  21.7199
+#>    1.8970   1.9715  -1.2359   1.0619   9.6286  -2.9894 -12.5589   1.5861
+#>   -5.1145   5.7224 -15.1579  12.4867  -1.6384  11.9790   2.1090 -10.0775
+#>   -3.0872   7.7246   5.7323   4.7021  23.8640  -1.3152   5.0465   1.0895
+#>  -18.0686  -7.3630 -11.8334  13.1770  13.5497 -10.7829  14.9718  13.9289
+#>   -1.3626  -2.7023  -3.1864 -12.9975 -15.3546  -0.7801   0.1602  15.4471
+#>  -18.7058  -0.9208 -15.8708  11.7466 -14.2495  11.2790   6.0995  25.0583
+#>    9.1240  -1.4579 -20.3905   5.6573  -0.7184  -5.6087  -6.3994  13.0453
+#>   -2.4425  -0.7989   6.2329 -13.7502  -6.8374  10.4169   0.8800   2.4768
+#>   -9.2049  -7.4061  -2.7655  -6.5597  -0.7682  19.4496   3.7899   3.6785
+#>   13.0414   4.9492 -11.5959 -21.0042  15.5289  -3.2080 -14.2575  13.0175
+#>    9.2355  -1.7161  -1.4585  -1.9030  -4.3101  12.4132   0.0779  10.9919
+#>   -1.0625   9.6083  -7.3377   3.5531  -3.9269   1.4615  24.4355  14.5865
+#>   -3.5285   1.3685  -5.5002  -5.1391  -7.4575  -1.4907   4.1108   4.2759
+#>   -4.8534 -18.5523  -4.9735  -2.6078  -3.6643   4.0607   1.4195   7.1534
+#>   -5.0158 -11.4926  -4.3159  -6.3126 -20.6569   3.9367  10.3564   2.0483
+#>   -1.6701  15.4250  -8.6130  -7.0511  -4.8020   1.9636   8.4343  -4.1786
+#>   -0.9257 -19.0060   2.1396 -10.7141 -11.3384  -8.6834  11.7603   0.7399
+#>    2.0826 -15.4453   0.2871   6.8400   0.7255   1.9984 -11.5148  -1.4323
+#>  -12.0348 -20.5719  10.4165   1.0737   7.7002  -3.7523   1.1721  -1.6846
+#>   -9.7998   9.9020  -8.4249  13.2797 -10.6123  -1.7268   7.7460 -22.1409
+#>   -9.1708   8.8775 -19.2015  10.1956  -0.8793  -3.3652  -7.5626 -20.8103
+#>  -15.5172  13.8040   5.4655   4.2733  -6.6152   3.4871  17.4479  -0.1538
+#>   28.4350  10.8799 -11.8118  -1.1315   5.3741 -19.2895  -0.1006   2.0684
+#>    5.7723   5.5244  -4.1145   8.3152  12.6638  -4.6514   0.9151  -8.6046
+#>   -5.0254  -2.3961 -13.0114   2.8701  13.2636  -3.8114  11.2217   6.7448
+#>    8.7154  -0.9041 -11.4255   2.0557  -1.8496 -18.7856  -2.5219  -7.3274
+#>    7.9779 -24.7783  10.9781 -11.2546  -7.0707  -1.2906  13.9495  -1.3816
+#>    2.4164  -2.9488   0.4156  -4.9939   8.6019  -1.6816 -18.8522  -5.1339
+#>  -13.3422  -6.2524  -0.1487   8.1645  -7.4022   4.6731   1.7811   1.5432
+#>   10.4236 -16.5892 -13.3360  28.1620  -1.8771  -9.6515   9.4042  -0.3092
+#>   -8.9722 -21.2798  22.1872   3.7967   0.6152  -1.5334   4.3920  -8.5679
+#> 
+#> Columns 25 to 32  -1.6424  -2.4765   9.3092 -11.3510   9.9788   5.8714   5.4902 -15.9698
+#>   12.4106  -0.6024 -11.0791   8.7927   1.0994 -17.3039 -26.4455 -13.0543
+#>    2.2594  -5.4852  -3.4057 -12.2934 -12.9057   4.9216   3.0801   5.6059
+#>   -1.8950 -11.5884  -2.7283  -1.9462   1.7399  -5.8023  -3.7597 -10.0243
+#>  -14.3559  10.5322   6.8887  -0.4751  15.8738  -0.9240  10.1749  -4.9763
+#>   -2.6932   0.5575  -0.6663   1.8782   4.0940  -6.2931   4.2382 -16.0185
+#>    2.6599  12.1880   2.8019   2.6629  11.2282   4.3514  -5.3328   0.9144
+#>   -8.6377  19.9402  -3.4128  18.9311 -10.6883   5.2146   0.0675  -7.2361
+#>    4.0462   5.1910  -6.2741   2.8696   8.4610  10.5984  12.8530   9.1585
+#>  -12.2206   1.3749  13.5455 -12.3921   2.5813  -5.1370  -4.7817   6.4752
+#>   20.4107  20.8562 -14.5576  -6.0688   0.1366   1.5356 -10.7728  -3.7383
+#>   -1.8856  12.4265 -15.4950 -18.6076   4.0694  14.0289   6.8498   5.6447
+#>   -6.9311   3.9407  -0.4233 -10.8357  -3.1333  -0.6132  -9.7033  -9.6968
+#>   -9.2422   1.9215 -12.0507   9.8397  -6.2344   1.0666   9.6721   2.1904
+#>   12.5287  15.5544  16.8050   2.7052   0.9099   9.5372   6.1023  -6.0156
+#>   13.4405  -7.6034  -4.4066  -8.2761   3.8420  -9.2116  -0.8592  -4.7290
+#>    1.4604  -9.0550   3.2961  -2.5062  -6.5379   1.2413   9.0860   6.8267
+#>    1.1640  -8.4296  -0.6172  -6.5487  -5.0897  -2.5419  -3.1881 -11.7350
+#>   -4.8023 -18.6926  -5.3272   4.1101   1.2480  -1.6659 -17.4415  -6.0486
+#>    7.2006   3.4588  20.5035   4.3221   4.4193   7.5548  -2.9902  -2.9720
+#>   -9.4837  -8.7489 -17.2334  -2.5913   9.0866   8.9060  -1.0552  11.8623
+#>  -23.3699 -13.4060   0.2063  -0.1960   0.0870 -16.9977   1.6882   2.1954
+#>    9.0821  -2.3378   2.1622  -0.3262   2.0681   4.5297   5.4300   5.7771
+#>    6.7068   6.7965  -2.2693   2.7424  -5.4353   7.3393  20.1439   4.3987
+#>   17.7561  10.4224 -12.1841 -10.5163  14.1629  -3.1522 -18.3901  -8.9442
+#>   -2.9116 -16.6984  -9.8374  -4.9267  14.1725  13.5858   3.1658  -6.9405
+#>   -2.5623  12.7188   9.3896   4.3581  -7.7263   1.2252   3.7008   0.0457
+#>   10.8466   7.9068   7.7756  -2.1754   6.9143  -6.5399   3.6235   1.2716
+#>    0.2789  -1.6467  16.3595   0.5726   5.6597  13.2393   5.3822  -8.2157
+#>   18.8431   3.2904  13.0234  -9.0880   7.3523   3.3317  15.0342   4.2243
+#>    7.8590  -6.2939  10.7964  -2.9619   7.0402  -5.6279  -1.4979   7.6217
+#>   32.7250 -11.7462  20.2352  17.2657   4.4180 -13.8541  11.9462 -20.9230
+#>    1.1396 -12.7938   7.1452   0.5081  -4.8631   5.3434   4.9601  -2.3946
+#> 
+#> Columns 33 to 40  -0.2619  12.7161  10.7242  15.4972 -12.7757 -11.5587  -8.9834 -12.7978
+#>  -13.9851  -2.0933  -6.0986  -6.2693  -0.6607  23.6650  -7.4972  -0.4155
+#>    4.3713   6.8333  -1.7626   5.5788   0.8095   7.2280 -10.1679  -0.3738
+#>  -10.6305  -6.8019  11.1522  -0.0075  -7.1234   3.4740  -1.1015 -10.4914
+#>  -17.5438  24.0233 -17.5758  -0.3541  14.3009  -7.6145   7.9742   6.2297
+#>    5.7447  -5.7513  -9.7239  -2.2230  -2.7103  -7.5661   4.4542  -5.4678
+#>    7.4828  -1.8481  -7.2217   1.1582   1.7979 -10.9675  10.1704   7.2448
+#>    6.3663   9.9856   4.2320  14.0903  -2.8358  17.7403 -24.4428  -6.7781
+#>   11.9790   2.8801  -4.8997  -0.3844 -16.0260   1.7895 -14.0649   6.7327
+#>    3.8283 -20.5070 -13.9063   3.6265  -1.7224   4.8390  11.2439   8.2543
+#>   -4.5104   1.0990  -7.9801  -0.3267  -9.5771  15.7856   1.2381  -2.8860
+#>    2.1250   8.0618  -4.4884 -17.2684   0.6274   9.9014   8.3939  13.9658
+#>    8.2922  -1.9465   3.2701  -4.6437   4.2321   2.4474   0.8845  -2.2747
+#>  -13.0043 -16.3209  -9.6747   9.0214  -7.6230  10.8326  -7.8567  -8.1656
+#>   -7.0567  -4.7429   7.5084 -12.7796 -10.0529  -3.0678 -10.7715  -6.5500
+#>    4.8686   4.5588 -20.7367  -5.0031   9.2900  -5.3462   6.4064  -2.0021
+#>    7.0105  -8.8632   7.5515  -1.7277  -7.0501   3.2268 -16.9346   1.2673
+#>   -1.2122  -8.3589 -11.9257  -5.2638  10.6705  10.1972  20.4345 -14.5363
+#>   10.5597  -0.2706 -17.3127  -6.0143   2.6291  14.3160  -9.3253  16.0324
+#>    2.3626   7.3991   6.0970  -1.0597   1.6514 -14.9038  -7.1639  -3.1538
+#>  -10.3637  -9.0861  12.4378  -9.6552   4.5593  -1.1351  -0.8175   4.1669
+#>   10.5016   4.8836   4.7881   6.2513  -4.8725   8.8854  -5.2061 -12.0160
+#>   -3.8515   3.6451  14.1298  -5.3823 -15.4148  -1.7431   6.9765   4.0277
+#>   -3.8197   7.7999  -4.2841   3.9069 -19.1509   1.8881  -2.7426   9.3630
+#>    6.0422  -2.9553   5.0320  -2.1669   5.1896   0.2393   3.9858  -3.5060
+#>    5.3165   7.4829  22.5830 -10.8877   0.1544   6.5125 -10.4479   1.0537
+#>    5.2859  13.8024  -2.3394   6.6578  -9.8351  -7.5973 -11.8256  -3.4882
+#>   -2.9636   0.7640  -1.8511  10.2530  -0.3724   8.3940   8.0818 -17.0425
+#>   -2.5797 -16.9501  12.7641  -4.4602   0.3296  -7.6189  -3.5270  -1.6177
+#>   -7.8829  -8.2571  14.9438 -11.2490  -9.1534  -8.5257  -8.3059   9.0745
+#>    2.2279  -5.0084   2.0301   3.6759   2.2980  -9.7950   0.5601 -10.7693
+#>    1.7267 -15.1450  11.2855   1.0800  -2.0005   3.6635 -21.7263   2.5000
+#>   -3.5535 -14.2293   3.0653  -6.4028  -9.7550  -7.3614   2.3882  10.9056
+#> 
+#> Columns 41 to 48  -0.2569  -9.5139  -4.2866  -2.9261 -12.3800   2.6025  10.5975 -13.0938
+#>   -6.5529  -1.2092   3.5724 -10.5480 -11.9196  -5.3149 -17.9613   9.1899
+#>    1.3782   4.1817  -7.9139   0.1709   4.2652   4.2287   9.1069  -1.8842
+#>   17.8498   5.9753   4.0421 -11.4002  -4.4282  -3.4945  -4.7283  -6.2333
+#>   13.6016  -8.0730 -17.3541  -3.7150  -6.7762   0.1805  -3.1156  -7.3046
+#>    7.5814 -11.6236   8.9773   0.9687  -0.5822   3.8360   9.0609   2.7697
+#>    4.8157  -1.9007  -3.3362  -4.6769  -2.1371   1.1593  -6.6814 -13.3274
+#>  -10.6753 -16.6580  -5.7623 -12.5975  14.0259  -1.2657   0.0719 -11.5077
+#>   16.4422  12.9995   4.5997   7.5588   5.5025  -3.2281   3.3950   3.4887
+#>   -7.3649  13.8673 -19.2715  -2.2774 -12.7737  -2.3281   3.1734  -4.8774
+#>   -7.1085   2.0625   7.2735 -14.2468  -3.7661  -2.4637  -2.5334   7.2650
+#>    4.5905  21.6741 -12.1799   3.7120  -5.2773   6.8621  15.6738  11.2954
+#>   -1.3936   3.1606 -12.2960   0.0098  -7.2469   5.4153   6.4829 -11.2436
+#>    6.3742  -0.7576   4.1856   1.3057  -7.2457 -11.4027   1.6516  -0.2674
+#>  -19.8319   2.3468  11.4316  14.1998   1.8338   5.0958  -6.7178   2.4970
+#>    3.6518  -0.3007   4.1743 -12.7341  17.8384  13.4231  13.6527  -1.1151
+#>   -1.5634   4.3176   0.2550   7.8061  -7.5150   2.0949  12.4934   8.2512
+#>   -5.2670  10.3745   4.6012 -21.4855   0.3423   2.2103  11.3690 -21.6348
+#>   -8.8169  10.5383   0.4247   0.0161   2.6061  11.3677   4.2398  -6.3025
+#>   -0.6777   2.8157 -12.5293  -0.8502 -12.2659   8.5000  -1.4116   8.4668
+#>    7.5280   5.8360   3.6033  15.3199   6.4199  13.4714  -7.0552 -12.8062
+#>   -7.3435  -8.2162   4.7295  11.5650   5.9780   6.6564  -9.9283  -2.4459
+#>   20.3087   8.9068   5.2203 -13.4446   4.6801   1.6056   3.1806   7.3509
+#>   -6.7363  -1.3154  -3.4538   6.5529   0.7935  -2.6589   4.7125   5.5222
+#>   -2.0835  -5.3218 -18.6172 -11.0490  -2.2373   7.8153 -12.7983  -7.3268
+#>    1.7505   6.4871  -5.2299  -8.5925   2.4491   8.8033   4.8963  -6.5391
+#>    6.2808  -3.8198 -17.8336   6.1843  16.0659  -1.4005  13.6031  -9.3461
+#>    6.4763 -13.9038   3.5939   9.4351  -9.8249   2.5886  -8.1894  -8.6189
+#>  -10.4219   6.3559  -1.9367   3.6815 -12.9632  -3.0950   1.5879  -0.3378
+#>  -13.0734  -9.6310 -12.4734 -15.8620 -13.2280  25.8353   1.9425   5.3443
+#>   -1.0289  -8.7732 -12.8032   3.3817   9.9112   7.1954   4.1474  -5.3028
+#>    9.4079 -10.5230 -12.3552   1.5561  11.0246   2.5046  -4.1856   7.8783
+#>   10.7292   2.6453  -1.6227   4.8396   6.4339   8.1401  -0.2312  -0.1694
+#> 
+#> Columns 49 to 54  -8.5980  -6.5352   1.1090  -0.7174   3.1088   3.6585
+#>    7.2771  16.4819  -4.7899   0.3398   2.7190  -6.9382
+#>   -1.0847 -14.3245  -1.4476   3.7634  -1.1584   1.3540
+#>    5.8353 -19.0570  -8.7585  -8.0196   1.2538   2.6888
+#>   -5.8740   5.2906  -1.1292   1.9004   3.7302  -2.8623
+#>    8.4203   2.1144   8.5652   6.3670   5.8550  -1.8026
+#>    0.0371  17.3777   9.7877   3.2500   1.9215  -4.5138
+#>  -11.1362 -11.2049  20.1779  -1.5833   5.2968   0.2506
+#>    2.9318 -10.4597   7.5047   1.9741  -1.1214   3.3391
+#>    4.3523  10.0785  -2.7405   1.8364   0.4232  -3.5657
+#>    1.8942  -9.2101   5.7043  -6.8886   2.1016  -1.0968
+#>    3.6432  -5.1992 -12.4703  -3.3176   2.4760  -0.4288
+#>   -9.1095  -8.0913   5.7376  -3.6167  -4.1750   1.4780
+#>   -1.5986   7.1605   2.6784 -12.7939  -5.6978  -2.1152
+#>    3.4665   7.1060  -8.1521  -2.9817  -3.6896  -3.6559
+#>   -8.3366   7.4986  -5.1208   1.1779  -2.3170   1.2639
+#>    0.5367   0.7394  10.1420   3.0481  -1.0416  -3.6655
+#>   -0.0778   6.1623  -5.8228   3.2258  -5.6881   1.3782
+#>   10.4587  -2.5889  -1.9695   3.4009  -5.9180   2.5792
+#>   -3.6272  -6.1581   5.3198  -1.2878  -2.7789   2.4301
+#>  -10.0869  -3.6536  -1.9521  10.3700   1.1489  -3.8861
+#>   -1.0637 -15.3969  10.2382   4.8546   4.3392  -0.8770
+#>    6.0377  -8.2065   6.4166  -2.2024  -7.9182  -0.0882
+#>    3.1750  -0.5693   7.1182   2.9453   0.0960  -6.6956
+#>   -5.8022 -10.2795  -8.4465  -0.4040   0.5724  -2.2128
+#>    0.7772   3.8755   1.6506   1.9345  -2.5504   1.3202
+#>   -8.0783  -2.7738  -3.6769   9.8188  -7.2214   2.4513
+#>    4.7323   2.3894  -0.2689  -6.2017  -4.2145   3.0817
+#>   -8.0090   0.0731  -6.2789  -7.6835  -3.4595   6.5530
+#>    6.2038 -11.0191   0.9809  -3.1851  10.0065  -2.9394
+#>    1.4329 -16.1954  -7.7916   4.3592   2.5994  -1.6995
+#>   -0.9031  11.7154  -4.9832  -8.6200  -0.8326  -1.8789
+#>  -10.6094   1.5200  -6.3085   6.3027  -3.7259  -1.5837
 #> 
 #> (10,.,.) = 
-#>  Columns 1 to 8   2.2363  -2.1157  -3.9890  -1.4472 -12.1587   1.9112  -0.6772   2.6816
-#>   -0.5097   0.8234   2.5852  -2.9076  -6.4124  -4.2266  -6.0710   1.3366
-#>   -5.7555   2.2560   4.2838  -1.0270   8.4408  16.8556  -2.1342  -1.6465
-#>    0.5127  -2.0896   1.1144  -0.6333  -3.0143  -9.4323  -1.9678   1.8573
-#>    1.4213  -2.9222   6.7214  -9.3739   3.3599  -8.5956  -6.5851  -9.5308
-#>   -4.4239   7.0383   1.5490  -1.8766 -19.2846  -8.9789   6.5690 -12.5547
-#>   -4.9949   2.4635  -9.9984   3.4919   3.7359  -2.8006  -7.1007  19.4063
-#>    0.9228   1.9885  -0.7494  10.7508 -14.2172  14.6552   7.3359  -7.5264
-#>    8.4246  -6.7451   0.3721   4.4351   3.0836  -5.5432   9.8858  13.3133
-#>   -4.4414   2.3311   8.9908 -10.3337 -15.8835  -5.9958  -0.2566 -16.4942
-#>   -6.2991  -1.0376   8.1352   2.4895   7.1721   5.1369   5.8482  -9.7590
-#>   -0.2901 -10.0629  -0.6162   4.4312   3.4176  -8.6422   5.5362  -2.1296
-#>   -2.1814  -7.6086   5.7908  -6.4812  -8.6060   5.2281   4.8243  -3.4728
-#>    3.2813  -1.3609  -0.0201 -12.3297  -2.0040  -1.9826  -3.4319  15.8522
-#>    0.5173  -4.4348   2.7485   4.3449 -20.0676 -13.4316   6.2142 -15.6838
-#>    1.4934   1.6267  -5.8460  -4.2022   7.2496  -0.9272   0.6064   4.2118
-#>   -7.5285   1.3303   3.2797   6.9902 -10.4241 -16.2242   9.1904   4.0080
-#>    5.1461  -5.2510   0.6457   1.7364  -2.2899 -17.4661   4.1976  10.2075
-#>    5.8689  -0.0052 -11.2868  -1.8224  19.4149   8.4131  -1.6432   4.1972
-#>   -1.6723   5.1238  -1.1278  -8.6816  -5.3589  16.0121  -6.3449 -11.0641
-#>   -3.6626  -7.9673   3.6362  -7.0441   6.2639  15.9587 -14.8696  14.3943
-#>   -1.4947  -3.7085   3.2552  10.2461   2.8955   0.1107  13.2408  -2.0492
-#>   -0.9754   5.7900  -2.2947   0.9517  18.2419  -2.0235  -7.2679  -3.8560
-#>    3.6322  -3.7929  -5.4700   2.1984  -3.2789   8.3245   5.7207  18.7523
-#>    5.2059  -0.2214  -2.8451   1.5933  -0.0468  -8.6477   0.1390   1.3842
-#>   -1.2361  -0.2643  -4.9882  -0.0226   8.1592  -3.9915  -1.6402  -2.3398
-#>   -0.0395  -5.7144  -8.8134 -10.7175  -6.4796   0.8209   4.4671  -2.2380
-#>    4.9342   4.5964   1.2061   0.4309   2.9231   0.1605  -1.3336  -8.6909
-#>    6.0918  -0.9102  -4.8624 -10.4539  10.7629  -5.7115   8.8977   1.2112
-#>    4.2715  -0.6758  -2.4629   7.5929  -9.4385 -23.0817   6.5401   0.1794
-#>    1.2896   1.9627  -0.7723   5.5046   3.7965   0.8929   1.7333 -12.3365
-#>   -1.6969   0.4768  -2.0244   8.8202  -0.3477  -9.0021  -7.3399  15.2483
-#>   -5.3663  -2.8496   1.1370   5.4573  -9.0503   2.6561   5.2536  -5.2445
-#> 
-#> Columns 9 to 16  -1.9999   0.2756  -3.0347 -12.2593   3.9656 -27.9082   6.1364  -6.9188
-#>    2.1805   3.2268 -20.1301  -2.3668  -1.9788  -0.9791   7.7772 -13.9050
-#>   10.4914 -14.8245   5.8770  -2.1505   4.3295  17.9494   3.2332  15.6589
-#>  -11.3138  20.1980   2.4907   1.7703   1.7816  12.1020 -16.8125  19.6667
-#>  -12.8666   9.0573 -20.4490   7.0089 -22.6402 -28.0731 -14.0704   2.2834
-#>    3.1369 -21.4233  -1.3923  -9.6448  -9.6338   5.1032 -20.7964 -10.3669
-#>    4.1008   9.9791   9.0406  -1.1264  -4.4379   8.5063  -1.5124   8.5893
-#>  -11.0239  -6.4762  10.6674   2.0926  -1.1683  15.1673  10.6290  -4.3064
-#>    9.1525   0.5704   3.5861   7.8905  16.3161 -14.5311   7.6131   1.7053
-#>    5.6472 -29.9151  -6.5300 -20.4768 -10.8918  -3.2348 -23.2803 -10.3696
-#>    9.7585 -10.2933  -3.6008   7.3111   9.5163  11.7164   0.6670  -3.8617
-#>  -17.8701  -2.2579   4.9358  -3.7360  -7.5334   2.2365  -1.2352 -15.2224
-#>    4.6645   8.1105 -11.1601   3.5535 -20.6257 -13.1826  15.6582  -9.6127
-#>   -1.1510 -13.3487  -2.8594  -3.8370   4.1328   7.3028  -8.5192   0.5595
-#>   -3.8685 -14.6638  10.8199   2.5929 -21.6798 -18.7749 -20.0722  -6.4741
-#>   -3.6035 -13.9078  16.3609  15.9312  25.7936  -5.2368  -4.4059   1.2649
-#>   -8.0685 -13.5076 -16.3485   9.0274   3.7127 -14.7530 -16.8515  -5.1507
-#>    1.5580 -15.9283   5.5596  -4.7943   7.0539   2.2376  -9.1937   4.7592
-#>    2.2859   8.7128  -2.9579  -8.4410   1.2170 -10.6162   7.5178  -5.7933
-#>  -18.0433  -5.6030  -8.8691   8.4996 -26.8533 -17.3229  -0.6320   5.0746
-#>   -7.9684  18.0999  -9.5078 -11.4769   1.3021  13.0954  -2.8317  -3.7281
-#>   -2.6961   2.3346  -4.2845 -11.5774  -4.2805  -2.6057   3.8918  -0.9071
-#>    3.4042 -13.9383  -8.8197   3.8370 -11.1391   3.3404  -2.8338  21.0822
-#>   16.8104  11.1530  13.4996  -9.7915   8.7716  -2.5054  12.0095 -11.5617
-#>   14.1501   1.8761   6.3314 -10.1340  -7.7371  10.3086  13.3223   0.9572
-#>   -0.6895   9.7227   9.2780   1.8902  -8.7706  -9.8212   3.1535 -13.4027
-#>    9.2930   3.7463   1.2078 -19.1367  -7.3601   7.2493  -4.3065   4.2799
-#>   -3.2989  -6.0517  -4.5894   3.2898  14.5887   4.2458   4.4147  16.0410
-#>    5.0267   8.3714   0.1451   6.8633  16.1347  -1.1124   5.0697  20.6016
-#>   -3.2677 -17.3143  -7.0599   0.8349 -13.2482 -17.2869 -13.9283   8.5212
-#>   21.3014  -3.3553   2.7272  -0.7305 -11.4003  -4.8428   9.4332   1.6574
-#>  -15.5308 -11.2574 -10.1778 -22.1764 -18.2551   6.8741 -14.3040   7.3803
-#>   12.2890  -8.7037  -0.7001 -30.3416  -4.4090   7.3282 -19.0024   5.6816
-#> 
-#> Columns 17 to 24  11.3415   1.8475   1.1421  -1.9459 -20.8720  -4.1786  15.6824  -8.0598
-#>   -9.8086   1.3116   8.6122  -8.0612  -3.7797  -2.9260  -5.7208   0.0336
-#>    5.2273  11.1455   1.5187   3.8744 -12.5833 -14.4831 -12.2603   7.3923
-#>  -25.1665   6.8883 -12.9580   0.7983   6.3732   0.4855  -4.4769   0.0224
-#>  -23.7427   8.5282 -13.7325   2.0021  -7.4680   6.8203   1.4800   2.3046
-#>  -10.6971  -2.8438  -5.5806  25.1138 -11.0431  15.1867  -4.4431  15.7058
-#>   -9.2288   0.0881  -3.0729 -16.7718   0.6031  -6.5455  -2.6865   1.0175
-#>    4.0480  10.4326  11.0936 -11.2328  -8.6291   4.2397  -7.3878   0.1333
-#>    1.5080   1.1348  12.0883  -6.0027 -18.7562 -11.7351   7.0525  -7.5212
-#>  -16.6178  14.5494  -7.9325   1.3820  -6.5049   3.9571   2.1005  -2.2390
-#>   30.8940  13.3603  -0.6191   2.7103  -6.4713  11.2503  -9.1462  -2.0153
-#>   -2.8805  -5.2401 -11.1418 -16.3078  -3.2876   6.3194   4.8781   2.4407
-#>    7.1361 -22.5099 -10.3275   2.2974   8.9481  12.9058   9.1893  -6.4378
-#>  -23.3701   5.0497  -1.9278   4.7373  -1.8211  -5.9550 -10.0134 -10.5139
-#>  -11.5440 -10.2881  -7.1974  -0.4086  14.8722  15.4445   5.4690  -6.9529
-#>   10.5349   8.7367  -5.6713  -8.3642   4.2299   4.1340   2.8754 -13.9507
-#>   -0.5988   5.8704  -8.2566   5.5887 -14.3388   7.6471 -11.1970   3.2961
-#>   -0.2537  10.4046  -9.4422 -10.4438  -7.7936 -14.9798  10.1950 -15.7083
-#>  -21.3917   6.3735   6.5893  -2.8601 -14.1835   2.0417  -2.5771 -13.8505
-#>  -23.9282 -19.6325  -5.5975  -8.8023   1.6138   0.0271   5.3163   4.2856
-#>    2.1600  -5.2791  -5.4355   4.8082   1.0244   2.1854  -3.3372  16.7090
-#>    6.8382  -2.4120  21.7143   4.5327  -0.8197   4.1578   7.4679   3.8636
-#>    4.2070  18.5672  -7.0864   5.2927 -12.6891  -6.0291   0.3712   6.0586
-#>    8.6083  -6.0751   1.5819  -4.1973   6.8597  -7.1708   2.9634  -7.9239
-#>   -9.8341  17.0530  13.1495   1.2774  17.5114   1.3631 -12.2239   7.6702
-#>    0.1338 -17.9398   5.5986  -1.6880   3.3359 -13.7695  15.4135  -8.5532
-#>   19.4245 -18.9081   5.8314  -7.6547   8.9253  -9.3756   5.7626  13.6244
-#>    4.7026  22.9031   8.1282   5.4685  -7.1700  -1.5186   2.0961   5.5197
-#>  -14.1119   6.1434   1.1908  25.2888  -1.2966 -10.8851  -0.1169  12.7462
-#>    5.1816  -5.7916  11.5887   1.7730  15.3466   9.7472   7.4484   6.8338
-#>    1.6397 -10.5599 -10.0957  11.6892   8.6562  -9.3152  -3.9449 -10.9035
-#>   -4.4536   4.4691  15.2725   0.5402   3.1361  -6.1849   2.5591  -6.1711
-#>   -1.8157  10.1398  -5.5183   1.7201  -1.8657  -3.3718   1.1055   2.2464
-#> 
-#> Columns 25 to 32   6.4815   5.6893  -0.9227  -3.1664  -6.2062  -4.3568   4.5600  -3.0158
-#>   12.8531   2.1956   1.1480   9.7768  -3.6533  -9.2790  -7.2291  -0.9515
-#>    4.8664  -1.7881  -7.7357  17.3084  -8.7027  13.6272   1.0505  20.4756
-#>  -17.7767  14.0274  -2.2175   1.9212  -6.6565   5.5522  -2.8884  -1.8843
-#>   -0.8330  11.0690   8.7098   1.9187   0.7351   3.5510   4.4887   5.0016
-#>   -4.3257   8.1221   2.6468   0.4250   0.2066   2.7744 -10.2055   5.2186
-#>    1.2346   0.7154   1.0156   6.1360   4.9314  -6.6754  13.3389   1.6581
-#>   -1.7103  -5.5087  -3.7728  10.8461  -4.9028 -10.6431  -5.0634  -2.1074
-#>   -0.2228  -0.4752  -7.8371   1.3122 -15.3493  -8.4393 -11.0277   8.3516
-#>   -5.9093   9.9790  -1.4842   1.6851   1.4589  12.7874   4.4788   9.0027
-#>   -0.4178 -19.4052   1.5148  -3.4644  -5.5994  -8.1708 -26.6034   5.4712
-#>    3.9157  -0.2716  19.1154  -0.7381  -3.5439   6.2129  19.1589   4.5414
-#>    7.1570  11.1482  -7.5456  -4.1460  -4.8931  13.3967  -3.7882   7.5528
-#>   -1.6330  -0.4864   1.6889  -9.7207  10.3118  -3.4479  -9.1503  -2.1856
-#>    5.0068  12.6816 -11.8438   4.0864   0.2849   7.5290   8.5680  -6.5796
-#>    1.9399   2.8008  -0.7312 -21.1728   5.3786  -3.8231   7.9454   1.1585
-#>   -4.7803  18.4195  -2.4929  -5.0381  12.4804  -1.6395  -1.5868  -1.6330
-#>  -13.5353  -7.7027   0.2612   3.4031   2.1050   9.2101   3.0357  -8.1287
-#>    2.4584 -16.7182  -4.7704  -2.7177  -5.0968 -22.1300 -13.1682   5.2942
-#>    8.8140   6.0968 -12.3037  20.1467   3.0207  16.8412 -15.3083  24.1607
-#>    2.2434  -1.5179   9.1971  -1.3209  -6.3701  10.1950   4.6083   7.7751
-#>   -8.9829  -4.7765   7.2443  -5.7954 -16.3653  -1.3180  -6.4603  -1.6012
-#>   -2.6718  13.0925 -13.5202   7.0508   4.7404   7.2232   4.7078  12.7123
-#>   -0.9451   7.0915  -2.9803  -0.7490  -5.6806 -15.7917   0.3480  -9.7282
-#>   22.9171  -2.5859  -2.9637  12.2590   3.6550  -3.5028   8.7403  -2.8165
-#>   -6.8268   1.9172  -9.0970   4.3419  -7.7849   7.8244  -9.3282  -0.7085
-#>  -11.6038   0.0854   1.1722   3.9985  -2.3884  10.6390   4.8932  -1.6232
-#>    6.2525   1.6452   2.3449  10.9179   2.4850 -11.0971  -8.7406  -2.9657
-#>   -4.8382  -4.9508   1.7669   5.4140  10.2555   0.8560   5.6142  -5.5381
-#>  -12.4554   7.3132   3.8855   2.8679 -12.0267   1.9528 -11.5046   0.5150
-#>    1.5025  -1.3696 -11.2275  -4.3624  -0.0759   0.8373  -6.3970  -9.6812
-#>    4.5267   8.3538  -7.7346  -3.5750   1.7459   3.5132  -3.8898  13.1212
-#>  -18.6642   5.1290   7.8952  -2.3550   4.7407  -7.8264   5.7572 -14.2221
-#> 
-#> Columns 33 to 40   8.0496  -4.3295   1.8407  -8.9827   7.7951   0.9815  -3.3443 -10.9863
-#>   -3.0410  10.0320   5.7510   3.9766  -7.4743  -2.5651   2.2061 -10.4112
-#>  -10.6715  -1.7391  -1.1629   1.3610  -5.0009   0.7881   7.1177  11.1729
-#>  -18.8247   6.3556  -7.0350  11.2825  13.5470  17.5848  -3.1697   3.9358
-#>    0.6382   2.6878   3.0333  10.8931  -6.3605  29.8396   6.9935   7.7576
-#>    9.3820   1.4815  -0.3291  -6.9713  -3.5959   9.0425   5.1229  -5.8787
-#>    1.1551   6.9779   7.5625  -5.4963   3.3594  -3.2003  -8.1196   3.4297
-#>   -9.8684  12.0555 -15.4573   1.9966  -8.1726  15.2350   8.3469   1.2325
-#>    9.0008   5.6839   1.3163  -6.8948 -17.9637   1.1142  -2.0898  -4.6909
-#>   -3.6163   2.3786   3.5571  -3.2093   1.1328  16.8561  -1.2516   7.7491
-#>   -1.7443  -8.1475  -6.9335  21.6469   0.9863  -0.8011   5.3470 -11.8187
-#>   -4.3721   7.7496 -15.3290 -14.7002  -4.6889   9.6844   5.8437  -5.3226
-#>    9.4806  -3.6792  -5.3814   2.6639   0.3295   9.1594 -12.4843 -13.4395
-#>  -15.7484   6.3044  -6.4635  10.6292  -1.2039   1.8374   6.8766   6.9814
-#>   -4.5046  -9.6416  -6.1771   1.9989  12.0743   0.4090   9.4904  -8.5855
-#>  -19.5573 -13.9309   4.8088 -15.0457  15.1453   7.5878   1.0427  -0.1884
-#>   -8.1178 -12.3319  10.5809   0.4396  -3.8857   7.0447  21.8247   2.4879
-#>  -10.7616  21.6806  -9.9731  -0.2034  -8.8837  13.3950   0.4176   8.9088
-#>   -1.9661   7.1422   7.4603  -2.7760 -14.4412 -18.2499   3.2360  10.8341
-#>   -3.7387  -1.8276  -4.5835  -1.3713  -8.7792   8.4404   4.5047   5.8481
-#>    0.9969   2.9623 -11.4680  -2.5941  -0.0900  -2.7210 -19.6152   3.4699
-#>   -7.1210 -11.8914   1.7412  -5.4167   4.6137 -12.9696   2.6714   5.5981
-#>  -17.3747   1.1312   1.7433   8.5802  -1.5415  14.1470  -9.4081   3.8514
-#>    9.3312  -0.7556  22.4715  -8.4808   4.1353 -12.0296 -13.0816 -17.7008
-#>    1.0289 -14.4718   4.8107  13.3372  -2.9651 -10.1529  -4.4134 -15.8602
-#>   -7.1483   6.0918   6.7277   0.3636   5.1824   0.2863  -7.3600  16.8108
-#>    8.6143  12.9924  17.0444   6.9125  -0.3173 -12.9158  -9.7363 -15.0327
-#>   12.7596   7.4542   4.2434   9.1233  -2.8078 -10.4957  16.8506   4.0370
-#>    0.6383  -4.2978   6.9013   2.2868  -4.1186 -13.9069   3.5185  12.9860
-#>  -27.9366  11.4565  -8.4703   7.0572  -0.2669   2.7892  11.4372  -5.5336
-#>    2.1539 -15.5910   4.9511   5.3579   5.9842  -6.1158   2.0469  -9.6203
-#>   -3.7822   7.1565  -5.9621   6.0425  -6.5943   1.6693  -0.4180   5.4690
-#>   16.8042  -5.0099   5.0895  -8.8010   0.1436  -1.9357   3.0880 -13.2745
-#> 
-#> Columns 41 to 48   0.3146 -14.3353  -5.9365   7.4027 -17.7053  -7.8412 -10.0810 -18.0308
-#>    4.0662   7.7610   1.7881   9.0038   8.5724   7.1975  -7.4737  -9.6039
-#>    4.9590  -4.5158  -6.8833   4.6137  18.8929   2.1048  -5.4436   1.9336
-#>   -4.8528  -3.6431  13.7987  12.6808   9.2454   0.6520   3.5415   7.5007
-#>   -0.6831   6.4074  18.4871  25.3673   5.4122  15.7172  23.4501   4.4716
-#>   16.4760  -2.8680   0.7216   4.0668  18.1909   3.2577   2.4170  20.2340
-#>   -8.1073  -0.9868  13.7834  -1.9514  -3.2242   2.6656   3.5993   0.2573
-#>    3.5294  16.4313 -18.7772   4.2040  -2.7209   8.4672   8.0065  -2.4085
-#>   -1.4619  -3.0826   8.1454   5.4698  -2.2318  -1.3324  12.1368  -9.4877
-#>    4.4847 -13.7462  19.8069  15.7085  13.8782   1.7101  -5.4297   1.1614
-#>   -4.5525   7.3460  -3.3067  -9.1236   1.7664  -1.3738  -2.8044  -0.9467
-#>  -27.4670   1.2062 -18.4750 -15.0367   6.5579  -3.3533  -0.4274   1.1769
-#>   12.5707  16.2844   5.2190  -4.8410  -0.9088   5.8897   1.0212  -7.7595
-#>   10.1050   3.4524  -1.3630  -3.5027   0.3427  -5.4786  -1.7661  -1.0505
-#>    4.3743  -3.2571  -2.2518   7.7369   0.3507   5.3110   0.2836 -12.8981
-#>   -4.3187   6.1857  -5.9451  -8.2904   6.1723  17.5522   9.5769  -9.2986
-#>   10.3207  -4.8456   6.7989   5.4208   4.2143  -0.1745   3.9681  -5.4716
-#>   -8.8992  -6.6920  -4.0081 -19.4168   4.1258   0.2592   7.3254 -13.1612
-#>   -5.0748  -3.2694   3.9104  -5.0094  -4.3356   9.6339  17.6926   2.7489
-#>    7.9159   1.2296   5.7530   5.2686  -4.5178   7.0554   9.1664   8.6424
-#>    8.9341 -11.6695  -4.1183  -4.7487   3.2787  -8.7734  -8.8676   2.4187
-#>   -0.5846  -7.5384  -8.2017  -3.2226  -1.1003 -13.9250  -9.9109 -12.9002
-#>   -7.1770  -5.6694  13.1071  12.3576  -5.2566  -2.9248 -14.2614   1.8318
-#>    6.0011   8.2952  10.7749   2.2634 -13.4215  -1.2809   6.6806   2.5869
-#>  -12.1651  -4.9027  -9.3990  -7.3810 -19.8350  -3.1701 -17.9258  -5.2328
-#>   -3.2122   0.5319  12.5485   8.6093   4.8249   6.0053 -13.9898  13.6141
-#>    2.4600   6.9859  -0.7049   2.6028 -12.7993 -13.0092   2.9656  12.3087
-#>   -7.3083  -3.6930  15.8727  10.6552  -6.9516  -5.3436   5.7612   1.9576
-#>    5.3848  -2.5760  -9.2433  -0.3591  -3.4518   1.0722  10.9195  -8.5985
-#>   -8.0646   4.3434  -2.1281  -1.4199 -16.7954 -12.6490   0.7447  -8.1730
-#>   13.3801   4.9655  -2.5100  -0.3978  11.2956  -0.5729   5.2475   5.3386
-#>   -6.9883   9.6422  18.8881  -9.8414  -0.1467  -2.5258 -16.4064  -2.6196
-#>   -5.7340  -4.1890  -1.8319   4.0902   1.0645  -4.4699 -11.0413   4.2681
-#> 
-#> Columns 49 to 54 -15.0626   4.6630   2.1379  -3.9945  -4.5883   1.5795
-#>   17.6983  -1.3270  -3.1154   2.2603   2.6928  -0.7149
-#>   -0.2805  16.9501  10.5462 -11.3031  -4.7032   6.6998
-#>    7.2113  20.6352   1.0219   1.2684   1.2694  -4.6234
-#>    7.4568  -4.2505   2.3414  -5.1908  -2.3375  -5.6689
-#>  -18.9978 -11.5822  -6.5452   2.4236   2.7432   0.1641
-#>    3.4828   6.0013   3.6968 -10.0672  -6.4488   1.8463
-#>   -1.6269   8.8388   1.7039  10.8039   4.8676  -0.8757
-#>    8.0023   7.3665  -3.6373   0.8060   4.4465   2.0906
-#>   14.0379   5.0369  -8.3252   1.9085  -1.2375  -0.7666
-#>   -8.2903 -10.8521   5.5031  -3.2708   5.1663   9.9725
-#>   -4.5692   0.8189  -5.1834   1.5868   1.9432   1.3214
-#>   12.6362  -6.4726   5.5729  -9.4261  -0.5406   2.7630
-#>    6.3896   4.7119  -2.1385   0.5686   2.1703   0.6736
-#>   -5.1342  -2.3181  -4.9941   5.9961  -0.5455  -3.2953
-#>    2.0037  10.6868   5.2003   7.3448  -5.1892  -0.2359
-#>   -5.1612  -9.5109 -19.9095   5.0390   8.7671   4.5186
-#>    6.5665   0.5478  -0.2521   2.9676   0.2838   2.1890
-#>   -2.5895  -3.7767  13.7813  15.5430  -1.4989  -2.7487
-#>   -6.8314  -2.9919 -14.5160  -7.0359   6.2852   5.7361
-#>   17.4565  -9.9268  -0.5212  -2.3981  -5.5776  -4.8160
-#>   -4.4211  -1.5263 -10.7164   1.1898   7.4499   6.3166
-#>   -0.1586   1.6893   3.3332  -7.7713   0.1963   5.0846
-#>  -10.3228 -10.7535   5.2761  14.6063   2.9479   2.6619
-#>   11.5348   7.4959   1.8269  16.0055   4.7889   0.3962
-#>   -4.4697  -7.9666 -13.3593  -4.0063  -2.3265  -0.5683
-#>    9.0687   0.1223  -1.3498  -4.1347   1.9401   0.9255
-#>  -10.6185  -4.0579  -4.0332  -3.4902  -6.2295  -0.2965
-#>    0.9226   7.3937   2.3543   1.6309  -2.5689  -1.2707
-#>  -19.4518   6.9227   3.6227   9.0081   8.0081   1.1943
-#>    5.5501   3.1205  11.6577  -3.1306   2.7364   2.7878
-#>  -13.3373   6.9455  -4.3216  -6.7553   2.7839   0.4027
-#>   -0.9990   5.7631   0.4375  -1.4641  -1.9611   1.0508
+#>  Columns 1 to 8  -0.6611   0.8633   4.5181  11.8372  -7.9175  -5.1817  13.0507  -7.3446
+#>   -2.1008   7.8400  -2.8013  -4.0089 -21.4881  12.2031 -13.5760  -4.6295
+#>    1.0073  -3.1811  -0.8668  -8.1886   1.7477   0.9719  -4.7175  -4.5664
+#>   -4.6761   7.9701   3.6665  -4.0315  -5.4230  -0.1666 -12.6213  -3.2475
+#>   -2.4110  -5.0418   3.8829  -8.8623   5.4294   1.3300   1.1614  -0.1423
+#>    3.7318   1.1395   3.6796  -8.2124   0.6972  -4.5617   6.0870  -9.2689
+#>   -2.3908  -2.0876  -4.9067   9.0580 -15.1537  -1.2717  -4.1885  -1.7099
+#>   -0.7408   6.1000  -8.5557  13.3696 -12.4589   2.8744   2.2003  21.9139
+#>   -0.0889   3.0948   2.1662   3.8347  17.1659 -15.9021 -13.1386  12.5928
+#>    4.7578  -5.0339  -1.4616  -2.0266   0.9830 -10.5603  -8.0299  -9.0213
+#>   -0.9916   4.3759   3.0263  16.4362   1.6162  -1.2849  -5.0382  -5.7440
+#>    6.0196   1.4073   6.5023   8.2701   0.6183  -0.8181 -21.5906   3.6070
+#>    1.2873  -4.6194  -5.2792   0.3995  -7.0204  -8.1191   6.7674  -5.4531
+#>    0.4421  -5.3564   3.8015 -15.8994   3.0221   5.4989 -11.3123  -5.6424
+#>   -2.1676   0.9705  -1.5714  -4.1847   0.9129  14.4554 -14.7947  -4.9063
+#>   -4.7807  -4.6723   5.4802   2.9786  -1.8234 -12.7308  -5.6416  12.7399
+#>   -3.2166   0.2486  -4.0669  -1.7059  11.5235  -1.0892   3.4701  -0.3527
+#>   -0.6959  -4.0948  14.9871  -5.0513  13.7201  -3.3918  -8.4135  -8.7584
+#>   -3.5511  -3.6261  -1.5445 -17.9466   0.1794 -20.1560   8.7606 -10.5011
+#>    2.0773   1.0343  -3.0427  12.6182  12.5333 -10.1239  -2.4999   3.3978
+#>    0.8364   9.5756  10.6182  -2.4468  -9.6579   6.7550  11.4021  12.2040
+#>   -7.0416   9.6265 -14.7227   2.0501   0.5188  19.7729   1.6035   8.3055
+#>   -4.1370   1.7481   0.7671  18.4794   6.4262  -8.1979 -19.2873   4.3345
+#>    5.1181   1.7642  -1.3925  -0.1967   7.7939  15.3934   9.5939  11.5427
+#>   -1.4124  -4.6633   5.7139   4.4405  -5.2704 -13.5925 -22.4641   0.2215
+#>   -0.9937   1.6140   2.1396   6.5553  -1.7230   5.9863   1.3649  11.1956
+#>   -1.7816  -9.7140   8.7916  -9.5544  -4.2154  -4.6643  14.8769 -11.7290
+#>   -2.4798   0.2481   2.4461  11.5898   7.1405   2.0186   2.8154   2.9565
+#>    3.5956   2.0564  -5.1493  -0.0189  -0.5728  -2.0911   8.2910   4.7337
+#>   -0.0141   0.2039  -2.6486   3.4763  13.9171   5.4500   0.9061  -3.7668
+#>   -1.3178   1.7913   9.1127  -5.3201   7.6073 -13.6050  13.2268   3.2877
+#>   -6.0995  -1.4246  10.5710 -12.1160  -1.6117  -8.8540  -8.9954   7.9852
+#>    0.6081  -4.1462   7.5732 -11.3362  -4.7914   0.1691  21.8157 -16.5704
+#> 
+#> Columns 9 to 16  -1.3819  11.6872  -1.3054  -2.3566  -1.3418  -7.3552   6.4253   5.9929
+#>    0.8817   8.4100 -20.9406  -4.2649   3.8530  17.0495  -2.8058   2.0342
+#>   12.2871  -1.4566   1.4359  -2.7449   7.5748  -0.4008   0.3269 -13.3659
+#>    0.5256   6.0077  -0.3450  -1.5874  -1.4657   5.2771 -10.0302  -0.4749
+#>   10.7343 -15.8349  20.0424 -11.8831 -22.9319 -10.2548  -4.3428   2.2176
+#>    4.7120 -12.3315   8.3134   0.0723   6.8495  -4.1431   1.7737   3.9246
+#>  -15.9989   3.4592   4.0169  -7.9858  -4.9650   1.1620 -16.5610  -1.4037
+#>    0.2714  -0.6825   8.0453   1.8648  -8.9153 -11.7699  10.3799   3.9168
+#>   27.3824 -13.4740  -8.2957   2.1005   4.4899  -4.3035   3.5842 -14.0698
+#>    7.0218   0.1087  -0.7370  -6.5453  18.9192  12.9505 -18.0434   8.9276
+#>    1.8591  14.8530   8.3594  -2.4979  13.6234   2.9864 -18.4924   6.7571
+#>   20.5221 -14.5185 -18.4809  -1.3594  13.9749   1.6217  -6.5318 -29.1864
+#>    4.7099  -4.6973  12.8633  -4.8044   3.9348  -0.6298  -1.5247 -10.4734
+#>    6.2318 -11.9080   4.1567  -2.8397   4.2145   0.2820  -0.1832  -9.3342
+#>   -6.2136  13.4091  -3.9193   2.4275 -10.8753   6.4462  12.9351  -5.0812
+#>   -4.0848   4.7132  19.5432   0.8093   3.5631   0.6383  -0.4498  -4.9942
+#>  -12.1719   7.7549   5.5829   7.3103  12.2272  -5.0218  -6.4758  11.2600
+#>    0.0171  14.9763   3.5854 -10.8155   1.2998   3.4217 -16.8142  -6.0228
+#>  -17.4808 -11.8566  -6.4099   0.0224  -7.2964   2.7588   2.4149   9.9422
+#>    7.4637   8.0378   1.2751  10.2065  -6.8412   4.1106   6.3912   0.1307
+#>   -4.0417  -9.4326   7.1181   3.9033  11.8671 -12.0753   7.2711  -3.8711
+#>    7.9301  -1.9583  13.8083  -3.4949  -0.8190   2.3821  -0.2717   9.6731
+#>    5.5368  16.6715  -9.6847  13.4369   2.0342   7.8106   6.7349 -13.5362
+#>   -6.7949   4.3114  11.3014  -2.9501  -5.0100  12.0658 -10.7322   1.7625
+#>   11.7377  -7.5089 -12.6801   3.9749   7.1767   2.2325  -4.2643  -7.6218
+#>   14.0514  -6.3722   4.2894  10.4985  -9.8281  -2.8144   2.6320 -10.3078
+#>    8.9473  -0.4528   8.6707  -6.9895  -9.9284  -2.9170   5.7764  -5.2314
+#>   -1.4368  -6.4265  -9.5321  -8.5625  -4.5725  -4.2898  -1.7135  -6.9696
+#>   -3.4091  -9.2516   4.9343  -0.8970   5.5795   4.8368  -1.3720  -7.7676
+#>    5.4554   9.7498 -21.7355  22.8640   7.1671 -13.7954   7.6228  13.5363
+#>  -14.2094  14.6133  -7.4670   2.5511  11.7978  -4.5729   0.9206  -1.9211
+#>   -9.5800   7.0509   2.6303   2.9259  -1.3507 -11.3461  10.9748  12.0294
+#>  -17.2297   9.3269  16.5957  -5.4184   2.2671   0.6134   7.4270   8.6542
+#> 
+#> Columns 17 to 24  -6.2700  21.5020   0.3924 -11.1093   2.9142   4.9612  -1.9417  11.1971
+#>   -2.1305   7.9089  -3.3748   3.5597  -2.6662   8.1207 -16.9857  -2.4668
+#>   -3.0714  10.3198  -8.7881  -3.9100   0.5989  -2.7594   4.0918   5.2642
+#>    4.3615  12.3107   4.8404  14.6435  11.4549  -7.5473   7.1962   1.8677
+#>   13.2755  -7.0422   1.5451  -1.3846   4.1460  -4.2754   7.5327 -17.9277
+#>   -2.3761  11.8222  11.8904 -11.7886   7.5151   4.9451   6.3646   2.9077
+#>    2.4230  -1.8104   9.5953   1.7579   3.0649   4.9760   5.5875  11.8555
+#>    2.5610   1.1922 -11.3001  -4.3650 -19.3118  -5.0179 -13.9261  10.7239
+#>   -3.0733  -1.0531   5.3610  10.0634  -3.2905   1.4873  -4.7693   4.9162
+#>    4.8763   3.7815  -0.9533 -13.4261   6.0837  -3.2680  -7.9161  -2.6066
+#>   14.9359  -6.0152  -2.2771  10.9814 -14.7333   2.2851  -1.5146  -2.7263
+#>    5.6751 -11.1411   2.2567   8.2682  -1.4867  -0.8107  -0.6556  -0.4739
+#>   14.9541   2.7392  -5.8034  -1.9498   1.1576 -13.7552   2.2034  -4.3756
+#>    2.9570   8.1543   2.5831  -0.0346  10.0457   7.9946  14.5249   0.9140
+#>   -3.5943  10.2665  -8.3933   2.3322   1.1761   8.1180   2.8477   6.9188
+#>   17.5834   4.9643  -2.2898  21.1595   2.1176   0.1345  -4.6422   1.0019
+#>   -3.0537   4.3746  -5.7071  -3.8752  -0.8578  -2.8941   8.3767   9.7314
+#>   19.8109  15.4926 -15.4173  -6.3662   1.9449  -2.6532  -0.3744  -5.7484
+#>    1.0483   9.8707  -1.4664   9.7985  20.9499  11.9705 -15.2943  -3.0298
+#>   -1.5864  -2.9502  13.6922   5.0553  -7.3467  -1.6777 -10.3997  -5.2738
+#>    9.2870  -3.9020   5.6074   4.9261  -2.6122  -8.8907  18.6157  21.1201
+#>    3.3948  16.0149 -17.9195  -7.5514 -11.7402  -7.7145  -4.0195   2.5575
+#>   -1.5184  18.2546 -15.3843   2.7558   5.9218  18.4407   7.7432   5.0433
+#>  -13.6985  -0.7083   7.9733   5.6892  -2.2178   1.1768   7.7553  20.1054
+#>   -2.8000  -1.7509   4.1501   2.3787   4.9932 -28.4399  -6.3160   1.4748
+#>   -6.7018   5.6688  -5.0720  -3.7965  12.7174 -17.0035   4.6901  23.8814
+#>   11.6533  17.6957  -6.3218  -8.3857  14.5962  -6.7053  -6.2555  -2.8368
+#>  -13.2520   4.0038   1.1624   4.3522   2.3364  -9.7136  -6.7032  -1.1039
+#>  -13.5111   4.9304  17.6480   6.9267  12.1660   0.3082  -6.3641  -2.5587
+#>  -10.5522  -3.1797   9.3514   1.4593  -1.5527   4.6419  -6.2983 -13.9793
+#>   -2.6949   9.4026   0.2440  -6.1458   1.3880  -4.4307   2.4953  -2.6057
+#>  -10.5914   6.7204 -17.3545   1.5614   2.6628  13.3528  10.3756  -6.4603
+#>   -6.7977  10.1937  15.7656   2.4495  16.4780   0.0115   8.4397   8.3374
+#> 
+#> Columns 25 to 32   5.6433 -11.5957  -3.2475  -1.9969   9.8283 -15.1986  -0.3113   5.2194
+#>   -7.7169  -5.6458  -3.2262  15.2147   8.3005   3.9418  -8.3297  -4.5994
+#>    9.7193  -0.7962  12.6110   7.5069 -12.4057   5.7746  -0.1675  -6.9663
+#>   -9.1199  -8.3813  -9.2700  -3.2203   7.3467   4.5803   6.0129 -11.5480
+#>   -0.8221 -10.5491   2.8890   2.2817 -14.8863  20.3709   4.4128  11.8123
+#>    3.3845  -4.7599   6.9778   0.0233   1.0711   5.9510   7.4425  27.4308
+#>   -8.6123  -1.8007  -9.7699   3.1298  -4.6956  -4.6226   3.3166   6.6086
+#>    2.9795  -7.0045  11.3283  -1.2507   1.5304  -4.3956   4.0280  -3.5158
+#>    4.1307  -3.0581  13.3507  -9.1241  -0.0047  12.7855  10.7001  -1.6175
+#>   -6.9317   9.6178  -8.8974   5.7012  -1.7189   3.3241   1.2916  -2.5986
+#>  -10.4041  14.2560  -4.6121  -5.6433  14.3503  -3.5790 -14.4444   4.3369
+#>   17.0635  -7.9951  -1.1646  -6.6689  -5.4740   1.2155   1.6072  -2.1184
+#>   -6.6551   7.4389  -1.3174  -3.5804  -1.6672 -10.2388   2.1840   1.4120
+#>    1.0087 -19.9252  15.6099 -16.4470  -7.3322  15.5909   7.8169   3.3432
+#>    2.5525 -18.7971  -2.5102 -14.5601  -7.7290  -7.3098  13.3930   3.3696
+#>    9.9850  13.5237   0.7382  -2.0884 -13.5249  -8.0925   1.2982  10.5711
+#>    3.9558 -14.3177  -0.0545  -3.0658  -2.6066  -4.4142  10.0609  14.9270
+#>    5.4432   5.2410  -6.8290 -20.5748  -0.0284  -8.3720  -1.6086  10.6433
+#>  -11.9347  -5.8632   9.0586  -0.3780   0.0432  -3.3674   1.5463  -4.0893
+#>   11.2623  -6.1923  18.5771  -1.2367   7.3070 -10.5772  -3.8635  -4.1126
+#>    5.6362  -3.1024   0.5384  14.1959   6.7002   1.5288  -9.8320  -1.5188
+#>   -0.0173   3.0810  -0.6256  -3.2138  -1.8541   3.2328  10.2637  13.0101
+#>   -8.9923   3.5888  21.8703  -5.2004  11.6331  -1.6214   1.5849  -0.5848
+#>    7.3452   6.8070   1.6576  -2.2558  -0.9527   4.1380  -8.0898   1.7644
+#>  -13.1351  15.4876  -6.3969   3.9100 -16.2921  -1.3991  -5.7334 -12.3163
+#>   -6.6052   1.6109  -3.3378  17.0653  -7.9649   0.3000  -2.8696  -1.1269
+#>    7.6506 -11.1752  11.8160 -12.1253 -22.6390  -3.7539  18.1623  11.5049
+#>    3.3027 -10.1043   3.4715   1.8730   0.7828   0.0773  -3.0767   1.0280
+#>  -10.8444   0.7015 -14.7629  -0.7397  21.7868  -8.4905  -2.6188  -4.6749
+#>   -8.6479  18.4425  -3.5645  13.9119   7.6663 -24.8181  -4.6933  -2.1963
+#>   14.7601   8.3412   4.0687 -11.7805  -2.8518  -7.1183  -8.2542  -2.7343
+#>  -16.0110  -4.9783  10.6725   7.4092  -1.1833   6.3177   2.6677  14.9228
+#>  -12.0955   0.6484   1.8794  10.4174   4.6666   0.3868   2.2247  12.5836
+#> 
+#> Columns 33 to 40  -0.2962   9.6208  12.4398   8.1668  -3.2740  -7.4233  -4.8652 -10.3557
+#>    4.2657 -11.5017   5.4273  -8.7239   5.4944   8.6025  -3.8007   8.1229
+#>   -8.5016  -7.0981   5.6711   2.0142   2.6624  11.2980   1.2697  11.7873
+#>   -2.9312  -5.0712  -5.0920   6.0426  16.1940   2.1960  -3.4998   3.2446
+#>    0.3163  -9.8282 -11.0563  16.8790  -1.4579  -4.4826   0.2670  -5.4285
+#>    6.3637  -1.5199  -7.3059  -4.9864 -12.0109  -2.1307  16.6465   1.7417
+#>   -1.0534  -1.6207   1.4929 -12.2766  15.0021   7.2522  12.2082  -3.2669
+#>   19.6507 -18.2253  18.6699   6.9492  -1.7718   3.0325  14.6211 -14.4381
+#>    0.8946  -3.7411   2.2395  17.5453   0.1537  -7.2091   8.9026   4.7404
+#>  -16.0100   9.2009   4.9782  -8.9104   4.5533   4.5445  -1.3998  -7.9842
+#>    4.9243 -15.5702  12.2728 -20.6112  23.8211  -1.8126  -5.4976  13.8467
+#>   -1.1646   1.1321   6.5600  11.7968 -21.2288 -12.8414  10.9074  12.0915
+#>   -3.1935  -8.6543   3.1002  -7.9556  -0.9918   7.6725   5.9351  -3.1350
+#>   -4.5953  -5.4783  -7.5917   3.4128  -0.9738   2.1710   7.5060  -7.8006
+#>    8.9759   5.5037  -3.4227  -1.4271 -10.2973   4.2965  -1.5208  -4.6537
+#>   -7.9960  -4.4194 -17.5747  -0.3333  -4.9945  -2.4433  14.5897  13.0269
+#>   10.5848  -5.2157  -2.2250  -3.4535  -9.3965  -0.9765   4.1304  -6.7142
+#>   -4.8346   5.1334  -6.3334  -1.8039   3.9677  18.1494 -13.2672   0.7295
+#>   -1.4480 -16.5949 -17.0096  -7.6926  -5.1956   9.8031  -1.2939  -5.9984
+#>    5.4517  -9.8674   4.9495   2.5906  -0.0842  -2.5847  -1.3374   1.4544
+#>   -2.9599  11.4454  -3.1959 -11.2170  -7.1678  -4.1683  13.2276   1.9520
+#>    5.5110  -6.6832 -14.8506  -2.9290  -4.5262   9.5364  -5.3194  -4.3918
+#>    2.1519  -2.9140   1.3189   5.1745  -8.2314   3.8524  -9.0984  12.9303
+#>    8.0010  -1.8399  17.9560  -7.7676   3.5190   2.2511   3.7484  -0.7531
+#>  -13.1318  21.6737   9.4858  -1.4879   7.4344 -10.5489 -11.7592   4.3336
+#>    4.6551  17.8564  -0.0870   0.6019 -10.0032  -9.3478  -5.4171  -5.9865
+#>   -5.7263  -3.3877  -0.9149   4.7041  -7.8953   3.7291   2.7934  -7.3602
+#>    2.6266  -3.5659  -4.1171  17.0871 -11.5948  -6.4614  -3.5060   4.1321
+#>  -10.4943  -4.3512   7.2377   0.4649  10.9122  -5.1840   5.8800  -4.5709
+#>  -10.7941  11.6392  11.6164   0.2958  10.5741 -20.1508 -10.0372 -14.8401
+#>    3.7105  20.0499  -7.9650   5.4661  -6.2146 -13.2299  -5.2941 -11.7309
+#>   -7.9633  20.7494 -10.8635   4.5992 -10.8483  -9.4899   2.9116   2.4278
+#>  -10.3842 -11.6309   1.2829 -18.7227   2.0096  20.9153   6.3166   0.5752
+#> 
+#> Columns 41 to 48 -11.2703  -9.3680  -0.6615   4.2703  -9.2414  -4.0478  -9.2309   2.4971
+#>  -10.8318  10.6628  -6.2700   6.8731 -20.5364  -1.8110  -3.9961   8.8645
+#>    2.9298  -7.3589   7.5589   6.3543   3.3836  15.8111  -2.9703  -3.8744
+#>   -8.6284 -15.1688  -0.5740  -8.7777   0.6015  25.6741  20.9157  -3.7018
+#>   -5.5992   4.0974   6.8765 -24.2026  12.2104 -11.4697   0.1142 -15.2414
+#>    4.7343   1.6210  -3.5755  -4.1490  -2.8426 -15.8829   9.5526  -3.5506
+#>   -0.2106   0.3444  13.3444   0.4776  -7.2632 -12.1724  -4.9528   4.1733
+#>   -1.2222  17.4621  -3.8672 -10.7412  -5.0470 -12.5450  -9.5587 -10.1776
+#>   10.2154  -6.1850  -8.3131  -3.0441   6.7150 -11.6100   5.6020  15.0109
+#>   13.7385  -2.7406  14.3025  -9.7996  16.2925  13.6234   0.8185  -3.0422
+#>   -9.0469   0.6524  -2.0106  -0.9984  -6.7472   6.4343  10.1060  19.3191
+#>   10.7824  -5.0717  -4.2690 -10.8837  17.0596  -3.1963  19.6327  22.1616
+#>    2.9831 -16.0540  26.6622 -10.2835   1.5282  -7.1523   3.4325  -8.0947
+#>   -4.8666  19.0890  -5.7237 -21.0986   2.2810   0.0252   4.5897  14.8064
+#>    4.1780  -1.6036 -19.3588   7.1971  10.5707   9.3126  -3.3774   1.3225
+#>   -8.0808   9.0564  15.7622   0.9649  -7.1808   2.2131  17.3118  -1.1069
+#>    1.6990   9.8326  -2.8595  -8.5353 -13.1813  -3.0796  11.5611   3.0494
+#>   -7.1744  -3.4298  11.6984   9.4984 -17.2590  23.4537   8.3871  -2.4809
+#>   -3.3677  17.9684   9.2585   6.5045 -11.8390  20.1953   9.1470  -8.1887
+#>    2.8777  -6.3442   4.1901  11.3522   4.2888  -3.3249 -14.2432   6.0946
+#>   -5.9562  -1.5024   5.2301  11.1633  15.5780  10.8966  13.4162  11.4966
+#>   -5.0927   3.2306 -13.3710   8.2159  -9.2961  -1.5798   9.3789  -1.5476
+#>    3.9564  -0.9356  -0.2322  40.1798  -5.9086   1.2422   8.4549   9.4432
+#>    6.3274  -4.4504 -11.1821   6.8195   1.7822  -2.0375  -3.7111   2.7139
+#>    5.0028 -27.6794   6.9989  -4.3020  -4.8355 -15.1243 -14.3543   0.7256
+#>    1.9120 -34.6642   0.6808   1.0192  -5.4055  -9.7786   5.1670  -8.4873
+#>    4.8587  15.7434   2.1805  -5.4320 -11.9293  -7.6229   1.8188   1.6439
+#>   -8.7987   5.5679   2.0597   2.9719 -10.0983 -20.3033   0.5446  -3.6602
+#>   -1.5910  -4.4877  13.5183 -14.5505  -1.7806   2.5759   8.7403  -7.3604
+#>    8.9671 -17.7200   1.9284   0.4708   3.7125  -9.0244  -1.7188  -4.1713
+#>   -8.9352  -4.5148   4.9734   3.8490   4.2269 -10.3070  -6.5548  -4.2115
+#>  -12.5068   5.6170   7.0301 -13.1469   1.9742 -10.0978  -2.2247 -17.4883
+#>    1.2284   2.7850   6.9505  12.5677  -2.8584  13.6294   4.2878  -3.3950
+#> 
+#> Columns 49 to 54   8.5138   5.6686 -10.1385  -1.9343   4.2850  -4.9671
+#>   -6.4914  -7.1378   0.6255   2.1053 -11.9386   0.1720
+#>  -10.8129   0.5106   2.9359  -4.8550   2.5544   2.3402
+#>   14.7798  -7.1283  13.0324   3.6824  -6.7595  -6.6901
+#>  -19.9904   5.3055   0.9131   1.4752  -7.4882  -0.3692
+#>  -13.0732   0.4756  -6.9973   0.0914  10.4741   7.9303
+#>    9.2921  -8.1054 -14.8033  -8.9238   2.6688   0.8758
+#>   10.5006  -2.7311  -1.3946 -13.4778   3.8063   7.5441
+#>   13.9077  16.5992   2.0752 -10.2177   4.2673   6.3937
+#>    1.7830  -2.8624  -2.5858  -3.7568   1.3992  -0.0297
+#>   -5.2593 -17.6484   5.1824  15.7374  -2.6939  -3.0136
+#>    4.0844  -4.3191  -6.6714  -0.3591  -4.2012   1.2912
+#>    8.6858  -3.9376  -7.5272   8.1540  -4.1832  -2.1365
+#>  -13.7469   1.1257  -3.2148   6.5900   8.6270  -1.0831
+#>    0.1524   7.6185  -5.2935  -2.7522  -8.5996   0.4535
+#>    8.9265   8.8100  -7.9665   2.8130   1.1212   6.1957
+#>    0.9362 -11.6891   4.3630   3.4745   6.1096   3.4688
+#>   -0.8777   4.6962   4.9339   7.8613 -14.1701   2.6426
+#>   -2.8167   3.5472  -2.4456   2.3522  10.9154   2.8535
+#>   12.7140  24.1550  -4.5807 -11.9758   0.2368  -6.0218
+#>    7.5006  -4.3749  -5.7951   8.0049   1.6383  10.3586
+#>   10.9017   8.0931   5.5237   8.5042  -0.1454   7.7731
+#>    9.3341  11.5738   2.2706   8.3116   2.0555   5.3507
+#>   13.6485  -5.9455  -1.2566 -12.1174   0.6620   3.5176
+#>   19.6660   1.7354  -0.5429 -15.3752 -12.5786  -1.4114
+#>   11.8797  11.1055  -3.0169 -10.4807  -1.4093   2.4014
+#>   10.6284  -6.2706  -2.1637   0.8827   6.1283   0.0022
+#>   23.6576  10.5404 -12.5570   0.7822   1.2994   1.3891
+#>   11.9360  15.4484  -1.8128  10.1307  -5.7153  -6.8657
+#>    0.6321   5.6983  12.0583 -10.3662 -14.0226  -3.1265
+#>   11.1468  -2.2041  -6.7250 -12.3004   4.4522   2.7180
+#>  -16.3479   6.1125  -4.6348  -3.1702   2.7357  -0.0780
+#>    6.0281  -6.9400   9.2174  21.0303  -1.3634  -2.4955
 #> 
 #> (11,.,.) = 
-#>  Columns 1 to 6 -3.8360e+00  1.5939e+00 -3.7936e+00  1.8678e+00 -1.8081e+01  4.4164e+00
-#>  -1.1471e+00 -2.8029e+00 -2.5762e+00  5.1902e+00  6.6967e+00  7.1688e+00
-#>   2.5485e+00 -4.8021e+00 -3.1657e+00 -1.4638e+01  1.5666e+01  3.7014e+00
-#>  -1.6291e+00  1.9018e+00 -4.8757e+00  1.5393e+00 -2.5465e+00  1.8616e+00
-#>  -1.8028e+00 -2.3302e-01  5.7494e+00 -1.0798e+00  1.0129e+01  9.0177e+00
-#>  -6.6964e-01  1.0868e+00  6.3221e+00  5.0807e+00  4.7175e+00  2.8995e+00
-#>   3.0178e+00 -3.2517e+00  5.1202e+00  5.3788e+00  1.1414e+01 -1.3915e+01
-#>  -4.1800e+00  1.3642e+00  3.0657e+00 -1.0666e+00 -4.3976e+00 -3.3562e+00
-#>  -3.0942e+00 -9.7947e+00  9.8732e+00 -1.2089e+01  9.8079e+00  4.0579e+00
-#>  -3.1702e+00  4.5015e+00  2.2437e+00 -7.6928e+00 -7.5949e+00 -1.4488e+00
-#>   2.8843e+00 -2.5495e+00  5.6960e+00 -4.4285e+00 -6.2185e+00 -2.1271e+00
-#>  -6.1734e-02 -2.3013e+00  4.0962e+00 -2.6230e+00 -5.5801e+00  6.3505e+00
-#>   5.1560e+00  2.2354e+00  7.4479e+00 -2.7796e+00  6.7787e+00  7.4205e+00
-#>   8.6645e-01 -4.0719e+00 -1.4128e+01  5.4836e+00 -1.8982e+01 -1.4326e+01
-#>  -1.6762e+00  1.3226e+00  6.9057e+00  7.8310e+00 -4.7439e+00 -4.6881e+00
-#>   3.0498e+00  7.4968e+00 -6.1214e+00  8.7065e-01  3.5176e+00  1.2662e+00
-#>   1.5084e+00 -3.7030e+00  5.4233e+00  1.1332e+01  9.5553e+00 -3.7449e-01
-#>   1.7620e-01 -7.8269e+00 -1.3969e+01 -5.6421e+00 -2.0002e+00 -4.6152e+00
-#>   3.1169e+00 -8.0233e-01 -3.1288e+00 -1.1862e+01 -6.6224e+00 -3.2739e+00
-#>  -2.7442e+00  2.3985e+00  3.2201e-01 -6.7745e-01 -4.5126e+00 -7.6190e+00
-#>   3.8572e+00  1.3669e+00  4.1118e+00 -1.1800e+00  1.6259e+01 -7.0737e+00
-#>   3.7801e+00 -1.9738e-01  9.7668e+00 -3.9714e+00  4.9906e+00 -1.9493e+01
-#>  -1.6236e+00  6.5810e+00 -6.8311e+00  3.3564e+00 -2.2126e+00  1.1171e+01
-#>   7.3322e+00 -2.2437e-01  5.6662e-01 -1.2073e+01 -9.4299e+00 -4.0470e+00
-#>  -1.7680e+00  1.6458e+00 -7.3639e-01 -6.0911e+00 -1.2497e+01  1.1654e+00
-#>  -7.3167e-01  1.8133e+00 -2.0562e+00  5.6627e+00 -3.3552e+00 -6.1999e+00
-#>   5.3507e+00  2.7325e+00  4.9057e+00 -3.1438e+00  9.1261e+00 -6.7998e-01
-#>  -3.0363e+00 -8.1935e-01 -3.0917e+00  5.6052e+00  6.7996e+00  4.9521e+00
-#>  -1.8488e+00  3.3741e+00 -8.2848e+00 -5.4631e+00  1.0128e+01  5.8941e+00
-#>  -1.2998e+00  1.3414e+00 -4.5637e+00  4.8459e+00 -8.2052e+00 -1.0843e+01
-#>  -5.1679e-01 -2.2994e+00 -7.7908e+00 -5.5937e+00 -1.0556e+00  2.5691e-01
-#>   3.5901e+00 -2.1805e+00 -6.4358e-01  6.8101e+00 -1.3151e+01 -3.8822e+00
-#>   3.8115e+00  4.7139e+00 -3.8519e+00  2.4953e+00 -2.6490e+00  1.2095e+01
-#> 
-#> Columns 7 to 12  1.1355e+00  8.0373e+00  9.8857e-01 -3.5068e+00  6.9162e+00  9.3497e-01
-#>   2.8756e+00 -2.6017e+00  1.1818e+01 -8.8997e+00 -3.1929e+00 -7.8767e+00
-#>   9.8390e+00  8.1572e+00  1.4665e+00 -1.1117e+00 -4.8860e+00 -3.9219e-01
-#>   7.8412e-01  2.0463e+01  3.2234e+00  2.9016e+00  5.3400e+00  3.6160e+00
-#>   5.6648e+00  1.6924e+01  1.1407e+00  4.5146e+00  8.7476e+00 -3.6236e+00
-#>   1.2562e+01  7.6311e+00  5.9790e+00  5.1021e+00 -4.0275e+00  6.5509e+00
-#>  -1.3679e+01  1.5848e+01  9.6090e+00 -1.3240e+01 -9.9157e+00 -1.6351e+00
-#>   4.1676e-01  4.6696e+00  2.1223e+01 -1.5720e+01  1.4410e+01  4.6688e+00
-#>  -9.9709e+00  5.2292e+00  5.8353e+00  7.9057e-01  3.5686e+00  2.8194e+00
-#>   1.9564e+01  1.1953e+01 -1.2233e+01  1.0940e+00 -8.2733e-01  2.0802e+01
-#>   7.7502e+00 -7.1128e+00  7.5082e+00 -2.4273e+00  6.4676e+00  4.0435e+00
-#>  -5.3417e+00  1.2981e+01  8.5225e+00 -1.3927e+00  1.4157e+01  3.2939e+01
-#>   9.7841e+00 -7.4163e+00 -6.2632e+00  1.8813e+01 -4.8736e+00 -5.7193e+00
-#>  -5.2379e+00 -1.6308e+01  8.9360e+00 -2.1223e+00 -9.0632e+00 -8.2404e+00
-#>  -1.5874e+00  2.3141e+01 -2.1215e+01 -1.1144e+01  6.3798e+00  7.7378e+00
-#>   2.7432e+01 -6.5000e+00 -9.0771e+00 -1.0587e+00  5.5313e+00  7.7932e+00
-#>  -4.2467e+00 -2.2803e+00 -1.1580e+01 -7.6551e-01 -1.0155e+01 -3.6816e+00
-#>   3.7888e+00  1.7301e+00 -8.4693e+00 -3.7254e+00 -6.6073e+00  1.8567e+01
-#>   2.5772e+00 -1.0706e+01  9.7069e+00  4.7409e+00  5.5050e+00  6.3782e+00
-#>   2.6113e+00 -6.4904e+00 -1.4243e+01  1.3646e+01  1.1248e+01 -1.3836e+01
-#>   1.9483e+00  8.2244e+00  5.3361e+00 -1.8463e+00  3.8943e+00 -5.7493e+00
-#>  -1.2684e+00  1.0993e+01 -6.9079e-01  9.9783e-01  1.4274e+00  1.8779e+01
-#>  -3.4843e+00 -4.9284e+00 -2.8626e+00 -1.5959e+01  3.7211e+00 -1.3182e+00
-#>  -1.2014e+01  2.7731e+00 -8.7801e+00  1.4702e+01  1.3477e+01  2.8389e+00
-#>  -7.1466e+00  1.6777e+01  1.2249e+01  6.4850e+00  1.4657e+01 -3.4285e+00
-#>   3.8257e-01 -9.9398e+00  2.2121e+00  6.9386e+00 -1.5108e+00 -7.0370e+00
-#>  -1.9808e+00  1.1421e+01 -1.3566e+01 -1.3428e+01  5.7615e+00  1.9043e+00
-#>   9.5193e+00  5.8011e+00 -1.1230e+01 -2.5744e+00 -9.9539e-01 -1.8106e+01
-#>   7.9076e+00  6.8125e+00 -1.8321e+00  5.3211e+00 -4.6525e+00 -8.1244e+00
-#>  -3.5788e-02 -2.8294e+00  1.1478e+01  4.6395e+00  6.3767e+00  2.9761e+00
-#>  -8.7438e+00  8.3134e+00  5.7363e+00  1.3889e+01 -5.7648e-01  2.6462e+00
-#>   1.5458e+01  4.3068e+00 -4.7382e+00 -2.4897e+00 -9.8575e-01  1.3976e+00
-#>   9.4647e+00  1.0196e+01 -9.4177e+00  9.1721e+00  3.3925e+00  1.1460e+01
-#> 
-#> Columns 13 to 18 -1.0379e+00 -5.1405e-01  7.7139e+00 -1.0642e-01  8.3741e+00  1.9785e+00
-#>  -7.2977e-01 -1.2779e+00 -8.6253e+00  5.1659e+00  2.0163e+00 -7.9648e+00
-#>   1.9047e+01 -7.8211e+00 -1.7251e+01  1.0182e+01 -3.4768e+00  3.5280e+00
-#>  -6.8537e+00 -8.3516e+00 -1.0993e+01  6.2705e+00 -1.7694e+01 -4.0229e+00
-#>   1.1758e+00  5.2104e+00 -6.0128e+00 -1.2200e+01 -1.4170e+00  1.2333e+01
-#>   1.8981e+00 -7.7969e+00 -8.2999e+00  7.0828e+00  6.2507e+00  2.2976e+01
-#>   1.0308e+01  2.0559e+00 -1.7678e+01  2.1769e+00 -3.3622e+00 -1.1646e+01
-#>  -8.8872e+00 -1.0547e+01 -1.0122e+01 -1.6779e+01  4.8407e+00 -1.0983e+01
-#>   1.9676e+00  6.8608e+00  1.3503e+01 -1.0649e+01 -3.7704e+00  6.5000e+00
-#>  -5.4224e+00 -2.0540e+01 -3.5309e+00  3.0255e+00 -1.4995e+00  1.4269e+01
-#>   3.9895e+00 -6.3869e+00  5.0963e+00 -4.7499e+00  4.6711e-01  5.8398e+00
-#>   1.1262e+01  1.1528e+01 -4.1378e+00 -8.8242e+00  2.8453e+00 -4.3191e+00
-#>  -7.2129e-02 -2.3884e+00 -2.0270e+00  5.6990e+00 -2.8940e+00  1.8328e+00
-#>   9.1767e+00 -4.4345e+00  1.6328e+01  2.1799e+00 -7.5807e-01 -2.0310e+01
-#>   2.5884e+00 -1.7368e+00 -1.2152e+01  1.0830e+01 -7.4779e+00  3.1142e+00
-#>  -1.3374e+01 -1.4053e+01 -4.6780e+00 -1.5641e+01 -1.0669e+00  2.0404e+00
-#>  -1.4194e+01  1.7131e+00  6.0105e+00  7.9512e+00 -6.1213e+00  1.9179e+01
-#>  -5.0826e-01 -6.4392e+00 -3.9512e-01  4.7914e-01  8.4086e+00 -4.8526e+00
-#>   1.5437e+01  1.6998e+01  1.0508e+01 -8.6180e+00 -3.1337e+00 -7.2217e+00
-#>   2.4477e+01  1.0709e+01 -9.2398e+00 -5.8075e+00 -1.3620e+01  2.6313e-01
-#>  -3.0655e+00 -1.0128e+01 -6.8721e+00  4.9362e+00 -1.2731e+00 -8.4537e+00
-#>   1.6656e+01 -1.2754e+01  1.5588e+01  1.4962e+00 -1.3077e+01 -1.4743e+00
-#>   2.4511e+00 -2.1280e+00 -1.1724e+01  5.4683e+00  9.7342e+00  8.6476e+00
-#>   1.1642e+01  5.3199e+00  1.3054e+01  8.3679e+00 -1.0100e+01 -1.1214e+01
-#>   1.3779e+01  8.0466e+00 -1.1280e+01  1.2072e+01  1.1482e+00  1.6226e-01
-#>  -3.8898e+00 -1.2344e+01  6.9595e+00 -4.3888e+00 -1.5284e+00 -5.0525e+00
-#>   9.0238e+00  1.5506e+01  4.0511e+00 -7.3665e+00 -1.4392e+01 -1.0761e+01
-#>   8.3401e+00  2.9931e+00 -9.2663e+00 -7.3426e+00 -2.0989e+00  1.3918e+01
-#>  -5.0650e+00  3.7366e+00  1.7401e+00 -3.5033e+00 -1.0104e+01 -7.8859e-01
-#>  -3.7941e+00  9.6133e+00  5.1849e+00 -5.6589e+00  7.7796e+00  2.6745e+00
-#>  -1.9627e+00 -5.5493e+00 -8.1682e+00 -4.2417e+00 -2.1606e+01 -2.9949e+00
-#>   7.0163e-01 -1.0841e+01 -8.8551e-01 -8.6048e-01  1.1358e+01 -1.1409e+00
-#>   1.2621e+01  1.4819e+00 -3.9977e+00 -4.2345e+00 -1.2149e+01 -6.6803e-01
-#> 
-#> Columns 19 to 24  4.0309e+00 -9.1221e+00 -3.9438e+00 -1.7986e+01  1.8854e+00  9.0591e-01
-#>   5.4623e-01  5.4963e+00  2.4605e+00 -4.4006e+00  1.0538e+01  1.6970e+01
-#>  -1.0713e+01  6.1633e+00 -2.0267e+00  3.7437e+00 -1.0923e+01  8.9042e+00
-#>  -1.1127e+01  8.0173e+00  2.9128e+00  3.2007e+00  4.7226e+00 -9.0789e+00
-#>   6.1319e+00  1.1253e+01  6.9746e+00  2.8675e+00  5.8655e+00  1.0044e+00
-#>  -1.6951e+00  9.3632e+00  6.8477e+00 -7.9658e-01 -5.9129e+00  1.8410e+00
-#>   7.1308e+00 -2.0794e+00 -7.7926e+00  8.8771e+00 -4.4297e-01  8.4418e+00
-#>   3.3092e+00  2.3140e+00  6.0336e+00 -4.7366e+00 -7.6794e+00  4.8340e+00
-#>  -8.0594e+00  3.9725e-02 -9.1456e-01 -4.6298e+00 -3.2125e+00  1.3890e+01
-#>  -8.9300e+00  1.3681e+01 -9.5088e+00  1.2522e+01  7.4292e-02  6.4323e+00
-#>   2.0328e+00  2.6531e+00 -1.2640e+01 -9.4904e+00 -1.5841e+01  5.4049e+00
-#>  -4.0114e+00  1.4736e+00 -1.1151e+01 -8.2712e+00  4.5151e+00 -7.3063e+00
-#>  -4.3667e-01 -2.3232e-01 -3.1493e+00  1.7114e+01  7.8338e+00  1.8030e+01
-#>  -4.7537e+00  2.0193e+00 -1.0430e+00 -1.0014e+01  3.9959e+00 -2.3063e+01
-#>  -9.8200e+00  1.0670e+01 -5.9706e+00  2.4227e+01 -2.7277e+00  5.0799e+00
-#>  -6.7298e+00  4.2245e+00  2.7938e+00 -2.8428e+00  2.2324e+01 -6.9202e-01
-#>   8.1541e-01  1.5880e+01 -1.3547e+01 -9.0919e+00  4.8563e+00 -1.3871e+01
-#>  -1.3599e+01  1.1264e+01 -2.1159e+01 -3.1379e+00 -7.7998e+00  9.0941e-01
-#>  -1.5343e+01 -2.8572e+00 -5.8122e-01 -3.8335e+00 -9.4521e+00 -4.6786e+00
-#>   7.6362e-01 -2.8310e+00  9.1508e+00  6.4683e+00  5.6846e+00  4.6314e+00
-#>   9.5910e+00  3.8809e+00  8.4984e+00 -3.5116e+00  1.0446e+01 -1.0756e-01
-#>  -4.8337e+00  9.8921e+00 -5.1602e+00 -7.1428e+00 -1.0196e-01 -7.2319e-01
-#>  -1.2248e+01  1.1094e-01 -5.7928e+00  7.0802e+00  5.0713e+00 -3.1212e+00
-#>  -2.4178e+00 -1.0519e+01 -2.3732e+00 -2.7023e+00 -5.9592e-01 -7.1338e-01
-#>   6.3050e+00 -3.9132e-01  7.2173e+00  9.4026e+00 -2.7707e+00  9.6987e-01
-#>   2.0358e+00 -1.3545e+00  7.1738e+00 -3.6405e+00  3.7646e+00 -1.6337e+01
-#>  -6.3457e+00 -1.4147e+00 -6.5676e+00  1.1348e+01  1.1023e+00  9.8485e+00
-#>   1.5842e+00  3.5686e+00  5.5394e+00 -1.1322e+00 -3.6309e+00  5.8952e+00
-#>  -8.2128e+00  9.2595e+00  1.7783e+01 -4.6057e+00  3.9662e+00  1.4638e+00
-#>  -1.2341e+01  2.5213e+00  1.2736e+00 -2.4336e-01 -1.6478e+00 -1.7396e+01
-#>  -9.3529e-01  2.8352e+00 -6.1613e+00  1.5112e+01 -1.2392e+01  1.0438e+01
-#>   1.2253e+01 -4.4346e+00  6.0388e+00 -2.3199e+00 -1.7197e+00 -7.9958e+00
-#>   1.8004e+01 -3.0359e+00 -1.5593e+01 -4.7352e+00 -9.6357e-02  1.7268e+01
-#> 
-#> Columns 25 to 30  4.5417e+00  9.9356e+00 -7.9734e+00  8.6843e+00  1.9243e+00  7.8274e+00
-#>   8.7007e+00 -4.7809e+00 -3.4190e+00  7.8836e+00 -5.8560e+00  4.9606e+00
-#>  -2.3389e-01  1.1928e+01 -9.1595e+00  5.2450e+00  1.2959e+01 -8.0261e+00
-#>  -2.3453e+00 -1.1489e+01  3.8974e+00 -2.7657e+00  6.5121e+00  8.7784e+00
-#>  -2.8829e+00  7.7365e+00 -2.3080e+01  1.0360e+01  4.4827e+00 -1.6566e+01
-#>   1.7922e+00  1.4484e+01  3.0660e+00 -1.2048e+01  7.5874e+00  6.2713e+00
-#>   5.5566e+00 -1.4929e+01  5.3574e+00 -5.9776e+00 -9.7370e+00  1.1398e+00
-#>   2.4957e+00 -1.4012e-01 -2.0386e+00 -2.8146e+00  1.4249e+01  2.2646e+00
-#>  -4.2497e+00  1.3767e+01 -3.7657e+00  7.9206e+00  2.9652e+00 -9.6100e-01
-#>  -1.6080e+00  1.4514e+01 -2.0387e+01 -1.7081e+01  1.1001e+01  1.1186e+01
-#>   2.7661e+00  2.3567e+01  9.5477e+00  1.2551e+01  8.0121e+00  3.5173e+00
-#>   1.3002e+01 -6.6859e+00  1.1111e+01  4.0720e+00  2.4169e+00  9.7231e+00
-#>  -7.4153e+00 -5.0619e+00 -1.1198e+01  1.4229e+01 -6.1807e+00  4.0081e+00
-#>  -1.9085e+01 -7.4257e+00 -1.2958e+01  8.1068e+00  2.7206e+00  2.1854e+00
-#>  -1.0049e+01  5.6731e+00  8.5600e+00 -1.7086e+01  8.3425e+00 -8.1282e+00
-#>  -1.0292e+01 -1.0067e+01 -2.2118e+00  8.2715e+00  6.0819e+00 -3.0324e+00
-#>  -3.3625e+00  4.6885e+00 -9.0741e+00 -1.1488e+01  6.0181e+00 -2.8482e+00
-#>  -7.8853e+00  6.8509e+00  4.0404e+00  5.2101e+00 -3.8899e+00  3.3171e-01
-#>  -9.4386e+00  1.4857e+01 -2.6685e+00  1.1545e+01  9.6147e+00  1.5497e+00
-#>   2.8378e+00 -1.1177e+01 -1.1782e+01  3.5629e+00  1.0285e+01  4.6724e+00
-#>  -1.3464e+00  3.1451e+00  1.8565e+00  1.0258e+00 -1.0872e+01  9.4655e+00
-#>  -1.1968e+01  6.8576e+00  7.9288e+00 -1.9535e+01  1.2262e+01  1.3630e+01
-#>   5.4541e+00  7.6256e+00 -1.1775e+01  8.9696e+00 -1.0420e+01 -1.0184e+01
-#>  -7.0127e+00 -1.0383e+01  5.1228e+00  1.2812e+01  1.4420e+01  6.0488e+00
-#>   4.6218e+00 -4.0049e-01  1.6324e+01  2.4842e+00  9.8292e+00  5.9595e+00
-#>   5.6140e+00 -1.7067e+01  6.4086e+00 -3.8410e+00 -6.4977e+00  4.8475e+00
-#>  -4.9237e+00 -6.8551e+00  7.2600e+00  9.0261e-01  7.7543e-01 -1.7415e+01
-#>   7.6608e+00  1.7461e+01 -9.9003e-01 -1.1081e+01  1.3593e+01 -2.8464e+00
-#>  -4.0654e+00  1.7042e+00  1.9005e+00 -2.1048e+00  2.3070e+00 -8.9824e+00
-#>   4.1790e+00  2.9341e+00 -4.1916e+00 -8.8421e+00  1.0892e+01 -9.1511e+00
-#>  -6.2952e+00  7.3589e+00  1.1996e+01  1.7767e+01 -1.4724e+01 -6.8176e+00
-#>   1.6905e+01 -5.8042e+00 -2.2659e+00 -1.0988e+01  1.2340e+00  5.7880e+00
-#>   2.8737e+00  3.9366e+00 -6.4715e+00 -8.6918e+00  7.6655e+00  8.2046e+00
-#> 
-#> Columns 31 to 36  4.9854e+00 -3.8726e-01  6.5628e+00  6.7788e-01 -5.2493e+00  4.1982e-01
-#>   1.4268e+00  2.0383e+00 -2.1152e+00 -4.2073e+00  9.1627e+00  1.5472e+01
-#>   8.2502e+00 -1.0515e+01 -1.5214e+01 -6.1506e+00  5.3685e+00 -1.2504e+01
-#>   5.2293e+00 -9.7807e+00  4.1479e+00 -2.4804e+01 -5.1699e+00  7.7294e+00
-#>   5.3212e+00  1.3778e+00 -3.7450e+00 -1.1298e+01  4.2642e+00  4.4505e+00
-#>   1.5083e+01 -1.1646e-02 -6.4796e+00  9.1971e-01  7.9833e+00  1.7602e+01
-#>  -2.6859e+00  2.7046e+00  9.2215e+00  2.6360e+00 -4.5803e+00  2.3114e+00
-#>  -1.6276e+00 -9.2005e-01 -2.5471e+00  1.0154e+01 -1.1071e+01  2.7301e+00
-#>  -4.7220e-02  1.0298e+01  5.7568e+00  3.7165e+00  5.3836e+00  1.2636e+01
-#>   1.3372e+01 -4.1079e+00  3.2401e+00 -1.5440e+01  1.0938e+01 -2.2595e+00
-#>  -6.2048e+00  1.0810e+01 -4.6557e+00 -5.7423e+00  1.9922e+01  6.8378e+00
-#>   1.6257e+01  3.3141e+00  2.6023e+00 -7.5886e+00 -5.4325e+00  1.6676e+01
-#>  -1.5218e+00  1.2508e+00 -2.9083e+00  1.2418e-01  1.8454e+01 -3.9098e+00
-#>  -7.6743e-01 -1.0442e+00 -6.1045e+00 -7.6160e+00 -7.2777e+00 -8.3719e+00
-#>  -5.6189e+00  1.0683e+01  4.7618e+00 -1.0877e+01  1.6728e+01  2.3661e+01
-#>  -3.1498e+00  4.8652e+00  7.7276e+00 -1.7810e+00  5.4526e-02  1.3808e+01
-#>  -1.6673e+01  2.2490e+00  4.5205e+00 -7.0093e+00  2.0609e+01  5.7754e+00
-#>   1.0943e+01 -1.8617e+00  1.0474e+01 -7.6102e+00 -1.5107e+01 -2.9932e-01
-#>   2.8436e+00  2.7909e+00  2.2022e+01  1.6944e+00 -1.2976e+01 -3.5025e+00
-#>  -5.4795e+00  3.9904e-01 -6.4733e+00  6.7021e+00  7.2860e+00 -2.8783e+00
-#>   2.6436e+00 -5.1108e+00  3.1331e+00  1.4062e+01  5.9850e+00  6.8239e+00
-#>  -8.2778e+00  5.1245e+00  3.0526e+00  6.2872e+00  1.4696e+01  1.1970e+01
-#>   8.6024e+00 -5.6751e+00 -9.3626e+00 -1.4129e+01 -3.8140e+00 -1.1897e+01
-#>  -4.4061e+00 -2.3178e+00  9.2872e+00 -8.1672e-01  1.2030e+00 -1.2162e+01
-#>   5.3659e+00  7.7213e+00  5.7185e+00  9.0630e+00 -5.1373e+00 -3.1592e+00
-#>   3.2050e+00 -2.3414e+00 -6.9714e+00  7.1941e+00 -9.8566e-01 -3.3091e+00
-#>  -1.6919e+01 -1.2293e+01 -6.6814e+00  3.7901e+00  5.9487e+00  8.8619e-01
-#>  -1.2029e+01 -7.6166e+00 -4.6589e+00 -1.7603e+01 -2.6050e+00  4.8997e+00
-#>   2.4186e+00 -5.6908e+00 -2.5564e+00 -1.6754e+01  8.0733e+00 -1.1196e+00
-#>  -8.7796e-01  6.5197e+00  6.5565e-01 -8.6806e-01 -2.6335e+00  6.8496e+00
-#>   1.1316e+01  2.8948e+00  4.4362e+00  4.8354e+00 -2.3211e+00 -1.2829e+01
-#>   8.5893e+00 -3.7059e+00 -8.4572e+00  6.7882e+00 -8.4576e+00  8.2972e-01
-#>   1.6636e+01 -8.8189e+00  6.4427e+00  6.5319e+00  6.9739e+00  1.1096e+01
-#> 
-#> Columns 37 to 42  4.4878e+00 -2.3642e+00 -9.1210e+00  1.5661e+01 -1.9917e+01 -6.3785e+00
-#>   1.1384e+01  3.5865e+00 -2.5288e+00 -1.7164e+01  8.7337e+00 -1.0503e+01
-#>  -6.2520e+00  3.9174e+00 -1.2509e+01 -5.3240e+00  3.7293e+00 -1.5637e-01
-#>  -1.9859e+00 -6.6096e+00  1.2515e+01 -1.4296e+01  3.2204e+00 -6.3126e+00
-#>   1.6918e+00  2.0285e+01 -3.0068e+00  3.2718e+00 -1.4610e+01 -1.4036e+01
-#>   9.0918e+00 -5.9544e+00  7.9972e+00 -1.1101e+01 -1.3772e+01 -1.9439e+01
-#>   1.8324e+00 -3.4902e+00  1.1935e+01 -2.5032e+01  1.8524e+01 -5.9759e-01
-#>  -3.1921e-01  3.2242e+00  2.3243e+00  1.0866e+01  5.6447e+00 -4.0742e+00
-#>   2.0699e+00  1.4610e+01 -1.7303e+01 -2.6887e+00 -1.1823e+01  1.4420e+01
-#>   5.1856e+00  2.0304e+00 -7.1594e+00 -5.7598e-01 -1.5643e+01 -1.7092e+01
-#>  -5.6571e+00 -6.3264e-01 -1.8718e+01 -1.6022e+00 -1.6949e+00  8.5261e+00
-#>  -2.5168e+00 -3.4719e+00  6.6238e-01  7.8183e+00 -7.9532e+00  7.6461e+00
-#>   5.2890e+00  4.8125e+00 -9.6915e+00 -3.7230e+00 -5.9298e+00  6.9522e+00
-#>  -2.5441e+00  7.1421e+00  2.4809e+00 -3.4307e+00 -1.7882e+00  6.1988e+00
-#>  -7.7466e+00  9.6058e-01  1.4210e+00 -8.9925e-02 -5.7440e+00 -1.0791e+01
-#>  -5.5046e-02 -1.0465e+01  1.9446e+00 -4.2025e+00  5.9414e-04 -7.0922e+00
-#>  -1.1868e+01  2.7056e+00  1.4955e+01 -4.6315e+00 -2.0707e+00  1.0120e+01
-#>   2.2510e+00  5.3352e+00 -6.9040e-01 -3.6846e+00 -4.9931e+00 -2.6069e+00
-#>   3.9388e+00  3.7917e+00  1.4283e+01 -2.5320e+00 -5.9300e+00 -9.6440e+00
-#>   9.9951e-01  1.2395e+01 -1.7485e+01  6.0842e+00  4.0708e+00 -1.4335e+00
-#>  -5.4947e-01 -1.1178e+01  1.4815e+01 -4.7548e-01  1.6524e+00 -6.1346e+00
-#>  -1.8547e+01 -3.5868e+00  5.7109e+00  1.0381e+01  5.3679e+00  4.0875e+00
-#>  -6.2024e+00 -3.8099e+00  3.1032e+00  8.7504e+00 -2.4905e+00 -3.1567e+00
-#>  -5.7587e+00  2.6403e-01 -6.2622e+00 -3.2774e+00 -3.4080e+00  1.7630e+01
-#>  -6.5531e+00 -5.1616e-01 -2.5381e+00  4.3673e-01  1.3827e+00 -1.7608e+00
-#>   1.3258e+00  7.4871e-01  7.2413e+00  2.9663e-01 -6.5212e+00  7.2607e-01
-#>   7.7659e+00  7.0330e+00 -1.2670e+01  8.9590e-01  1.4491e+01  1.8087e+01
-#>   6.2934e+00  8.0264e+00  3.6147e-01 -7.0904e+00  7.8345e-02  2.3515e+00
-#>  -1.1642e+00  1.2683e+01  6.3582e+00 -1.7169e+01  6.1291e+00 -2.4972e+00
-#>   1.1013e+01  1.7106e+01  7.4904e+00  7.7218e+00 -1.7584e+01  4.4086e+00
-#>  -1.2509e+00  1.5248e+01 -3.4874e+00  1.3855e+01  5.7941e+00 -9.0559e+00
-#>   2.0705e+00 -1.1685e+01  3.1573e-01 -1.4187e+01  6.1962e+00  1.0098e+01
-#>   4.0630e+00  7.0444e+00 -6.7645e+00 -1.2123e+01  1.0104e+01  6.0825e+00
-#> 
-#> Columns 43 to 48 -1.3225e+00  7.5155e+00  3.5457e+00 -7.7401e-01 -7.9913e+00 -2.8263e+00
-#>  -4.4239e-01 -6.5029e+00 -5.3231e+00 -1.2749e+01  1.2061e+00  1.3572e+00
-#>   6.6092e+00  7.3148e+00  4.0908e+00 -1.6144e+01  4.0651e+00  9.7613e+00
-#>   1.6555e+01 -4.9039e+00  2.0471e+00  2.6363e+00  1.6037e+01  7.5261e+00
-#>   5.6720e-01 -1.8519e+01 -1.4613e+01 -1.0380e+01  7.9935e+00 -2.4992e+01
-#>  -9.9498e+00 -1.4021e+01  3.0135e+00  9.9928e+00 -1.4162e+01 -7.7319e+00
-#>   4.5391e+00 -9.1612e+00  8.2764e-01  1.4789e+01  8.5675e+00 -4.3570e+00
-#>  -1.1531e+01  5.0008e+00 -1.0593e+01  6.8532e+00 -7.9979e+00 -2.3096e+00
-#>  -6.9519e+00  9.2419e+00 -5.3410e+00  4.6456e+00  1.2305e+01 -1.4149e-01
-#>  -5.0381e+00  4.8369e+00 -9.1629e+00 -1.7132e+00 -5.3706e+00 -1.9919e+01
-#>  -2.1738e+01 -6.7851e+00 -8.8066e-01 -8.6924e+00 -1.1466e+01  9.4515e+00
-#>  -9.2470e+00  4.1282e+00  6.4433e+00  1.4437e+01 -5.9255e+00  5.0156e+00
-#>  -3.8086e+00 -8.3930e+00 -8.6682e+00 -2.9186e+00  8.8949e-01 -7.8708e+00
-#>   9.3219e+00 -2.1789e+00  2.8101e+00  1.1117e+01  3.1088e+00  3.8468e+00
-#>  -4.7248e+00 -1.1072e+01 -4.4787e+00  1.2252e+01  2.8345e+00 -1.1620e+01
-#>   2.5926e+00  1.7134e+01 -7.0157e+00  7.1150e+00 -1.0001e+01  7.1872e+00
-#>  -9.4637e+00 -1.9046e+01 -4.2980e+00  5.0236e-01  8.4141e+00 -5.4341e+00
-#>  -8.8937e+00  2.2311e+01 -1.7071e+01  9.9567e+00  1.3399e+01 -7.8803e+00
-#>   9.9015e+00  7.5980e+00  1.0382e+01 -6.2705e+00 -5.7278e+00 -1.9555e+00
-#>  -1.4069e+01 -1.9179e+01 -8.3042e+00 -3.1122e+00  1.2979e+00 -1.2923e+01
-#>   3.4753e+00 -3.3074e+00  6.5502e+00 -2.8901e+00  4.0644e+00 -1.2498e+00
-#>  -8.5863e+00 -1.1009e+00  4.9903e+00 -9.2786e+00  8.5524e+00  4.5484e-02
-#>  -2.3117e-01 -6.9398e-01 -2.4986e-01 -7.5937e+00 -1.0116e+01  1.9911e+00
-#>   9.7825e+00  3.6422e+00  8.0094e+00 -2.3208e+00 -7.2454e+00  1.1305e+01
-#>  -3.8630e+00  8.8709e+00  7.7070e+00  2.0446e+01  1.2637e+00  8.0608e+00
-#>  -6.2783e+00 -9.8522e-01 -1.4196e+00 -8.4213e+00 -2.2625e-01 -1.6038e+00
-#>  -8.0083e+00  7.4533e-01  2.5935e+01 -1.0613e+01  4.8281e-01  2.1464e+01
-#>  -2.3609e+00 -1.1517e+01 -3.8818e+00 -1.6465e+01 -5.4758e+00  3.3605e+00
-#>   1.6953e+01  1.1260e+01  9.7832e+00 -1.2325e+01 -4.8735e+00  1.1821e+01
-#>   5.7424e-01  9.8175e+00  2.3017e+00 -1.5989e+00  3.4220e+00  1.0485e+01
-#>  -6.5441e+00 -8.2770e+00 -4.5998e+00  1.2889e+01 -2.1546e+00 -1.7968e+01
-#>  -5.9971e+00 -1.7395e+01 -8.5698e+00  1.1068e+00  6.6695e+00 -4.2924e+00
-#>  -1.5701e+00  9.1711e-02 -1.4028e+01  9.8882e+00  3.9283e-02  1.3230e+00
-#> 
-#> Columns 49 to 54 -3.8444e+00  1.1858e+00  4.9792e+00  9.6627e-01  1.2525e+00 -1.7345e+00
-#>   7.1986e+00  2.9036e+00 -1.3004e+00  1.1473e+01 -4.6877e+00  1.6839e+00
-#>   3.4666e-01  3.0801e+00  1.3069e+01  1.7583e+01 -8.5806e+00  4.5497e+00
-#>  -2.2805e+00  1.7387e+01 -1.5286e+01 -5.8295e+00  4.2371e+00 -2.7819e+00
-#>  -2.4679e+00  7.7251e+00 -2.3069e+01  8.4689e+00 -3.3133e+00 -8.6648e+00
-#>  -3.6375e-01 -2.5602e+01  1.0000e+01  3.5638e+00  4.2016e+00  9.5066e-01
-#>  -1.1269e+01  6.4835e+00  3.1930e+00 -4.3767e-01 -1.6418e-01 -6.3701e+00
-#>   2.7810e+00  5.8144e+00  4.0933e+00 -1.3157e+00  1.1435e+01 -3.0485e+00
-#>   8.1508e+00  1.4555e+01 -2.8499e+00 -2.8760e+00  8.9614e+00  2.1232e+00
-#>  -8.3261e-01  2.7100e+00  8.7359e+00 -8.2285e+00 -6.2578e+00 -5.3856e+00
-#>   2.2066e+00  1.0378e+01  9.5569e+00  9.5588e+00  1.6320e+00  1.0084e+01
-#>   1.4588e+00  9.2064e-01 -7.1439e+00 -5.2017e+00 -2.4140e+00  5.3526e+00
-#>   1.3612e+01  5.6620e+00 -1.1620e+01  2.3625e+00 -1.1330e+01  1.5248e+00
-#>  -3.5426e+00 -7.0384e+00 -2.9504e+00 -9.5061e+00  4.3853e+00  4.7988e+00
-#>  -9.7679e+00  1.7907e+00 -7.9331e+00 -6.1811e-01  5.9971e+00 -3.7143e+00
-#>   7.6239e+00  6.9541e+00  5.5589e-01 -4.7093e+00  2.4645e+00  5.1278e+00
-#>  -9.8085e+00  8.3869e+00  8.0053e+00 -9.6987e+00  8.2273e+00  5.5820e+00
-#>  -9.3834e+00  6.4200e+00  1.0142e+01 -8.7463e+00 -4.5796e-01  3.4890e+00
-#>   7.2693e+00  2.4162e+00 -4.3967e-01  4.9801e+00 -6.2719e+00 -4.2498e+00
-#>   5.4299e+00 -4.0730e+00 -4.8699e+00 -3.7527e+00  2.8188e+00 -6.4849e+00
-#>  -1.6949e+01 -7.3988e+00 -6.1241e+00  7.4695e+00  1.5441e+00  1.4237e+00
-#>  -9.0940e+00  6.1137e+00  9.6657e+00 -2.4854e+00  7.8322e+00  2.6870e+00
-#>   1.3036e+01  1.8695e+01  2.8623e+00  1.2134e+01 -8.4328e+00 -2.1355e+00
-#>   1.8717e+01  7.8995e+00  7.2458e+00 -7.6360e+00 -9.9172e+00 -2.4202e+00
-#>  -3.4935e-01 -4.2851e+00 -3.0568e+00  1.6756e-01 -2.4791e+00  2.2609e+00
-#>   8.0906e+00 -7.4999e+00 -4.3320e+00  1.7309e+00 -6.8967e+00 -3.5653e+00
-#>  -1.0714e+01 -1.1555e+01 -2.1318e+00 -1.1573e+01  1.0170e+00  1.3795e+00
-#>   2.5068e+00 -1.0478e+01  1.8676e+01  2.4759e+00  1.5531e+00 -4.1684e+00
-#>   8.1488e+00 -8.4433e+00 -7.5819e+00  1.6040e+01 -4.2868e-01  4.2592e+00
-#>  -1.5774e+01 -4.5053e+00 -2.0056e+00 -1.8684e+01  1.1285e+01  5.9708e+00
-#>   1.0074e+01 -1.4951e+01 -4.2046e+00  1.2449e+01 -3.6492e+00  5.5716e+00
-#>  -5.2416e-01  1.1741e+01  1.2132e+01 -4.0776e+00 -2.5397e+00 -3.4564e+00
-#>  -7.4641e+00  3.1837e+00  1.8403e+01 -2.4848e+00 -5.9302e-01 -9.3820e-02
+#>  Columns 1 to 8  -1.8278  -1.9992  12.0137   0.7742  -4.3592   7.2621   8.1447   7.4280
+#>   -1.7553   4.2251  -2.8547  -7.8134  -9.7632   0.0592   4.3939 -18.6819
+#>    2.9278  -8.9142  -6.8949   2.4460 -12.0093   0.8691   1.1334  -5.5789
+#>    1.0978  -1.4901  -1.0441  -7.7989  -3.0274  -1.1836   0.8401   1.7227
+#>    1.3817 -10.0784   8.7597   8.8902  12.2989  14.1231   4.9507  -1.3499
+#>   -3.3349 -17.0731  -4.9188 -13.6262   4.9822  10.3247 -19.9588  -5.4589
+#>   -9.0754   3.3522  -4.5549   2.3084 -13.7354  -6.1428  -3.4103  13.2395
+#>   -0.6018  -7.1244   7.2283   8.9938  -3.4478  -2.9633   4.0425  -3.1454
+#>   -1.7671   1.4279  15.3357  -9.2404 -12.3784   7.3627   3.8409   7.3812
+#>    0.2318   4.4662   8.2655   8.9807 -10.3119  -7.4330  -2.4399   5.8157
+#>   -1.2583  -4.1496  -6.0251  -6.1863   4.0612 -10.3667  -0.6054  15.0585
+#>    1.0225   1.7582   3.4433 -13.3864   7.3118   5.4278  -4.3103   2.2919
+#>   -2.0022  -4.6432  -3.4370   2.3838   2.0596  -3.8474  -4.7552   9.8073
+#>    1.2191  -0.4041   8.8284   1.7027  -3.4531  15.1536  -3.0719  -4.3556
+#>    0.6785  -0.2781  -0.2994   2.9963   7.8222  -6.5244   2.3621 -10.7839
+#>   -4.2437 -11.7469  -1.8946   5.1964   5.0602 -12.2637  -5.4357  -4.9670
+#>    3.2541  -6.0174  -4.7469   3.9014  -5.6371   6.1688   6.9332   2.7519
+#>    3.9222  -6.0205  -6.8673  21.9821   0.4441 -10.1525 -11.5251   6.3250
+#>    2.2674 -13.1944  -7.3018   0.6320  -6.4537  -0.3320   1.2296  -6.7274
+#>   -4.7276  -4.1470   4.0511   6.6782  -4.1980 -11.0360 -10.3439  13.8242
+#>    4.1586   7.2105  -5.9131 -10.6936  -6.4093   0.5122 -14.9520  -0.0415
+#>   -3.4269   5.2123   9.6663  18.2697  13.5317   0.9326  -3.9832  -7.3083
+#>    2.9821   1.4320   5.1003  -3.9346 -12.4614  -0.0960   0.6008   1.0544
+#>   -3.8915   4.3737  -3.5099  13.1990   9.2120   6.7522 -16.2094  12.1509
+#>   -4.9955   5.8569   9.9225   4.4663  -8.7695  -0.7714  -3.1265  -0.1352
+#>    0.0058   1.3676   5.6374   8.3946  10.9936   9.2321  -2.6340  -5.0205
+#>   -5.6368  -4.7662   7.2619  10.5504  -5.3719   2.5085  -0.8908   0.1013
+#>    1.9447   0.6654   8.5346  -8.5676  -4.4802  -2.4839  -1.0572  -8.3232
+#>    4.0766  -9.7166   1.4965  -7.6203   7.8403  -5.2291  -4.4750  -0.2420
+#>    3.2524  -3.2185   3.7339 -14.8517  16.9115  10.5897 -10.1153   7.9807
+#>    1.6478 -10.4716   7.3207  -0.9193 -11.4669  -8.8775  -4.5622  -7.5420
+#>   -0.3521  -1.9796  -0.1008  -8.4364  -6.5148   4.5422  -4.6248  -4.0213
+#>   -0.8232  -8.6533 -18.9698   8.5933 -13.4778   3.6377  -6.1830   4.5912
+#> 
+#> Columns 9 to 16  16.3031  13.3162   0.0441  -9.6332  -6.9281  -7.0784  10.1682 -15.0466
+#>  -14.3116 -18.6331  -2.0147   0.0963  -4.5613   4.5297  -7.2080  -6.5696
+#>   21.7557  -8.0346  12.6664  -1.0153  -3.4079   7.1477  -6.6836  -5.0452
+#>   -0.7489   9.0841   0.3517  -2.2716  -5.5166  -7.2953  -6.1782  -8.0897
+#>   10.9753   4.1531   1.0763  -1.3994   1.0888   0.3557  -2.2661   3.0627
+#>  -12.6661  -1.3871   4.7954  -0.8387   9.6736  -2.1913  -0.2700  -0.6660
+#>  -11.2423   7.9876 -21.8495   4.6218  -2.4685  -6.2939  -0.5695  -1.1823
+#>   -5.1893   1.0698   2.3094   5.6548  -5.5176   1.8193  -9.7336 -13.2829
+#>   -4.2866   5.2463 -11.3882  -8.7327  -8.5612  -2.2047   7.6348   0.7481
+#>  -18.1333  14.9107   1.1302   5.7585  -7.2209  11.4020  -3.8563  -1.8114
+#>  -10.8373  -7.0064   7.8502  -6.7826  -0.1871 -15.9070  -4.1897  -8.2457
+#>    7.8334  -2.7950 -10.3673  -7.8100  -7.7432   7.8274   5.8466   3.0237
+#>  -11.4523   7.2507  -9.3757   6.8782   0.9394  12.7514   8.3915  -6.6221
+#>   -6.4298  -0.2166  18.2928  -2.0460   8.1428   3.1872  -4.6073   2.1645
+#>   -1.8766  -6.7421  -1.5052   3.4517   3.3160   4.1193   0.4470 -10.5357
+#>    1.8184  -3.3434  -4.9086   4.9641  12.3714  -2.6826   6.5897  -8.1739
+#>  -10.1332   5.5788  -9.5882   1.3482  -1.3735   5.2645  11.8176   0.0861
+#>   -5.9807 -12.4909  31.0489  -6.8092  -1.6492 -13.5120   1.0149 -14.4195
+#>   -5.7085 -26.2626  -8.2131  -3.0630  -0.5366   6.7058  -1.8958  10.0420
+#>    0.7780  11.5537  -1.8851  -2.1664  -2.2292  -9.4855  10.2857  -0.0787
+#>   -1.7473  12.8294  -7.4605   5.8547  -6.8567  -3.5955  -4.5156  -4.2422
+#>   -5.3133  13.5608   7.5875   0.7132   9.4777  -6.0114   9.9703 -11.1773
+#>   20.2499   3.5352  14.5475 -13.3674   3.4155  -2.5999  13.1113  -8.1474
+#>    3.7472   5.3684  -1.9821   4.4593   0.2074  -5.2935  -5.6149   0.1898
+#>   -1.6840   3.5513   0.9613   1.7634  -7.2022  11.3500 -13.4342  13.6657
+#>    7.1025  14.6000   5.7225   8.9757  -7.4920  10.2955 -20.1670   3.6177
+#>   21.4246  -3.4838  -9.6575  -8.5567  -3.7634   2.6889   2.1730   7.8899
+#>    1.9030  -6.2530   3.0929  -7.3119 -11.1117 -11.5511  -5.6199 -15.5764
+#>  -17.8055   3.6392  13.2821  -0.3390  -9.5785   4.0666 -16.5819  -2.7986
+#>   -6.0328  19.2414 -13.0046  -2.9299  -2.6221   5.2583  -5.3400  -3.4284
+#>    0.4950  17.2507  -1.0922   3.8960   7.1311  -7.5944   7.2633 -12.3290
+#>   -5.9907   2.4158   3.3354  -3.3003  18.5557  -1.3097  -6.5577  -3.1158
+#>   -2.7529  -2.1604  -7.0972   7.4385  -4.7679   7.2684   0.1251  -3.2474
+#> 
+#> Columns 17 to 24  14.1471  -1.6221  10.5489  19.0133  10.9805  22.9026  18.4415   8.6729
+#>   -3.3398   5.1112  13.9802 -11.7035   1.9743 -11.2474   9.6685 -13.1868
+#>   11.4249 -12.5929  11.4603  -0.8304   9.9173   0.2101   9.0032 -12.0680
+#>   -5.4534  -3.3778  16.7488  15.4319  19.8737  26.0228   2.7345   1.4849
+#>   -9.3980   4.8794   0.5792   7.7060   3.7078   0.8509  -2.5579   4.9784
+#>  -11.1519  -2.1167 -14.5700  -1.5012   0.2889   9.5857 -13.2802  -3.4726
+#>    2.6271  -6.8448  -6.6396  -3.2060   5.9826  -9.6228   1.7255 -12.4407
+#>    5.7910  -9.1598   1.3664   3.5546  -7.4978  -0.0121  -9.0969  -0.9117
+#>    8.7712   2.1619   7.8947  -9.5589  -0.8316  15.2951 -16.3607   5.2267
+#>    4.0864  11.9321   2.2410  11.1664  -1.7695  -6.4023  -4.8266  -7.6543
+#>   -7.8193  -6.7269   0.0553   0.0495   7.5919  -6.0707  12.9644  -4.1214
+#>    9.8959  -7.3465 -11.0739 -20.9455  -7.1304  -0.7368 -16.6885  12.6987
+#>    0.6348 -15.9486   4.1711   4.0479  -0.0220   9.2770   0.3611  -0.8650
+#>   -6.9850  -0.6714  13.5144  -3.4413  -9.8248   8.4805   5.9827  -2.5657
+#>   -1.0861  -5.3642  12.4004  -1.4940   1.7654   3.8199   1.1674  -9.8535
+#>   11.8522 -14.4678 -20.0183  -6.0879   4.8844  11.6476  -4.8730   9.3403
+#>  -11.8238  -5.5082 -22.9524  -5.8221  -0.0325  -0.5233   7.4460  -3.0812
+#>   -3.7692  -5.1480   6.1451  -1.7952   4.5798   4.0911   2.6925 -16.5468
+#>   -1.1947   8.8372  -3.1783   3.0410 -15.8259   3.2736   0.6511  -0.5271
+#>    2.4455  -2.3751  -2.1832 -11.0963   5.4442   0.5341   0.2816   5.8151
+#>    9.8548  -9.4681  -1.9801  -9.6426  12.5498 -12.8970  -5.7238  -5.1347
+#>   -2.9029  -3.0799   9.5785  -4.2105  14.8580  -4.1471  10.4564  15.2692
+#>   11.3599  -6.4316  -2.3842  -5.4518   3.6670  -6.7810  -2.8211  -2.3596
+#>    4.0217  -3.7635  -6.2476  -3.8510  11.7067 -20.7715  17.7182   1.6303
+#>   10.7473  13.8563   3.6357  -4.5467  16.0371   3.8422  -1.8105 -12.6746
+#>   -1.5486   3.2842  -2.6390   1.5962   9.7386  13.2267 -11.8230   6.7691
+#>   11.5688 -19.7452  -8.9386  10.1395 -13.1120  11.6775   3.4599  -3.8841
+#>   10.7813   3.1226  -7.0240 -19.7247  -1.7867  -0.1655   1.4773  12.6155
+#>  -12.6501  19.3373  -7.3785   0.5540  -9.3232   2.3527   3.7409  22.0885
+#>   -0.6158  29.1081  -7.7667 -13.0275  -1.6349   9.0042  -3.8634   2.3394
+#>   15.4306  -5.2225 -12.0317  12.3403  -6.3439   7.4529  -9.7596   7.6394
+#>   -8.2285  11.1189 -15.4673  22.6747  -1.9692  -0.1479 -10.5180  -5.7190
+#>   -7.5455 -11.5259   0.1797   7.6880  13.8323  -9.0994  12.2357 -10.6590
+#> 
+#> Columns 25 to 32  -3.8070   4.0626  10.0321   2.9133 -11.9386  -8.7714   7.9659 -24.7761
+#>   -2.1164  -7.0922  -1.4403 -11.0323  -5.9337  13.8197  18.1642  -7.3607
+#>   13.5477  -3.7010   8.6035   8.9446   9.4659   2.0519   0.7980   2.6327
+#>    3.6474  -5.2079 -12.3851   3.4422  -5.3575   0.5633  -6.8533  -3.2238
+#>   10.6037  -0.6468  15.9020  -8.7088  -9.9113   5.0383   7.7306  -6.9287
+#>    5.3263   2.4196  -0.2952   0.9148  13.2521  13.7524   6.0847  -1.7773
+#>    7.9296  -7.7511 -10.0406   7.5685  -6.6879   4.1834  13.2754  -5.4886
+#>  -14.2614   6.8018  -7.6910   5.2495   2.8189  -8.5175  -0.6668  -1.6376
+#>   11.0703   0.1423  -4.4549 -15.0306   2.0572  -8.6217   0.4768 -13.0389
+#>    8.0307  -9.6294  -3.2351   5.3387  -8.5965  -1.9904   0.3424   3.6076
+#>   -7.3230  -4.0098  27.4761  21.0948  -3.9312  -4.0498   1.0309   2.1574
+#>  -10.0410 -18.1823  -8.6550  -3.6033  -1.4088  -7.4984  -9.2645   8.7458
+#>    5.3361   2.6729 -12.3806  -0.9159  -2.6683   2.1241  11.3422   0.4183
+#>   27.1691  -3.1880   1.4364  -4.3836  -5.6235   3.2178  -2.5931 -16.2138
+#>   -8.2161 -10.2309  -1.9759  -3.3680   6.3068 -13.8779 -12.1415 -16.8366
+#>    6.6612   3.0679  10.4860  -4.2603  12.9672   3.0420  13.6200  19.1005
+#>   -2.3542  -2.6570  10.1231  -3.0807  10.5623  -1.6872  -0.2589  -1.1286
+#>    9.3881  -7.2766  -8.2052   5.4098   8.9287 -23.6507   9.9289  -5.0458
+#>   -6.6019   1.0735  13.7641  -8.0233   7.2367   1.2418  20.4790  31.1730
+#>   11.9357  13.7167   1.0437  -5.9436  10.4147 -17.2705  14.2735   2.1576
+#>    4.3458  -7.4362  18.7311  -6.3015  -7.7887  -1.2063   9.4937   7.5900
+#>  -11.1522   4.9899   3.2782   1.0124   7.6420   8.4002   2.9606 -22.6415
+#>   17.3937  19.3920 -11.4020  -3.3475   0.5184 -24.7598  -5.4586  -3.1849
+#>    7.1874 -11.5002  11.7036   7.1107   3.6857  -0.1435  -6.1266  -2.1732
+#>    6.5301  -3.5415 -13.6247  -5.4981 -33.6445   3.4628 -11.6791  -0.6334
+#>    8.7089  10.6086   4.1865   6.6284 -12.9061   4.4287  -6.3520   4.4147
+#>    3.8152   1.5419  -6.0620 -10.0631  -9.7848  -0.3725   4.2617  11.0266
+#>   -3.1238   2.6697  -2.5713  -8.7944  15.7555   6.5145  -2.5702 -11.7910
+#>   -2.8956  -9.4594 -11.0735  -0.2199   1.9091   0.0760  -6.8349  13.5783
+#>    5.5080  -2.7804  16.0789  -0.3762   5.0091   3.8944 -13.5909   7.5931
+#>    3.4339  -7.3356   1.5105 -12.9250  10.2782 -12.0469  11.4916   1.4644
+#>    4.2244  -2.2059  19.0733 -21.7453  -3.8929   0.0641 -26.1059   8.6540
+#>   20.8482   1.0211   0.2668   0.2010  -0.6060   2.6662  11.6536  15.4551
+#> 
+#> Columns 33 to 40  -4.1759   0.1792   6.3306   6.7061   1.1700   0.3439  -0.2116   1.5787
+#>    8.8917   1.2146  16.3504 -14.7963  -8.9506  -7.1300  -0.9518 -18.5146
+#>  -23.8418   5.1508 -15.5476   6.3501  10.8675   0.8243  -6.8501   1.7874
+#>   -8.9844   2.2340   8.9887   1.5850  -1.2605   2.1202   5.3737   5.1156
+#>   -9.3110  10.5398  -8.6976  -0.7967   7.4244  18.2843  -7.1259   7.5997
+#>   16.1473   4.0424  -6.3295  -2.7665 -14.5501  14.4538 -12.9577   9.4903
+#>    7.2387 -10.8876   9.4133  -6.2578  -1.8405  -9.1878   4.4472   1.0935
+#>   -9.1707 -11.0896   0.7047  10.7887   1.0210   2.0706  -2.4303 -10.6248
+#>   -0.0595   0.2143   6.3909   9.3707   7.9404   4.3553   2.7386  -2.5387
+#>    3.5608   1.4678 -12.8538 -10.7144  13.8932  -5.8710  12.9713  -4.2875
+#>    5.5676  -1.6633  -7.3778  -5.4125  -8.8952   5.7221   9.0450  -3.3968
+#>   24.4307   8.4293 -14.3973  -5.0354  -9.5832   7.0662  -7.5748   7.9493
+#>   -1.9314  -1.2003  -7.0768   0.9991  -1.6183  -9.8344  -7.3185  -4.3012
+#>    4.0534   1.2099   9.9007  -0.3536  -5.2408   3.7769   7.4002   8.1614
+#>    5.8442  -5.6219  19.0496  -3.1157  -3.1701   0.2326  10.2120   2.5049
+#>    0.8920   0.3652 -25.1249 -11.6316  18.6287  -8.7130   0.6353   5.2994
+#>    3.9898   8.8716   7.0474 -13.7945  -6.0692  -5.0922  11.3454  -5.7957
+#>   -9.2591   6.2370   2.3607 -12.0233  -3.0580  -0.5462  23.3562  -0.3377
+#>   -7.2532   8.2455  -5.0663  -4.7884   4.5751   1.0790  -4.0215  -4.0910
+#>   10.3187 -18.9974   1.4884   3.6102   7.5574  11.1837  -4.6688  -3.3943
+#>    7.8745  -2.3474   2.5750   3.3975  -2.7609   0.8566  -0.0632   3.8676
+#>   15.1801 -10.1422  -1.0819 -12.3601  -5.0162   6.5887   0.3800  -5.3402
+#>   23.3701  -7.1501   0.3758 -10.2395  12.1159  13.0760  17.4721  -0.0280
+#>    2.1601  -5.2557  10.3193  -6.4740   1.6852 -16.3359   3.3005  -7.3239
+#>   -9.9006  -3.4947   5.3245   3.4283  -0.0992 -11.3374 -13.7996  -3.6147
+#>   -6.0846  12.2785   7.9146  -5.6806  -6.8068   7.7441  -3.0438   0.7109
+#>   -9.6249 -11.1059 -15.9537  14.1597   8.3405  -7.1891   5.3502  -0.6583
+#>    4.1647   6.5661   5.9930  -1.3052  -7.6803  -3.6735   0.2886  11.7744
+#>   -6.4241  10.4401  -0.0793  11.7241 -10.1892  -8.0390  -8.3995  15.4166
+#>   17.5252  -5.8873 -12.0533   5.7357  14.8050  17.6203 -17.7314  -1.7866
+#>   -6.1603   7.8373   0.3816   0.3329   8.5804  -9.1982  -6.5034  -5.7855
+#>   -1.2352   7.1834  -3.4181  -9.7114  36.2866  -1.0136  10.0630 -10.2468
+#>  -13.3563 -13.3916   3.5529   0.8110  18.1894  -7.3221  14.0168  -5.2680
+#> 
+#> Columns 41 to 48  -0.4203  -5.3919  10.1363  -7.1045 -11.6670  -6.9815  14.7989  -5.8473
+#>    0.7813  -9.1485   7.1495   2.5925  14.0859  21.3443 -13.7789 -23.8908
+#>    6.3872   3.3134  11.0106  -9.2730   4.3757   2.1895   9.1677   5.0160
+#>   -6.2241   9.6219   5.9175   0.0616 -10.5226  -6.9645  -5.2340  -6.0893
+#>   -9.8494  -4.9540  -6.1446 -10.2490  -7.5325 -12.5611  20.3521  -3.1144
+#>    6.5833   0.7438   0.2831   3.8526  -7.4652   5.5956  21.8544  -3.6685
+#>   19.5054  -9.5118   4.6375   7.2778  12.2823   5.4768  -2.3338  -7.7278
+#>   -2.5981  -0.1810  -7.1396   5.1544   9.2667   4.5204  13.8634   8.2614
+#>   16.8392   3.4734   3.8301  -7.0999   5.7907   1.4268   0.0401 -14.9712
+#>   -0.2924  -8.1524   3.3882 -17.1059   0.6868 -12.9145 -19.1256 -10.5931
+#>   -1.6853  -6.0860  -7.0604  -1.8475   5.6664  -0.5509  17.1244 -12.0091
+#>  -11.0133  11.3395   4.0484  -1.3233   7.6724   4.7709  11.2414  -9.2826
+#>   -0.3401   0.4798   7.4614  -4.7404   4.1452 -12.6775  16.5851 -10.9787
+#>   -1.0534   4.8472   0.3311  -2.8344  -0.2976  13.5925  -3.7241 -18.2202
+#>    3.1590   4.5599   4.8182 -11.2759   2.1343  12.7832   7.7773   8.4494
+#>    4.1884  -1.9868  13.3732  -0.0600  10.0132   3.1800   8.7206   4.8105
+#>   12.0572 -14.9972   0.0707  -1.5617   2.9682  -4.8372 -21.5722   9.2030
+#>    3.5336  -7.6226  -1.2871  -1.7145   5.0222   9.3502   0.8102   9.6789
+#>    0.8985  -5.4043  12.0690   9.3996  15.3610  -9.1556 -18.7414  -7.9379
+#>    4.5196  -3.6074   2.2545  -0.9103  -5.2378  -5.8195  14.1626  12.5672
+#>    4.0691  -1.3085   3.3314 -11.5787   6.3989   4.8375   4.8127   5.2556
+#>   -0.0278   3.3645 -12.2148   8.2952  -6.0585   3.5579  -3.8833  -0.1563
+#>    3.6362  -7.7613  -7.4743  -3.6094  -8.3921  -0.8610  -8.1597   7.8543
+#>   17.1323   2.6529   0.3249  -3.9764   2.8374   8.6943  -6.2051  17.6460
+#>   -4.2134   5.9890   2.3131  -3.9054  -5.0735   3.7393  -1.4429 -12.5281
+#>   -2.2282  -4.9036  -1.6334 -10.6815  -6.5110 -13.8104   0.2534   9.8076
+#>   -3.5680   5.0634   5.9500   7.9774  -7.8356   3.4612   9.7782   9.7200
+#>   -3.9451   3.4529   1.1338  -3.4034  -5.8660   9.3493  12.5976  -3.0950
+#>  -10.5798   5.6987  10.6136  -7.9722  -1.3188  -5.8405  -3.6458 -14.3120
+#>    7.7916  -5.7089   4.6727 -20.9463   2.4246  -8.1153   5.8464  -3.5677
+#>   11.7358   0.3333   6.2847 -22.3661  -4.6214  -4.4525   6.7179   0.9785
+#>   -4.7052 -18.9650  -3.1811 -15.5645 -11.8443  14.4625   4.6566   7.8471
+#>   15.9889  -6.2135   7.9098  -7.7195 -11.2409   4.5874   2.8155  11.0690
+#> 
+#> Columns 49 to 54 -23.7621  -2.0672   5.1202   2.8693  -3.5247  -1.1408
+#>   -0.5914  15.5443  12.9888  -2.8760  -9.3422   5.9938
+#>  -18.5729  -5.6788   1.0306  -3.8441  -6.0085   1.2521
+#>   -9.5754 -18.4452 -16.0340   3.8308  10.5140  -2.4937
+#>    9.2793 -24.4750   6.3770  13.0336  10.5139   2.6575
+#>    0.1169 -19.7163   8.7442  -7.4142  -0.3510  -1.3783
+#>  -20.4549  12.7792   1.3947  20.0064   0.2754   6.7898
+#>    2.3452  -4.8371  21.0461  18.3207   4.3536   0.6791
+#>   -1.6700   0.3947  -2.3495  -4.5524  -4.9053   2.6297
+#>    0.0885  15.4859  -3.0774  -7.5609 -10.5294   3.6316
+#>    6.2226  14.6700  10.8972  -2.9759   0.9148  -4.2356
+#>   29.9997   9.5160   0.7651 -18.8166 -20.9002   4.1593
+#>   -8.5250  -0.1514 -10.3785  -3.6711   1.9608   5.1791
+#>    9.1076  12.1738  -5.0964  -2.1688  -5.8933  -3.4682
+#>  -13.8920  -1.6102   3.0901  14.5004  -6.1199  -1.8855
+#>    3.6077  -3.8308 -11.6881  -4.0734  -9.5537   4.4149
+#>    9.9225  -0.1492  -6.4503   6.9651  -4.6334   1.3232
+#>  -12.8566  13.6474   4.2665   5.5423  -1.5723   0.2077
+#>    3.2742  -5.8253   1.1354 -14.6569 -12.1710   3.1549
+#>  -10.4806  -0.3699   0.9769  10.6879   3.8503   5.4269
+#>   17.8956 -11.3729  -2.0963   4.5150  -4.6084   2.9865
+#>  -12.6670 -19.6047  -3.4059  -6.0671  12.6761  -3.2279
+#>  -16.8891  16.2462   4.3667  -4.0076  -6.0983  -4.3323
+#>   -7.3072   2.0849   9.6141  15.3027  -6.0594  -4.7458
+#>   -5.2251  14.7473   2.6430 -10.7033   3.4242   5.2835
+#>   -3.2220 -10.7773   4.0306   1.7556  -0.8909   0.1858
+#>   -1.6882   4.1642  -0.8937  -4.1788   5.2175   1.8850
+#>   13.4893  -2.5422  17.0995   8.1240   2.2313   6.1781
+#>   10.8599  -5.8666  -5.0010  -7.6735  11.1980   2.3107
+#>   13.3253   4.4453  16.6472   6.6833  -5.4616   5.4430
+#>    1.8033   1.1504   5.3888   8.4619 -11.5018   5.4091
+#>   -9.3979  13.8763  -9.2113  16.0083 -10.2207   1.1649
+#>  -14.9877 -17.5661 -11.5863   2.2509   5.4443   4.4319
 #> 
 #> (12,.,.) = 
-#>  Columns 1 to 6  5.6711e+00 -9.7715e+00  8.0139e+00 -3.1456e+00 -8.2597e+00 -8.6188e+00
-#>   4.5534e+00 -2.6602e+00  5.7101e-01  4.5924e-01 -7.1426e+00 -7.6855e+00
-#>   2.3944e+00 -1.8833e-01 -1.2372e+00  1.3183e+01 -1.1774e+01 -1.2614e+01
-#>  -3.7058e+00  1.2332e+01 -7.1135e+00  1.0252e+01 -1.1324e+01  1.5886e+00
-#>  -1.3778e+00  1.3744e+01  1.0130e+01  1.7991e+01 -9.5907e-01  4.4537e+00
-#>   4.3923e+00  2.0173e+01 -1.1748e+00  2.2084e+00  1.0591e+01 -2.1620e+01
-#>  -4.6225e+00 -4.0510e+00  4.9409e+00 -8.6606e+00 -4.5434e+00  4.4733e-01
-#>  -1.6025e+00 -6.5477e-01  1.8544e+00 -2.0565e+00 -2.4117e+00  3.3357e+00
-#>   2.8552e+00 -1.0342e+01  1.0375e+01  4.7829e+00 -7.2180e+00 -8.8688e+00
-#>   1.0235e+01  9.7207e+00  3.1417e+00  4.0030e+00 -7.3955e+00 -6.9170e+00
-#>  -5.7200e-01  5.5964e+00 -1.5976e+01  8.3666e+00  6.3403e+00 -7.6915e+00
-#>   3.4703e-02  3.5169e+00  3.8781e-01  1.0636e+00  4.0983e+00 -2.7044e+00
-#>   4.6174e+00 -2.4054e-01 -8.3332e-01 -1.0104e+01  5.7178e+00  5.9626e-01
-#>  -3.6530e+00  6.3614e+00 -1.4573e+01  1.8428e+01 -1.0996e+01  7.7436e+00
-#>  -7.8050e-01  1.7219e+00  1.0551e+01 -7.6440e+00 -1.9886e+00 -2.9778e+00
-#>   3.0868e+00 -2.7772e+00 -5.4846e-01 -4.6270e+00  7.8908e-01 -1.3109e+01
-#>   2.8907e+00 -1.3217e+00 -1.0009e+01  1.4561e+01  4.7109e+00 -3.6655e+00
-#>   2.1374e-01 -3.3225e+00  1.0521e+01  3.3724e+00 -1.4473e+01  1.0237e+01
-#>   2.7409e+00  1.5870e-01  8.3594e-02  1.3877e+00  6.6924e+00 -2.4924e+00
-#>  -2.1034e-01  1.1392e+01  4.4399e-01 -2.0182e+00  6.3715e+00 -1.6278e+00
-#>  -2.7978e+00  2.1835e+00 -3.4727e+00 -1.1099e+01  1.1308e+01  1.9844e+00
-#>   1.4916e+00 -4.6658e+00  7.5224e+00 -4.0165e-01  4.3843e+00  2.9431e+00
-#>  -4.8740e+00  3.3266e-01 -1.0033e+01  2.4769e+01 -1.0689e+01  5.7757e+00
-#>   9.7244e+00 -2.0223e+01  1.5571e+00 -6.1857e+00 -4.8121e-01  5.3037e+00
-#>   2.1985e-01 -6.2206e+00  1.8928e+00  4.3969e-01 -4.0883e+00  1.2525e+01
-#>   3.6974e+00 -6.7978e+00  1.4884e+01 -1.1140e+01  1.9589e+00  2.8536e-01
-#>  -2.5475e+00 -1.5448e+01 -9.8222e+00  2.7567e-02 -1.6978e+01 -1.3631e+01
-#>  -2.5236e+00  7.1482e+00  9.9684e-01  1.6637e+00 -1.8525e+00 -3.7578e+00
-#>   6.8408e-01 -4.5971e+00 -2.3067e+00  1.6949e+01 -1.8636e+01 -1.2232e+01
-#>  -6.7232e+00  9.1325e+00  2.0426e-01  2.6954e+01 -8.9083e+00 -1.2419e+01
-#>   2.1591e+00 -1.0398e+01  1.2408e+01 -2.9166e+00  6.0366e+00  1.0637e+01
-#>  -2.0474e+00  9.3563e+00 -5.2706e+00 -7.9547e+00  7.8486e+00  1.2689e+01
-#>   3.8286e+00  6.1758e+00 -7.4604e+00  2.8888e-02 -1.1185e+00  1.1341e+01
-#> 
-#> Columns 7 to 12 -1.1826e+01  2.2621e+01  5.7299e+00 -1.0211e+01  2.0500e+00 -2.0580e+00
-#>   2.2837e+00 -4.1392e+00 -1.3015e+01  4.3746e-01  1.4448e+01  4.2647e+00
-#>   1.8809e+01 -5.0689e+00  1.1920e+01  4.9381e+00 -1.1874e+01  1.6306e+01
-#>  -7.0689e+00 -8.3381e-01  1.5031e+01  5.1379e-01  9.0282e+00 -1.0080e+01
-#>   9.0968e-01  6.8403e+00 -1.2286e+00 -7.2483e+00  1.4132e+01  1.1597e+01
-#>  -1.2094e+01 -3.4152e+00  2.6146e+00  1.4391e+01 -4.1691e+00 -3.0251e+00
-#>   1.3840e+00 -6.1495e+00 -6.8200e+00 -7.8748e+00  1.6628e+01  8.4340e+00
-#>  -1.9125e+00  8.5565e+00 -3.7415e+00 -9.7756e+00 -1.5671e+01 -1.0357e+01
-#>  -2.6787e+00 -3.3692e-01 -1.3210e+01  4.1409e+00  5.5250e+00  6.2864e+00
-#>  -6.5676e+00  1.9990e+00 -4.1325e+00  1.8645e+01  4.8182e+00  6.1783e+00
-#>   1.2527e+01 -9.8162e+00 -3.1660e-01 -4.4354e+00 -1.9086e+01 -7.7893e+00
-#>  -2.1272e+00 -1.3353e+01  7.4139e-01  1.5879e+01  1.2691e+00  8.5015e+00
-#>   4.1590e+00  4.9067e+00 -1.2046e+01 -1.4323e+00  1.0125e+01 -2.5866e+00
-#>   3.5672e+00  4.8377e+00  3.1483e+01  4.4150e+00  2.4836e+00 -7.8096e+00
-#>  -4.1977e+00 -1.7131e+00  5.3965e+00  6.3721e+00  6.7158e+00  1.0346e+01
-#>  -5.3131e+00 -3.7630e+00 -3.8306e+00 -2.7599e+00  5.5997e+00  1.4647e+00
-#>   1.8103e+00  6.5942e-01  7.3887e+00 -9.9311e+00  8.3289e-01  5.5044e+00
-#>  -4.5116e+00 -5.5039e+00 -1.5688e+00  1.6182e+01 -3.9394e+00 -1.0381e+00
-#>  -2.6093e+00 -3.5263e+00 -5.2329e+00 -3.5045e+00  5.7408e+00 -3.6470e+00
-#>   1.3142e+01  1.6011e+00  4.2480e+00 -1.1670e+01  1.3931e+01  2.5416e+01
-#>  -4.3017e+00 -2.4624e+00  8.3654e+00  6.6246e+00  3.9868e-01 -1.0769e+00
-#>   6.2551e+00 -3.5745e+00  7.6361e+00 -5.0444e+00 -7.1267e+00 -1.1137e+00
-#>   2.0551e+00 -9.9614e+00 -1.9466e+00  7.4334e+00  1.4679e+01 -4.0151e+00
-#>   4.6821e-02  1.1432e+01 -3.3222e-01  4.6008e+00 -7.8557e-01 -1.3359e+01
-#>  -2.3926e-02  6.8382e-01 -4.8464e+00  7.1137e+00  2.9912e+00  4.5205e+00
-#>   4.2275e+00  6.5084e+00 -1.7213e+01  8.4103e+00 -5.6102e+00  3.6258e+00
-#>   1.3469e+01 -2.8784e+00 -8.9576e-01  2.5106e+00 -5.1594e+00  1.0925e+01
-#>   2.6589e+00  2.8767e+00  3.8252e+00 -1.2938e+01 -2.2467e+01 -3.1135e+00
-#>   5.5026e-01  2.3104e+01  1.6266e-01 -8.8421e+00 -2.3897e-01 -1.3208e+01
-#>  -3.0139e+00  7.5189e+00  2.7617e+01  1.6454e+00 -2.2950e+01 -7.1717e+00
-#>   2.6843e+00  1.2358e+00 -8.7613e-02 -1.3074e+00  1.4932e+00 -4.6459e+00
-#>  -7.2866e+00  6.1055e+00 -1.9969e-01  1.4076e+00 -7.0442e-02  8.3251e+00
-#>  -7.3165e+00  6.8355e-01 -6.6699e+00 -9.3733e-01  1.8232e+01  2.1962e+00
-#> 
-#> Columns 13 to 18  1.9015e+00 -7.1054e-02  6.5036e+00  1.8471e+01 -7.8308e+00 -1.3398e+01
-#>   1.4368e+01 -8.2049e+00  8.0801e+00 -3.8359e-01  7.3189e-01 -1.1775e+00
-#>  -1.4420e+01 -4.6961e+00 -1.6440e+00 -7.7410e+00 -6.0583e+00 -3.1094e+00
-#>   3.3701e+00 -3.4220e-01 -1.1748e+01 -5.6438e+00 -3.8476e+00  1.4843e+01
-#>  -9.6171e+00 -6.9606e-01 -4.0281e+00 -1.1368e+00 -6.0511e+00 -1.3137e+01
-#>  -9.2864e+00 -2.6392e+00 -1.1496e+00 -1.6844e-01  1.0979e+01  7.7849e+00
-#>  -9.9460e-01  1.0206e+00  4.4142e-01  1.1463e+01 -7.1979e+00 -1.1835e+01
-#>  -8.3785e+00  2.4509e-01 -4.0796e+00  3.0417e+00 -3.3664e+00 -1.6919e+01
-#>  -2.7345e+00  6.1984e+00  3.6193e+00  1.6176e+00  1.8195e+01 -8.8061e+00
-#>  -6.9595e-01  1.5538e+01 -5.7967e+00  4.6592e-01 -7.5308e+00 -2.6751e+00
-#>  -8.8344e+00  1.1063e+00  1.5160e+01 -2.0454e+01  3.6625e+00 -7.1725e+00
-#>  -5.0083e+00  1.1612e+01 -1.2413e+01 -1.8029e+00 -1.0512e+01 -4.8151e+00
-#>   7.4919e+00  7.1667e+00  1.0053e+01 -3.3491e+00 -1.1306e+01  6.5037e-01
-#>   2.3728e+01 -8.9091e+00 -1.0776e+01 -8.8852e+00  4.8242e+00  5.6458e-01
-#>   8.7586e+00 -9.7872e+00  1.0937e+01  2.2949e+01 -7.2551e+00 -6.7513e+00
-#>   9.4950e+00  1.3832e+00 -1.6443e+01  2.3965e+00  4.3156e+00  4.0602e+00
-#>  -3.6678e+00  3.3257e+00  4.8965e+00  1.3294e+01  6.1973e+00 -1.0398e+01
-#>  -2.4726e+00  2.3567e+01 -2.0121e+00  4.4666e+00 -2.9066e+00 -2.0574e+00
-#>   6.9333e+00  1.1008e+01  9.4940e+00  2.6360e+00 -2.3520e+00 -9.7747e+00
-#>  -6.8040e+00 -6.3517e-01 -1.3560e+01  3.8996e+00  4.3230e+00 -1.5269e+01
-#>   5.5237e+00 -5.9878e+00 -4.8637e+00 -5.7554e+00  3.2201e+00 -9.9667e-01
-#>  -1.5524e+01  4.4229e+00  1.0867e+01 -6.8404e+00 -8.2170e-03 -1.1930e+01
-#>  -8.9235e+00  2.8391e+00  7.8580e+00  5.2690e+00 -1.1646e+01 -1.6284e+01
-#>   1.1135e+00  2.0755e-01 -5.3085e-01  4.4674e+00 -2.2772e+01  1.2490e+01
-#>   3.5108e+00 -1.7097e+01 -1.0687e+00 -6.3410e+00 -1.7460e+01  3.0247e+00
-#>  -1.5075e+01  4.4179e+00  2.9446e+00  4.5553e+00  3.9893e+00  1.0847e+01
-#>  -3.8475e-01 -3.0224e+00  1.3595e+01 -6.9657e-01 -1.1146e+00  7.6620e+00
-#>  -2.6613e+00 -1.5902e+01 -9.7376e+00  5.7318e+00  5.6692e+00  1.2321e+01
-#>   5.4724e+00 -1.1878e+01 -4.7547e-01  9.2209e+00 -3.4451e+00  1.6410e+01
-#>  -2.8532e+00  3.8302e+00  1.2172e-01 -6.0661e+00 -4.2698e+00 -1.0130e+01
-#>   1.6465e+00 -5.3229e+00  7.0761e+00 -1.5705e+00 -7.0562e+00 -2.7597e+00
-#>  -6.1864e-01 -3.3426e+00 -9.9110e+00 -9.6252e-01  7.0601e+00  6.6784e+00
-#>   1.9348e+01  3.2684e+00 -5.1465e-01  3.5049e-01 -1.0837e+01  9.5411e+00
-#> 
-#> Columns 19 to 24 -3.8948e+00 -2.6723e+00 -6.6318e+00  1.6838e+00 -3.7660e+00  7.2476e+00
-#>   1.1216e+01  8.1438e-01 -3.6275e+00 -2.4376e-01 -9.8312e+00 -7.3129e+00
-#>   1.5957e+00 -1.2974e+01  1.8953e+01  9.7969e+00 -1.1822e+00  4.6118e-01
-#>   1.1569e+01  2.7565e+01  6.6624e+00 -1.0608e+01 -4.3190e+00 -8.5527e+00
-#>   1.7083e+01  4.3328e+00 -4.9356e-01  3.5066e+00 -7.9629e+00  1.8458e+00
-#>   6.6433e+00  2.2971e+00 -1.3667e+01 -4.0295e+00 -1.0085e+01 -4.5433e+00
-#>   1.5841e+00 -2.4033e+00  5.9013e+00 -2.2349e+01  1.7666e+00 -1.9011e+01
-#>   8.8770e+00  1.1447e+01  4.4222e+00  2.0156e+00  3.1334e-01  6.5320e+00
-#>   4.4325e-01 -9.0440e+00 -5.9457e+00  3.4535e+00 -1.6740e+01  5.7113e+00
-#>   5.1010e+00  6.8260e+00 -8.1804e+00 -2.4639e+00 -1.3020e+01  4.3330e+00
-#>   1.1201e+01  2.3794e+00  2.9129e+00  1.5070e+01 -1.1008e+01 -5.5706e+00
-#>   4.8113e-01  8.7237e+00  1.0651e+01 -1.0053e+01 -1.4475e+01  9.6086e-01
-#>   4.8966e+00 -4.8608e+00 -4.7656e+00  4.5974e+00 -5.3435e+00 -3.1645e+00
-#>  -2.8056e+00  6.0239e+00  4.9811e+00  9.3587e+00 -1.2518e-01  1.4811e+01
-#>   1.5243e+00  5.9463e+00 -1.9694e+01  2.4770e+00 -7.4116e+00  4.2682e+00
-#>   1.7486e+01  5.6142e+00 -2.6774e+00 -5.4607e+00  3.1753e+00 -7.7612e+00
-#>  -4.7767e+00 -4.1876e+00  8.2306e+00  1.8335e+01  1.6702e+01 -6.1712e+00
-#>  -1.0461e+01  7.7642e+00  9.8448e+00 -3.9824e+00 -1.4456e+01  1.0921e+01
-#>  -3.0338e+00 -3.8039e+00  3.7088e+00  1.1555e+01 -6.6480e+00 -7.0763e+00
-#>   1.2589e+00 -9.1476e+00 -1.0267e+00 -9.8251e+00  1.3203e+01 -1.1121e+01
-#>  -7.1104e+00 -2.1209e+01 -4.1617e-01 -1.6712e+01  7.2966e+00  8.6806e-01
-#>  -1.3472e+00  2.4518e+00  2.5488e+00  5.1634e+00  8.3433e+00 -6.9640e-01
-#>   4.9083e+00  1.1894e+00  1.6084e+01  1.8053e+01 -8.3202e+00  5.9620e+00
-#>   1.4155e+00 -1.2155e+01 -8.0382e+00  1.2269e+00 -2.4019e+00 -4.7523e+00
-#>   5.7603e+00  2.8282e+00 -6.3438e+00  9.6194e-01  4.2417e+00 -8.6298e-01
-#>  -6.2398e+00 -1.3997e+01 -1.0611e+01 -1.2185e+01  5.8461e+00 -1.5036e+01
-#>  -9.8415e+00  7.8770e-01 -7.4182e+00 -8.3712e+00 -5.5750e+00  5.2374e+00
-#>  -6.6236e-01  8.8197e+00  2.5809e+00  9.2737e-01 -2.7482e-01 -3.0782e+00
-#>  -4.8489e+00  1.2334e+00 -8.1420e+00  8.0714e+00  1.8021e+00  1.0030e+01
-#>   2.2816e+00  2.9167e+01  5.3001e+00  1.2647e+01 -1.0302e+01  1.7506e+01
-#>   4.0581e-01  4.3354e-01  4.6884e+00  2.4039e+00 -7.0114e+00 -2.3394e+00
-#>  -1.7465e+01 -2.9286e+00 -3.3655e+00 -5.9884e+00  1.8096e+01  4.9700e+00
-#>  -8.3931e+00  9.1925e+00 -6.2284e-01 -3.7626e+00  7.2228e-02 -1.4662e+00
-#> 
-#> Columns 25 to 30  2.0364e+00  3.4924e+00  8.8576e+00  1.9560e+01 -5.3159e+00 -6.7273e-02
-#>   8.5199e+00  1.2460e+01  1.0849e+01 -1.2488e+01  1.1534e+01  8.3744e+00
-#>  -5.2998e+00 -1.3045e+01  7.0058e-02 -1.6556e+01 -5.1436e+00  1.2320e+00
-#>  -1.9245e+00  2.6752e+00  2.5943e+00  3.8957e+00  1.9414e+00 -4.5554e+00
-#>  -1.7785e+01  3.6812e+00  1.3851e+01  7.9270e+00  1.4260e+01 -5.3344e+00
-#>   1.7070e+01  7.5528e+00  2.5537e+01  2.0648e+00  9.1443e+00  4.2659e-01
-#>  -5.8135e+00 -1.1264e+00  5.3336e+00 -7.6384e+00  7.7942e-01 -1.4103e+00
-#>  -6.6164e-02 -1.1689e+01 -5.0414e+00  2.4737e+00 -8.3804e+00  2.4184e+01
-#>  -1.2746e+01 -1.4828e+00 -5.7543e+00 -5.3110e+00  5.5415e+00 -1.6850e+01
-#>   3.1513e+00  1.5225e+01  6.6620e+00  1.6091e+01  1.0120e+00  7.5204e+00
-#>  -1.2891e+01  7.4804e+00  8.0265e-01 -1.2574e+01  9.4596e+00  7.2928e+00
-#>  -4.2098e+00  4.1588e+00  6.9672e+00  2.2453e+01  9.1519e+00  1.8179e+01
-#>  -4.1001e+00  1.8540e+01  4.0887e+00 -1.0073e+01  1.8330e+00 -1.4200e+00
-#>   5.6480e+00 -1.3350e+00 -1.2036e+01 -9.5990e+00  7.4656e-01  3.2980e-01
-#>   3.4966e+00 -1.6570e-01  1.8722e+01  2.3947e+00 -2.9675e+00  2.2816e+01
-#>   2.8179e+00 -3.1789e+00 -1.1038e+01  5.5216e+00  5.2880e+00  4.8121e+00
-#>  -1.4641e+00  7.2584e+00  7.5047e+00 -1.4729e+01  1.1529e+01  4.0212e+00
-#>  -2.1840e+01  1.6311e+01 -2.2091e+00  8.8685e+00 -2.6726e-01  1.0857e+01
-#>  -6.0878e+00 -1.9527e+01 -2.7790e+00 -2.3459e+00  9.2915e+00  3.6225e+00
-#>   3.0910e+00 -8.8166e+00 -1.2194e+00  2.1275e+00  1.4776e+01 -6.3646e+00
-#>   3.4392e+00  1.2337e+00 -2.4477e+00 -4.2940e+00  1.2196e+01 -8.3100e+00
-#>   2.1095e+00 -1.6299e+01 -1.8262e+00 -8.5428e+00  5.4610e+00 -1.8177e-01
-#>  -8.0396e+00 -3.5281e-01 -4.7305e+00 -2.0184e+00 -1.3290e+00 -1.2946e+01
-#>   6.7542e+00  2.7995e+00 -2.6588e+00 -5.7228e+00 -1.7821e+01 -1.2070e+01
-#>   8.5798e+00  1.3482e+01  5.4124e+00  4.5472e+00  4.2665e-01  9.1178e+00
-#>   7.9867e+00 -1.7613e+01 -3.2339e+00  4.7845e+00 -1.2815e+01  5.4119e-01
-#>   6.9358e+00 -7.6092e+00  8.9106e+00  1.4474e+01 -5.1735e+00  6.0925e-01
-#>  -3.7007e+00 -1.1889e+01  1.2766e+01 -4.3393e+00 -8.2446e+00 -5.4101e+00
-#>   1.4022e+00 -2.8613e+00 -1.5519e+01  5.5836e-01 -5.3816e+00 -1.6839e+01
-#>   3.9563e+00 -1.7790e+00  1.2531e+01  2.5413e+01 -6.9938e+00 -5.0654e+00
-#>  -1.3314e+01  1.3742e+01 -5.1880e+00 -2.6514e+00  7.9159e+00 -3.9425e-01
-#>   5.2115e+00  5.2123e+00  8.2073e+00  4.7590e-01 -1.2351e+01  6.1773e+00
-#>  -1.2535e+01  1.6295e+01  5.8316e+00  1.8009e+01  7.4288e+00  4.5441e+00
-#> 
-#> Columns 31 to 36  1.2023e+01 -6.7178e+00 -5.8166e+00  1.1401e+01  1.5205e+01  1.9883e+01
-#>  -1.2687e+01 -8.2098e+00  5.3369e+00 -8.2628e+00  1.0745e+00 -1.1268e+01
-#>   9.6230e-01 -1.3118e+01 -6.6789e+00  1.8343e+01  4.4205e+00 -7.8813e+00
-#>   4.5552e+00 -2.2099e+01 -1.1815e+01 -8.8782e+00 -3.4950e+00  4.7670e-01
-#>   1.8077e+01 -6.6238e+00 -1.3619e+01 -7.2434e+00 -8.6265e+00 -7.9528e-01
-#>  -1.0967e+01  9.8506e+00 -2.7321e+01 -1.0223e+01  6.2490e-01 -5.6411e-01
-#>   1.4522e+01  8.1797e-01 -8.0892e+00  4.3848e+00  1.1477e+00 -3.4365e+00
-#>   4.7297e+00  6.4393e+00 -8.8589e+00  6.6986e+00  1.0247e+00  3.6755e+00
-#>  -1.7666e+00  7.6427e+00 -2.5547e+00 -4.3410e+00 -1.5351e-01 -1.1309e+01
-#>  -3.9239e+00 -2.5789e+01 -8.5965e+00  1.2510e+01 -7.7351e+00 -1.1317e+01
-#>  -7.5335e+00 -1.1775e+01 -6.7104e+00  1.6478e+00  1.5399e+01  3.0482e+00
-#>  -1.0282e+00 -3.0531e+00 -1.1335e+01 -1.5999e+01 -1.5555e+01  1.6338e+01
-#>   7.3122e+00 -8.6837e+00  6.5041e+00 -1.4024e+01  4.4772e+00 -5.9621e+00
-#>  -2.8027e+00 -5.2603e+00  1.3327e+01  5.9999e+00 -5.2299e+00 -3.3551e+00
-#>  -2.3727e+00 -1.3826e+01 -1.2738e+01 -1.9202e+01 -7.7574e-01 -1.4906e+00
-#>  -8.8552e+00 -2.1012e+00  1.3027e+01  8.1915e+00 -6.9022e+00 -4.1185e+00
-#>   1.3801e+01  1.0830e+01 -5.0325e+00  4.9789e-01 -2.2028e+00 -1.6712e+01
-#>   1.0335e-01 -3.7909e+00 -6.2674e+00  1.4032e+01  1.0125e-01 -4.1395e+00
-#>   9.5018e+00  1.9901e+00  1.2336e+01 -1.1491e+00 -1.4146e+01 -9.2964e+00
-#>   1.6288e+01 -1.8714e+00 -1.2278e+01 -6.2869e-01 -2.4788e+01 -1.2325e+01
-#>   1.3845e-01 -4.5797e-01  6.1840e+00  8.4224e+00  9.5258e+00 -1.0124e+00
-#>   1.2613e+01 -1.3998e+01 -1.5340e+01 -4.1053e+00  6.9608e+00  4.7141e+00
-#>  -5.0590e+00  1.6985e+00  9.5863e+00  8.1763e-01 -6.1154e+00  5.2919e+00
-#>   4.0963e+00 -1.0605e+01 -5.2540e-02  6.2528e+00  1.6638e+01  1.9486e+01
-#>  -1.9335e+01 -3.2307e+00 -2.5775e+01  2.1714e+00  9.3489e+00  5.1945e+00
-#>  -1.1523e+00  6.8666e+00  6.6111e+00  1.3814e+00 -8.8345e+00  8.1187e+00
-#>   2.5454e+00 -1.1054e+01 -3.9274e+00  1.4365e+00  7.5614e+00  2.1234e+00
-#>  -1.0611e+01  3.2993e+00 -4.9833e+00  5.5129e+00 -1.5963e+00 -3.0104e+00
-#>   3.0948e+00  7.7989e+00  2.8320e+00 -6.8328e+00  5.3871e+00 -7.3793e+00
-#>   1.0356e+01  1.0498e+01 -2.1031e+01 -2.4123e+01  2.2733e+00  1.2569e+01
-#>  -5.8569e+00 -2.2212e+00  8.6687e+00 -1.8951e+01  1.2854e+01  9.3725e+00
-#>  -1.1501e+00 -1.2831e+01 -6.0599e+00  6.2460e+00 -4.7948e+00 -3.0693e+00
-#>  -2.7834e+00 -1.5981e+01 -2.3720e+01 -5.7574e+00  4.2840e+00  2.6247e+00
-#> 
-#> Columns 37 to 42 -9.1028e+00  1.2591e+01  1.0310e+00 -5.8421e+00 -1.1338e+01  4.1153e+00
-#>   3.1706e+00  2.9428e+00  1.4845e-01  8.9454e+00  1.3134e+01  6.5534e-01
-#>   4.5841e+00 -8.7716e+00  8.0809e-01 -1.6475e+01  4.4807e+00 -1.0819e+01
-#>   2.3524e+00 -5.6207e+00 -2.0095e+00 -4.4384e+00  1.3316e+01 -6.8045e+00
-#>  -2.4933e+00 -3.9729e+00 -7.7103e+00  1.1894e+00 -1.1831e+01 -5.8958e+00
-#>   4.1788e+00 -4.9825e-01  1.6073e+01 -3.8185e+00  1.1832e+01 -8.7843e+00
-#>   8.6549e+00 -4.8510e+00  2.8659e+00  1.5565e+00  5.0214e+00  3.4118e+00
-#>  -7.3109e-01  9.8369e+00 -4.7401e+00  9.5182e+00  5.5939e-01 -4.8440e+00
-#>   5.2137e+00  1.8856e+01 -3.0772e+00  1.2977e+01 -1.7254e-01  3.3656e-01
-#>   1.0825e+01 -4.8495e+00 -1.2025e+01  1.1839e+00 -1.6483e+00  1.5632e-01
-#>   7.4011e+00  1.1780e+00 -8.3667e+00  3.9612e+00 -1.0989e+00 -1.1149e+00
-#>   7.2148e+00  9.1705e-01  1.3965e-01  1.2729e+01 -8.3906e+00  6.4803e+00
-#>  -8.2265e+00  3.5863e+00 -6.5682e+00 -4.1911e+00 -4.0294e+00  1.6310e+01
-#>   6.1826e+00 -8.1974e+00 -1.2904e+00  1.0439e+01 -7.9703e+00 -7.8956e+00
-#>   5.9539e+00  1.3534e+01 -3.9589e+00  6.4846e+00 -3.7262e+00 -9.1065e+00
-#>   7.2161e+00 -2.5541e+00 -9.6471e+00  2.1303e+00 -5.7376e+00  3.6745e+00
-#>  -5.3257e+00  4.5439e+00 -1.3490e+01 -5.4855e+00  9.1892e+00 -9.2638e+00
-#>   1.5159e+01 -8.0027e+00 -1.8171e+01  3.2348e+00 -1.1226e+01 -1.2237e+00
-#>   4.0426e+00  4.7529e+00 -3.9383e+00  4.2050e-01  7.3034e-01  1.5667e+01
-#>  -1.0191e+00  1.3406e+01  8.5509e+00 -4.3237e+00  1.7792e+01 -1.0651e+01
-#>   9.1773e+00 -5.7015e+00  5.4302e+00 -3.8808e+00 -7.5434e-01 -8.2778e+00
-#>   1.1293e+00  1.3398e+01 -6.2309e-01  1.1985e+00  1.2024e+01 -1.1281e+01
-#>   4.1953e-01 -1.8520e+01 -1.6476e+01 -2.2505e+01 -8.8054e+00 -3.2068e+00
-#>   1.7972e+00  1.9958e+01  2.3866e+00  1.9869e+01  5.1840e+00  2.0026e+01
-#>   7.1083e+00  6.2996e+00  2.2023e+00  5.7702e+00 -3.2933e+00  1.0233e+01
-#>   3.9714e+00  2.4181e-02  1.1250e+01 -1.2165e+00  1.1519e+01 -5.1492e+00
-#>  -4.7458e+00  2.2553e+00  9.3586e+00  7.0260e+00 -8.9473e-01  3.7660e+00
-#>  -9.3217e-01  2.3749e+00  2.4645e+00  7.2412e+00 -1.8842e+00 -3.2332e+00
-#>  -3.2802e+00  1.5417e+00  6.4068e+00  1.9257e+00  2.9942e+00 -1.0548e+01
-#>  -8.2871e+00 -2.6355e+00  4.1110e+00  4.1654e+00  9.5214e+00 -1.0637e+01
-#>  -8.8577e+00 -1.2026e+00  3.2324e+00 -1.0275e+01  8.1027e-01  5.3804e+00
-#>   1.2246e+00 -4.9161e+00  6.2280e+00 -1.3336e+01  3.3344e-01 -1.7871e+01
-#>  -2.5499e+00  3.3478e+00  6.1585e+00 -9.2499e-01 -4.8937e+00 -7.7639e+00
-#> 
-#> Columns 43 to 48 -1.8949e+00  3.1792e+00 -2.1589e+00  6.3949e-01  3.2063e+00 -1.3578e+00
-#>  -1.0469e+01 -5.6708e+00  7.5980e+00  9.6664e+00  6.7643e+00 -6.5708e+00
-#>  -6.1512e+00 -2.0264e+01  3.4358e+00  6.0738e+00 -1.3076e+01  2.5472e+00
-#>   7.7560e+00  1.4065e+00  1.2090e+00 -8.3934e+00  1.8032e+00  8.1045e+00
-#>  -1.9726e+00 -3.6610e+00 -2.0917e+01 -5.1021e+00 -8.5341e+00  9.6339e+00
-#>   3.1744e+00 -1.5621e+01  3.4864e+00 -1.6545e+00  4.0293e+00 -5.3260e+00
-#>  -8.8298e+00  6.7961e+00  1.1437e+01 -2.5266e+00  3.7172e-01  9.4313e+00
-#>   3.0545e-01  1.8151e+00  9.5635e+00 -1.7820e+01 -6.6283e+00 -8.8928e+00
-#>   1.2522e-01  4.0336e+00 -7.4177e+00  9.1586e-01 -4.6258e+00  3.6882e+00
-#>  -1.2605e+00 -9.0548e-01  8.1084e+00 -6.0548e+00 -1.6634e+01  2.2741e+00
-#>   1.8113e+00  1.5702e+00  5.0019e-01  2.8852e+00 -4.3100e+00  2.9644e+00
-#>   7.3638e+00  3.9861e+00 -3.0309e+00 -2.8950e+00 -3.3484e+00 -6.0369e-01
-#>   7.5221e-03  3.2168e+00 -1.2645e+01  1.7435e+01 -3.2664e+00  3.6868e+00
-#>   3.1162e+00 -5.5322e+00  1.7958e+00 -8.8683e-01  2.2310e+00 -1.3305e+01
-#>  -1.9391e+00  3.5524e+00  6.6669e+00 -3.1837e+00 -1.0453e+00 -9.0635e+00
-#>   2.0948e+00  1.3118e+00 -6.5282e+00 -1.7253e+01  2.3108e+00  8.9559e+00
-#>  -1.7999e+01 -3.3407e+00 -5.3289e+00  4.9456e+00 -1.0145e+01  8.4892e+00
-#>   2.7427e-01  8.0205e+00  1.4450e+01 -1.3079e+01 -1.2436e+01  4.7463e+00
-#>   4.4269e+00  5.9370e+00  9.3929e-01 -1.3290e+01 -1.3577e+01 -9.1268e+00
-#>   1.6066e+01 -8.7729e+00 -2.0591e+00  4.5703e+00  4.9376e+00 -7.4957e+00
-#>  -1.2894e+01 -1.7018e+00 -6.3262e+00  2.8053e+00  3.8833e-01  1.0766e+01
-#>   2.5407e+00  1.3492e+00  5.4623e+00  4.9909e-01 -1.1941e+01 -3.1936e+00
-#>  -4.5454e+00 -4.9026e+00  7.8692e+00  1.5897e-01 -8.2075e+00  7.7568e+00
-#>   1.6057e+00  1.8280e+01 -6.0971e+00  1.3906e+01 -2.5730e+00 -3.1180e+00
-#>  -2.2184e+00  2.2779e-02  6.5701e-01  2.4582e+00  9.5279e+00 -3.7017e+00
-#>   2.0165e+01 -1.5902e+01  9.6445e+00 -7.0694e+00  7.1113e+00 -1.9141e+01
-#>   6.5520e+00  9.2279e+00  8.5650e+00  1.9217e+01  4.2980e-01 -2.1048e+00
-#>  -1.4847e+00  7.6447e+00 -1.0691e+00  6.1191e+00  4.5094e+00 -4.1965e+00
-#>   4.9171e+00 -3.5977e+00 -5.1303e+00  9.4302e+00  1.0969e+01 -2.8175e+00
-#>   1.7654e+01  1.2303e+00 -1.8604e+00 -6.7042e+00  8.0041e+00  2.6355e+00
-#>   3.4652e+00 -8.8835e+00 -2.4696e+00 -6.1194e+00 -2.3170e+00  1.8372e+00
-#>  -3.2396e+00 -1.2917e+01  1.1964e+01 -2.1103e-01  1.5640e+01 -9.4318e+00
-#>  -7.8141e+00  4.2299e+00  2.8658e+00  7.7334e+00 -2.7448e+00 -8.3782e-01
-#> 
-#> Columns 49 to 54  1.2333e+01 -1.3767e-01 -3.5458e+00 -2.4091e+00 -4.3892e+00 -1.8596e+00
-#>   3.0590e+00 -8.6098e+00  1.2912e+01  1.1340e+01 -2.3048e+00  1.8209e-01
-#>  -1.4247e+00 -1.1627e+01  2.7382e+00  2.6747e+00  1.1848e+01  8.3825e+00
-#>  -1.0018e+01  6.1788e+00 -8.4593e+00  7.7510e+00  2.9908e-02 -1.5930e+00
-#>  -6.7280e+00  1.1571e+01  9.2223e+00  4.1258e+00 -8.2129e+00  7.0230e-01
-#>   1.0891e+01  9.3680e-01 -7.9255e+00  4.5878e-01 -7.3924e+00 -4.2198e+00
-#>  -9.4668e+00 -5.4795e+00  1.5067e+01 -3.1557e+00 -4.1782e+00 -1.7167e-01
-#>   3.4064e+00 -4.4980e-01  1.1592e+01  7.2165e+00 -2.5246e+00 -2.8478e+00
-#>   1.0886e+01 -1.5934e+01  1.2154e+00 -6.4077e+00 -4.7183e-01 -4.6793e-01
-#>   1.0714e+01  4.9262e+00 -1.0091e+01 -4.0105e+00 -2.2166e+00 -6.6209e+00
-#>   2.1358e+01  4.1649e+00 -2.9043e+00 -6.3058e+00  2.6100e+00 -5.6352e-01
-#>  -4.0338e-01  1.6002e+00 -8.1261e+00 -5.7080e+00 -2.5969e+00 -1.8784e+00
-#>   7.2134e+00 -4.0635e+00 -1.1044e+01 -7.3213e+00  4.8594e+00  2.2916e+00
-#>  -8.6441e+00  2.0574e+00 -2.0262e+00  8.5201e+00  9.9343e+00 -2.0685e+00
-#>   8.1453e-01 -3.3945e+00  1.0309e+01  6.3051e+00 -2.7420e+00  8.4013e-01
-#>   5.6428e+00  1.0729e+01  6.6803e+00 -2.7419e+00  1.7320e+00 -1.8016e+00
-#>   1.6561e+00  1.4647e+00 -7.4047e+00  9.6098e+00  7.3993e+00 -9.3957e-01
-#>   6.3324e+00  6.9388e+00 -5.6508e+00 -1.0262e+01  6.1348e+00 -3.7880e+00
-#>   2.7018e+00 -4.5579e+00 -5.6484e+00 -1.1570e+01 -1.1231e+01 -6.9494e+00
-#>  -1.4790e+01 -5.7864e-01  5.0066e+00  8.4038e-01  9.4816e+00 -3.1154e+00
-#>  -1.1103e+01 -2.8144e+00  8.4787e+00  7.7905e+00  6.3268e+00  8.5337e-01
-#>  -5.4284e+00 -9.9734e+00  8.3666e+00  4.5437e+00  4.6585e+00  2.5157e+00
-#>   1.3540e+00 -1.0940e+01 -5.3762e+00  1.2806e+01  1.4043e+00  7.3552e-04
-#>   6.4825e+00 -8.2156e+00 -4.1786e+00 -1.4547e+01 -8.1273e+00  6.3691e+00
-#>  -1.3291e+00 -2.9907e+00  4.6872e+00 -4.9224e+00 -8.3817e+00  5.2724e+00
-#>  -3.5581e+00  2.5445e+00 -7.8640e-01 -2.6496e+00 -6.5729e+00 -4.9490e+00
-#>   5.3952e+00 -1.0344e+01  1.2800e+00 -1.8468e+00  3.4351e+00  2.9873e+00
-#>  -5.5920e+00  3.8213e-01  9.8507e+00  2.6040e+00  2.8464e+00 -1.8464e+00
-#>  -1.4649e+01 -1.0690e+00  5.6015e+00  1.5896e+01 -5.8986e+00  8.7044e+00
-#>  -1.1121e+01 -4.9316e+00  2.6215e+00  2.8442e+00  4.1814e+00 -4.4011e-01
-#>   3.7989e+00 -9.5559e+00  1.2084e+00  6.3876e+00  7.4033e+00  5.4886e+00
-#>  -1.5470e+01  5.8021e-01 -1.7981e+01 -6.3462e+00  5.2296e+00 -3.3604e-01
-#>  -2.3717e+00  8.1532e+00 -7.8498e+00 -2.2843e+00 -3.7729e-01  8.6050e+00
+#>  Columns 1 to 6  5.6421e+00  5.9514e+00  8.7758e+00  1.1337e+00 -1.1600e+01 -2.4098e+00
+#>  -3.3859e+00  2.5444e+00 -1.2338e+01  1.5982e+00 -6.8984e-01  1.4842e+01
+#>  -2.4738e+00 -1.1530e+00  7.1141e+00  2.1818e+00 -5.2785e+00 -4.2995e+00
+#>   1.6312e+00  6.0255e+00  4.6905e+00  3.0506e+00  4.7901e+00 -7.3336e+00
+#>   1.6581e+00  1.8267e+00  2.3954e+00  3.6370e+00 -2.7057e+00 -1.2975e+01
+#>  -8.8971e-01 -1.1515e+01 -1.2797e+00  3.8290e+00 -1.0209e+00  4.2400e+00
+#>   3.4707e-01 -5.0225e-01  1.6227e+00 -1.0604e+01 -9.6857e+00  7.7212e+00
+#>   8.4716e-01 -5.2614e+00 -1.1029e+00  4.7822e+00 -7.9505e-02 -1.3063e+01
+#>  -2.4924e-01  4.7284e-01 -1.3204e+00 -1.0713e+01 -1.3655e+01 -1.3402e+01
+#>  -1.1695e+00 -8.2465e+00  7.5490e-01  1.3367e+01  1.9606e+01  1.7177e+01
+#>   3.2295e+00 -5.8235e+00 -1.0723e+01  2.3644e+01  8.6335e+00 -3.6504e+00
+#>  -6.5045e+00 -7.6210e+00 -6.0815e+00 -4.0829e-01  4.3174e+00 -2.4908e+00
+#>   1.0245e+00 -5.6323e+00  4.6078e+00  5.2485e+00  8.9380e+00  1.0256e+01
+#>  -6.6109e+00 -2.5496e+00  5.6999e+00 -5.0328e+00  1.6521e+01 -5.6609e-01
+#>  -4.4788e+00  4.2669e+00  3.9316e+00 -4.0342e+00 -4.5785e+00 -5.5384e+00
+#>  -2.1253e+00 -2.4201e+00  2.3308e+00  3.8880e+00  5.4073e-04 -4.0642e+00
+#>   2.8740e+00 -1.3873e+00 -6.3214e+00  8.7914e+00  1.2729e+01 -6.3549e-01
+#>  -3.6005e+00 -8.1801e+00 -2.8416e+00  8.4031e+00  2.3685e+01 -4.3816e+00
+#>   3.8497e-01  8.7318e+00 -1.4292e+00 -2.3954e+00  1.9753e+00 -4.1494e-01
+#>   1.0812e+00  1.1659e+00  3.1414e+00 -3.4453e+00 -1.4109e+01 -5.6707e+00
+#>   2.0676e+00  5.7838e+00  2.0268e+00  1.0947e+01 -3.7929e-01 -1.1772e+01
+#>   3.8142e+00  7.7487e+00 -5.9545e+00  8.4684e+00  3.1953e+00  1.0657e+01
+#>   1.3140e+00  9.3326e+00 -1.5039e+00  5.7755e+00 -1.1504e+00 -1.1975e+01
+#>  -1.1858e+00  2.2620e+00 -1.1432e+01 -3.7626e+00 -1.1710e+01  6.5101e+00
+#>   3.5093e+00  1.8896e+00  2.5598e+00 -2.9082e-01  7.8567e+00  8.2710e+00
+#>   2.0613e+00  6.3802e+00  7.9352e+00 -6.7460e-02  1.3046e+01 -1.6659e+00
+#>  -2.1816e+00  9.4957e+00  7.0137e+00 -1.8771e+01 -8.3001e+00  5.4263e+00
+#>  -6.5825e-01  8.0723e+00 -8.9852e+00 -1.9129e-01 -1.4055e+01 -1.4530e+01
+#>  -1.6265e+00  5.7905e-01  5.1807e+00 -1.6214e+00 -6.6498e+00 -1.2818e+01
+#>   6.0556e+00 -4.2692e+00  3.6834e+00  9.5250e+00 -1.3678e+01 -7.1343e+00
+#>   5.5887e+00  1.4082e+00  7.6184e+00  1.4062e+01  6.1112e+00  7.0163e-01
+#>  -7.7306e-01  5.8380e+00  1.7265e+01 -3.4233e-01  3.5887e+00 -1.3688e+01
+#>   4.3715e-01  5.8768e+00  3.3457e+00  5.2262e+00 -9.6363e+00 -4.4150e+00
+#> 
+#> Columns 7 to 12  1.8717e+00 -3.8462e+00 -7.0571e+00 -1.9892e-01 -3.1488e+00 -4.6469e+00
+#>   2.0289e+00 -1.6235e+00 -2.0590e+00 -2.0266e-01  1.1852e+01  6.7884e+00
+#>   2.5091e-01 -6.5207e+00  5.5788e+00  2.3274e+00  4.7012e-01 -3.7623e+00
+#>  -1.3844e+01  8.6533e+00  4.3534e+00 -1.3939e+01 -3.5549e+00  3.0343e+00
+#>  -1.3927e-01  8.1590e+00 -1.7224e+01 -2.4374e+00 -1.3905e+01  2.6416e+00
+#>   7.5704e+00 -9.6043e+00 -2.1781e+01  5.7073e+00 -1.7014e+01  1.0927e+01
+#>  -6.8453e+00 -2.1309e+00 -7.0816e+00 -2.1569e+00 -8.9727e+00 -1.1888e+00
+#>   7.4492e+00 -9.8248e-01 -9.9627e+00  1.5302e+01 -4.8582e+00  6.1168e-01
+#>   6.3909e-02  7.0717e+00 -3.7311e+00  1.1669e+01 -6.3676e-01 -1.1931e+00
+#>  -7.4045e+00 -1.0613e+01 -9.4320e+00  2.9507e+01  3.7915e+00  4.2571e+00
+#>  -8.1949e+00 -1.8488e+00 -2.0948e+00 -1.0077e+01  4.5891e+00  9.0483e+00
+#>   1.5817e+01 -8.8738e+00  2.2322e+00  1.3272e+01 -4.5677e+00 -1.3289e+01
+#>   1.2123e+01  2.5267e-01  4.4614e+00  1.1802e+01  1.1462e-01 -5.5703e+00
+#>  -3.8266e+00 -1.4259e+01 -7.2775e+00  6.6205e+00 -1.4896e+01  3.3552e+00
+#>   6.9123e+00 -8.6870e-03  6.1058e+00 -2.4124e+01 -3.5880e+00 -9.3689e+00
+#>  -6.0387e-01 -9.3752e+00  7.2756e-01  3.6307e+00  9.9265e+00 -4.1738e+00
+#>   6.0102e+00 -8.5303e-01 -1.3038e+01 -4.3333e+00 -3.4516e+00 -5.1734e-01
+#>  -1.4759e+01 -1.3811e+01  5.0702e-01 -6.6804e+00  6.2980e+00 -2.8277e+00
+#>  -2.5279e+00 -8.3110e+00 -5.6109e+00  6.5594e+00  4.0858e+00  4.5019e+00
+#>  -3.1133e+00 -1.3220e+00  1.0349e+01  3.1432e+00  1.3162e+01 -1.3658e+01
+#>  -1.9074e+00  6.1141e+00 -7.7314e+00 -6.1643e+00  1.9595e-01 -2.2582e+00
+#>  -7.1831e+00  1.0418e+01 -1.0707e+01 -1.1143e+01  1.0416e+00  8.3760e+00
+#>  -7.1877e+00 -1.2295e+01 -1.5401e+00 -6.6699e+00  1.9101e+01 -1.2868e+01
+#>   5.2554e+00  1.2176e+01 -1.0990e+01 -8.8784e+00 -9.1264e+00  8.7949e+00
+#>   4.1445e+00  1.4764e+01  7.2696e+00  1.1312e+00  4.6407e+00  3.8955e+00
+#>   4.2100e+00  4.1919e+00 -3.1944e+00 -1.0460e+01 -2.8014e+00 -3.5093e+00
+#>  -3.6877e+00 -8.7712e+00  2.5401e+00  8.3708e+00 -9.1170e+00  1.3523e+01
+#>   5.4637e+00  3.2682e+00 -6.0641e-02 -2.5750e+00 -8.2128e+00 -1.3273e+01
+#>   1.8581e+01  1.6255e+01  1.2747e+01 -6.8879e+00 -2.1825e+00 -2.5067e+00
+#>   1.2481e+01  4.5978e+00 -7.3489e-01 -1.3409e+01 -6.0107e+00 -2.7504e+00
+#>   1.7816e+01 -3.5718e-01 -1.1780e+00  1.6923e+00 -4.5382e+00 -2.0238e+01
+#>   6.5862e+00 -1.3457e+01  8.5478e+00 -1.3558e+01 -3.4814e+00 -1.5367e+00
+#>  -1.1326e+01  7.2230e+00 -3.6186e+00 -8.4180e+00 -6.4513e+00  7.9379e+00
+#> 
+#> Columns 13 to 18 -8.0057e-01 -1.3328e+01  9.8669e+00 -2.5136e+01 -5.3349e-01  3.6857e+00
+#>   1.0354e+01 -1.4761e+01  1.1679e+00  1.4412e+01 -1.2440e+00 -1.1661e+00
+#>  -6.5192e+00 -7.1688e+00 -2.9187e+00 -5.2366e+00 -4.9826e+00  3.7546e+00
+#>  -3.1300e+00  7.3697e+00 -4.1025e+00 -5.7561e+00 -6.4322e+00 -4.9924e+00
+#>   1.0797e+01 -3.0347e+00  1.1637e+01 -1.8523e+01  2.2189e+00 -5.2789e+00
+#>   6.5284e+00  1.3239e+01  1.2901e+01 -1.0149e+00  1.1488e-01 -7.5773e+00
+#>   1.3891e+01  1.2985e-01  9.3304e+00  1.1353e+01  6.6067e+00 -2.0041e+00
+#>  -5.6352e+00  1.5733e+01 -8.8031e+00  5.5721e+00  3.4226e+00 -9.7906e+00
+#>  -1.1781e+00 -2.5947e+00  3.6996e+00  5.0159e+00  1.3265e+01 -7.8744e+00
+#>  -6.6246e+00 -2.3119e+01  5.5166e+00 -1.0686e+01  1.0688e+01 -7.0927e+00
+#>   1.9604e+01  3.1592e+00 -6.8978e+00 -4.5115e+00 -1.4070e+01  1.8832e+00
+#>   1.3349e+01  3.5876e+00  8.9876e+00 -1.3067e+01  7.4223e+00 -1.6353e+01
+#>  -8.8548e+00 -7.7655e+00  5.2639e+00 -1.5279e+01  1.3111e+01 -8.7192e+00
+#>   9.2301e+00  1.2825e+01  3.8590e+00 -8.9350e-02 -3.3228e+00 -6.6612e+00
+#>   1.1719e+01  8.8251e+00  1.0758e+01  1.1020e+01  1.7316e+01  1.0903e+01
+#>   1.0597e+01  3.0815e+00  5.4455e+00 -1.1521e+01  3.5875e+00  1.0045e+01
+#>  -4.6327e+00  2.6468e+00  9.0459e+00  3.5549e+00 -1.7233e+00 -3.7580e+00
+#>   5.5239e-01 -5.8831e+00 -6.4494e+00  6.9075e+00 -7.5687e+00 -4.6467e-01
+#>   5.6701e+00  1.5749e+01 -7.3728e+00  3.2362e+00 -7.7297e+00 -9.7480e+00
+#>   5.6887e+00 -1.1115e+01 -1.2254e+00 -5.2029e+00 -2.1490e+00  5.6609e+00
+#>   7.8467e+00 -7.5810e-02  7.7566e+00 -1.0227e+01 -3.7912e+00 -3.1270e+00
+#>  -4.7080e+00 -4.5689e+00 -7.6721e-02 -5.8001e+00 -9.5479e+00  8.3101e+00
+#>  -1.5013e+00 -3.6316e-01 -3.1087e+00  6.1050e+00 -1.5130e+01  1.5111e+01
+#>   3.4386e+00  9.8310e-01 -2.0312e+00  1.0758e+01 -3.3430e+00  5.0932e+00
+#>  -1.3168e+01 -6.7728e+00  1.6069e+00 -4.6869e+00  4.6065e+00  1.3057e+00
+#>  -1.6464e+01  5.7022e-01  1.0934e+01  8.7831e-01 -2.6596e+00  2.1941e+00
+#>   1.2239e+00  2.0631e+01  3.1795e-01 -6.3166e+00 -7.4960e-01  1.2326e+01
+#>  -1.1780e+00  1.4964e+01  1.4871e+01 -6.6004e+00 -2.4907e+00 -4.5097e+00
+#>  -3.2585e+00  1.6227e+01  1.4389e+01 -1.4555e+01  1.3821e+00 -1.6833e+01
+#>   2.2738e+00 -1.8023e+01  7.3146e+00 -1.3987e+01  3.7863e+00 -1.3842e+01
+#>   1.3159e+00 -6.0347e+00  2.4591e+00 -6.5787e+00  9.6240e+00  5.3632e+00
+#>  -3.8836e+00  6.9408e+00  1.7871e+01  2.8488e+00  1.1042e+01  2.4041e+01
+#>   4.5042e+00 -3.3415e+00  4.6526e+00 -1.9363e+00 -5.7645e+00  4.8845e+00
+#> 
+#> Columns 19 to 24  2.1816e+00  1.1398e+01 -2.0100e+01  6.5745e+00 -7.2871e+00 -1.7311e+01
+#>  -1.1820e+01  1.2387e+01 -4.6485e+00  1.2276e+01  4.6670e+00  2.7533e+00
+#>   4.0599e+00 -1.0616e+00  1.3343e+00  5.2283e+00  1.6967e+00  1.7240e+00
+#>   2.0656e+00 -1.1546e+00  3.8879e+00 -6.8895e+00  7.4366e+00  5.5816e-01
+#>   5.9550e+00 -1.4099e+01 -7.4364e+00 -1.8394e+01  1.4459e+00 -4.3082e+00
+#>   6.3243e+00 -1.3173e+00 -1.5185e+01 -3.2522e+00 -1.0327e+01  8.6914e+00
+#>  -5.9197e+00  7.5110e+00 -9.7248e-01  5.1201e+00 -2.8549e+00 -1.2483e+01
+#>   8.3908e+00 -1.6846e+00  3.8770e+00 -1.9234e+00  1.0995e+01 -2.1494e+00
+#>  -3.8551e+00 -3.2826e-01  1.6225e+01 -2.6102e+00 -3.8071e+00  1.4121e-01
+#>   1.0434e+01  2.7217e+00  3.9908e+00  2.0863e+00  1.8488e+00 -1.4448e-01
+#>  -9.7046e+00  6.4612e+00 -4.5994e+00 -3.9989e+00  3.6108e+00 -1.7516e+01
+#>  -2.2860e+00 -1.9504e+01  2.7930e+00  5.0934e+00  9.8681e-01  5.5149e+00
+#>   1.9064e+01 -2.3494e+00  1.3243e+01  1.6121e+00  1.2582e+01 -5.4996e+00
+#>  -7.0795e+00  2.1689e+00  9.7785e+00  2.1558e+00  8.3160e+00 -2.0315e+00
+#>   1.9758e+00 -1.0558e+01 -3.7069e+00  3.8015e+00 -9.3428e-01  6.8966e-01
+#>  -1.9669e+00  1.7340e+01 -5.4626e+00  2.6395e+00 -6.7900e-01  4.5230e+00
+#>   5.6454e-01  3.9365e+00  3.7507e-01 -3.5966e+00  7.3264e+00 -1.4597e+01
+#>   3.9740e+00  6.0941e+00 -1.2176e+01 -1.9403e+01 -4.5312e+00  6.1493e+00
+#>  -3.5115e+00  6.1865e+00  1.2620e+01  6.3291e+00  7.9115e+00  1.8524e+01
+#>  -3.5108e+00  2.5150e+00  3.5326e+00 -1.1806e+01 -4.4001e-01 -1.7239e+01
+#>   3.7839e+00  6.4758e+00 -1.4811e+00  1.1773e+01  6.4469e+00 -7.4049e+00
+#>  -1.1164e+00  9.4232e+00  3.1854e+00  1.1059e+00  9.7922e+00  4.8349e+00
+#>  -6.3895e+00  8.3978e+00 -2.9053e+00 -1.1393e+01 -7.3833e+00 -1.0466e+01
+#>  -4.5210e+00 -9.0873e+00 -3.9491e+00 -2.0665e+00  5.5214e+00 -1.6368e+01
+#>   5.9251e+00 -4.5887e+00  1.7094e+01  8.2391e+00 -1.6594e+00  1.8229e+01
+#>   1.5021e+01 -9.5044e+00  9.0490e+00 -4.7405e+00 -9.9340e+00  4.3366e+00
+#>   1.3484e+01 -9.3071e+00 -1.6405e+00  1.1124e+01  2.9578e+00 -5.1350e+00
+#>  -1.6481e+01  4.3615e+00 -9.7366e+00 -9.5827e+00  3.0091e+00  2.2236e+00
+#>  -1.8121e+00  6.1377e+00  2.6013e+00  8.3860e+00  6.7466e+00  8.8319e+00
+#>   1.1963e+01 -6.6936e+00 -9.5687e+00 -9.3228e-01 -1.7769e+01 -2.6604e+00
+#>   1.3484e+01  3.2324e+00 -5.0558e+00 -8.7698e+00  1.2637e+01 -6.5493e+00
+#>  -6.0411e+00  6.4152e+00 -1.2675e+01 -9.6146e+00  8.2074e-01  4.9775e+00
+#>  -1.3057e+00  5.5402e+00 -1.4782e+01  8.4229e+00  3.9776e+00 -1.2717e+01
+#> 
+#> Columns 25 to 30  4.2712e+00 -1.8313e+01  1.2869e+01  1.0771e+01  9.3267e+00 -8.9804e+00
+#>   3.1789e+00 -5.3914e-01  4.8706e+00 -7.7735e+00 -1.0663e+00  3.6529e+00
+#>  -3.1381e+00 -5.4898e+00 -5.0572e+00 -1.5204e+01  6.8879e+00  1.5140e+00
+#>  -5.6012e+00  4.8856e+00 -1.1568e+00 -1.0985e+01 -1.6185e+00  2.6259e+00
+#>   3.9710e+00 -2.6853e+00  9.4064e+00 -1.5029e+01  2.2575e+01 -5.8623e+00
+#>  -5.5364e+00 -8.5947e+00  3.2784e-01  1.4177e+00  1.3231e+01 -8.0045e+00
+#>   1.5313e+00 -5.1651e+00 -1.4980e+00 -3.6880e+00 -3.4109e+00  8.9855e+00
+#>  -1.1175e+01 -1.3714e+00 -1.0365e+01  4.7220e+00  5.3410e-01  1.7925e+00
+#>  -4.5655e+00 -6.0778e-01 -3.1458e+00 -6.3064e+00 -6.5464e+00 -1.8196e+00
+#>   1.4990e+01  8.8926e+00  4.1020e+00 -2.1035e+00  6.3438e+00  7.0223e+00
+#>   2.5534e+00 -5.8687e+00  2.8717e+00 -7.4900e+00  6.2523e+00  8.9355e+00
+#>   1.4022e+01  1.0725e+01  1.5612e+01 -8.7809e+00  3.9462e+00 -1.3987e+01
+#>   1.0360e+01  2.7338e+00  4.0706e+00 -1.2100e+01  1.0978e+01  5.0141e+00
+#>  -7.5337e+00  6.6931e+00  8.3354e+00 -6.1995e+00  2.4049e+00 -1.0162e+01
+#>   2.2830e-01  5.2956e+00  1.6554e+01  1.5506e+01 -5.9415e+00 -9.6459e+00
+#>  -5.4945e+00 -4.1556e+00  7.5814e+00 -1.0024e+01 -3.3956e+00  5.4715e+00
+#>  -1.8974e+00  4.0738e+00  4.3747e+00  5.3339e+00  1.0370e+01  3.5695e+00
+#>  -6.3953e+00  8.1155e+00  2.6668e+00 -5.7607e+00  9.3227e-01  9.6839e+00
+#>  -6.8767e+00 -3.6269e+00 -8.4079e+00  3.5210e+00 -1.5193e+01 -2.7687e+00
+#>  -1.1708e+00 -1.3841e+01  4.0027e+00  1.2555e+00 -4.2463e+00 -2.2155e+00
+#>  -1.0225e+01 -1.7509e+01 -1.6429e+00 -1.6112e+01 -5.7260e+00 -1.9660e+01
+#>  -5.5449e+00  1.9966e+00  1.0666e+00  3.4531e+00 -1.5475e+01  3.3974e+00
+#>  -1.3588e+00  7.5988e+00 -1.8388e-01  7.8243e+00 -1.5999e+01  1.0581e+01
+#>  -8.8002e+00  8.7883e+00  1.1669e+01  4.5411e+00 -9.6372e+00  4.4150e+00
+#>   4.1970e+00  3.1673e+00  9.9109e+00 -2.0725e+00  1.4861e+00  8.6069e+00
+#>  -8.2932e+00  7.1537e-02  1.0104e+01  1.3536e+01 -1.4057e+01  4.9435e+00
+#>  -3.1966e+00  1.6351e+01 -3.0836e+00  5.8257e+00  7.8308e+00 -1.1259e+01
+#>  -2.9138e+00 -1.9039e+01 -3.7361e-01 -1.1853e+01  2.7653e+00 -6.2049e+00
+#>  -3.4330e+00  1.8411e+00 -6.2346e+00 -4.5798e+00  6.0478e+00  1.1831e+00
+#>   4.3443e+00 -2.8794e+01  5.0782e+00 -2.0471e+00  2.9354e+00  1.5715e+00
+#>  -9.7509e-02 -1.1681e+01  6.5869e+00 -3.6830e+00  9.8735e+00 -7.4123e+00
+#>   2.3951e+00  1.6265e+01 -4.8599e-01  9.5051e+00  3.5874e+00  1.2326e+01
+#>  -1.1905e+00 -5.8175e+00 -3.5236e+00 -1.7360e+01 -1.2221e+00 -1.0987e+00
+#> 
+#> Columns 31 to 36 -5.6949e+00  1.3361e+01  2.3993e+01  6.7339e+00 -3.0410e+00 -1.9543e+01
+#>  -1.9175e-01  1.9476e+01 -8.1250e+00 -1.0638e+01  3.8086e+00 -1.4532e+00
+#>  -1.7334e-01  2.9818e+00  1.5608e+00  1.4213e+01 -1.3916e+00 -6.3702e+00
+#>  -2.5140e+00 -4.6497e+00  1.2057e+01  7.0821e+00  6.0922e+00 -2.0688e+01
+#>   1.5012e+00 -1.3077e+01  1.5612e+01  1.1582e+01  9.8053e-01  8.3894e+00
+#>   4.0044e+00 -9.4430e+00  1.9241e+01  1.6960e+00  2.6499e+01  1.2580e+01
+#>  -2.1191e-03 -2.8668e+00 -8.1291e+00  6.1644e+00  2.1598e+01  1.7666e+01
+#>   4.6148e+00  9.9687e-01  7.2539e+00 -6.8223e-01  4.6920e+00 -3.5754e+00
+#>   1.6859e+00 -7.5914e+00 -5.8969e+00  4.6902e+00 -5.8453e+00 -3.9254e+00
+#>  -2.5888e+00  5.3150e+00 -5.4238e+00  2.0027e+01 -2.3618e+00  1.2522e+01
+#>  -5.9867e+00 -6.4037e+00  1.3977e+01 -4.9397e+00  7.3722e-01  3.8638e+00
+#>  -1.1407e+01 -4.7524e+00  1.8301e+00 -4.3101e+00 -1.5760e+01  1.5808e+01
+#>   1.1545e+00 -4.5749e+00  3.9353e+00  8.2907e+00 -6.0958e+00  1.8457e+01
+#>   1.7913e+00  1.6733e+00  3.3430e+00 -5.1444e+00  1.1334e+01 -4.8110e+00
+#>  -7.3777e+00  1.2817e+01 -1.1085e+01  1.9805e-01 -4.1106e+00  4.4191e+00
+#>  -1.6404e+01 -9.1713e+00 -1.0913e+01  1.3962e+00  8.4605e+00  8.7429e+00
+#>   4.8666e-01 -1.1638e+00 -1.3549e+01  4.1671e+00  9.1208e+00  1.2404e+01
+#>  -1.3434e+01 -2.3100e+01 -4.2072e+00  7.5665e+00  1.7500e+01 -3.3207e+00
+#>   1.1719e+01  7.1473e+00 -1.1867e+01 -8.7646e+00 -1.1132e+01  4.0229e+00
+#>   4.4853e+00 -3.3253e+00  4.9775e+00  9.0879e+00  5.9107e+00  7.4434e+00
+#>   1.8142e+00  1.6967e-01  1.7707e+00 -7.8519e+00 -1.2571e+01 -2.9638e+00
+#>  -7.3384e+00 -1.7540e+01 -4.1018e+00 -7.7791e-01  1.2584e+01 -4.1257e+00
+#>  -1.2738e+00 -9.7462e+00  5.6063e+00  7.9758e+00  3.4011e-01 -7.2899e+00
+#>  -3.6470e+00  4.9329e-01  1.0851e+00 -8.8301e+00 -5.7992e-01  9.0049e+00
+#>  -9.5606e-01  2.8600e+00 -2.0506e+00  7.6949e+00  5.8242e+00 -4.7942e+00
+#>  -7.5989e+00 -6.2703e+00 -1.0120e+01  1.4883e+01 -8.2820e-01 -7.1206e+00
+#>   3.7361e+00 -2.6730e-02  1.1486e-01 -9.8292e+00  7.5682e+00  9.1253e+00
+#>  -9.8936e+00 -1.1247e+01  1.4983e+01 -7.0797e+00  3.0183e+00 -1.3355e+01
+#>  -2.5079e+00  9.1406e+00  1.0627e+01  2.1261e+00 -4.9782e+00  7.8488e+00
+#>   4.2701e+00  1.0740e+01  5.4955e-01 -3.6912e+00 -4.8871e+00 -6.4244e+00
+#>   3.0608e+00  1.6989e+00  1.6671e+01  6.5070e+00 -9.4148e+00  1.5586e+00
+#>  -2.0385e+01  3.9488e+01 -4.0000e+00  1.5101e+01 -3.2709e+00 -9.0798e+00
+#>   1.2203e+01  7.4935e+00 -7.6433e+00 -5.0494e+00  5.9084e+00  1.4025e+01
+#> 
+#> Columns 37 to 42 -3.5567e+00  4.9884e+00 -2.0162e+00 -9.8983e-01 -8.1743e+00  1.8105e+00
+#>  -1.0130e+01  1.0737e+01 -1.3030e+01 -2.4097e+00 -7.1381e+00 -2.2804e+00
+#>  -1.0712e+01 -4.6281e+00  8.6404e+00  5.0877e+00 -1.2724e+00 -1.3629e+01
+#>  -1.4130e+01 -6.7262e+00 -6.5459e+00 -5.0702e+00  5.8389e+00 -6.6099e+00
+#>  -1.0146e+01 -2.3493e+00 -5.7388e+00  2.3630e+00  7.7276e+00  1.9727e-01
+#>   9.4642e+00 -1.6979e+00 -1.6908e+00  4.0453e+00 -9.3064e+00 -4.9294e+00
+#>  -1.7900e+01 -1.0781e+01 -2.2128e+00  3.4972e+00  4.5195e+00  2.1352e+01
+#>   1.4080e+01 -1.7566e+01  1.7405e+01  7.3100e+00  5.6941e+00  6.8552e+00
+#>   2.8797e-01  4.2590e+00  9.6440e+00  3.7909e+00  6.8174e-01 -1.1576e+01
+#>  -1.3965e+01  2.2105e+00 -3.2947e-01  2.6924e+00  2.5126e-01  8.7139e+00
+#>  -1.2245e+01 -1.3169e+01  2.3058e+01  4.0956e+00  1.4707e+01 -4.5470e+00
+#>   1.0996e+01 -5.0388e+00  6.5529e+00  7.1346e+00 -1.0733e+01 -1.8054e+01
+#>  -4.9208e+00 -6.2861e+00 -1.5915e+00 -1.7812e+01 -6.9850e+00  1.1769e+01
+#>  -1.0391e+01 -2.5315e+00 -2.4661e+00 -1.6226e+01  1.2565e+01  1.1249e+01
+#>   4.8863e+00  7.6682e+00 -4.4582e+00  1.3917e+01  2.2450e+00  4.1614e+00
+#>  -9.7247e+00 -5.3544e+00  2.3091e+00 -5.2290e+00  6.1582e+00 -1.7322e+00
+#>   4.6380e+00  7.5338e+00 -2.4575e+01  5.9214e+00  7.2310e+00  1.1455e+01
+#>  -3.4109e+01 -1.2936e+01  7.2160e+00  9.2824e-01  6.4974e+00  8.5199e+00
+#>   6.4172e+00 -1.0787e+00 -9.8345e+00  7.6054e-01 -6.6593e+00 -8.7434e+00
+#>   6.0668e+00 -3.8605e+00  7.7222e+00  1.3185e+01 -8.8122e-01  2.1869e+00
+#>   8.5262e-01  1.2873e+01 -9.3571e+00 -4.0499e+00  2.7709e+00  7.4133e+00
+#>   3.8747e-01 -1.2859e+01 -3.6468e+01 -1.4145e+00 -5.2340e+00  1.3269e+01
+#>  -6.7104e+00 -6.0494e+00  9.6427e+00  2.2719e+01 -1.8434e+00 -1.1373e+00
+#>   8.3560e+00  2.1794e+00 -8.9665e+00  4.2148e+00  4.8866e+00  9.1118e+00
+#>  -1.9341e+01  4.2699e+00  3.9515e+00 -1.1069e+01 -2.4024e+01 -5.1794e+00
+#>  -4.9691e+00  8.9193e+00 -5.9994e+00 -1.3280e+00 -1.0818e+01 -3.2965e+00
+#>   1.9666e+00 -3.0454e+00  9.0428e+00  1.3853e-01  4.1911e-01  9.6931e+00
+#>  -7.9676e+00  2.1590e+00 -1.2932e+01  1.2422e+01  8.0138e+00 -5.9471e-02
+#>   8.2083e+00  7.7886e+00 -1.2701e+01  1.6966e-01  1.0069e+01  7.4440e+00
+#>  -8.3398e-02  7.0096e+00 -8.7646e+00  2.5291e+01 -1.1063e+00 -1.0413e+01
+#>   1.8087e+00  1.3406e+01 -6.4398e-01 -6.0747e+00  6.3313e+00 -3.4190e+00
+#>  -6.2972e+00  3.5243e+01  6.8191e+00  1.2734e+01 -6.4595e-01 -8.1191e+00
+#>  -6.4373e+00  9.3643e+00 -1.7571e+01 -8.0169e+00  9.8722e+00  1.5727e+01
+#> 
+#> Columns 43 to 48  7.9088e+00 -5.6556e+00  4.8938e+00  1.0608e+01 -6.9977e+00  5.4173e+00
+#>  -1.1238e+01 -1.3247e+01  9.6207e-01  3.6348e+00  1.4571e+01  1.0120e+01
+#>   8.0333e+00  2.7531e+00 -4.3690e+00 -6.9672e+00 -9.6015e+00 -3.1335e+00
+#>  -1.1907e+01 -4.2905e+00  6.9964e+00  4.0701e+00  5.0200e+00  1.0520e+01
+#>  -1.7194e+01  8.4560e+00  4.9195e+00 -1.8132e+00  3.3464e+00  9.0690e+00
+#>   1.0605e+01  3.6800e+00  1.9141e+00 -7.7366e+00  8.8173e+00  1.6478e+01
+#>   7.6670e+00 -4.4036e+00 -2.0118e+01  3.7706e+00  4.1627e+00  2.3883e+01
+#>   5.7098e+00  7.1396e+00 -6.4154e+00 -7.3870e+00  7.1860e+00  2.8694e+00
+#>   7.8667e+00  1.2995e+00  1.5219e+00 -1.5403e+01  1.4408e+01 -8.8732e-01
+#>  -8.8178e+00  4.1856e+00  3.4311e+00 -3.3330e+00  1.1866e+01  2.7426e+00
+#>  -3.2672e+00 -2.0059e+00  1.0937e+01  1.8572e+01  1.3395e+01 -8.7461e+00
+#>  -4.6177e+00  2.8059e+00  1.3937e+01 -4.1939e+00 -7.0773e+00 -1.0014e+01
+#>   1.0897e+01  1.2618e+00 -5.3603e+00  1.6801e+00 -4.7967e+00  1.2367e+00
+#>  -1.0665e+01 -1.6151e+01 -2.8564e+00  6.4615e+00  1.5634e+01  8.7862e+00
+#>  -8.4990e+00 -1.8268e-01  1.0268e+00  4.8410e-01  3.3697e+00  6.8629e+00
+#>   2.1992e+01  9.2984e+00  2.2908e-01 -7.1116e+00 -5.9029e+00  2.3546e+00
+#>  -6.6385e+00  6.8041e+00  5.2392e+00  3.6962e+00  5.2319e+00 -4.3501e+00
+#>  -1.4431e+00 -1.4766e+01 -3.2638e+00 -9.8882e+00  3.5751e+00 -1.4642e+00
+#>   1.9757e+01  6.0210e-01  1.8813e+01 -1.7528e+01  1.3589e+01 -7.2073e+00
+#>   2.2319e+01  1.0252e+01 -8.5589e-01  2.3263e+00 -2.1076e+00  4.7572e+00
+#>   2.5049e+01  4.3311e+00  3.0760e+00 -2.1143e+01 -7.4189e+00 -5.7686e+00
+#>  -6.0083e+00  7.5146e-01 -2.0329e+00 -2.1183e+01 -1.4873e+00  4.5826e+00
+#>   9.2355e+00  6.3142e+00 -6.5020e+00  6.2657e-01 -4.6874e+00 -4.8295e+00
+#>  -9.6173e-01  6.2268e+00 -7.0243e+00  3.9041e+00  6.7295e+00 -1.3892e+01
+#>  -3.6028e+00  1.2075e+01  3.5572e+00 -5.3261e+00 -2.6636e+00  1.1129e+01
+#>   7.4841e+00 -9.2742e-01  8.9835e+00 -8.4415e+00 -7.2958e+00  7.8062e+00
+#>   2.2400e+01  2.1623e+00 -3.0189e+00  7.1862e+00 -6.3412e+00  1.0901e+01
+#>   3.6117e+00 -7.0971e+00  2.6814e+00  5.4192e+00  3.5838e-01  1.1047e+01
+#>  -9.8853e-01 -1.7515e+01  2.4676e+01  1.4085e+01  8.2997e+00 -4.8444e+00
+#>  -4.5072e+00  5.5580e+00  2.3702e+01  4.8946e-01  7.3788e+00 -1.6394e+01
+#>   2.5949e+01  2.4009e+01 -8.7447e+00 -7.0793e-01  4.5913e+00 -4.3254e+00
+#>  -9.8891e+00  8.4424e+00 -4.3977e-01  1.0079e+01  7.7925e+00  2.1888e+00
+#>   1.4065e+01 -1.0794e+01 -7.0287e-01  2.4411e+00 -9.2024e+00 -4.9945e+00
+#> 
+#> Columns 49 to 54  1.2240e+00 -1.2818e+01  9.5647e+00  6.5460e+00 -1.5940e+01  1.1664e+01
+#>   4.5038e+00  8.0489e+00  4.2433e+00  6.2366e+00  4.6840e+00  4.4786e+00
+#>  -1.6363e+01 -1.4942e+01  7.2150e+00  5.6045e+00  4.0751e+00 -2.1345e+00
+#>  -1.0558e+01  5.4887e+00 -1.2569e+01 -3.2999e+00  8.2827e+00  1.8991e+00
+#>  -3.8149e+00  6.1766e+00 -2.0876e+01 -6.9356e+00  8.3863e+00  6.9102e-01
+#>  -2.7710e+00 -4.1936e+00 -1.1201e+00 -5.6233e+00 -1.3735e+01  8.6153e+00
+#>   1.5911e+01  1.3823e+00  3.4872e+00 -2.3034e+00 -3.7417e+00 -4.8286e+00
+#>   1.5463e+01 -2.4522e+00 -4.8940e+00  1.0528e+01  6.3326e+00 -4.1093e+00
+#>  -1.3318e+01 -6.1879e+00  1.4634e+01 -6.2361e-01 -5.1670e+00 -2.5203e+00
+#>  -1.5569e+00 -9.1091e+00 -2.2504e+00 -2.4537e+00  5.0198e+00 -1.3754e+00
+#>   1.1113e+01  2.0553e+00 -1.1050e+01 -5.2365e+00  2.9043e+00 -9.1591e-01
+#>  -1.8847e+01 -8.8923e+00  1.0530e+01 -2.0581e+01 -6.0758e-01  9.6417e-01
+#>   9.6848e+00 -9.3837e+00  1.3975e+01 -5.5193e+00  3.6848e+00  1.5615e+00
+#>  -1.2492e+01  5.5814e+00  6.1893e+00 -7.8973e+00  2.9681e+00  6.9512e-01
+#>   1.1504e+01  7.6018e+00  6.9501e+00  7.5726e+00  6.8567e-01 -2.4113e+00
+#>   6.7309e+00  5.1267e+00 -9.7423e+00  3.6266e-01 -1.6027e+00 -1.7865e+00
+#>   1.6913e+01  1.1763e+01 -6.8572e+00 -1.0648e+01 -8.2093e+00 -3.2516e+00
+#>   4.6734e+00 -6.2526e-01 -1.5552e+01  1.4470e+00 -3.3042e+00  2.4781e-01
+#>   1.1611e+01 -6.9366e-02  4.6982e+00 -6.0526e-01 -1.9356e+00 -4.0384e+00
+#>   1.7692e-01 -1.7620e+01  8.2326e+00 -3.1783e+00 -3.9814e+00  4.4209e+00
+#>  -1.0654e+01  8.7792e+00 -2.7505e+00 -1.5891e+01  1.6422e+01 -1.3983e+01
+#>   1.7992e+00  8.1260e-01 -8.6144e+00 -2.9489e+00  7.1418e+00  1.2316e+00
+#>  -1.4337e+01 -6.8194e+00 -1.7666e+01  2.5777e-01 -1.2038e+01 -3.0056e+00
+#>   7.4552e+00  9.3896e+00 -1.7496e+01  1.4413e+00  4.9794e+00 -8.6028e+00
+#>  -6.5202e+00  9.1406e+00  1.1102e+01 -7.9955e-01  1.4321e+01  3.1646e+00
+#>   6.7650e+00 -3.1582e+00 -2.6166e+00 -3.2835e+00  8.4187e+00  2.4054e-01
+#>   1.5329e+01 -5.0739e+00  7.8081e+00 -2.6967e+00 -5.3574e+00  1.0191e+00
+#>  -2.0436e+01  8.6585e+00  1.1495e+01  1.8867e+00 -3.0256e+00  2.2732e+00
+#>   1.8309e+00  8.7694e+00  7.3270e+00 -7.8188e+00  1.1073e+01  3.1894e+00
+#>  -1.1917e+01  6.3724e+00  1.4758e+00  9.3766e-01 -2.3483e+00  9.6741e+00
+#>  -1.7902e+00  6.6935e+00 -5.0005e-01  1.1740e+01 -5.9915e+00  3.6669e-01
+#>   2.5202e+00  2.1117e+00  4.2934e+00  9.3296e+00  4.1314e-02  1.2129e+00
+#>   6.5548e+00 -3.7900e+00 -9.4372e+00 -9.1410e+00  1.8987e+00  2.0038e+00
 #> 
 #> (13,.,.) = 
-#>  Columns 1 to 8   0.6225  -1.5633  -1.0825   5.1088   5.7446  -3.3708   3.6607  11.8080
-#>   -5.9414   4.0864  -1.2822  13.1553 -15.7463   6.3882   1.5634 -11.4881
-#>    3.8365  -8.4567   4.2103  13.8929  -3.8816  -6.1134  -0.6663  -8.0852
-#>   -3.0222   6.8643  -2.3826  -0.7312  -5.0444  10.4230   3.6401  -5.1320
-#>    2.9885   0.6388  -1.7581  -0.5176   8.0534   9.4546  17.4677   2.8854
-#>   -0.0614   2.0208 -14.5500   0.9292  16.2407   1.4846   5.7644   0.9823
-#>   -5.0461   4.9234   2.7604   1.9248  -0.2402  -1.4744   6.1263 -18.5446
-#>    1.5126  -6.5236   7.4569  -6.4902   0.9539 -34.3821   4.7376  -6.4473
-#>   -1.8858  -1.7096  -1.9603   0.5967  -2.9680  -0.6489  -4.0181  -0.8944
-#>   -0.4119  -1.9274   1.7104   6.9449   2.2967  14.1728   4.3677   5.9133
-#>    3.1806   0.2433  -6.9227  -6.6097  -3.4068  -1.5102   1.1768  -7.3246
-#>    5.1976   3.0144   2.7979  -7.2828   7.7405   4.1381   7.9900  10.3155
-#>   -1.6468   0.6064  -5.1478   9.8157   2.6589   6.8723  -2.0592  11.5436
-#>   -0.2124  -2.1939  -5.1560  13.0934  -6.1557   5.6263 -11.1988   0.6105
-#>   -0.7770   5.0216  -9.4610   3.9703   5.4409  11.2737  12.1204  -2.3762
-#>   -4.3581   6.0825   2.3620  -0.5420  -0.5396  -2.1976  -2.9201 -17.2563
-#>    1.2618   5.1261  -7.6242   3.7173   9.8331  17.3887   2.6580  -6.2977
-#>   -1.8163  -2.6399   9.1186  -5.1573   2.7076  10.3487  -5.5950   6.4227
-#>   -2.2549   2.9415  -1.3807  -0.5117 -13.7537  -9.9439  -7.9778 -11.1709
-#>   -0.0283  -3.4669   0.2005   4.9303   1.9195 -14.3671  -4.5576   6.9302
-#>    1.2863   6.2312   5.7416  -1.8370  -6.7823  11.3672   5.8564   1.9807
-#>    1.8187   2.6548   4.8856  -0.5090   3.4795  -2.6129  -6.3327   3.1360
-#>    0.8995   7.3851  -0.9977  -1.0382  -0.0063   7.6577  -0.6447 -18.8161
-#>    0.1038  -3.5217  -9.7995   3.8576 -10.7382  -3.8209 -21.4523  -5.3858
-#>    0.1988  -0.2407   2.9182   9.4107  -7.8575   8.6792  11.2470   1.5597
-#>    1.0691  -7.2791  10.4935 -11.5572   2.8200 -11.1891   0.6554   4.3099
-#>    3.9875  -1.9605   1.6879   8.9195   6.2382  12.0618  -4.7136  -1.3624
-#>    1.0449  -2.2370  -5.4446   3.2154  -7.8951  -3.5772 -16.4724  -0.0223
-#>   -4.2487  -0.0201  -5.0634  15.8200   3.4258   2.2750  -6.9639  -1.5070
-#>    7.8249  -2.3258 -11.5096   6.5237  25.4575  12.2537  -2.4187   9.4461
-#>   -4.5060   3.7490  -4.8310  -6.0221  -7.5922   4.1831  -8.2478   1.6914
-#>    4.5197  -6.3969   6.6634  -7.9376  11.1415  -1.7244   3.6967  -5.8130
-#>    1.6796  10.2047   2.9963   0.4611  15.4367   8.6640  12.3652   9.3268
-#> 
-#> Columns 9 to 16  -1.2855   0.0185  -9.0798  23.0098   1.5778   7.3526  15.9490  -2.5839
-#>   12.9727  -1.6580   6.0191  -1.8907   3.2326  -4.4916 -12.2917   9.3610
-#>   -9.4650  -3.3754   9.2407 -20.8298  -4.0272  -4.0452  -8.5894 -13.6104
-#>    4.6112 -15.6477   8.9300   6.9903  -3.0968   0.7586  -4.6015 -13.4000
-#>    8.4996  -4.4138  -5.4577  -7.4059 -19.6451  -5.3132 -17.6285  -8.1284
-#>   17.0900 -12.0884   7.6606  -5.2643  -3.9708   8.9101 -12.8181   1.2724
-#>   11.7912   3.3413   1.8484 -10.1811  -3.7404   5.9754  12.8199   3.5822
-#>    9.0085   0.0326  -8.7861   4.5516  -3.4405 -11.4276  -1.4082 -14.7301
-#>   14.3183 -13.1309  -5.4163  -2.6748 -15.5476  -1.9758  -9.0136   7.4097
-#>   -2.9459   1.3235  14.2310  -4.8895   1.0256  18.3548   3.3083   8.2479
-#>  -10.8172  -6.8999 -12.8204 -11.6486  -5.6255   2.9987   7.2506 -16.9556
-#>    8.6517   2.0668  -5.2789   2.6444  16.9798   5.7512  -6.2904   9.3246
-#>    8.5083  -4.5114   2.7383  -3.9136   2.9081  11.4299  -2.0102   4.7976
-#>  -14.5124   2.3619  11.0900  13.8907   5.7813  -8.9663   6.9377  15.0322
-#>    9.4700  -5.7718  21.7840   1.7330 -13.4566   9.4243  11.2065   3.2765
-#>    7.1058  -3.1070 -12.9466  -0.9314   6.2793  -5.7981  -1.6923   4.2464
-#>    9.8869   2.1072   0.9882  -8.5688  -6.7740   5.2935  -1.1322  -6.0721
-#>   13.6170  10.1805  -0.6006   5.0214  24.5619  17.5630  12.9840   8.9247
-#>    0.7140   1.3764   5.9720   0.2036   2.1168  20.9245   2.4649 -10.0140
-#>   -5.4863  -4.5560   6.1671   2.2992  -9.4693   3.2962   8.1224  -7.1518
-#>    4.0313  -3.1694  -3.7682   8.7248   6.9283  -6.7974   1.0717   3.5391
-#>  -12.6850 -11.1544  -3.5832 -17.0292 -10.2922  10.5102  -8.5805 -16.6701
-#>    1.6295  -9.6518  -9.2382 -21.4327  -4.1098  14.8623   6.2514 -18.4324
-#>   -4.7173 -13.7172 -15.1778 -13.1720 -12.6725   4.9590  -3.7975  -8.5846
-#>    4.4336  -0.8218   7.3717  -5.4434  -4.8480 -20.0243  -2.8340   7.2669
-#>   -3.9407  -5.8612  -9.2493   2.7658  -6.3236   7.3682   0.8614  -2.2647
-#>   -5.1057  15.3606  17.8084   0.8658  -4.1440  11.4043  27.9573  -0.7385
-#>  -14.7314   4.1165  11.2977 -13.5234 -15.3198 -13.3219 -12.4830  -9.5864
-#>   12.5962  -2.0848  20.9790   6.7316  -7.9422  -7.3325 -11.7881   2.1356
-#>   -2.2800   5.7363  11.4628   7.3722  10.9769 -12.4952  -6.5846   4.3698
-#>   -5.5631   6.5627  -2.8050  -8.0543  13.7225   5.1217 -13.3229 -10.1688
-#>    1.1927   8.0427  -9.9975   1.4130 -20.1412  10.6100  13.6041  -6.3356
-#>   -3.2324  13.7741  -1.9013  -4.1233  -2.3054   2.5272   6.2873   4.8383
-#> 
-#> Columns 17 to 24  18.1636   2.6890   5.8660  -0.9663  -7.8354  13.3825  -5.7938  -3.0844
-#>   14.9101  -8.1264   9.3529  -5.6501 -13.8379  -5.1529  12.0903   6.6171
-#>  -12.9787  -4.5172  -3.5492  -0.1676  -0.3024 -20.2456 -17.2140  -3.7271
-#>   15.0217 -17.9985  -2.7245   0.7354 -10.6781  -4.0954  16.4370  -3.9958
-#>   19.8714 -15.6207   3.7955 -11.6631   0.5926   3.2357 -10.5831  -6.8354
-#>    7.3107  10.1958 -12.5282 -15.6849   9.5947  -0.6776  -3.5694  -6.8568
-#>    4.1721   6.1826  -3.5171  -1.6282  -4.5980 -12.7351   2.4134   1.4011
-#>   -3.8929   0.8567  -2.3181  10.7230  -0.4437  -1.1194  -2.6472  -1.0988
-#>    0.5700  -4.2833   0.8950   9.9760   0.1999  -7.7351   8.4031  -1.9698
-#>    8.6987   1.8773   5.9658  -6.3321  10.9693   8.2531  -7.0048 -16.8536
-#>  -25.2377 -12.1537  14.2061  -8.3470   7.8966  -2.2503 -19.2228   6.2746
-#>   -4.2712  -3.8228  -5.6966 -14.1824 -10.1671 -10.6292  -2.9392  -2.9507
-#>   -1.0011  -7.9093   8.0571 -11.0527  12.1593  -3.7356 -10.7960  -6.1334
-#>   -0.5150 -10.1990  13.7810  -3.9238  -9.3145  12.6934   7.1857   6.6778
-#>    6.2490  -4.6729  -2.4496  -4.4163   1.3302  24.1066  -2.6552 -12.0586
-#>    3.7691   3.7795  -1.8822   9.3887  -7.6862  -0.9493   0.4402  -6.8497
-#>    7.8804  20.2961   4.4132  -0.6763   1.9556  -9.0676  -0.4367  -9.1325
-#>   -8.1008  11.3290  19.9934  18.0099 -12.1368 -15.7701  15.0487  -3.7897
-#>    3.9090 -12.1362  11.0174   8.8886   1.6636   4.9241 -14.1295   5.4046
-#>    2.5688  -3.1138 -14.4614   0.4418 -14.4975   8.2753 -17.5903  -9.1665
-#>    9.6189  -2.4555   4.4813 -10.1254  -0.2373  -9.0948   8.7192   4.3740
-#>  -16.1791   5.0023  -5.0693  12.8547  -3.6058  11.3588  -5.5056  -5.0991
-#>    5.2873  -8.9815   3.3710 -13.0669  14.1989  -8.1130  -9.1110   9.0331
-#>  -23.5598 -30.5964 -14.4885   4.7473   2.7410 -11.7885  -4.4209  -2.4463
-#>   -0.0130 -15.6218 -14.0751   1.8073  -3.6559   1.4849   7.6913   4.5255
-#>    4.9514  -0.2254  -3.8016  15.9771   1.8918  12.4013  -6.2868  -1.3038
-#>  -19.2015   2.9579  -6.1137  19.9605   3.5282 -10.5849   6.1530   0.7995
-#>   -0.4617  -3.9831  -4.4857  -0.1848   1.6324   9.0417  -0.4186 -10.2384
-#>   -1.0973 -10.0601  -0.4049   9.9800   4.7209 -10.4516  18.6407   1.3664
-#>    2.4310  13.5651 -10.4308  18.9115   8.8863  17.2386  15.7466   5.9440
-#>  -19.0119  17.7831  11.2511  -6.0253  -4.8593  -1.7501  -1.0230   5.3960
-#>    3.3324  -6.8259 -16.2877  11.6091  16.1198   6.0207  -9.4439   6.7558
-#>   -8.2590   8.0797  -6.6256  10.1228  -0.1918 -10.1314   1.6924 -16.9398
-#> 
-#> Columns 25 to 32   9.7602  -7.7804   8.1099 -10.5406  -0.8016   4.2707   1.6632   6.3764
-#>   -6.5471 -11.5912  -6.5146   0.6060  -2.6289   6.8809  -8.7830  14.0514
-#>   -8.0664  -0.9994  24.0894  -6.0419  11.4410   7.0581   0.7835  15.4865
-#>  -12.7290   8.7368 -21.3734  12.8849  -0.0553   3.9926   3.6383   1.3704
-#>  -13.4071   3.8879 -24.2288  11.2635   1.5915   7.5209   6.7797  -0.2068
-#>  -17.5532 -16.1468  -2.0649  10.6989  -0.0317  -5.5492  -0.6623  -1.1360
-#>   -5.5422  15.5088 -10.3694  -7.2312  -0.0041  -6.3378  14.6600 -10.6514
-#>   -7.5865   1.1804  -1.6392 -22.0979 -11.5349  -7.3807   9.2519   7.6128
-#>  -10.6179 -12.5250   1.7685   8.5996  14.2387  -8.7196  -6.3942   6.2967
-#>   -4.5517   3.1659   1.0517   5.2230   1.6644   7.6189   7.8894  -9.2089
-#>   -0.7227  -8.7862  21.3328 -13.5036   9.0519  -3.5375 -10.7146   7.8418
-#>   -7.3776  -9.2339  -9.0334   4.7565   9.8160   3.7270  15.0688   7.1635
-#>   11.3902 -10.1863   1.0542  -6.8610   2.8108   3.0329 -15.4997   3.2724
-#>   -1.3498   1.5421   4.7036   2.1384   1.6352  -6.2726   8.5540  15.2884
-#>    4.6787  -2.6138  12.6692   5.2700  -5.3515   0.3652  10.9815  -3.3373
-#>   -2.6148  11.6006   1.1225  -5.9275   5.0149  -0.7353   2.5467 -10.4715
-#>    3.8180  12.0771   8.0940   0.3466 -15.9737 -17.4497   7.3648   3.8229
-#>   -1.6993  19.6717  -6.1632  -9.6537   8.7153  -3.2533  13.1750   5.6769
-#>   -8.5416 -11.7091  -9.0161 -15.5721 -10.8837   2.5474   8.6095   1.1659
-#>    8.6536   2.1465   4.0729  -7.8629 -11.2442  10.7150   0.3810  13.0305
-#>  -23.0618   1.2090   1.6410   8.9639   2.0674   0.9350  -4.3420   6.6411
-#>   -5.8685   3.1908   9.4136 -13.6394 -15.1120 -19.4785   5.9027  11.2434
-#>    9.5898   2.5199   9.4495   4.7774   8.1902  15.0195  -1.8043   7.9227
-#>   11.5086 -11.9990   0.6903 -15.1623  -4.1851   0.9766   6.7075  16.3212
-#>   21.3647 -18.4335  -2.4270   7.1990   4.3983   2.1114   0.8842  -0.2500
-#>    3.3664  -6.8320  -6.3752   8.8395  -9.9285   1.5100  -6.8595  -5.1740
-#>    9.0478  -0.2612  10.8768 -13.0471  -8.4355  -2.3569  -8.9594  -6.3812
-#>    6.6571  -4.9372   4.6812  -3.2220  -4.3514   8.6508  -2.3734 -10.8352
-#>   -9.3337  13.9356 -11.9485  16.9562  -3.8194   1.4336 -10.1326   4.5468
-#>   11.5920  -1.3552   0.0446  -6.0608 -11.8932  -7.2355 -17.2701   6.8176
-#>   13.2213  -3.6443 -12.2204 -14.1070   7.7651   0.5954   2.4866  11.5992
-#>  -23.3829  12.7079  -3.0813   7.9698  -1.7549  -0.0543   3.8429  -3.8380
-#>  -11.4183  11.7327  -9.6460 -18.0117  17.6143  -1.6256   9.8787  -2.0024
-#> 
-#> Columns 33 to 40  -3.9341  -2.0305   0.6524  -5.1456   0.6766   5.5498   1.0476  -0.1857
-#>   -6.8117   2.9403   7.8930  -7.9923   3.1811  -1.0809   1.7309  -3.3199
-#>    2.2975 -11.8925   3.8148  10.5640  -6.8398  25.4739   1.6435  14.8361
-#>  -10.5531  -3.5991  -4.7588  -8.2663   7.6058   2.9165   0.2993 -17.7117
-#>   -8.8943 -11.8832  10.5742 -11.1564   0.5177  -2.3430  -7.9058  -8.9088
-#>    4.5182   4.0905  -3.4065 -13.8010 -11.5456  -3.7047 -10.3269  11.0212
-#>  -11.1254   3.4482  14.1089 -15.2183   2.6742   4.0473   3.2420 -20.2393
-#>    0.8347   2.2787   4.5881   2.3749   8.7257  -4.3932   7.1358  -8.3630
-#>    5.0211   9.5129 -15.4526  -4.9501  10.4043  12.4970  -3.7509  23.3894
-#>  -10.2102   3.5111 -12.3805   3.7743  -6.8626  -0.0747  -2.9689   9.1161
-#>    8.2601   3.4696 -12.2933  -0.6306  -1.6229   4.7930  -7.3460  32.7231
-#>    2.5878  10.6950 -19.4801  -2.9348  -1.4626  -0.3699   2.5573   5.6626
-#>   -6.8752   0.9755  -4.6640 -17.8662  -0.8453   3.5109  -4.6187   9.9316
-#>    1.6028  -0.1389  -1.2994  -6.2128  10.0624  -2.3837   2.1566   8.8314
-#>  -14.0722   1.5607 -20.0004  22.8786  -0.1401   2.0176 -14.8383 -13.5920
-#>  -18.6510  -0.4177  -2.9582   4.9505  12.8327   7.7842   1.7678  -8.5907
-#>    7.1897  -2.1071   1.5479   6.3302   1.3973  -2.8158  -8.7517   1.4023
-#>   -6.8289   3.7599   1.2994  -1.9434   0.1248   5.1686   1.8590   8.0154
-#>   -1.5625   2.7974  14.1518 -10.1855 -17.9860  -7.5408   8.3709  10.3564
-#>    0.7383  -5.3753  11.1572   9.0830  -4.7921   5.3966 -17.6511   0.3852
-#>    1.2908 -10.6373   9.6046   0.0826  -3.8387   0.5976   2.8609  -9.5399
-#>   -2.6528  -1.6465  -1.9558  13.9648   7.2380  -0.6112 -14.5066   2.6307
-#>   -2.3397  -0.2024   0.8239  -6.0347   4.0913  11.3913  14.4397   4.5889
-#>   11.2896  10.6101 -10.7042 -15.5441  -9.2189  -9.8412   5.1248  32.5710
-#>   11.0244   5.5595  -7.1076  16.0597   6.2505   6.3358  -0.2796  -2.1579
-#>   -9.9230   0.2472  -9.2166   5.0312   3.5981  -7.8504  -7.2184  -1.3071
-#>   -2.4879 -11.3545 -12.4110  24.2905  -8.7519 -17.0857 -10.2919  -3.4404
-#>    4.1187  -2.6143   1.0428  10.6671 -13.0915  -3.5546   0.5629   6.4200
-#>   -3.3104 -12.0760  -0.7076  -3.0841  24.2404  -3.1691  -6.9119   1.7941
-#>    3.1939  -8.4331 -17.0943  10.9742   6.5465   8.0851   0.8856  -8.9553
-#>  -14.2499  13.0122  -2.6418  -2.2588  -0.4159  14.4637  -3.2607   8.2385
-#>   18.2165  -3.9107   0.1991  -8.0249   0.6290  -2.4179  11.3165   6.8118
-#>    3.9876  -3.3598  14.0990  -5.3588 -12.7347 -11.6581  -0.1995  11.9736
-#> 
-#> Columns 41 to 48   2.6384  12.6728  -3.7734 -20.3231  -0.2065  -1.2895   5.8673  -0.0072
-#>   -0.5856  -7.6191   9.3754  -7.0757  -1.0990  -0.6045  -2.5725   0.2833
-#>  -11.6955   1.5578  -5.1756  -6.8691 -13.8330   1.5411  -2.2008  13.6485
-#>    5.0361 -17.7714  10.7466  -6.2763  13.0006  -3.0142   2.9823   1.0376
-#>    8.8774 -10.9829   0.9405  -9.5145   2.8328  -4.1716   0.8532   2.6348
-#>  -10.9172   3.7905   8.3874   7.9075  -8.7231   7.7682  -9.8785  -1.4428
-#>    4.1869  -4.6672  -1.5456  -1.7447  -8.1386   3.2080   0.5161   8.1082
-#>   -0.7679   5.4786   5.5345   8.9032   5.1916   1.9881  -1.1946   8.5584
-#>    2.3608  22.4209 -18.1195  -9.3485  -0.4505   0.1381 -21.7665  -0.1648
-#>   -8.8235   4.9310  -6.2566 -16.0138   3.0802  -1.5473   1.1070   1.7021
-#>    1.9942   9.4574   4.0311  11.4089  -5.2419   4.3092 -11.9660  12.5830
-#>   -2.6881   3.7668   2.4198  13.9039   5.7139  14.8638  12.3989  -1.7952
-#>  -22.4898 -14.8158  -6.6019  -5.1961   5.4858  -0.3881 -13.2620  -0.4163
-#>   -4.5293   8.1611   5.8838  -9.0136  -5.5433 -14.5050  -8.3331   9.2668
-#>    7.0383   0.8013   0.0123   7.6101  25.3196  -5.0556  -8.8034   6.1687
-#>  -13.9259  -4.7097  -6.8706   4.4354   4.7897  29.0184  -1.8586  -9.1406
-#>   -8.2563   5.5888  -0.1159   8.7976  -2.7876  12.0876 -13.8446  21.1724
-#>   -4.6292   2.1923  -8.3140   1.8247   4.4025   0.4105  -1.3992   6.8583
-#>    7.0735   2.5204   0.8192  -3.7221 -21.1281  -4.5568  -1.9114 -10.8041
-#>    0.4631  -5.1953   3.0787   2.2137   9.7525  -2.0934  -5.1761  10.7299
-#>    3.2992   2.7955   6.4101   6.6697 -15.3544  -2.3311  10.5766  -5.4545
-#>   -4.7215  12.9134   5.2489  11.9368 -10.3907   0.9388  -7.1882  20.6407
-#>   11.2167  -1.1713  -4.6229 -26.7602   8.4682   6.1601   7.4882  -2.4842
-#>   11.0823  13.9282  -1.9220  -9.2037  -4.9446 -20.9689 -21.6739  -3.2960
-#>   22.2801  11.3753   4.0951  17.2048   6.1801 -12.2547  10.9081  -2.8286
-#>    6.4324  -4.8378  -0.7057  -3.4801   0.3702 -13.0156   3.2830 -12.0592
-#>   -4.2670   7.1882  -2.7934   2.9694  10.5591  -1.4846   6.7703  20.9930
-#>    3.9284  -4.1591  -8.8767  -2.8856  -2.4679  -2.1686  12.8365   9.9197
-#>    1.9951 -20.0595  -4.7230 -15.3246  16.8989   2.5943  -2.6376   3.5854
-#>    0.8159   7.6094 -14.7591  -1.7713  14.2379   1.4623  -9.3418   0.7935
-#>   -5.7499   8.6248  12.7241  -6.9128  -4.7761  -6.7299  -4.8388   0.9684
-#>   24.4516  -7.3553  -1.6329   0.4585  -2.1951 -10.1090   9.4520  -1.1079
-#>    2.9964  -5.9975  10.7465   8.8183  -7.0952  -0.7106   4.2629   1.4456
-#> 
-#> Columns 49 to 54   8.2625   0.9033   1.5123   3.2150  -4.9727  -0.7974
-#>   -9.1411  -6.7768  -3.3312  -7.6552  -5.8629   1.5708
-#>   19.4400  -2.8266   0.6595   2.9002   2.2003  -3.4884
-#>   10.0413   0.8295   9.7387  -5.7218   2.9886   1.8164
-#>    0.7244  -4.3088  15.7654  -5.2667   6.0944  -3.1314
-#>   -3.9781  -0.5492   3.8910  -8.0746   9.1950   0.4971
-#>   -4.2548  -0.8059   1.0549   7.2977  -5.5230  -1.4808
-#>    6.5204  -2.4275   3.5413  -2.1586  -8.4760   1.7143
-#>  -17.1326   3.4917   3.2997   2.7240   0.5655   3.8225
-#>   -7.7256  -0.1063   4.6172  -5.3888   7.1422  -8.5851
-#>    7.6347  -2.0811  -0.6710  -2.1723  -7.2139   1.1386
-#>   -1.6509   1.1366  16.1597  -1.2614  -3.2961   1.4994
-#>    4.6051  22.8175   1.6403  -9.0837   7.6439  -2.1571
-#>   -3.5945  -6.0262 -16.3896   0.2016   0.9746   1.5790
-#>   -7.6154  -7.2821   4.4517   5.6029  -6.9573  -4.2717
-#>  -10.4473  -0.8336  -9.6946   9.4865  -7.1611   1.8194
-#>   11.3759  -6.8616 -11.5624   3.5180  -1.9766  -3.6941
-#>    2.0064  -1.6333   4.0976   0.6205  -2.6042  -4.9661
-#>  -12.8823  -7.8360   1.0782   5.6972   5.6660   2.1111
-#>    1.9252  -9.6661   8.4916  -9.2061  -4.0192  -8.4211
-#>    6.6718  -3.4963  -4.0314  -7.0760   5.7587   3.5493
-#>   12.1766  11.8530  -6.2429  -4.2156  -5.7470   1.1385
-#>   -3.2129   3.4765   6.8705   2.2789  -1.6691  -9.8592
-#>   -3.0113   9.5897  -7.1651   3.0243   4.2135  -0.0837
-#>   -7.6214   2.8420   1.4394  -0.0282  -3.7063   6.0629
-#>    8.3831  -2.2977  16.5964  -4.5028   2.6921  -2.9995
-#>    7.3325  -0.6589  -8.6266   0.7577  -7.1119  -4.5866
-#>   -5.9435 -12.6229   2.5341   8.3034   5.0543  -2.7943
-#>    1.8347   6.9768   7.7201   0.5520   6.6773   1.3603
-#>   11.4995   9.5357  -4.6271   4.5563  -4.1167  -1.1788
-#>    1.0763   0.1444 -15.0228  -0.1753   2.3464  -6.2756
-#>   11.7925 -10.6398  18.1778   6.3685  -4.0435  -2.9618
-#>    6.0329  12.3226  -8.7054   2.7213   1.1492   1.0349
+#>  Columns 1 to 8   0.1502  -0.6327  -5.1165  11.6773  14.3463 -16.5857 -23.2968   1.7444
+#>    0.0141  -4.0258   4.1974   8.9528  -9.9707  -8.5931  -3.2126  -1.9768
+#>    5.8446  -6.9787   8.9577  -0.7572   3.3967   4.2266  -5.3755  12.2286
+#>    0.7716   6.1096  -0.6805   2.2177   2.3422  -6.7453  -0.7093  -3.9760
+#>   -3.5904   1.2967   3.7849   1.1136  12.1699   1.5712   3.1402  -4.8506
+#>   -4.9829   2.7822  -8.2093   2.0288  22.9273 -13.8559   7.9110  -0.1632
+#>    0.9712   4.1440   4.5902   3.1206   8.4393   8.0681   2.4422  -6.5444
+#>   -4.1766   6.5025  -8.0135  -0.7057  -4.7804  10.9730 -14.6142  -6.0363
+#>   -1.2651   7.5690   0.0950  -4.7592   1.2051  -8.0752   8.2937   6.4398
+#>   -2.5072   2.2357  -6.0020 -17.4529   6.3990   7.8647  11.7904   2.6119
+#>    1.3361 -10.6175  -0.2354   2.7564   7.6634  -1.0736 -14.1549  -8.6648
+#>    0.3114  -1.5055  -0.3231   1.0974  -6.9884  -9.7584   0.9568  10.1247
+#>    1.6453  -5.7996   3.5452   6.3986   9.3010  -0.6876   1.1725   5.0440
+#>   -5.3501   1.5010   3.2083  -0.1423   6.0821  -4.6799  10.5806  14.2973
+#>    5.5053  -3.4022  -9.2779   3.2031 -17.1135 -14.0270  -7.7946   8.4620
+#>   -1.0949  -0.3882  13.5959  13.5594  -6.5282   3.5082  12.8191 -11.8238
+#>   -5.4553   4.9206   5.0066   0.6998   2.7967  -3.7996  -3.5733  -5.6437
+#>   -2.7895  -7.2860   4.7593   2.8820   6.8320   0.6149  -2.1145 -10.6138
+#>    1.4033   6.6825  14.9566   2.1453   2.2461  -3.6996  12.8808  -5.8650
+#>    0.7737  -0.9963   4.8971  -1.6165   2.1081   3.5645  -0.0486  -3.1669
+#>    2.4272  -6.0975  -8.8798  -4.1698  13.6304  -7.3528  15.2820   8.6944
+#>   -1.3885   7.3461  -5.3565  -2.9205   4.6033   5.2413  -4.7957  -7.1355
+#>   -2.7855   0.4383  -2.0094   5.3199  11.6295  13.1496  -7.0949  -3.9079
+#>    7.6482   4.3722  -1.2289  -9.8542 -14.4585  -0.2983   3.6377  -1.7866
+#>   -2.0140  -5.7528  -2.2515  -6.1826  -3.5021  -4.1251   5.6230   6.0546
+#>    2.3866  -2.8660  -2.6130   2.1253  -5.8539  -7.4563  14.0099   9.4419
+#>   -1.9124   1.7875   4.3873   8.6576   8.1988   1.7990 -20.1503  -0.2226
+#>   -1.3105   0.7783  -2.3225  10.2201   3.3992  -8.8959   0.0339 -13.3772
+#>    1.2475   4.8226  -4.0829 -10.4948   0.0785 -14.0801  20.2100   2.5078
+#>   -0.8625   1.8876  -9.3857  -1.3849   0.8895 -29.8070   6.9208  -1.5414
+#>   -5.8760   0.3630 -10.4666  -6.4514   5.2589   3.1505   8.0784 -13.7259
+#>   -6.8187  -0.2258  -0.3130  -0.8500  -3.7169  -8.6896  16.5658  -0.4019
+#>    2.9267  -2.9513  14.0778  -5.9623   9.5521  -5.5139   5.5557   1.7858
+#> 
+#> Columns 9 to 16   1.7864  -5.0491  -0.5680 -13.1897  -3.9846  -3.8399   2.7190  11.2911
+#>   -7.3188 -12.3025  -4.9221   1.7435   8.5387   5.5209   0.6618  10.5780
+#>   -5.5940   9.3218  10.8462 -15.2501   2.6757  -7.6190  -5.6595   5.6197
+#>   -2.5642  -7.0475 -17.7531  -6.4610  -0.2528  -2.6088  -5.9995   6.3092
+#>  -18.1159  -9.3682  -5.4989 -12.7824   7.5788  -3.4397  -8.2629  30.7666
+#>   -2.1718  -0.3497  -3.1785  -6.8824  -4.1225 -13.7171  14.8214   6.4650
+#>   -7.4951  -5.4636  -5.1662  -3.3794  -0.5324   8.7031  -7.3074   0.7377
+#>   10.7573   1.2803  -2.8465  -7.6641  16.8036 -17.2760  17.4993  -3.7881
+#>    6.5791   4.9164   4.6700 -19.1050  -2.1860  -3.5184  19.9508  10.7379
+#>   -0.1242  -4.1409  17.6556   1.3901   3.6041   7.7495 -15.2813  -8.3537
+#>    8.5827   6.2256  -5.9061  -0.5912  -8.7770 -15.7100  -5.5663  -4.7435
+#>   -3.7580  15.1016  17.3715 -15.2703   0.3931   5.8730  -0.7814  -8.5623
+#>    6.3721 -10.4422  -2.1435 -14.6633  -8.7295  -2.0260   3.0931  -6.2568
+#>   -2.6818   4.1069 -21.5864  -6.9363  18.0050 -11.0571  -5.2293   0.4206
+#>   -2.5457  -8.8578  -5.3770   4.6376  -3.6898  -9.7821 -14.0432  -5.2195
+#>    4.6188   3.7077   1.7767 -21.9502  -7.9970  -0.6138  -9.0604  -8.8355
+#>   13.8772   1.5815  -3.6044  -4.8382  -6.5071  -2.6235  -5.2686  -4.5968
+#>   -1.5794 -22.0361  -2.2059   1.1024   8.4288   4.4157  -5.9486  -2.7891
+#>    8.1590   3.8084  -5.2762  -8.1521  10.3248  -8.1053   5.0296 -20.4825
+#>   -8.1600   3.9923   3.1615   3.3658 -12.1478  -0.4582   4.4161  -7.0431
+#>   14.1191  -2.9299  -8.3054  -5.0831   7.5364   4.5573 -12.9784   6.0358
+#>   -2.2307  -8.5232  -1.5594   7.7415  -4.2493   9.5669   6.8826  11.5447
+#>    2.0640  -1.1619   8.9586  12.2880  -8.5156  -0.8169   3.9353  -2.8133
+#>   -5.9796  -1.3531  -5.1153   5.4403   6.6031   1.6265  -9.1132  -2.4750
+#>  -12.1595  14.5719   7.3589  -0.8528 -12.0797   6.5165  -6.5158   9.1897
+#>   -7.7593  -7.5360  -4.2608   0.6677   5.6086   7.3084  -1.1450  -0.6650
+#>    2.1285   5.0666   3.8205 -19.9812  -5.3825  -6.2388  -5.0001   5.1073
+#>  -11.4498   7.9439   2.5264   1.7372  12.4589   4.7601   0.8722  11.8413
+#>    4.5614   0.2426  -1.1967   0.3214   8.3549  -3.9374  -1.9127 -30.0761
+#>  -10.6920 -17.6461  19.2189  18.9718 -17.5786 -13.3243   3.8246 -20.3998
+#>    1.9363   2.6901  11.4226  -0.8000  -8.4201 -10.7214 -15.2685  -1.2141
+#>   -6.4414   5.5103 -14.8857  16.8497  -6.3816 -15.7200 -11.5210   3.0433
+#>   12.2955 -23.6837  -9.0876   2.5366 -11.2098  -4.3643  -6.7683  -6.3079
+#> 
+#> Columns 17 to 24   8.8265  18.9406  -9.4133  -2.5348   7.2665   6.5188  -4.3930   2.1048
+#>    2.8716 -16.0539 -11.8397   5.5599   3.8261 -27.1507   7.7110  -5.4979
+#>    2.3130  -0.5389  -9.2514  -8.8838  -0.5748   0.2937   7.8625  12.3091
+#>   13.4952   5.9597  -5.5660  -3.7617  -3.2340   8.2575  -6.2173  12.0081
+#>   -4.9171   8.4606   7.0278  13.6968   8.3683  -7.3618   6.8984 -11.2440
+#>   -3.2943   8.3736   3.7466   9.5787  -8.9279   6.5024 -18.1383   0.4755
+#>   -0.4949   5.5721   6.9379  -7.8568   2.8956  11.2934   1.4106  -1.6120
+#>   11.9518  11.8384   2.5023 -11.5562   9.9104   1.2761  12.6963 -14.4882
+#>    5.2451   4.2083   9.0829   0.8515  -6.3686   2.6889   0.3345  10.9705
+#>  -12.5338 -10.2935  -1.3780  -4.7442  -2.3097   7.1551  -0.1443   9.5783
+#>   25.3115  -3.0398 -14.3625  -1.2636  12.7686  -3.3883  13.4861 -11.1937
+#>   -4.5211   2.1346  12.5857  -8.1697 -14.3383 -12.2419   8.2375  -5.4028
+#>   -2.0818  -7.1800   0.7283   2.0070   1.7882  -2.7738  -3.0338   9.9644
+#>   -6.3256   9.6141  -8.1130  12.2936   8.5317 -11.5170   5.0715   7.5243
+#>   -2.7383  -9.7668 -16.7810 -10.9428   3.4754  -8.3081  -5.1900  -4.5701
+#>  -10.2102  11.2480   6.5073  -6.5978 -13.8941   5.6511   4.0472 -10.9076
+#>   -5.5926  -1.5593   5.7522   4.2259  -1.7510  -4.6279  -4.5535   5.6030
+#>   14.6310  -3.2063 -10.0208  16.2688  -2.3531   3.7047 -11.1398   3.4741
+#>    1.2885 -12.0961   4.9428 -12.3416  -1.2494  14.2553 -13.7385   5.5886
+#>   -3.4728   4.1505  12.0654 -12.3847  -6.6229  -0.7823  -3.0840  12.5855
+#>   14.5684  -0.3764  -7.1810  -6.3587  14.2933   9.1784 -11.0523  -3.9788
+#>   -0.3546  11.6056   1.1543  -4.5159  -8.4306  -9.4144  -5.4696  -8.1684
+#>  -15.3279   3.0093   4.7932  13.0679  -9.1388  -5.4328  22.8265   0.3704
+#>    7.6645   2.6893 -14.4119  -2.8792   4.4938   8.0528   3.1974  -8.6381
+#>    3.3738  -7.1812  -5.0788   2.8829   5.2612   1.0766   1.6137  -0.3375
+#>    5.5916  -8.0586   6.7475  -4.2617   2.6975   4.4982  -3.8766   9.6640
+#>   -5.5459  18.6531   1.8227  10.8455  -4.3877   2.5642 -12.4524 -15.8890
+#>   -7.4719  23.2260   4.1229   4.9025   6.2354  -9.6361   0.0786  -8.6084
+#>   11.6038  -4.1914   3.5742  -8.2894  -1.1181   9.8882  -3.5115   5.7064
+#>    9.0505 -17.6689  11.3823 -12.1330 -14.1131   8.9340  -5.1862   4.9549
+#>  -12.8777  -9.1465   1.0852   6.5645   1.3628  11.2346  -2.7197   3.3082
+#>  -15.9775  -3.1818  -4.5302  10.2368  -2.1682   2.2111  11.4631   1.7134
+#>    8.4578  10.4538 -14.2874  11.6500  -3.2734   5.6058 -14.4480  12.7504
+#> 
+#> Columns 25 to 32   4.5362  -6.3846  -9.1947  -1.4785  -4.4318   2.7225  -3.3800   8.8245
+#>    3.6867 -12.9515  -7.3310   6.4607  23.0355   8.0931  20.4505  -3.2334
+#>   12.1664  -2.9485  -6.0665   0.1145 -10.9513   0.0830  -4.1382 -14.1843
+#>   -3.6729 -10.5543  -6.1293 -10.1133 -10.5977   7.9843 -13.1441  -1.9370
+#>    5.4258   3.2411  -6.3558  -8.8474   4.2419  -4.6076   2.1246  18.6591
+#>   12.4402   5.9000  -9.1547  -3.6564  -2.2394  17.8233 -26.5256   8.5273
+#>   -0.2354   3.5966  -4.7936  -3.3391  -1.6590  -0.3001   4.9479  -1.6429
+#>   -2.5284  12.9843  -1.1777  13.4040   7.5414   7.3708   6.3774   5.7664
+#>  -11.7246  -7.4048   5.6794  -6.5819  -9.6858  -6.8145 -26.7890  15.7343
+#>   -6.4861  -3.1409   6.0822   2.7258   7.0966   1.6550   2.2907   1.6123
+#>   12.6386   7.5574 -17.8627  -4.7396   2.8532   4.6215  -4.0242   0.3481
+#>  -13.9927   1.0784   8.9757  -2.3967   3.4257 -14.6185 -16.4024  -3.1132
+#>   -6.5631   3.2515   1.3259   1.5545  -5.2817   6.8179   2.4699   7.5062
+#>    7.6563 -18.2317  -5.9616  14.4318   2.4898  -1.0191  -2.6948  13.5260
+#>    5.0151 -18.5543  -0.4373   1.6251   3.7774   4.4423   3.4176 -18.3328
+#>    0.2585   3.5288  -7.7036   8.1640  -0.5656   1.0587  -3.1771 -10.6755
+#>    1.1604  10.2628  -1.4101  -1.9162   0.8279  -9.7483  -5.9036  -0.6826
+#>    2.0231 -14.9928 -17.9465  12.3793  -7.7265  27.5180  -8.1285 -20.4272
+#>   -7.4535   1.1490   1.7212 -11.0781  -4.4916  10.2222  -4.3039 -14.2790
+#>   -8.5627   1.9616   4.6415 -16.0075  -1.2049   5.0215 -10.4812   7.2736
+#>    7.8055  15.1741  -3.3337   0.9341  -5.7580 -12.2163 -14.0667  12.3840
+#>  -10.8541  13.6793  12.8612   1.0421   9.4164  -5.6323  -7.4601 -10.8486
+#>    0.4551  -0.9507 -14.4768  -4.5887 -16.6857  -2.4758   0.4379  -4.1190
+#>   -1.1506  -5.2307  -1.4388  15.7405   2.6871 -11.9327   9.2181 -12.6076
+#>   -4.4578 -11.9439   2.8058   5.7662  26.6840   9.0248   2.8032   4.2246
+#>  -10.3300  -9.6205   4.1342  -2.7574   9.2160   3.0372   1.0326  -8.0832
+#>    6.9992   6.7391   8.4815   8.7365  -2.1005  -2.3835   1.9618 -13.2855
+#>   -3.9171  15.1513  -0.8424  -1.0606   3.2967  -4.1415  -5.7364  -4.2694
+#>  -14.0996   4.1496   9.1888   2.7240  -2.9387  -2.3929  -4.0292   6.5702
+#>    0.5942 -13.2248   1.4505  -9.6488  -5.8043  10.6196 -11.6304  24.4467
+#>   -2.7895  -4.9634  -5.1119  12.7252   7.1553  10.3754 -19.7647   0.2771
+#>    8.7760  -8.9328 -12.1186   0.7542   6.9072  -5.5977  19.3347   2.9616
+#>   10.7522   7.7890 -17.6029 -10.4164 -21.0359  -8.3593   4.2845  -0.8286
+#> 
+#> Columns 33 to 40   8.2293  -5.1600   2.4583   2.5163  -1.0784   9.5208 -15.3089  -1.7521
+#>    9.2848  -9.5671 -14.9465   2.1182   4.4059   7.3621   5.9493   1.2706
+#>   17.0957   1.0428   5.7024   1.9221   8.5981 -15.9624  -5.3606  -6.3537
+#>   14.9423   3.4573   6.8398   8.1765   4.7029  -1.8634 -17.5590  -0.9544
+#>  -12.7008  -4.0907  -4.9392  -3.9029 -19.0786   6.2376  -3.0325  22.7123
+#>  -15.3415  -9.7980   5.7301  10.5588  -1.5494  14.9005  -1.5633   5.6834
+#>    4.9847  -2.1010 -13.1506  -7.0318 -16.9139  -1.7904   5.2275   6.5568
+#>   -6.2782  -4.1310   5.4280   7.1735   4.1964  -8.1535   2.6125  23.0726
+#>    0.6093   4.6743  12.5202   4.8873  -7.3422 -10.9752  -0.0823  10.2310
+#>   15.8328  12.4338  -1.4879 -12.5920   1.4926   2.2287  12.7423   8.4751
+#>    6.4443   8.5776   1.8122   8.8571  -5.5670   1.4204  -0.4372   9.1904
+#>   -8.4314  -3.8573   2.6211  -5.6659   5.3228   3.5362   0.8988   7.5738
+#>    2.0035   7.1615 -10.6302   2.9194   6.9806   9.4983  -5.4700  11.1988
+#>   -5.3793  -5.6574   6.8903   2.4261  -2.8877  -7.1431   6.0547   0.1253
+#>  -10.6209   6.6820   7.0431   0.9343   5.0204 -16.7050  -8.7897 -11.5912
+#>   -6.4801  -0.3452   4.4141   0.4342   9.4311   8.6459 -16.5092   2.4078
+#>   -9.5491 -11.3154  -8.8797  -5.5353   6.2301  -3.0708   9.0026   6.3432
+#>   17.7493  13.4238  -9.5344 -21.4205   0.7244  -7.3182  -7.6547   0.4352
+#>   -4.1389 -22.7554  -4.8486   4.0074   9.3635  -2.8265   4.1809   1.0639
+#>    4.8367  -0.1893  -1.2777  14.8569   5.5085  -7.2500   1.0789   2.7517
+#>   13.1695  11.4243  10.3081  -0.4784  -1.4638  12.9745 -13.0287  -1.4219
+#>   -6.5016  10.9037  21.7328  -3.0877  -1.4793  10.7955 -20.6368   5.9169
+#>   14.2078   3.3812 -13.7932  -8.9849  -8.9467  14.5796  24.6045   0.8933
+#>   -7.5996   3.5048  22.0829   2.5851   8.0192 -13.6655  -4.3487  -3.7454
+#>   14.5181   4.7206  11.7895  -1.5944  -1.3652  -6.5049   8.1058  -0.1272
+#>    0.8222  -2.5204   1.9191  -2.2974   5.8231   3.0175  -9.3610 -13.6032
+#>    3.6852  -5.0083   0.2621  -0.3519   1.8717  -0.2546   9.9030   3.9011
+#>   -0.2744  -7.0254  -3.2710   2.9479   2.9215   4.8423 -17.4727   6.9660
+#>  -13.3550   0.9482  22.9237  16.7080  20.9580   5.3614  -2.4341  -7.4789
+#>  -13.3255 -17.2470  12.6000   0.2714   2.5818   4.9169  -4.2032 -15.0249
+#>    6.1829  -1.3314   0.1468  18.2705  -2.9847   5.8745   4.7609   0.4977
+#>   -0.4490 -24.9648  -3.1186  -8.9949  13.4103  -8.0018  27.0184  -7.6923
+#>    7.8353  -5.8453   2.2504  17.2211   5.8414   5.0291  -5.5030   1.7877
+#> 
+#> Columns 41 to 48   4.0916   3.3849   4.7426  -1.3405  10.1076  16.2660  10.6860  -6.1657
+#>  -19.7972  -5.1898  -4.8162 -10.3623  10.6441  -3.9971  -8.4521  -7.2454
+#>   -9.4200  -1.9175   5.8213  12.5241  18.8085  -5.1161   5.2490   8.6126
+#>    2.1210  -0.8823  -5.0897   2.6917   1.2090  15.9057   2.4324   5.3099
+#>   20.1725 -10.2449  -8.4428  -5.3989   2.6726  -1.9335   1.5664  -0.2841
+#>   -7.7274  -8.1707 -19.7667   3.5507 -12.9730  23.8874   3.7928   9.6482
+#>    3.4079   8.7545  -2.4725  -7.2952  -7.7775 -15.5888   1.6426 -10.1780
+#>  -12.2906   0.5766  -5.7266  -9.7333 -24.4787  -8.5168   5.8254 -14.4201
+#>   -7.4206  -2.5292   6.2337  -5.6087  -7.9186 -18.1385   2.1090  -9.9210
+#>    3.5527   7.2321  18.0147   2.9648   0.2699 -18.7313   5.7041   2.9222
+#>   -3.7048   1.2978 -12.4707  -7.6922   7.0586   5.9125   6.4581  -1.0633
+#>   -3.6145 -22.2058   2.8666  -4.8607   2.6456 -10.1019  17.1695  18.6168
+#>    4.2739  -3.9789  20.4105   4.6241   8.9154 -10.1849   6.2545   5.0430
+#>   -0.2601   2.5646  -1.2959  -7.4923 -14.5485   7.5918   8.7384 -17.6047
+#>   21.9885   0.5478   2.0581  -6.5970   2.2651   3.1050 -13.9449 -19.2407
+#>    3.6517  -8.8977  12.0841  18.7272  -1.2955  -8.1800   4.6427   5.9706
+#>    9.1178   7.7942   5.9473 -18.4547  11.2522   2.0277  -3.8946   2.1382
+#>    9.6460   2.5854 -11.6359   7.5342 -10.0983  -8.3343 -13.1944  -0.1597
+#>    2.2920 -12.5184   6.8451  14.7949  -3.9423  -9.1176   4.3645   4.6963
+#>   -1.5796 -12.6671  17.2917  -7.0164   7.4424 -10.2480  12.6685   5.7798
+#>   -5.4726   5.6697 -10.1118 -15.8220   0.1771  -2.1173  16.5993   4.6345
+#>    2.0185  -1.7008   0.6645   3.0304  -1.5405  12.6602 -13.5373   9.3011
+#>    3.5688   2.3965 -11.0686  16.3498  -6.6246   3.4093  -4.8364   5.5354
+#>    6.6491  21.9286  -3.4636  -2.0928  10.7779   6.7079  -6.7564   6.8138
+#>   -5.0027   0.2820   2.3162   7.4655  -7.6358  -8.0254 -10.0874  -1.0490
+#>   16.2587   0.4248 -13.0793  16.2570   2.3401   3.0679 -10.8025  15.4457
+#>    2.9828   0.1336  10.0219 -11.0347 -15.0825  -4.9162   8.7773 -10.9194
+#>   -7.1183 -11.7349  -6.9990  -5.8554 -22.7783   4.5044  -0.2356 -11.5437
+#>    5.6971   1.9848   3.5486  -1.4376 -11.2005  15.1607  16.3215   3.7568
+#>    1.9127  -3.7770 -11.8825   5.1776  10.8349   9.3161 -13.8649   9.8430
+#>   -0.8122   6.2886  12.2971   0.0506  -0.5405  -1.1465   8.9510  -4.6449
+#>   19.2128 -13.2209   0.0055   1.4520  -4.1927  -1.8904  -5.9837 -12.5014
+#>  -11.3788  12.8628  10.0100 -23.3328  12.1723  13.6150   0.2646   2.8340
+#> 
+#> Columns 49 to 54  -7.4826   0.3962   3.7641  -1.1592  -3.7543  -3.7121
+#>    7.9611   2.5701  -0.4473  -9.0542 -11.3221   3.8200
+#>  -25.5656  -4.4084  -2.6764  -2.7857   8.7528   5.1498
+#>  -10.9501  -9.3092 -13.1660  -1.3856   3.2337  -2.7899
+#>    0.5280   4.3192   4.8902  10.1795   2.4985  -3.8265
+#>    8.3818  -7.1733   6.0724  -0.6022  -1.3414   0.6556
+#>    9.2775   4.6217  -5.8455   3.5248   0.6118   9.2599
+#>   18.7403 -12.7942 -10.3054   8.0210  -2.2770   3.3177
+#>    9.1307  -6.8422  -5.4990  -0.1244   5.8394   5.7740
+#>    2.8057   6.9146  -8.7885  -9.1022  -3.3002   3.7682
+#>   -2.3153  -1.6333  -1.5210   6.5993  -2.9678  -3.2647
+#>    9.3252   6.6995   0.1459 -13.1617  -2.7700   3.0207
+#>  -11.0236  -7.9975   3.2984  -4.9898  -2.6405  -1.1378
+#>   24.2149  -3.8564  -9.6210   8.6175  -4.2841  -0.5845
+#>   -5.5724  -4.8857  10.0525  -0.0648  -8.1390  -3.6214
+#>    6.2020   0.1164  -2.5706 -14.6186   1.3396   7.3444
+#>   13.0301   9.3384   2.4529   3.4333  -5.0788  -0.8720
+#>   -1.2997  -6.2029  18.7258  -1.0295 -12.3016  -1.7109
+#>    3.2627   1.8864  -2.1414 -10.6178   1.5966   4.1469
+#>  -11.8324  11.3675  -2.3672 -10.3189   3.6826  -1.7218
+#>    5.7709 -12.1997  -9.2240  10.8699   8.0572   8.1489
+#>   -9.9726  -4.7731 -10.8461   0.6376  -0.7442  -5.3611
+#>   15.7831  -1.7745  14.6319   3.0108  -6.2453   0.4071
+#>   13.3609   0.9176  -3.8600  10.5494   0.6311   2.1822
+#>    1.6072   7.8351 -17.8949 -15.1692  -1.0781   1.0849
+#>   10.4373  -1.5727   4.1686  -7.1519   0.6870   5.7558
+#>   -3.9311  -2.1158   1.6981   0.0296   1.9939  -2.8001
+#>   -6.9511  -1.2249   0.4667  -2.8986   4.0628  -1.9922
+#>   -9.7188  -6.9660 -14.2356  -9.4817   4.1744  -9.6052
+#>   -3.0306   7.9162   9.8755 -14.9937  -4.3824   4.2074
+#>   10.9695  -5.6113   0.2919  -5.8939  -1.1476   6.1831
+#>    2.7958  14.0478   2.1897  -0.3721  -7.0056   1.1450
+#>   -8.2584  -4.7282   1.8714   3.5121   3.0562  -0.5528
 #> 
 #> (14,.,.) = 
-#>  Columns 1 to 8  11.9867  -8.2984  -1.3146  -3.6354  -8.8529   0.2349 -13.9622  -3.0493
-#>    6.4384   6.9374  -6.6913   7.1586 -13.3554   1.9107   0.1068  -1.9499
-#>   -1.5772   0.2219  10.3118 -10.6105  -2.3904   0.7299   8.2559   3.1453
-#>   -0.2696  18.1811   0.1068   4.8140   7.3397  -9.6986   2.2253   4.3820
-#>    6.5211   9.0709  10.7426  21.6991  15.5631   1.3897   4.9629  -9.8507
-#>    4.8956  11.2489  10.0215  12.3291   6.3059 -13.7399 -11.5145 -16.4382
-#>  -13.6097   1.8697   4.8802  -6.9369  -8.3503  -5.2314  -4.0539  16.6210
-#>   -8.3542  -3.8929   5.4088  -9.4494   1.2373   2.9799  -0.9539   5.0020
-#>    7.5931 -16.0784  -0.6130   4.4867   1.8511 -10.0700  -6.7818 -12.6482
-#>    6.8347   7.8955  15.9790   5.6663   0.2463  -7.2013   0.4226  -2.7929
-#>   12.1881  -6.6606 -13.5196  -0.0920  -4.2267   3.3949   3.1965   1.4301
-#>    1.1594  -2.0000   7.4615  -1.4799  10.2481 -19.5450  -6.4259  -7.2719
-#>    6.6965  -1.9487  -3.2567   2.7485  -5.5610  -3.7478  17.9432   9.7405
-#>    9.2307   7.2232 -16.9909  18.2683  -5.5177  18.6778   6.8506   8.0292
-#>    1.0844   5.2928  12.9493   8.5347   5.1063 -22.1321  -3.8124 -10.9738
-#>    0.1242  -0.8362   0.9491  -2.1039   9.1767 -10.5851 -22.4580   0.0073
-#>    4.1889  -6.7578   0.6158  10.6993  20.2475  -2.6803   1.4228   2.6368
-#>    2.6003  12.7629  -5.6436 -10.5305  16.5167 -12.4023  -1.8523  -2.2946
-#>    1.3399  -0.8541  -0.2925  -1.9588  -3.1782   7.4115  -7.8541  -7.5948
-#>    1.3034   5.2238   8.1993  11.3116   1.9543   6.8484  19.9934   1.7398
-#>   -9.7040   1.3257   0.3924 -16.1826   0.2296  -2.0072  -7.8176  16.1278
-#>   -2.4648  -0.0334   8.7678  -7.9293   4.9946   2.8806  12.8632   3.9208
-#>   -2.9757  -2.2769   7.5569   5.6977 -13.9843   3.7280   0.5950   5.5230
-#>   17.0316  -4.8927  -7.4138 -23.9129 -16.3091  15.0418  12.8346   4.5024
-#>    0.2592  -3.1293   5.7996   9.5155 -17.7701  -0.6414  -0.9153  -3.0268
-#>   -4.5513  -0.0853   3.2202  -2.6910   4.5919  -0.7635  16.7898  -1.5109
-#>   -1.3973  -1.0692 -12.9523 -30.2056 -19.6100   5.0399  13.8209 -17.2502
-#>    2.2549   2.7606  -5.4813   0.8905   5.9473   8.5687  -7.6555 -13.5678
-#>   -0.9458   7.3816  -7.9265   2.9466  -7.6597  -5.3167   8.1822  -8.8753
-#>   -1.8768   8.9373   6.3757   4.5212   5.9159   0.5309  -3.3872  -5.6588
-#>    4.2539  -4.2977   2.2383   4.8838   3.9870   5.2324  12.4818  -3.0790
-#>   -4.5559  10.2216  -4.1422  -6.7525   7.4266   1.5538   1.6988  16.4077
-#>    4.8749  15.8087  -3.5180 -10.5315  -0.0794  -6.0009   1.2058   3.4717
-#> 
-#> Columns 9 to 16  17.1385  -1.7777  -3.5087 -12.1099  -3.3899   5.2232  10.7039  -0.3877
-#>  -13.5326 -20.5020   3.1858  -1.0639   2.6542   6.7054 -14.8185   4.5870
-#>    5.9097 -11.6857 -10.4692  11.6955  -0.4011  -2.0465  -2.4632   6.9488
-#>  -14.1932   9.3081   3.2745   8.7878   5.5004   0.1761 -14.6211 -10.5968
-#>    1.7818  -3.1959   3.9106  -5.2226   0.7281  -2.4754 -10.0146 -12.3474
-#>   -6.9109  10.7561   4.2927   3.9453  -0.1506  -6.4972  -4.0949  17.0473
-#>  -10.4787   8.9851  -4.0980  -3.8323  18.6653  14.4851 -15.8948  -3.3505
-#>    2.8277 -15.8193  -3.6551   1.9299   0.3159  23.0975   6.4151   5.7881
-#>    7.1573  -2.5123  11.9745  -6.2904 -11.5491  -2.1794  -3.4662  10.7510
-#>   10.7676 -11.3751   1.2635  -1.9772   5.0680 -24.8550   3.4418  -7.2806
-#>   14.6815   9.9974  -4.7321   6.8144   4.9209  -5.8303  -0.1228   2.7059
-#>   -7.8457   2.3963   8.4782  11.1909  11.9533   5.7583  -7.5739   0.8734
-#>   17.4703 -11.4663 -10.2138  10.8576  10.6437  -6.2552   7.0891   6.3207
-#>  -17.5415 -19.9175   6.7340   2.6164  -5.2055  -2.9767   3.3248  -7.6270
-#>   13.8038   8.1952  15.0012  -1.9493 -11.4842  -2.7522  -1.7889   7.3613
-#>   -9.8399  -5.3707  15.4129  13.8695  17.0482   4.0208 -13.5116   0.9707
-#>   -9.0796   2.1351  -9.5623 -11.4923   1.1438  -4.7025  -0.7771  20.3234
-#>    1.8303  -4.7797   9.7657  -7.4804  14.0735 -10.1588   8.9940   2.3020
-#>    8.9459   3.2066  15.0946 -13.5423   8.3755  28.6378   0.6761   3.6107
-#>    2.0416 -12.4461  -8.3948  12.2424  -6.6186   2.3483   6.4522  -8.7031
-#>  -23.7058  11.7056  20.3069  -2.6221   8.4900   9.8045   7.4044  -1.5314
-#>    1.4330   6.2013 -19.5130  -3.8664   3.1176   6.3897   3.1219  22.7205
-#>    7.3159  -3.7498  -0.9872  -9.3042  -9.2403  -1.6763  -0.1357  -9.7366
-#>    2.7242  -2.1545  -1.2852  -8.8201   2.2035  -5.8164  -9.1770   7.0053
-#>   -3.7575 -21.8548   7.5362 -27.8420  -9.4957  -0.5877 -18.0524 -18.7607
-#>    3.0822  21.1661   3.2644  13.9712  -8.8555  -9.6856   3.4375   0.3204
-#>   -7.6758   1.7168 -13.5409  -0.6890  -6.3293 -11.9538  -7.7964   3.2132
-#>   -1.1651  -1.6261   8.0642 -12.0310   7.2915  -9.8800  -0.7667   0.9424
-#>  -10.3043   3.9737  -5.0173 -13.3134   5.6635   5.2256  -6.4463   3.3400
-#>    6.5242 -11.1280 -29.3445 -12.3845 -20.2978  -2.3998  -5.4099   6.7119
-#>   12.9018   3.5089  -1.3941  -5.3138  -3.4071   3.0327   9.5516   0.7164
-#>   -7.0746   5.9619  -4.6661   8.6978 -15.0780  -2.8786  -3.4132  -4.3365
-#>   -8.3428   5.4275  -7.3953   7.4961  24.9181  -2.1467  -8.6705  -3.6659
-#> 
-#> Columns 17 to 24  11.5171 -13.5963   2.5003   6.5864 -14.5384   6.9039 -18.7580   0.8669
-#>   -1.2107  10.7887   6.0819 -11.5975  -2.2031   6.5728   2.1513  -7.7375
-#>   -0.8223   3.6664   2.4372  -5.6217  12.5742  19.4965   0.0526   6.5964
-#>   -6.2581   3.5735   1.2482   8.0885  10.9134  -4.5984  10.8286   3.3268
-#>   -3.3290  -4.1021  11.1832  16.9330   4.6907  11.7326  29.6726  -0.0969
-#>    0.5262   4.0599  14.4213 -10.2620   6.5553 -11.4401  20.3248 -26.7666
-#>    2.4857  11.3926 -14.0582  -2.2133   8.6209  -7.9137   4.1798  -1.4231
-#>    5.2495  -0.5675  -2.2643  -7.5104  10.9990   3.7068  -6.7966  13.7726
-#>  -19.1619  10.3072   6.5310  -1.0618   5.0327   5.1669  14.8004  21.6915
-#>  -16.9673  -8.3238 -10.4902  18.6426  11.9763 -15.1956  -3.9650   2.7984
-#>  -11.7504   0.0668   8.7361   0.3543  -6.3230   8.1647  -6.3996   3.7345
-#>   -7.2738   0.4172   5.9568  -0.7624   4.1710   0.5172   3.5015  -5.8163
-#>  -15.7977   1.5926   6.3499  -7.6268 -13.1398  14.6137  -6.5688  -8.8103
-#>    0.7629   4.0633  -4.5180  -0.3671   5.7061  -1.2815  -5.2170  -8.4716
-#>  -11.7767   0.9209 -18.8888   4.4665   9.5710  -6.4211   1.0147   1.5405
-#>   -8.4317   9.7537   1.9881   4.6121   0.4938  -6.5393  -0.0713   4.9697
-#>  -15.2060   4.7632   1.2563  12.2544   6.4372 -11.2441   5.9420   0.2902
-#>  -20.6139  -0.3935 -16.5252  -0.3189  15.2685 -21.3963  -1.8786   9.5508
-#>   -1.3409  -5.6721  -2.4587   4.6961 -12.9813  -0.7488   4.6960   6.8340
-#>    1.4624   1.1889  -4.2159   4.0801   2.9847  -1.3100  12.9584   1.6323
-#>   23.1831   2.5809  -5.2848  -6.6509   9.1711  -1.1559 -11.4886   1.3686
-#>   -0.6514   6.4197  -7.5416   6.3921  15.6665   0.7941  -8.1033  14.6386
-#>  -12.0915  -0.6876  13.3686   5.8638  -0.8935   3.7644   7.6196   0.2200
-#>   -1.6452  -1.5316   7.2437   4.4482 -12.4975  17.8050 -11.5380   9.0727
-#>    7.0956   3.6777  -3.2342  12.4985   9.9739   5.1889  13.3412  -8.5417
-#>    6.1458 -12.5611  14.1390 -12.5054   9.4268  -0.0469   2.0785   0.5694
-#>   -2.4983  -8.0480  -4.1426   1.6966  -8.7688  -7.3796 -19.6699  10.0829
-#>    9.6098   8.0111   7.6299   5.3213  -1.2586  -9.8363  14.0288  -0.7746
-#>    6.7143   0.5243   1.7446  -9.3635  -3.6307  -1.0245  14.2477  -7.5727
-#>    0.0423   7.0274   4.0858   9.4900  -4.4992  -6.8116   8.3830  -0.1123
-#>  -13.3977   7.0101   2.2001   0.4743  -2.4360  19.8296  -4.3704   5.9854
-#>   17.4984 -14.0824   0.6152   4.4569  16.7575  -3.1909  -0.1884  14.3114
-#>    1.5785 -11.2978  -3.4116  15.3848   3.2945  -3.2251   1.2248   5.3961
-#> 
-#> Columns 25 to 32  -8.1145  -8.5795   5.9131   2.4377   7.5201  -4.6986  -7.5430  -9.7138
-#>    3.7041   3.2672 -11.4226  -7.4969   2.8387  -8.5854  -3.1510  -4.9110
-#>   26.2928   3.8416  -3.0306   1.4455 -18.7729   5.4855  -0.0242  -3.6069
-#>    7.4172  -7.8073  13.4746  -4.8996   8.2154  11.3917   6.2756  -6.2200
-#>   -6.6342   8.8344   6.9771   1.2821   8.9269  15.9903  12.5617  -2.2922
-#>   -6.2727   0.8454  -2.9785  16.1220  12.7200  -8.8158  13.0746 -13.5324
-#>   -4.1055  -2.8899  -2.6837 -10.1672 -14.6019   4.6622   2.9432   6.0218
-#>  -11.6356  14.2121   1.5131  -4.1166  10.9975  -2.0590 -14.1106   7.0641
-#>    1.2110  12.9263  -2.7073   9.8011  -4.0078   4.8110  -5.7098  -7.0139
-#>    2.5019  -4.6459  14.4936   6.9029   6.8220  -6.1545   4.6982 -13.7960
-#>    3.8880  24.0579   5.2231  22.2702  10.4620  -6.8659  -7.0574  10.2661
-#>   -4.2881   5.0802  -7.6389   1.0890   5.0370  18.1272  -6.3395  11.5115
-#>   -6.9992  13.3076  -3.4046  -7.0203   2.2997  -4.9772   3.6725   1.6278
-#>   15.8015   5.4040  -0.5921   1.0171  13.0780  -0.1570 -21.2774   4.6421
-#>  -14.5559  -4.4831   4.5585  -9.3380  16.2462   0.5818  12.1105   6.9620
-#>    1.8914   4.9895   2.9694 -11.0574 -16.9952  12.5293 -15.0569  18.8390
-#>   -4.6414  -9.7090  15.4887   5.8955  -1.0994   2.9043   1.4137   4.9682
-#>   -2.6412  -7.0216   5.2076 -16.7756  10.3232  17.7661 -11.6975   3.4358
-#>   10.6992   9.7291   6.3405   7.0945  -3.1234  -4.5210 -12.6039   2.7055
-#>   -0.9061  -3.8552  -3.4819  -5.2309  -2.0386   7.6870   8.0089   5.3179
-#>   -9.2466  -1.9734 -17.7327   1.4467  -4.5935  -2.4652  -7.3554  -4.5967
-#>   -0.0229  -3.8766   6.8379  15.7671  -4.2786   8.3920  -1.8504   0.6503
-#>   13.0757  -6.5117   6.0628  -2.8543  -5.9146  -3.3948   7.3113  -8.8372
-#>   12.8777  10.6938  11.7491  13.2491  -6.4082  14.3670   8.2762 -10.5485
-#>   20.8602   5.2665  -2.1849   8.4760   4.6720  -9.7380  14.8252   2.4705
-#>   -2.9191   2.4862   5.1356   3.3433   5.7164  10.1240  10.1418 -12.5545
-#>    7.7613 -17.3256 -14.2300   3.0357  -8.0037  -9.3167   5.4464  -9.5051
-#>    0.3402 -13.6247   8.6591  -8.0319  -4.1533  -5.3817  -8.5196  -2.2487
-#>   12.3752 -11.3022  -5.1591  -7.1980  -4.7129  -1.5999   6.0342   0.5003
-#>   -8.3133  -7.0496  17.7399  11.1410   0.1447   5.0498  -7.6757 -12.1452
-#>   11.3473  -1.1404   0.2236   3.8494   6.2571  -3.1258   6.3751  -2.4485
-#>   -6.3385  12.0757   2.5867  -5.1963   3.9730   2.3733  -4.1170  -6.3596
-#>    6.0022  -3.2219   4.8640   2.0869   3.6794   0.9510   3.1537  -5.3880
-#> 
-#> Columns 33 to 40  -9.7750   4.3122   7.7314  -0.3517   6.2544  -6.1334   0.5192  -9.4471
-#>   -8.7052   3.9934  23.8465   8.6498   2.4528  -4.0451  -4.4595  15.7021
-#>   10.9452   6.8185  23.5919  10.0206   7.8168  -9.7620   0.6737  12.7527
-#>    8.2015  -5.9087  11.5235  -4.9371  -8.2185  -6.3917  -5.5924   2.4774
-#>    8.4680   6.6133  -5.3330   5.5582  11.6515  19.1548  10.5538   9.0030
-#>   -9.4199   2.2284   6.7835   8.8719  -8.4643  -8.7587  -6.3010  -1.5074
-#>   -1.1034  -8.4749  -5.3583  14.8149   8.7404  20.1333  -4.1000   1.9190
-#>   -7.1595   4.5246  -1.5435  -8.6677  -2.4809 -10.2686   4.5958   1.0697
-#>   -3.0526   5.9754  -1.7177   0.9364   7.2218  15.6521   5.0914  15.7620
-#>   -2.3993   3.3825  17.5335   7.8474  18.1009  -8.3220   6.9126   1.3761
-#>   -9.2370   5.9037   4.8368 -10.3760   1.5467   3.6940   5.9009   2.6261
-#>   -6.8671  -5.5725  11.4637   1.8629  -4.3842  -8.8654  -5.1225 -19.9867
-#>   -2.1253   8.3315  -0.9771   7.8220  11.6228   3.0225 -10.3436   4.4569
-#>   -0.0098   0.1681   7.3899 -14.7242 -10.5119  -3.0313  13.5913  16.3598
-#>    2.0848  13.5177  -1.9371  -9.8813   7.2109   0.1098   2.8727   5.3730
-#>    9.2950  -5.0439   1.8789  -8.6116   0.4375   0.2704  -4.6135  -0.4481
-#>  -15.4528   6.8408   4.1062  -1.2382  -1.1624  14.0390  -3.2953   9.6390
-#>   -2.4949 -13.5640   2.6893  -2.2827   2.5954  -0.7560  -3.5461   7.4404
-#>   -4.1343 -22.1728 -12.7734  -8.1149   0.2042  -6.0266  -0.7888   5.2665
-#>   18.2782   1.1584   9.0241   2.2745  21.5432   4.8197  -8.2883  -2.4910
-#>    9.1289   5.9599   6.8255   7.6997  -0.3532  -4.1977   8.8119   0.5369
-#>  -10.6763   6.4529   2.8220   1.1191   1.3069  -0.0376  -0.4241   1.5943
-#>    3.2870   1.2273  15.5045  11.5385  12.0046   2.5891  -2.5266  10.8700
-#>  -10.8855   2.7544 -10.3731  -7.6112   5.5987   2.8348   0.2626   4.2871
-#>    1.8254   3.8367   3.7787   0.6154   1.4018  -6.2460  -8.3149  -3.8919
-#>    4.5782 -14.2862  -4.3419  -2.8506  12.8187   6.0623   9.9037  -8.7852
-#>   -7.6454   7.2889  -7.3449 -14.5525  -4.5972   8.2140  -0.5723  -8.6042
-#>    8.7800   0.2766   6.5277  -5.1133   1.4634  -2.1700   5.2418  -1.3187
-#>    4.1663   6.2197   9.3275  -3.1985  -8.7473   2.1206  -2.6255   6.9047
-#>   -7.2054   0.4252   0.9457 -13.5936  -9.6846   5.9752   9.3577   7.8062
-#>   -7.9872  -9.4947  -3.2777  -1.0915   7.9006   4.6527   2.2801  -0.2176
-#>    9.3088  -7.8706   0.1679  11.2275   8.6029   8.0369  -5.1506  -6.3863
-#>   -9.3878   3.4855  -3.5517  18.5091   4.3952  13.0829  -0.8334  -5.4597
-#> 
-#> Columns 41 to 48   6.5011 -16.1431  -4.3466 -14.9900 -11.7130   7.8898  -2.9533  -2.2251
-#>   -0.9887  -5.6208   3.8264   6.5192  -9.3852   7.1299   3.5867  -6.3774
-#>    0.2947  -3.7010  10.4453  -2.2354  -0.0995   9.3341  13.2049   6.7727
-#>   -6.2542   8.5937   1.3642 -10.2065  -0.6303 -10.4907  -6.5015  -0.1814
-#>   11.8759  14.5775   7.2311  -4.1882  -3.3403  -1.6617 -13.8284 -15.8210
-#>    8.9016  -4.4975  -2.8111  -3.9421  -8.8392 -15.6729   5.8049  -9.0963
-#>  -22.4988  -3.1013   5.6064  -1.3716  -0.4750   4.9586  -0.3411  -1.9196
-#>  -11.7136  -0.1444   0.6934   4.2718  -4.5249  26.0992  -3.3704  -2.0736
-#>    3.3940  -4.8608  -6.7398   2.2891 -11.9959  19.4911  -4.5567   7.0563
-#>   -2.8931   0.6912   2.3908 -20.5148  -8.7039 -21.1667  -5.3784  -4.7638
-#>    3.5246   2.6905  -2.7401  12.7459 -13.7531  -1.9057 -24.9776  -5.5714
-#>   -8.2436  12.1626   8.5889   0.0052   1.3230 -19.5498  -0.2498  -2.6468
-#>   -4.6138  -4.6218  -2.2591   8.9042  -4.3583   6.2058  -3.3368   1.2475
-#>   -8.1050   7.6328 -15.1233  -5.7670  -5.6250  -2.8747 -12.5963  17.1892
-#>    3.3816   9.4714   9.2402   4.2461  -1.8082   1.3889 -18.4537  -7.4553
-#>   -6.8785  -3.8493   4.7792   8.7750   5.6983   3.3413  -2.4371   7.8816
-#>   14.1797  -8.5735   1.2438  11.8582 -10.3231  -2.7905   7.6571  -1.2268
-#>  -18.1333   1.1585  -0.1916  -7.3058   3.1073  -9.1316 -14.7963   4.7654
-#>   -8.7561 -18.2628 -10.8891   4.9068  10.5885  -1.6268  -4.1198   6.0831
-#>    0.5767  -5.7960  14.2352  -6.2464  -0.0318   4.7683  -3.9952 -16.9078
-#>    4.2374   6.0610   8.8447  -2.9822   9.9090   7.2188   8.9347   9.0626
-#>   -1.2002   2.3952   6.9179   6.7029   0.9641  11.7896   4.9396  10.7570
-#>    8.7479   3.4975   6.1097   3.5737  -9.6930   2.4751  -0.9593   6.4604
-#>    8.7248  -4.2146   0.1575  -8.3680  -4.5994  -3.6923 -11.4719  -6.3495
-#>    8.2636   8.0527   1.2133  -4.7065   7.7905   4.3437   1.3388   6.6275
-#>   11.6542 -13.8952   6.7065 -26.8512   8.7871 -20.8686  13.1924 -11.7947
-#>    3.9899   0.6227 -15.7883  -3.1894  -3.9283  -0.7139  -5.5691 -12.3483
-#>    3.3880  -5.5641  -5.1218  -7.5778   7.3389   1.1221  15.2900   1.8776
-#>    0.4520  -0.0513  -6.6910  -1.2088  14.5641  14.3009   8.1152   2.6959
-#>    2.3548   1.1817 -16.2080   1.7125 -10.1196   9.7649   3.8981   3.3544
-#>    7.7892   8.2897   0.6544   8.3422  -3.1110  -0.1419   2.9984   8.1290
-#>    2.9468   7.7314   1.1350 -15.7203   7.8031 -26.0266   4.3703  14.3819
-#>    6.5798  21.6411   6.7600  11.7019  -0.3172  -9.8948  -6.3248   9.0993
-#> 
-#> Columns 49 to 54   5.0734  -3.1985  -3.1484 -18.5449   3.5854   2.6389
-#>   -9.9198   1.8784  -2.8820  -2.0754  -2.6528  -2.0361
-#>   13.2967  -9.1146 -10.2911   8.6274   2.0238   0.7990
-#>    4.9978   6.5459   5.7172   1.0634  -1.8348   0.2181
-#>   -6.6560  -6.9395  -2.7945 -13.4165   0.3720  -5.3638
-#>  -13.8443  17.2028   1.6859  -3.0717   7.0974  -2.4310
-#>  -11.1912   0.7051   0.4575  -1.5135  -5.2907   3.6489
-#>   -0.9601  -1.2169   5.9644 -14.7754  -2.9408   9.7640
-#>    6.9968   3.5194  -5.5172  12.0147   0.2977  -1.2645
-#>    4.0465 -13.0005  -4.5254  -6.8678  -5.6679  -0.6868
-#>   11.8746   9.7976  -6.3857   0.3301   6.3049  -1.9373
-#>   -4.6632  10.6690  11.6423   1.4945   0.5620   1.4400
-#>   -3.6426  -3.7228  -9.2963   2.9672   8.7875   0.7035
-#>   -8.5415  -6.0032   8.7938  11.3102  -3.4259   1.2885
-#>   -4.5487  10.1005   8.8807 -16.3164 -13.8255  -7.4006
-#>    4.4433 -13.8975  -6.4573   1.1518  -4.9269   3.9875
-#>  -16.5790 -15.7375   1.8982   9.4079  -4.0299   2.0403
-#>    5.4469 -11.0663   3.1077   5.7961 -17.1680   6.1061
-#>   11.3496   7.0669   8.9131   1.8658   6.6558   4.8192
-#>   -1.0470 -21.5752  14.9542   8.4510  -1.2142  -0.3483
-#>  -10.2189  -3.1078  -1.9010  -6.4678   1.8478  -1.3810
-#>   11.3472   6.0519   4.0031   1.0326  -0.1843   5.3557
-#>    5.0970  -4.6407   5.5243   0.9926  -4.7836  -8.5118
-#>    5.8961  23.3211   7.8782   7.2337  11.0661  -2.1141
-#>   -1.7034  11.4659   6.8905  -1.7727   1.0258   4.5055
-#>    3.9860  -2.7932   6.7847  -3.8188   0.9939  -9.3229
-#>   -5.9320   4.4995 -13.8126  -5.9319  -6.1371  -5.0455
-#>    7.7796  -4.0133   4.3013   2.0061  -6.8397  -7.0406
-#>   10.1231   5.2064 -10.2621  -0.2242   0.5774  -5.2299
-#>    3.2700   5.6559   3.3086   2.3718 -15.8170   2.4293
-#>    3.7895   3.7239  -2.7195  -0.8528  -0.1359  -2.3084
-#>   -2.4946   0.8061  17.5376   4.8175  -4.9913  -5.3384
-#>   -6.9893  11.9924  -3.7919  -3.3553   0.5683   0.4901
+#>  Columns 1 to 8   2.0483   2.8171   2.1817   7.0182   5.1270  -7.9854   6.3096  10.3784
+#>    2.6052  -3.8704  -3.5973  -7.3507  -2.4192  -9.0850  -0.6693  -2.1878
+#>   -1.9051  -8.2699   4.6939  -7.8013 -16.2656  -7.9998  -4.0699  -0.1254
+#>    3.0601   3.2293  11.0668   3.2417   6.1561  -8.0102  -6.5164  13.2991
+#>   -1.4684  -1.3421   8.1883  -4.2006   1.0094  -0.7288   4.6309  11.9590
+#>   -2.9930   1.6211  -2.6800  -8.3973   0.8828 -25.0192  -8.4731  -7.3463
+#>   -2.7903  -6.1639  -0.4059   2.6958   7.1756   0.5466 -12.5127   4.9266
+#>   -1.8259   5.8285   2.9052  -0.5450  18.2841  -4.1749   8.3484   3.4708
+#>    0.7899   1.6427   1.4980   2.6466   3.0877  -5.3801  -7.0835   7.4466
+#>    1.5463  -3.1601  -0.7089 -16.6168 -10.8211   5.3150  -1.3674 -10.9028
+#>  -10.2982  -2.1975  10.4029   0.8741   3.5458  -0.3036   0.1896  -8.7427
+#>   -2.0001   1.7725   9.2228  14.5550  -4.8925  10.8646  -5.8209  -3.9372
+#>    4.3509   3.3802   3.9177   0.2544 -12.7767   7.7736   9.1449   4.0556
+#>   -2.7691  15.4949  -3.5401  -4.9217  20.0237  -4.4575   7.9682  16.4020
+#>    7.7987   2.5612   1.9509  16.0925  11.5299   5.4404   5.5854   0.7955
+#>   -6.1617   3.3401  -2.4637   6.3745   3.1217  -0.6190  -6.3978 -15.2816
+#>    1.6892  -4.0921   1.7836  13.7757  -0.4059  -7.8691   4.5175  -6.2729
+#>   -0.4282  -3.1315   4.1787  10.8730   8.4929  10.0354   8.5351 -17.6654
+#>    3.1141   0.2154  -1.9783 -13.6804  -8.9142   6.5795  -5.1016   0.1180
+#>    5.5324  -1.0862  -2.1182   3.3810  -1.8815  14.4442 -12.4813   6.7168
+#>    4.4775   6.0969   3.3110   4.4587 -14.6556  -3.1994  -5.8314   7.5657
+#>    9.2710  -5.7303   3.6967   1.8235   6.8391 -17.6486  10.0485  -4.2315
+#>   -1.1558  -1.5740   9.4869  -6.0505   3.7959   1.1696   2.4942  -4.4600
+#>    4.5613 -10.1540   3.2603  -0.0989   3.3937 -10.0462  16.6124   4.4885
+#>  -11.4087  -2.7969   9.7151  -0.5111   4.3528  17.4705  -0.0372   0.3828
+#>   -1.3272  -0.7748   3.8768   7.2436  -6.2681   0.5956  -1.7136  13.4700
+#>    2.5192  14.0231   2.1485   6.8496  13.4577   9.1016  10.8995   4.2937
+#>   -3.1544 -11.1025   3.1132   4.2347  17.9970  -2.9984   0.5567   1.8000
+#>    1.7558   2.6680  -2.5301   0.6804  -5.6686   3.8812   7.3442   5.9400
+#>   -2.2579  -9.3441   0.2845  -1.2302  -9.1843  10.7536   3.4147   3.4666
+#>    0.6889   2.6817  -2.6930  -1.1192   4.1683  16.7969  -5.8238   5.3186
+#>  -10.9125  16.3318  -8.4345   4.1855   9.5059 -16.0090   7.7850   4.6360
+#>    6.8469   4.2239 -12.7071  -1.2498 -12.8182  -9.5137   8.5964  -2.5512
+#> 
+#> Columns 9 to 16  -2.3523   3.1771   4.8633  -1.8067   7.6656   7.2096  -0.5283   1.9938
+#>   -0.3175  -2.5652 -20.3070   7.0495  -2.7538  -5.0101  18.2074  -1.1214
+#>   13.9994  -2.1166  18.2260   5.9787   4.0359   5.0937  -3.4541 -10.2350
+#>   11.7015  -0.7359   3.5844  19.4892   5.7383   3.8325  15.9566   1.1753
+#>  -10.0147   6.8668   7.8851   5.2145   3.1191 -17.1693   2.5109  -3.7144
+#>  -10.5480  -9.1764  -4.3777 -13.3429   0.7453   3.2906   7.1985  -0.1988
+#>   15.4651   4.6034 -14.7709  -3.8301   4.8131  -8.3639   6.4618   2.2399
+#>    7.8516   4.0185   3.0853 -14.0399  -0.2901   8.2163 -14.6712  -4.0115
+#>    1.0268   8.5333  20.5010  -0.8681  -3.7316  -0.6342 -16.6141   1.6666
+#>  -16.1924  10.9530  -1.9614  14.2302  19.3348  17.9828   8.2189  18.9737
+#>    2.9337  -0.3779 -15.6893  -9.1333  17.2737   4.6129   8.3059  -1.2873
+#>  -19.9053  10.7389  -6.4006 -10.9317  -6.4664  13.4150  10.5242  13.1741
+#>   -0.3921  19.7432  -0.4637   0.0092  15.1561   4.9532  -5.2236   6.2684
+#>   -9.6794   0.3285   1.6589   4.7561   6.9246  -1.7614   0.8781   7.1642
+#>   -7.8437  -1.1849  -6.6127   4.5542 -13.5849  -7.4552  10.3579   7.2606
+#>    8.9161  -7.2971  -4.7357  -8.2574  -2.6160  -4.2778   0.6448  10.9217
+#>  -10.3911  -4.8965 -11.8475   5.2317  -3.3840  11.2177   1.8663  -4.9843
+#>   10.6675 -11.0052 -15.9643  -4.3065  -0.6591  -0.3957   0.8641  12.9674
+#>    7.5725  -8.0643 -10.6968   9.1187   1.9843  28.3923 -16.6680   7.4496
+#>   -4.6260  10.7212   5.6854  -6.2471  -6.2971  -0.9964 -14.6339  -1.9753
+#>    1.8728   2.3720  -9.0977  -0.4728  -3.4320  -1.8218   2.7187 -10.6726
+#>  -13.9849   3.5338  -5.6726  -5.5427   0.1115  -4.5678   4.8786 -10.0060
+#>    5.8341   0.2327  17.1221  -3.4749   2.6155  -2.1026  -1.9057  -0.3805
+#>   -6.7081  -2.1162  -5.1836  -7.3530 -18.4240  -8.5944  22.7212  -4.4626
+#>    7.2314  14.8975   9.6902  10.3583   2.8377  -7.1998  11.5357  -0.5511
+#>   -5.2351   6.0340   2.5682  -6.9786 -12.3467  -9.0609   2.7647  -2.1528
+#>    1.3923 -11.0850   2.9330   7.7764  -5.4198   5.6025   0.1478  -1.8264
+#>   -2.3406  13.3075   7.4693 -10.6425 -12.6399 -19.7282  -4.6386   3.1225
+#>   -5.5779  15.4978   7.9327   5.4769   0.0235   6.7429  -9.6360  16.0188
+#>   -9.7868  -6.6325  -8.5226   5.4234 -10.6048 -23.8821 -11.4005  -4.3078
+#>   13.5427  -3.9301   3.6775   0.8042 -12.6796   5.9962   6.7765   4.2510
+#>    7.0764   2.1136  12.1797  23.4169 -15.0435  -2.0228   3.4215  -8.3234
+#>    1.8828  -1.3288  -2.4978  23.3028   1.0032  -2.7889  -3.6193  -8.0648
+#> 
+#> Columns 17 to 24 -10.2348 -10.3924  28.3538  23.5301  -1.6500 -10.1804 -10.0480 -15.2089
+#>   -8.9161   5.8792  -3.2699  -6.1232  12.9389   5.0260   5.7754   0.3234
+#>  -15.4402  -1.9281  -7.6691  16.2835   0.4242  -8.6604  -3.8395 -10.5689
+#>   -2.1347  -6.7671  14.9591   5.0173  13.4562   6.1168 -23.7830   6.4363
+#>   12.7730  -6.3039   0.5441  -4.7507   3.2715  -3.8849   2.3002   9.2171
+#>   -5.3868 -23.7438   3.7346   1.6850  -3.1972   2.0915   7.9911  -3.4694
+#>    5.9121  -5.1906  -3.0446  -8.3304  -5.7022  11.0526  11.6562  -2.9498
+#>  -17.2484 -10.6187   1.4781   8.3619   7.5431  -2.8201  -5.6948   8.7816
+#>   -4.7339  -3.1140   0.8105   7.1769  -9.5033 -14.8670  -5.1933   4.7480
+#>    1.6259 -17.2838 -14.6744 -14.6320   1.5441  13.9075  16.0969  -5.9717
+#>  -12.3126   9.4735   3.1425   8.1340  14.8011  16.8741  -6.0387  -3.2562
+#>   -4.7323   4.1972 -10.1715   6.3317   0.5642   4.6339   7.4144  17.3780
+#>   -7.3307   6.3004  -2.1724  -2.1328  -1.1456   6.2455  -0.2815  -0.3057
+#>   -3.0029  -5.2331   5.3913  -5.6159  -0.1986   8.4029   1.4879 -10.6394
+#>   11.6895  18.7261   7.2288  -3.5144  -8.8820 -13.3798  -3.0939 -10.1335
+#>    2.9795   0.2223 -11.0558   5.5597  -1.1708  10.5407  -1.9903  -4.8966
+#>   13.4637   4.1461   9.6946  -4.4173  -9.4509   0.7498   3.1487   1.2051
+#>   10.6868   5.9891 -15.9531   6.0373   3.7671  22.5487   8.1482 -12.9323
+#>    1.0647   3.6761  -6.6383   7.8673 -11.3775   6.0606   3.4448  -3.0820
+#>    2.4590  -2.6091  -2.5434  13.3988   1.6054  -7.6742   5.7351  -8.5271
+#>  -20.7212  -6.6214   1.7931  11.1464  -6.0346  12.6079  -7.6567  -1.7598
+#>   11.3305  -5.3623   1.9753  -1.6536  -0.8524   1.7750  -1.4573 -12.3850
+#>   10.4981  -2.9261  -4.7843 -10.2992  -5.5276   6.9699  -6.6368  -1.6385
+#>    1.2611  10.0195   4.3912  -2.0264  -3.4332  -2.8160  13.1815  -0.2798
+#>    4.0165 -18.8673  -0.9714   1.5572  -0.1710   7.8988  -0.7883   1.7974
+#>    1.8325   0.7240  -3.1175   6.3640  -2.0914  -5.3480   5.9340  -1.5273
+#>   -4.7145   8.8223   4.8152   5.9093   1.3893 -15.7724 -13.5039  -1.8057
+#>    9.0114 -13.7019  12.0908   9.7055  -3.3504  16.4803  -2.4017   3.5436
+#>   -0.0961  -4.5853  -8.7671  16.7746  -3.1751  10.5036  -2.7427   0.4062
+#>    4.2938  -1.5279   4.8288  -3.5993   8.1544  18.6168 -10.0799   6.6257
+#>  -10.4978 -22.2202  12.8952  -0.2795  -0.2268  17.3916  -9.8229  -4.5361
+#>   10.3517  -4.3437   0.8798 -12.8159 -16.6323  -0.2328  -5.1834 -14.1388
+#>   -7.8771   9.1761  -5.3219  -2.0524  -4.2862  -2.6113   5.0800  -8.5416
+#> 
+#> Columns 25 to 32  -0.9159  13.4203   6.5323   7.5950  13.8688  -6.9460  -0.1792 -16.4621
+#>    6.9652  -2.4826  -9.9765 -19.0936 -30.2546 -11.6350  -1.9545   0.5948
+#>  -10.4552  -0.8037  -2.5198   3.4383  10.2861   3.7880  -3.5247   8.1405
+#>    7.4821  -3.8323   5.4414  -1.2910   4.2027   1.7808  10.0969   0.3735
+#>    8.3640   4.7307   3.8161  16.9917  -1.7441  -5.2732 -16.7697  -9.1089
+#>   -0.4242  -8.8090  -1.9215   8.1409  -5.8040  -0.9260  -9.6041  -4.5747
+#>   -6.4201   6.3877   9.5317  -3.8934  -6.3176  -3.7419   6.3712  -6.7074
+#>  -21.5882  14.1418  -0.6532  -3.6961  -0.4233 -14.6904  -0.6364  -5.9010
+#>   18.6200   7.4164  10.3222  -0.1400   9.0266  -7.2192  10.3088   4.9905
+#>    6.8061  -5.0992 -10.0704  -9.0681   5.0483  13.1940   9.5656  11.5561
+#>   -5.8975   1.2614  -4.3039  -9.0968   9.5044  -0.2363  -3.8939  -3.7445
+#>   10.8748 -11.1940 -14.2923  -0.4156   0.4765   0.9525   8.4732   9.5188
+#>    7.0825  -8.1913   4.2862  -8.7126   6.4783 -11.0109 -11.2965  11.6086
+#>    5.9322  -0.8264  -6.4513  13.2633  -6.0782 -12.1683  13.7421   9.4527
+#>   -4.3458 -19.1304  11.3339  -1.4040  -1.8719   6.7522   2.3990   0.5421
+#>  -15.5739  -8.1081  11.7671  -7.1687   3.0119 -11.2907   8.4919  -2.2549
+#>    3.4496  -7.5678   7.8432 -10.9499   6.9601   2.1162  -3.6398   5.1774
+#>    5.8573 -16.8634  11.2962 -10.6024  -2.1106   9.3152   5.0799  -3.2991
+#>    3.5228 -12.3862  19.4526 -12.3581 -10.8081  -0.2490  -3.4801  10.2120
+#>    1.9833  -1.1323  15.5792  -3.3417  13.6646  -3.1423 -10.8112   2.0569
+#>   -2.8532 -20.8118  17.1460   5.1504   6.7858   8.9569   9.5942  -8.1865
+#>  -11.6045   2.7472   6.8489 -17.4607   7.4088   7.1632  -0.6412  -6.1571
+#>    4.6663  -0.0405  11.8816  10.4567  24.6613  -3.0307   3.9927 -10.8379
+#>   -3.9458  -0.2320   4.1353 -12.4750 -13.3768  13.9787   0.6004  -6.3795
+#>   -0.1637  -0.9540 -16.8383   8.0502  -3.2454  -2.7919  -1.1321  20.5537
+#>  -10.5922  -5.9244  -3.6159   0.7996  -2.2223  13.7091   3.0005  -4.1734
+#>   -2.1762   9.7432   6.9444   7.7467   4.5227 -20.9266  -8.7350  13.4254
+#>    1.9172  -3.8725  -0.0471   1.6605   4.0943  -8.7245  -0.2301 -17.9943
+#>   17.3466  -3.8644   3.9618   1.4779  -0.9825  -5.0319   3.4316   3.1853
+#>    2.8328 -19.3811   2.0911   2.8130  -1.9346   8.6048 -15.1948  -2.9528
+#>   -7.0527 -21.1938  17.2286  -0.7637  10.8436  -0.2206   8.4798  -3.9594
+#>    2.7843 -11.4965   1.3322  13.7793   6.6676  -9.4186  -2.2445 -15.0435
+#>    6.0846  -7.4912  19.7761   0.4588   5.7567  -3.9668  -8.8055   7.0106
+#> 
+#> Columns 33 to 40  18.6396   7.1904   0.0509   4.4550 -16.2284   6.6531  13.7506  -2.6644
+#>    6.6045   1.9599   1.3909   0.0837   9.4284  10.7175 -11.2015 -11.1156
+#>   -7.3619  -6.9144  -0.3754   3.9721  -3.8965   5.6269  -4.2670  -2.9937
+#>   -4.2725   8.3841   5.9296  -5.5361  -2.6811  -1.3009   2.3152   1.7558
+#>   -3.1047   8.4638  -3.5369 -10.2167   8.7408  15.1797  16.7173   2.1196
+#>   17.4756  -5.6791 -17.2619 -12.4031   8.2213  -6.4023  14.2544   6.5709
+#>   -8.3391  -1.8479   8.5197   1.9586  -7.0193   2.8856  -2.3662  -6.9705
+#>   15.8320  -8.4019   6.2829   8.6353  -0.8195 -10.5387  -8.0319   6.0173
+#>    0.1851 -10.3071  -2.3758  -2.1783  -3.3879 -10.7620  -8.7636   0.9066
+#>    1.9244  -4.0913   8.6562  11.9832  -2.7403   2.3984   2.8265   4.1045
+#>   15.7915   2.2120   5.1031  -8.1328  -8.7761   7.5448   3.7938 -11.0116
+#>   -0.1379  -9.2567  -0.9971 -12.5197 -10.7772  -2.9520 -20.5811   2.5617
+#>   16.7674  -6.0425  12.6763  14.3236  -5.7227   8.6642   3.8490 -13.0048
+#>  -18.5483  -6.2450  -5.8645  13.6455   8.1484  -2.9606  -7.2609  14.2135
+#>  -10.5598  -8.8060  -8.8076  -1.0404 -11.7503   0.2692   9.7035   3.3732
+#>    5.6073  -9.5617  -0.6657   1.6180   1.2846   0.9699 -11.6399 -19.3874
+#>    6.2403   6.5218  -7.6153   8.9744  -9.2173 -10.6295  -6.8481  -3.2323
+#>   -6.4528  -8.3036  -0.4019  13.1187 -10.7443  -9.0710   0.0980  -8.2492
+#>   13.9228   3.2469  -6.3787  19.7753  14.1159  -4.4331 -11.0666  10.8947
+#>   -1.4693  -0.8730  -6.9941  15.8055  -5.1114   0.3584   9.0701   7.9444
+#>   -3.0482  -1.2769  -8.7067  -7.8207 -10.1355 -11.6483  -6.9194   7.0103
+#>   11.3450   4.9000 -12.8353   5.5950   2.4555 -10.0292  17.2249 -12.3853
+#>   -6.8362  -1.2283  -1.8838  -6.3650  -3.0411   4.4704   7.9367 -13.7584
+#>  -14.2842   2.3737  -5.6434  -2.8510  -8.1665 -10.6020   6.2823  17.9619
+#>   -3.2322   6.7363  22.6806 -12.1045   6.1171  14.7458  -0.8419 -12.5650
+#>   -7.8762   0.6909   1.0330 -16.9335  -2.2932  -2.7218  13.9762   0.5134
+#>   -8.1761  20.5615   4.4597  -2.9705  -2.1040   7.0635  -9.4867  -0.3010
+#>   -4.3827   1.6377  -2.8162   6.3927  10.1479  -0.8918   3.9715  -3.6091
+#>   -1.4799  -3.7579  12.8654  23.8155   1.0990  -7.5794  14.5106  11.8321
+#>   20.8702 -15.5642   7.4340  -7.2400  -5.6068   1.4093   5.5772  -3.3926
+#>    4.6256   4.1646   6.5021  -4.7770   1.0834   5.4780   3.9725   1.2136
+#>  -14.0916  16.9296 -18.0211  -9.9699  19.3443  11.1253  10.8632   1.9144
+#>    1.2075  -1.2523  -6.7294   5.9618 -10.4208   0.3372   6.2555   5.8249
+#> 
+#> Columns 41 to 48   8.5576   7.5433   2.6797   3.0871   0.0767   4.7525   7.1795   4.3351
+#>    3.6851   7.4914  18.1230 -12.1484  -5.7966   0.4726   0.7648   7.6670
+#>    0.3106  -4.1319   5.5173   1.6237  -8.3065  -1.5440  -3.0000   5.4854
+#>   12.1217   0.0225   1.1910  10.8809 -11.7799 -10.1549  -2.2679  -5.0619
+#>   -4.3544  -5.4043  -1.6194   4.9990   4.1381   0.3335 -11.4544   6.4979
+#>   -0.0970  16.1548  -3.8121  13.4631   4.0613   2.3344 -10.0733   6.2545
+#>    4.1619  -3.9533   1.1096   1.2977  10.7076 -11.4872  10.5086   1.3446
+#>   -8.0701 -17.3169   7.5144   2.8291   5.3520  13.0136  -4.9439   0.6834
+#>    5.6396  17.1359  11.8835  -9.2396   1.5524   8.4900 -16.9708  -2.5895
+#>    8.7313  10.3437   0.2953 -11.7832  -4.6645 -13.1998   3.3757  15.5541
+#>  -10.2804   0.6929  19.8981   2.3942   2.0070  -4.3088 -12.2119  11.3075
+#>    8.5153  11.9262   8.1268 -16.4382  -6.0893 -10.5934  -0.6553  14.1596
+#>    6.8772  -4.7055  -4.1626  -5.7810   1.2507  -0.2576  -6.4328  -1.5552
+#>   -9.6600  -7.0994   7.8232  -1.6231   1.4039   3.8520 -12.9451  -2.9715
+#>   -8.9316 -13.7418   8.7478   6.0414   2.4571  -1.0620   9.6386   3.9649
+#>   16.4171 -10.0602  -9.5793   9.5198   5.2759  -8.3260  -1.7487  -1.0387
+#>   -2.2099  -0.9547   4.8525   0.4844  19.7238   2.9641  -7.6010   5.1132
+#>  -14.9295   0.9643   5.3782   6.2052 -14.3373 -18.5915  -9.6981  17.4214
+#>   -4.0840  -4.6899 -26.1996   3.8893  -3.0055  10.0548   2.2240 -19.3547
+#>   -2.9221  -4.8070  -9.3611  -9.6042  -2.7436  13.8186   3.2881  -8.2854
+#>    3.9435  -9.8249  -3.4478  -7.1110   5.7017 -11.0682   3.4407   1.2799
+#>   -6.2238  -6.1991  -4.1156   5.5609  -0.5584   7.1017 -15.8312  16.3446
+#>   -6.4259   5.4845  -1.9898  -7.9629  -1.0325   5.4699  -7.5042   2.7843
+#>   -5.8199   3.0797  15.3120   0.9456   3.8138  -7.8040   6.1432  24.1412
+#>    3.7536   9.8367   2.2500 -17.0865   0.7510  -6.5477  13.5176  -3.4241
+#>   14.1747  12.4147  -7.4757  -8.8212  -3.8793  -7.6130  16.1602   7.7567
+#>  -13.9329 -13.9696   0.6841   4.1562  12.1386  11.1311  -1.1848 -22.4898
+#>    0.8757  -3.5722 -16.4026   3.2604   0.3412   0.9699   0.8866  -1.3551
+#>    9.0806   9.5547 -13.6876   2.2703   4.3662  -7.1310   2.8670   3.8843
+#>   -6.2397   5.3594  -8.2173  -3.0896   9.3805  -6.6170  16.8530  12.8967
+#>   -1.2034  -1.6997  -7.8824   2.8570   6.0805  -1.6090   8.8409  -6.6037
+#>    3.1053  -6.8886  -4.5419  -3.2972  16.4854  -1.6520  25.8332  -9.2220
+#>   -4.6863 -16.3210   5.9312   0.2007   5.4184   6.4026 -12.7763  -9.0798
+#> 
+#> Columns 49 to 54  -3.9847   1.6295   2.5405  -1.5959  -0.1645  -6.1709
+#>   -2.4922   8.3674  -0.1290  -6.2076  -6.5233  -7.9762
+#>   -2.5895  -9.3321   4.5612  -4.0574   8.3154   3.1874
+#>    3.4926  -1.7231 -13.7573 -10.6458   5.6517   2.2175
+#>   10.4529   4.9393  12.7976  -8.3547   7.6803  -0.6987
+#>   14.4214  13.8274  -6.6765   4.4461   0.7199 -10.7774
+#>    2.8338  -3.3699   3.4500   4.0040   6.0632  -0.3591
+#>   -1.9455   1.2128 -10.9467   3.0886   6.4146  -5.8782
+#>    1.4996  -5.0270 -11.5554   7.0102  14.1503  -2.1967
+#>   -8.1273 -15.6613   9.4757  -7.3414  -8.4077   4.9521
+#>  -20.4313  -1.9778   4.8580   6.4747   0.3780  -5.9440
+#>    0.1205 -13.0912   9.8917  12.8688   4.2765  -0.9285
+#>   -1.0324  -2.5524   6.0610  -4.3612  -3.8263  -3.0147
+#>    3.9707   7.6382  -7.8979  -8.4047   5.3099  -7.3539
+#>   -0.5268  -0.5446 -18.5820 -11.4591   2.6708  -3.4061
+#>    4.9823  -4.0362   1.4343  -0.8815  -4.8684   0.8801
+#>   -0.1282   4.4348   5.3564   1.1135  -3.1455  -7.9813
+#>   11.9521  -4.7571  -1.4570  -6.3113  -3.4599   7.1929
+#>  -18.7061  15.4748  -3.5646   6.1948  -6.9990   0.1782
+#>    3.0922  -0.7009 -11.6909   1.7172   5.9202  -1.2143
+#>  -26.1815  -7.5516  15.6631   6.4886   3.6771   2.7164
+#>    7.9054  -6.0297  -6.6026  -8.0419  -9.2620  -1.7847
+#>   -2.5703  -6.3069  -1.0247   5.0530   5.6225  -2.2818
+#>   -2.3374 -10.2969   1.9019   5.3632   6.3660  -3.1475
+#>   -4.6428  -5.8613  -0.3048  -7.1832  -3.3850   0.3479
+#>    7.8183  -2.7134  -1.4327  -1.9916  -8.1875   3.9433
+#>   -7.5695   9.4886   3.9988  -5.2189  -2.7899  -1.1938
+#>   -0.2347  -6.1187  -8.4674  -2.1549   9.5906  -0.2897
+#>  -10.2787 -15.5042 -16.7421  10.1928 -11.7354  -1.7132
+#>  -16.1504   0.9005  -1.2476  12.9076   4.7648  -8.9070
+#>   -2.3000  -0.4876  -7.2084  -4.1374  10.0935  -4.1776
+#>   -7.0180   5.9344 -10.9723  -8.1919   0.6407  -3.8926
+#>   -0.5738   2.8627   1.9321   3.7696  -3.1981  -1.5959
 #> 
 #> (15,.,.) = 
-#>  Columns 1 to 8   3.1122   3.8611   4.8205  -1.6298   6.9329   9.8906   2.1740  -4.2024
-#>    8.5203   9.7147  -1.5809  -0.6421  -2.0104   2.4745 -14.1152  -2.4426
-#>    7.0298   6.1547  -9.4351  -0.5382 -19.4606 -12.2878  -1.7764  -3.0312
-#>    0.2865  -0.9511   5.3513   4.3796  -2.7623 -13.8982 -14.1224   0.6164
-#>    0.0973  -0.4816  -4.8435  -3.1812   0.4628  -2.6683  -0.5602  22.2579
-#>    0.4775   3.2235  -1.8869   0.2404  13.6895  -0.6795  15.2676   5.6317
-#>   -2.1784  -1.1375   4.5558   3.1385  -4.9571   3.7874  -5.9118  10.8517
-#>   -4.7388  -6.9078 -10.4307  -2.2684   6.6817  -1.7626 -18.4548 -10.9733
-#>    6.7687  13.5322  -5.5984 -12.1420  -1.2412   5.6165 -13.5941  -2.0271
-#>    2.0455   9.0392   1.6625   6.0172   3.8340   7.0512   1.2465  -1.9772
-#>   -2.1886  -6.4816   4.6749   5.9960 -10.1334  -6.3482  -2.7780  -6.9412
-#>    5.0527   4.9251   2.5347   7.6254  -1.3561   3.7016  -3.2117 -11.3581
-#>    7.7865   4.6978  -7.2020  -4.0605   8.9861  15.5755 -12.3648  11.8017
-#>    8.7464 -11.9171  12.6224   5.7931  -9.1825   1.7586   8.2715 -19.4382
-#>   -3.1150  -6.2088   0.4635   8.2511   8.0410   4.9534   2.9653  17.6959
-#>    0.9174  -0.7699  -4.1018   7.9598   0.7056  11.8100  10.3111 -20.1756
-#>   -5.3045  -6.8303  -2.2864  -6.5579  11.0886  22.3864   7.9265  -1.5356
-#>    4.6490  -1.5498   1.7408  14.1294   2.5618  18.0424  -1.0681 -17.9160
-#>    3.5954   9.3813  17.2237  18.8808  -6.6940   8.5774  -5.3845 -30.3545
-#>    5.2688  -3.6840  -5.1910  -5.7160 -11.5041   4.3481   3.6597   7.6335
-#>   -1.1897   0.6484   3.4448   4.7273  -4.5146  -2.8914   7.4103  30.1499
-#>   -4.3721   1.5688   2.7589   0.5297   1.1048   0.7333  -2.0578   0.0610
-#>    5.4195   2.0839  -1.5410 -10.0713 -13.5664   1.3268  -0.3629   4.4647
-#>    9.1930   9.5706  -7.9102  -7.7028 -10.7286 -22.2398 -20.2083 -15.1622
-#>   -5.6316   3.1994   6.1807   0.7756   1.6676 -14.9653  -9.9982  -4.2857
-#>   -5.1895   3.7136 -17.8547   3.9746   1.8439  -4.8682  12.8033  -7.1466
-#>    2.1943   9.0551  -0.9147  -4.1940 -11.3143 -11.8204  12.1720  16.6048
-#>    2.7969  -7.6488  -2.1236  -4.0005 -30.9301 -11.7749  10.6926  -4.1824
-#>   -1.0040   2.9021  -5.1487 -15.2700   2.6248  -2.1149   6.4104   3.2766
-#>   -3.7081  -1.6625   4.2692  -1.5709  11.0265  10.0323  11.2276  -8.9382
-#>    2.6578   5.1897   0.1735  -5.7386   5.8531 -11.7441 -14.0351  17.3496
-#>   -2.1352   6.3114  10.2460  -8.7306 -20.9668  -3.4366  25.2204  -8.0201
-#>    5.8776  15.5592  22.1419  -8.1323   9.0823 -11.0189  -2.7949  18.9450
-#> 
-#> Columns 9 to 16  -2.4820   8.6576   5.9839  -1.8184  -9.2920   5.6428  13.7748   8.7812
-#>   21.4926   4.5923 -11.8291   0.6987  11.9017   6.1472   3.6525   2.2308
-#>   -4.7901  22.6197  -4.5075 -11.4693  -5.4537   5.0686   5.0970   9.4705
-#>    7.0716   0.2369  -2.7498   1.5082  -6.0825 -12.4953  -6.6059  -1.0555
-#>   -2.6514  12.4401  14.5083   4.0852   4.0618   4.1491  26.6751   3.6393
-#>   -3.9949  11.5821   4.5012   7.7274  12.3697   3.9179  -5.3370   3.5276
-#>   12.0472 -18.0762   7.6975   5.1272   3.4334  -6.3926  -1.2136  -6.6883
-#>    4.8552 -11.6885  -1.4379  12.8208   0.1779  -9.7202  -6.0333   5.4248
-#>    5.1050   5.3917  -5.1519   1.0647  -4.9101 -10.4036   2.9865   5.9236
-#>   15.4829  12.2093  -8.3707  -0.1868  17.1047  -2.8015   6.6041  -1.1927
-#>  -10.6184   4.6587  -6.7819  -5.7780   4.3709  -7.5517   1.9509   2.6140
-#>   17.4269   5.2521  -7.8666   3.7005   0.9033  -2.5157  -3.4188   5.0107
-#>   -0.0271  20.8237  -0.5649  -6.5968   5.7183   8.3035  -5.4941  -7.4708
-#>    2.0521   5.0497  -8.4153   2.5045  11.8565  -9.7868  -6.0838  -5.0893
-#>    2.5230  -5.0851   4.8680  -1.1900   0.4753   0.8048   5.0609 -11.2180
-#>    6.2523   2.1532  -4.1158  -8.2963   5.3402  -1.7555   5.6635  -5.3381
-#>    8.4795  10.2285 -10.0182  -3.3915  14.9372   7.8887   9.9017  -7.2846
-#>    6.7871   2.0844  -7.8338   3.5193  14.5492   2.6393   7.4061 -12.4265
-#>   -8.6737   9.2819  15.6804   4.1469   1.2852 -19.8180  -7.3381  10.9151
-#>  -10.3532   8.9571  -0.5642  -3.9206   2.9838   9.9372 -15.1253   1.8576
-#>    7.4981  -7.1064   3.3215   9.7494   2.7893   3.1182  -2.1001  25.5508
-#>   -4.8811  -3.8547   0.6671  -8.4423 -12.9163 -11.9388  -7.4008   9.0730
-#>   -0.4422  -5.3445 -14.6563   5.8467   6.8341  -4.3484  21.7684  -0.6717
-#>  -18.2219  -7.6869  16.1875  -7.0942 -22.3077  -7.1760 -10.6216  -3.2437
-#>    4.1118 -20.1772  -7.1432  -6.9128 -18.2796   2.3830   0.2257  -3.6572
-#>   -3.8686  11.1459   2.9378   0.2278 -14.0622   9.4110 -20.2442   1.3321
-#>   -6.3837  -4.3450   9.4582   7.1413  -9.9865  -0.6224  -0.6129  -0.0896
-#>  -13.8273   0.5218  13.4575 -12.8272  -4.1247  -1.7526   7.2794   0.0497
-#>  -16.4962  -2.7740  19.6613   0.7536 -15.6204  -9.4792  17.8453  -7.5782
-#>  -14.1994  -2.2635  12.2250  11.7569 -18.9640  -5.6786   2.5413   4.4004
-#>   -6.9980  -4.5628   2.6912  -0.7503  -9.6685  -0.6845  15.9196  -2.9514
-#>    3.3739  12.9420  -3.2897 -14.8992  -4.0071  17.6254 -12.3500  10.5809
-#>    4.4941  -1.2783   2.5828  -7.5568   4.0072   6.3986   7.0076  -7.2597
-#> 
-#> Columns 17 to 24   2.7415 -14.5243   7.7590 -11.1564  -9.3308  -7.2492  -1.2720   2.2529
-#>  -11.5159  10.8135 -16.2901   4.3608   8.0637  -9.7066  -8.7165   1.8619
-#>    0.0860  12.3243 -31.5053   4.8606   2.1148   0.3605  -5.5557  -0.4323
-#>  -11.2532  -3.9996   0.5845   3.8652  22.2795  -1.9149   6.9725   8.2957
-#>   -2.9669  11.4869  -0.4343  -9.9054  -2.8446  -8.0256   1.0610   9.3770
-#>   10.0457  -3.4253  -4.7768  -7.3880  -3.9299   8.4463  -1.8519   6.3592
-#>   -1.4612   4.7071  -2.8134   3.0198  10.9095   1.7908  -3.9836   5.6276
-#>    6.5276   4.2167   5.7684  -0.7467   0.8570  -4.0737 -14.7975   9.9143
-#>   -5.9155  16.2076  -8.2101   8.2188  -2.8018   7.0341   2.4280   7.5394
-#>    1.4553 -11.1364 -17.4621  10.4478  -7.7759  12.9649   5.3697  -5.1533
-#>   -4.2117   7.7752 -15.3058  13.0305  -7.9705   0.6638 -10.2953  23.3320
-#>   -5.2965   1.2251  -1.9521 -11.8184   1.4711   0.9470   0.6935  -0.8830
-#>   -4.1606   4.4501 -20.9371  14.0476  11.0796   3.0351   2.3615  -5.1264
-#>    2.8865 -12.0706  -5.3796  -0.6273 -10.8796   4.0407 -12.8834  -2.8364
-#>    2.2295  -2.5573   2.4217   7.7431  -0.0052   5.6104   0.5390   5.1543
-#>  -10.9820  -3.3330   7.8870  20.0087  -1.9390  15.2596   6.6805   3.3873
-#>   -3.5583   0.9338  -2.4335   5.2367  -8.3011  -2.0619 -16.8000   8.4627
-#>    5.1808   1.4008 -11.4610  12.9522  -1.9121  -5.4007  -1.5541   4.0131
-#>   -5.7829  10.3096   6.7852  -5.5373 -18.5659  10.4546  -6.8009  -3.4478
-#>   -5.0784  10.0420 -11.7954   2.7051 -16.1865   3.7774 -13.4981 -10.3071
-#>    3.2760   5.2329  -1.7290  -3.1013   2.8472  -4.8237   1.2745  19.0922
-#>    6.6286   6.4568   0.0930  18.2459 -13.5861  -7.7898  -8.3763  -5.8059
-#>   -9.0000 -10.3056 -13.3352  -3.0509  -3.2558   3.6195  -7.3976  10.1592
-#>    1.1644  -4.5979  -6.7606  -2.0620   1.7103 -17.1890  -7.9710 -19.0924
-#>    9.3919  16.9688  15.8126   0.2450   7.8224   1.2130   2.5086  -3.5090
-#>   -1.9533   1.1459   0.4736  10.3822   1.3048  -6.4972  13.3227 -29.6419
-#>   -7.0148   1.7177   0.0313   6.3580   9.1552 -11.2252   7.7353   7.3579
-#>   -4.7833  -0.1028   2.7229  -5.2943 -12.8086  18.4304  -4.4705  -3.4158
-#>   -9.2536   4.5833   5.5145  -0.7567  23.3196 -10.2063  17.2270   3.5223
-#>  -15.1389   8.7041  12.2054 -12.8302  -0.7823  13.7584   2.4872  -0.4881
-#>   14.4617   3.9974  -7.9709  21.8895  -4.8486 -19.2757   6.6048   5.4127
-#>    6.0082 -18.5477   2.9759 -20.5861   8.7631  10.0609   4.0358 -11.9522
-#>   14.4334 -10.0376   8.1364 -16.9371  15.9354 -13.9389   7.9507  10.4880
-#> 
-#> Columns 25 to 32   4.4647  -7.0875   2.3327   1.8307   7.3989   0.4803  -3.6664  -6.0721
-#>    5.8742  -8.4197   6.2609  -6.9642  -2.8786   2.0274  -7.7869  10.8764
-#>  -14.0883   8.3286  -1.4974  -4.1067  10.7286   3.1396   0.8001  -1.4272
-#>   -8.3975   1.1112 -14.3248  14.9886   0.6196   3.2767  -2.5909   4.2835
-#>  -13.5545   9.2322  -5.9542  -2.5594   1.4618  16.6292  -8.4196  12.4791
-#>   -0.7116  11.6965 -10.5454  -3.5316   4.3152   9.0918   5.8082 -13.3541
-#>  -11.4994   4.4063  -5.8573 -17.3401  -6.0474  18.5556  -1.1967   6.8481
-#>   12.0583  18.5907   9.8987  -8.1644   0.4287   2.6380   3.0048   0.5976
-#>   -7.0870 -14.3711   0.4314 -16.2165  -2.3792 -12.6777  -4.3406   6.0900
-#>   -5.8610  11.7489  -7.8149  -6.6605   9.2336   2.3260   7.7857  -4.9880
-#>    0.3623  -1.4200  13.0666  -2.4665  -4.7151  -3.9452  12.5038   3.2693
-#>   13.9317  -6.6125  -2.8563   9.5944   0.0860   4.3587  14.8142  -6.9758
-#>  -13.4904  -2.4979   6.6735 -11.6457   0.9460  -1.9097 -13.3650   8.9596
-#>    1.9304  -4.0284   0.7302  14.4181  -7.0430 -10.5987   7.4161   6.4617
-#>   -0.0161  -4.2945   2.9026   1.7768   3.9085  -6.9946  -4.5485  -2.9448
-#>    6.8562 -10.8369  -5.6905 -11.0385   0.1285  -4.7716   2.3382  -0.4609
-#>   -6.1776  19.8724  18.4406  -8.7792 -20.6198   5.8089   7.3201   0.5096
-#>    1.2028   3.0662  -5.1415  -0.9428   4.9533  -6.4426   5.5798   0.8762
-#>    4.4632 -17.6349 -13.0296  -4.2663 -16.4941  -6.0344  -5.5801   8.4649
-#>   -6.9736  13.5637   2.4916  -1.7900  11.5020   8.7405   2.0377  -4.7403
-#>   -6.1378  -4.8473  -9.7515   2.3663  -1.1794  -0.7174  -5.0061   2.4843
-#>   -3.4997   3.5160   9.5409  -3.1535 -11.6830  -3.5705   2.6792   1.4836
-#>  -11.8763   4.6721  -1.8193   3.2386  11.2250   7.8626  -1.8528   0.4861
-#>   -8.0426 -14.7109   4.3708   2.2663  13.2169   6.3566  -9.9510   6.0865
-#>   11.0725  -3.9107   0.6830  -4.5783  -1.6197  -2.3960  10.7319 -13.7979
-#>    7.3039   0.8473  -9.9158   6.4912   4.9668  -9.9030  -4.0687  -9.6390
-#>    0.3819   1.3212  18.2018   8.2165   6.2052   4.6291   5.0011   0.7982
-#>    6.8205  -2.5124   9.6381   9.0118   2.8916  -1.5941   9.1080  -7.1449
-#>  -13.4964  -8.6642  14.5336  15.9082  -0.8775 -10.1957 -18.6116   3.5536
-#>    0.9977   4.0760  11.8884   2.6569   0.1379  -7.2319  -0.1400 -11.4456
-#>   -9.2941   1.0872   5.3523  -7.4826   2.3029  -2.4002  -1.3751  -1.8256
-#>   -3.2221   5.9747  -5.6958  20.7800  -4.3516   4.8361   0.0648   4.8459
-#>  -24.3830   2.8290   1.2183 -13.2337 -16.3716   6.1607   7.4295  -0.7610
-#> 
-#> Columns 33 to 40   1.8205  -1.9031  12.3617  -5.0926  -3.0377   7.9483  11.3565   5.3461
-#>   -0.4750   7.1601  -2.3730  -7.5397 -10.2509  -7.0522  -0.8622   7.4636
-#>   16.1951  -2.6339  10.4763   6.7128  -2.4033   1.5973   0.5560 -12.5422
-#>   -5.8209 -14.4871   6.3812 -20.7239   6.6207   4.5128  -6.8785  -0.2301
-#>   10.9282   8.5717   6.2680   2.4384   0.5621  -0.7995 -20.6212  -2.2964
-#>    9.4476 -14.1702   4.5617 -12.0078  -8.1397  -2.5639  -2.7928  11.8655
-#>  -12.6462   2.1810   1.2845   9.7156 -20.5358  -5.9804 -10.9644  -0.5939
-#>    3.5793   5.2351  -6.4354  -4.9213  -9.9919   7.4415  -3.4633  -9.8310
-#>  -12.8611   2.8465   0.1158   0.1859  -1.2493   9.6240   1.1403  -0.6776
-#>   -0.3449   7.4077  -0.7959  -7.9119  11.8331  -4.9754 -14.2622   6.4361
-#>    0.7595 -17.6125  -5.8273  -3.2121   8.0339   2.5602  12.3164  -3.8753
-#>   -1.0412  -5.4503  -3.5347 -13.2825   1.0322   6.5559  13.9156  15.9307
-#>   -9.2140   6.1835  -6.6971  -3.4164   2.1543 -10.5855  -3.5077   6.1390
-#>    4.4808 -10.5445   7.2821  -3.9587   9.7382  -2.9853   2.7431  -9.6809
-#>   -2.5871  -4.7222  -5.5093  -2.2494   0.6249   9.5892  -3.0757  10.6005
-#>    1.6629  -3.8746   5.5493   5.2350 -16.2316 -10.5616  -7.7546   3.6809
-#>   -0.4978  -3.4466  13.3552 -10.9518  -5.6727 -18.6570 -15.8404  -3.5805
-#>  -13.1766  -0.4777   0.2826  -0.6377  -9.3905  -3.6232  -9.2420   4.7829
-#>   -3.6613  -5.0635   2.4324  11.3758  -8.1947  11.6599   0.3691  -0.2076
-#>   12.5498   0.2379  11.0158  -4.5225  -6.0512  -6.8703  -1.2930  -6.1242
-#>    4.9730  -2.3177  -4.2980   5.2178  -5.1310   0.4080   2.9159  -8.6934
-#>  -11.7598   6.6139   2.1697  -0.6532  10.4587  21.6341  -3.2036  -3.5018
-#>   13.5214  -2.9392  21.8901   4.7567   0.3415  -9.1058  12.1258   9.6166
-#>   -8.3603   1.6233  -2.6932   7.0846  15.2218  17.6835  15.8797  10.3380
-#>    2.6707   0.4024  -3.1447   2.7802   9.8796  16.6959  23.8264   7.0454
-#>    1.6237  -1.3032  -4.3692   1.1793   9.3805  -1.8181  -2.0411  -1.3207
-#>   -0.9425   6.4807  -5.7085   6.3386  10.6126   2.3979  -8.2275   1.4569
-#>   11.1681  -1.0505   0.3317   1.7951  -0.9978   7.5406  -8.7477  -3.5775
-#>   -5.3785   7.9169  11.0730 -17.4246  -5.1151   6.9251 -13.2934  -0.3874
-#>    5.2756  -3.2304   1.5981 -14.7328  11.5731   7.4060   9.7360  -6.7432
-#>  -15.2574   4.5115   7.5608  14.3726  -2.1682 -10.3855  -5.8989   9.8928
-#>   -0.3856  -8.3256   2.6750   5.4766   2.3158   6.9830  10.7369  -5.5621
-#>    0.8368  -4.4529  -2.9222 -10.4655   7.5028   3.0049 -13.9050   6.4207
-#> 
-#> Columns 41 to 48  10.6267   2.0566  -0.1899  -4.2216  10.1488   4.8102  -8.9333 -12.9299
-#>    2.8428  -1.9617  12.4254   7.3913   4.2177   2.8855  -6.2039   1.6922
-#>   -4.9332  -2.4444  -0.4150   4.8136   8.5222  -3.2814  -2.5849  -4.5969
-#>    2.6271 -12.1750  -6.9400  -3.1252   9.6071 -13.4722  10.1572   2.5616
-#>   -5.8844  -6.8112  -7.7014  -8.0022   1.7783   4.1806  -2.3968 -17.2372
-#>   -6.7355   1.5915  -9.2817  -0.8666   6.2460  14.7628  11.8597 -12.6760
-#>   17.6917   5.5680   1.8349  -1.1989  -4.6840   7.0459  -3.3207  10.4828
-#>    5.6398  -6.0632   1.6924   0.6557  -4.7924  10.7048  -0.6390  -3.2781
-#>   15.3371   2.9338   8.0760  13.9686   1.7372  -3.0089  -2.3561   0.3753
-#>  -11.5604  -2.6880 -11.4365   2.3654   7.7114   2.9627  13.5443 -12.7850
-#>    9.6262   9.4812  -0.5602  -2.8509   0.3019  -4.0123  16.3873  13.6085
-#>   20.5345  -8.0568 -11.0704  -9.2712 -10.8319   0.2600   9.1865  -6.3293
-#>  -11.1006 -12.9762   4.1232  -8.3061  -6.4398  -0.4549 -17.1532   6.3581
-#>   -7.1097   4.8705  16.1682  -2.3171   4.2617 -22.1824   5.9422  -4.8015
-#>  -10.4708  -1.7752  -7.4766 -12.3306   1.3784  -6.3603   3.2971  -7.3621
-#>   -1.7025 -12.1037   9.3636   7.3521   4.8636   7.5377   2.7529  10.1537
-#>   11.0573   9.7915   7.8233  -2.0144  -3.4713  -2.7266  16.8523  -8.3005
-#>    2.3746   1.5750   5.2558   7.2855  -5.3842  -2.6478   9.7472   3.5639
-#>    5.4006  -0.8139  -6.0416   7.4403   6.9999 -15.6390  -5.8958  10.2864
-#>    5.7947   3.8600  -1.4406 -14.5901  -4.3508  -9.8071 -14.7991   1.8049
-#>   -5.0717 -16.6773   7.0603  -2.3248  -4.0577  -4.1785   0.2248   0.3476
-#>    7.0438  -5.2534  -8.0476  -2.4107 -17.2631  -6.7523   1.3226  -2.7569
-#>   -1.7567  -9.6459  -2.5314  -1.1619  10.4042 -12.3261  -9.5224  -4.6514
-#>   12.4365   2.8640  10.7389  -0.2652  -2.4801 -11.0003 -14.6895  -1.4004
-#>    4.1451   0.1326 -14.8814 -10.9389  14.0706  -7.5585   4.3588  -4.7046
-#>  -12.1984   7.8382  -6.0839   3.8610  -9.9043   7.9125 -14.7297   2.9360
-#>    5.7610  14.6969   7.7319   7.3925  -3.0844   4.3962   4.0488  14.4855
-#>   -0.1482   8.7862   1.5423  -5.4913  -0.2252  -7.1675  13.4628 -12.1098
-#>  -12.1577  -7.5669  12.8937   7.8985  -0.8192  -7.9449  -0.0794  -7.7434
-#>   -9.4886  -0.4424  -1.1893 -10.5959  -9.3650  -1.0334   1.2311 -10.4145
-#>  -16.8748  -7.4350  -2.3928  -9.6226   3.4923   3.5035  -0.6490   1.1591
-#>    0.8123   9.7618  -9.3335  -0.5018 -11.4629 -11.9040  -5.3682   7.9294
-#>    2.4693   5.7884  11.3076 -19.0914 -11.9091  -3.3234  10.0314  -6.7216
-#> 
-#> Columns 49 to 54   0.4406   1.6129  11.1615  -6.4500  -0.8354   0.9156
-#>   -8.4724   3.7934  -0.6207   7.8873   1.1957   0.5047
-#>    1.6362 -12.6187   8.1702   9.1539  -0.2782   4.0106
-#>    1.5858  13.5181   3.5678  -1.2366   7.3036  -0.1232
-#>    9.1165  15.2435  -6.9428   1.9598  -6.4923   3.1891
-#>    9.3808  -9.5047  -2.5205   5.0731  10.0928  -2.0794
-#>  -14.7524  16.4280   6.8513   0.7544  -8.9306   1.7082
-#>  -11.2358  15.5635   3.2996   1.5688   1.8301  -1.2618
-#>   -1.0093   0.3102  -0.9837   1.1016  -3.9838  -0.2361
-#>   12.1988  -5.1664  -0.1060   0.1254   0.0174   0.3351
-#>   12.4086  -1.4730  -5.9202  -0.6916  -7.4567  -2.5951
-#>    5.1866  12.7100  -2.6120  -5.1527   2.4302  -4.9550
-#>   23.0999  -1.2934  -6.9460   1.9001   1.8792   2.4004
-#>  -13.5533 -15.6596  -1.9504  10.2901   6.8115  -5.0187
-#>    0.1487  21.4774  -5.9941  -1.9582  -7.3697  -3.1072
-#>    0.5098   7.4638   3.3007   4.9267   1.0696   2.9138
-#>  -13.9605 -15.3375   2.3721   4.2856   7.6262   0.1114
-#>   -0.0946  -1.9983   7.4403  -1.6219  -3.5262   0.3071
-#>   -3.5941   6.7896   1.7399   4.9699  -0.1605  -0.2439
-#>  -10.2240   2.4551   6.6242 -12.3264  -2.7923  -6.0393
-#>    4.6394  -4.9876  -2.6554   5.6472   1.9260  -3.1263
-#>  -12.1955   8.9774  -6.0791  -7.6644   2.5696  -1.0618
-#>    3.4853  -8.6396   4.8650   4.8160  -5.9536   5.4277
-#>    5.7871  -7.7097  -5.2413  -6.2673  -2.1149  -1.7612
-#>   -6.8536   1.3883  -7.2248  -8.3984   0.0079  -0.3945
-#>    4.2400  -1.7337   3.1773  -7.3190   2.1686  -9.1689
-#>   19.0523  14.2516   7.0283  -3.9777 -12.5752  -2.5832
-#>  -11.7205  -0.0090  11.9184   2.2404   0.3422  -3.7061
-#>    4.4009  -0.8376   5.5358   9.0462   0.6890   3.6592
-#>  -13.7356   4.7588   4.9515  -8.4374   4.7421   1.8016
-#>    2.9980 -13.3701  -1.4301   3.5365  -8.7095   5.2190
-#>  -13.4774  -7.1882  -0.0140  -5.5037   2.3036  -1.9311
-#>   12.4994  -2.9208   4.1533  -4.6218  -6.7373   2.3593
+#>  Columns 1 to 8  -1.5480  -2.3226 -12.6145 -11.4732  -7.8165  -9.4983  -3.5700 -15.6556
+#>    1.0601  -4.6836  -4.3608  -6.0285   3.4574   9.6195   0.6524  -7.0485
+#>    5.6251   7.5929  -2.7877  -1.8816   4.9794   2.9114   4.6998  -0.4624
+#>    2.7641   0.4292  -3.0854 -14.3098 -10.5390  -5.3486 -10.4439  -9.0938
+#>    1.1096  -4.3114  -4.3827 -10.5254 -17.6882  18.4926  -5.6819  -1.4153
+#>    2.2133   0.2157  -5.5338 -17.4456  -8.1320   8.4154  -8.5801  -5.5160
+#>   -0.5291  10.5321  -1.4005   2.4837 -12.5371   2.5787 -11.4416  -5.5872
+#>    4.7381  -4.2311  -2.5796  -1.1170  12.5779  16.1060  -1.3978  -4.3200
+#>    3.4056   0.0756  -6.4885   0.0520  -4.1468   8.8738   8.6866  -9.8233
+#>    3.7480   1.8686   1.9059   9.0625 -17.1037  -3.2422  -2.5089  13.7175
+#>    2.1866   8.4718   0.0546  -9.8847 -24.0309  -4.8157  10.6850   5.7006
+#>    1.4033  -0.3059   8.7609   5.4726 -13.6462   7.7370   1.4408   8.9779
+#>    4.6412   6.3536  -6.1421   7.9591  -9.0173   9.5937   3.8149   4.3593
+#>    5.3953  -6.3292  -0.2669   5.3851  -0.7025  -3.3660  -0.1432  -2.4160
+#>   -4.1293  -5.5192   3.2517  -0.5529   5.5178 -15.3576  -7.3132  -4.2888
+#>    0.6848   4.4763   7.5650 -10.9784   0.2439  14.8388   1.9178  -2.3989
+#>    0.5960  -0.7973   4.7239  -5.1132  -6.0069 -13.6999  -7.3106   3.4710
+#>    4.1932   4.8065   4.9770 -17.2746   6.3694 -15.3226  10.8627  -3.2967
+#>    3.4117  -7.1715  -7.9748  -9.1588  21.0270   8.4184   3.5228  -4.5519
+#>   -2.3926   3.0979  -6.8742   2.9889   2.0614  -9.2233  15.1879 -17.0244
+#>    1.9140   0.7177   5.1660   6.4429  -2.7839  10.8547 -12.4839  10.7146
+#>   -5.0641   2.4166   6.4848  13.2724   3.0504   4.3973  -6.4006   4.7745
+#>    1.2893   0.9322  10.8218  -1.7183  -4.4995 -19.4674  16.5020   1.0697
+#>   -9.3303  -0.8401   8.5638   0.9223   2.4065 -12.5260 -16.0471  10.8620
+#>   -2.2359   3.5106  -1.2181  10.5718  -4.7001   2.8325   3.7966   8.1014
+#>   -3.8732  -1.1684   1.4643   7.9242  -3.8844   0.7609  -6.6007   0.2390
+#>   -2.1598  -7.4457   1.5657  -0.6186  11.0294  -4.3382  -3.4645   6.8675
+#>   -0.4584  -8.1934   4.4102  -5.8540   3.4823   8.6603  -5.4523  -1.6470
+#>    5.6762  -6.5943  -0.6382   2.8509   1.3007   3.9492  -8.7240 -15.6477
+#>   -3.0598  -1.1246  -0.3795  -7.7945  -6.5833  -6.1003  -2.1444 -12.0461
+#>   -2.1948  -4.9020   9.9337  -9.4449   4.7021  -4.3830  -9.7941  -3.8972
+#>   -2.7172  -4.0026  -6.8488  -9.6995   3.0726   9.4563  -1.8003 -14.0530
+#>    2.2239  -0.5233  -4.4547  -5.8050  -1.2404  -7.0466   1.7092   2.6113
+#> 
+#> Columns 9 to 16  10.9001  -6.9516  -9.9832  -7.7073 -21.7719  -3.9437 -15.9477 -18.6319
+#>   -3.7237  11.6001   8.5005   4.1985  11.0552   9.6969   3.6887   3.6450
+#>    4.0110   4.2526  -0.8369   9.4248   8.3532  15.4726  -3.0963   5.1259
+#>  -18.3138   6.0425   8.1423  -0.3699   4.1318   1.2594  -7.6515  -8.0281
+#>   -0.9162 -13.8312   2.7840  -5.4233   3.1747  -4.6699  -3.4324  -5.5202
+#>   11.1367   2.8137 -17.3162  -1.3527   1.3507  13.0210 -13.8186  26.8187
+#>    6.3357  -7.7561  -3.6762  15.1476  -5.7097  21.4325  -0.1750   7.5073
+#>  -11.2215   7.4079   6.6160  -9.4466  16.0628  -2.4552   2.4160  -5.8624
+#>   -7.4508   5.0155  -0.1214 -12.1375  16.9189  -4.7154   1.0027  -8.0713
+#>   23.4865  -6.0109  -4.5907  -5.7444 -18.8072  -2.4224  11.1117   3.5363
+#>   -1.1102  -2.5905   2.6187  10.5455   1.4857   6.4054  -9.6781 -13.4437
+#>   24.6610  -4.5083  10.4631 -14.3266   4.6868  -8.1047   8.6586   1.1920
+#>    6.5881 -13.6663  -0.9451   9.0214  -2.9227   7.7702   6.2728   5.1214
+#>   -1.0705  -7.4547   0.3573 -12.3091   9.6111   1.4313  -8.6579   9.8779
+#>   -5.1110  -9.0238   4.4972  -6.8154   4.4076 -10.9123   2.5202   0.8639
+#>   -0.5783  -9.5890   7.2320  -1.5034   2.2434   6.4146  19.6122  -2.1466
+#>   -3.7124  -6.7418  -6.3208  -2.0437   2.6679   2.0410   0.6387  -3.7876
+#>   -5.2312   6.5131   0.8760  14.7253  -8.0860  -2.4185  -2.6448   7.6081
+#>  -16.2556  -1.7937  -1.8222  -0.4736  10.3241  -0.3719  28.7630   7.5050
+#>    1.0380  -5.1460  -9.3551  -4.7798  -6.7171   0.1936   4.7133  -2.9299
+#>   10.8932   1.6696  -2.8752 -12.7621  -6.5751   9.2449  -8.1203  14.1162
+#>   -3.2963   4.3001  -8.4615   1.6528 -12.8636   9.9290   0.9848   5.5399
+#>   -9.3516   7.4380 -11.8734  -4.0161   3.8160  -6.4307   3.9864   6.7317
+#>   18.6668  15.8147  -5.8428   0.3814 -13.7620  -4.5685 -14.6382 -12.2606
+#>   -7.4702   5.5498   7.4251  -8.4443 -13.8517   6.9375  -5.9914  10.3369
+#>   -2.6617  11.2949   5.4276 -13.5057 -26.9067 -18.3660  -6.4792 -10.6989
+#>   -0.4519 -17.8086  -9.4939  -6.1366   1.4040   1.3275  16.4785  -2.9151
+#>   -3.5705   1.9239  10.5705  -4.0311   3.5690  11.8709  -3.3305 -13.0298
+#>    8.4804  -6.7775   8.2662  10.1708  -4.1508  -8.1485   3.4573 -10.4160
+#>    2.7779  -5.6409  -3.2864  -7.3414   7.5454 -14.8958 -20.6072   7.1933
+#>    7.0204   8.7989  -7.2620 -27.7442  -0.0752 -15.0155  -2.0379  -4.6041
+#>   -6.8519  -8.9223  -5.1441 -11.2840  15.1385  -5.0153  -5.8679  11.5172
+#>   -9.8625  -7.4851 -19.9489  14.0669   2.2448  21.1529   1.6980   5.5803
+#> 
+#> Columns 17 to 24   4.2597 -13.7939  -7.7846 -17.2603   1.7428  -2.4263  -4.0600   6.0426
+#>   21.4571  -0.6034   3.9594   2.5508   8.8771   5.5903 -14.4536  -8.9530
+#>    4.2347   3.8858  -0.6975 -11.7504 -11.3260  -3.9687   3.8406   0.3280
+#>  -16.9006   4.0892  -0.0560 -14.2800 -14.8899   4.8978 -13.6346  -0.7563
+#>  -12.5520  -6.8838  -5.3907  -6.0314   0.0258 -10.2345  -1.7311   6.0769
+#>    5.3802   8.6944   2.4642   1.1627   7.6099 -17.2395  -3.0697  17.0651
+#>   -2.6690   4.9361  -5.9838   0.4278 -14.3752  -7.1909  -0.6866   7.6025
+#>   -0.3400   1.1488  -0.8959 -10.7551  -2.9267 -10.6040  -7.8760  -0.2190
+#>  -11.8994  -0.6909  -9.4784  -2.7070   5.3315 -14.1667  -3.2866   1.4254
+#>   18.1085  15.4950  14.5003   4.0541   0.4752  -2.8371   8.4423  -4.6955
+#>    3.7797   2.6540 -12.3780 -24.3250  -4.5251   4.2954   1.7625  16.6302
+#>    6.7297  13.7620   7.4653   2.9460   4.2122 -12.6542  10.9362  23.1284
+#>   -2.6228   7.5622  -1.1427  -1.6263  10.7495   0.5432  -3.8417   5.2567
+#>  -12.0876   3.5873  -9.5721  -4.2999  -4.6252  -7.8572   4.9122 -15.0571
+#>   -3.8800   8.3790   5.1583  10.1358   4.2538  19.4156   2.0299 -23.0303
+#>   -4.4879  -6.4741   2.1706  12.3167   4.3516  -1.9165  -1.7489  13.6663
+#>    3.6242   0.2784 -10.3865   1.7086  -4.4610   1.3640  11.7412   5.1147
+#>   -4.5507   6.2862   6.3273  -3.3545  -0.6348   3.8607  -5.4115  -6.5705
+#>   10.1680 -18.2731   3.8560  -9.3279   4.6786  16.4668   2.7389  -9.3028
+#>  -11.7139  -6.4300  -7.7580   6.8943   9.9658  -7.6174 -10.6152   2.1464
+#>   -0.9245  16.6915 -18.8516  -9.7559  -7.6859   5.0988   7.4145   9.9781
+#>  -15.0904  13.0561   3.9313   3.1098  -0.3813   1.2831   6.7172  -1.5725
+#>   -4.1603  -9.2554  -6.3740  -2.8171  -4.5400  19.3786 -13.3872   0.1061
+#>  -15.0199   7.9187   2.8637   1.0640  -9.4443  -0.7164  13.8786  -4.9321
+#>    6.7410  11.0764   5.8694  10.5676  -1.4785 -10.4956  -2.1647  -2.4979
+#>   -4.8420   0.3223  -1.8641   2.2764   3.5282   3.1243  -3.8928  -0.7861
+#>  -15.6744  -4.9896   5.6997  -6.9834   6.3976   2.2967  -4.4781 -14.0095
+#>   -0.9168 -13.6836  -5.3883   0.2359  -7.2814  -8.4654 -15.1297   7.9175
+#>   -5.6942  -3.7938   6.3266   6.8379  -8.2443  -3.4863   5.9245  -3.7879
+#>    6.7252 -18.2728  -7.5994   0.7930   5.2211 -10.6935   6.9960  13.5182
+#>    7.7631  -4.5409  16.6383   1.1098  -2.2515   4.2739  -6.3872  20.7529
+#>   11.5400  -3.8880  -4.8843  20.8790  -1.9235   2.3904  -0.5893 -18.2680
+#>   -3.7502  -8.8683  -7.8292  -0.6399   0.3981  -3.2471  -3.3288   0.3509
+#> 
+#> Columns 25 to 32  -1.0282   0.4582 -15.3311  -8.6707 -13.5440   3.9472   4.6046   8.3668
+#>    0.1522   2.3482  -2.8826   2.4415   9.4128  16.4714  27.9099  -0.7864
+#>    9.4208  -1.2501 -13.7185   0.2158  -0.1669   7.8426  -4.5836   5.8697
+#>    6.2956  -5.5135 -10.9481   0.0356   7.6555   1.7165  10.4980  14.5851
+#>  -23.4205   5.2852 -10.4222  -6.3602  -8.3993  -9.9196  -7.4862  21.0609
+#>   -4.0632  -2.6121  -7.4521 -23.1105  -9.9574  -2.0262  -6.3906  10.4616
+#>    4.2014 -20.5212  -0.1920 -14.6375  -7.5854   0.4322   5.7235  -2.3992
+#>   10.9157  -1.1830 -12.9771   5.2316   0.9914   1.1060  15.9885   5.1279
+#>    9.6905   8.5292  -9.1180   3.6933  -2.1834   0.0974   5.8803   8.8771
+#>   -1.3064   9.2922   5.8561  -5.5635   4.3657  10.5714 -14.9753   5.2954
+#>   -1.3752  -3.3854   5.1899   3.2407 -15.6044   3.2689  12.1928 -10.5994
+#>    9.9412   2.0251   7.0985 -24.8129  -3.4721   0.9038   2.8694  -6.7046
+#>   15.2142 -16.6872   0.0271   3.6146  -2.9019   7.1541   0.9173   0.9618
+#>   -0.8263  -6.3602 -19.2822   7.8230   1.5329   0.3437  -8.3381  11.1148
+#>   -9.2765 -10.0126 -10.1303   9.1527  -2.0900 -10.1066   1.8279   7.8473
+#>   -5.3532  -0.8918  -4.5856 -13.6197  -3.7120 -10.6242  -6.2875  -1.0124
+#>   -0.1493  -7.5038  -5.5090   0.5891 -11.3242   4.7411 -19.3341  -7.6166
+#>   14.9721   0.1261 -10.9862  -2.3502  -0.5491  -3.7918  -2.1191  11.7330
+#>   -5.9561 -14.4365  -0.9097   2.7735  20.1569   8.6849  -6.5850 -14.8361
+#>   13.5322  -3.3381   1.5737  -7.6319 -17.5363 -16.1658  -7.4887  -1.9301
+#>    8.0783  -3.5145  11.6705  -8.6861  23.5980  -5.1621  -4.4400  -5.5569
+#>  -11.5186   9.8255  -1.8861   0.3933   9.2411  -1.6147  22.0441   2.4700
+#>   17.7148   6.9463 -11.0379   3.6581 -11.7796 -11.1479  -4.7689  -6.2846
+#>   -2.8002   2.3347 -10.8528   4.0733 -15.5047 -11.9804   3.0149  -2.1179
+#>   17.1219  10.0335  -5.3631   5.5909   6.5102  10.8323  14.5000  24.1880
+#>   27.5205  -2.0819  -9.4357  -2.8115   1.0335 -10.3944  -1.9690  10.5398
+#>    4.7011  -5.6275 -22.3002   4.7902 -10.4215   6.5076 -11.2655  -8.1794
+#>   -4.4113   5.3305   3.1989   1.8326   6.0643  -2.6075   1.4840  12.5007
+#>   -0.8936 -13.4811  27.4145  -5.7787   4.8533   1.6192 -10.1593   9.9651
+#>  -12.8642  10.6825   9.4401 -16.8412   1.1883  -5.4021 -12.4144   2.2342
+#>    0.1948   7.5835   4.8703   0.6781  -3.7487  -1.8040 -13.0540  17.2460
+#>    6.7588 -16.5598  -3.5190   9.9915 -16.6773  14.0928 -18.8078  18.4728
+#>    4.0239 -20.5449  -0.4540   7.6054  -8.4693  -3.3453 -11.0471  -3.3487
+#> 
+#> Columns 33 to 40 -10.0044   8.8446  -5.4153  -2.7522  -7.1615 -10.7928 -10.0474  -5.3207
+#>   13.5996  -2.4725  -2.3259   4.4499  11.4691 -15.1970  22.5800   6.1409
+#>   -1.9622  -3.2464  11.3140  30.6990  -1.4250   5.8495   1.4358   0.6305
+#>    0.2717 -16.3668  -6.9023  15.0843  -7.5832 -12.4764 -12.7843  -3.9944
+#>    2.4591  17.4164   5.2842   0.9430 -13.3163 -12.1380  -6.7616  -7.1223
+#>  -11.2559   9.0321   1.6306  14.1726  -7.6880  10.3294  -7.3962   8.1921
+#>   -7.0996 -10.0326   8.8506   9.7593  14.6049  -2.1472  -8.2472  -8.2946
+#>    7.4699 -16.4769  10.8192   1.6106  -2.4452  -6.5099   5.8837  -7.5283
+#>   -7.6865 -13.6181   5.6755  -2.6459  -5.2640  -6.7292   0.1028  -3.3698
+#>   -0.9626  -9.6243  17.1599 -18.2705  -2.6131  21.2705  21.4044  -3.4941
+#>   -5.9849   3.6610  13.6420  -0.9752  -2.7782  -0.4650 -13.0801 -21.8521
+#>   -5.0176  -8.3594  -3.3800  -0.0317  -3.1850   0.9126   6.6621  -7.9703
+#>   -7.4689   8.0285   1.1802   5.0644  -3.7316  14.8885   6.7005  -0.7299
+#>    9.2908  -0.1285   6.2898  -5.7670 -13.0555  -0.9993  -0.4255  -1.9627
+#>    7.2127   9.3229 -14.3159   2.2984  -4.7685 -19.1841   5.2852   4.9894
+#>   -4.1292   7.0298 -16.4050  14.0357   9.9598   6.2736  -5.6160   4.5912
+#>   -5.5833  -1.3794   5.9041  -2.4800  -0.4108 -10.4670   1.7214  -9.7661
+#>    9.6540  -3.0273  -5.6618  18.2986 -17.7096  -1.3812  -2.7293  -9.8936
+#>    3.2495  -7.0192   8.0887  -3.8346   1.4242   1.8322   3.7756   7.3413
+#>   -1.6291  -3.6846 -13.2125  -1.3214   6.7250  -1.9049  13.2982  -0.5769
+#>  -18.3707 -16.5870   4.2834   0.1563   9.2321   3.9839  -1.3116  -3.7813
+#>    1.7285  -0.3777   3.2121 -10.7871   2.7076 -12.3704  15.4249  10.5765
+#>  -10.9551  -0.9031 -10.2472   0.0455   3.5755 -10.1855   7.9231 -16.8731
+#>   -1.4941 -12.0283   8.3931  -3.3628  -0.6169 -14.8074  11.7382   3.7825
+#>    6.6997 -11.8422  -5.6994   1.4233 -10.1577  12.2058  10.3908   3.7953
+#>   -1.7648  -8.1783  -8.6478  -3.0441  -5.7994 -14.4964  -0.2980  17.7039
+#>   10.3389  -2.8890  -2.0172   8.9927  -8.2508   3.5769 -10.8726  -1.6017
+#>   -6.9268   9.0528 -10.6983   6.9228   4.5881  -6.5463   4.7896 -12.9723
+#>  -13.1831  -4.9624  -3.3134   2.8427 -17.2637  19.6480 -11.4416   9.9095
+#>   -1.4476  25.2428 -12.3396  -2.6832   7.4781  -9.5722  -8.0280   4.6569
+#>  -25.4606  -6.8473  -3.6761  -9.1680  10.9991  12.9768   5.6900 -11.3268
+#>    8.3571  12.0314  -9.0241   1.3981 -14.1026   1.8525  -4.5234   0.9328
+#>   13.5991  -1.3644   0.3049  20.6506   0.6737  -3.3638 -11.0013  -8.6448
+#> 
+#> Columns 41 to 48  17.0404   2.3907   8.1429  -2.9929   0.2796   1.2241  -9.8935 -11.5428
+#>   15.8002 -13.7289 -14.6515  -8.2285   4.4164   5.7381  -0.7154  -4.4316
+#>  -12.6148  -5.4966   0.5992   8.2136  13.4968  -6.4177  -3.5308  -8.0255
+#>   -4.0532   7.2112  -7.8960   3.7471   9.1279  -1.5569  -2.6160  -7.0760
+#>   15.1692   7.2893  -2.0220   5.0197  -4.2582  -1.2034  -4.7606  -2.1391
+#>    6.0665  -0.6873  -8.5629  -5.3530   0.0460   9.8676 -17.2723  -3.6176
+#>   -1.4594  -6.6986  -9.0179  -1.3675  -5.5377  14.7394   6.1867 -10.4132
+#>    7.5915   9.9949 -22.0853   4.0273  -8.9388  -0.0390   6.6909  -9.4642
+#>  -13.1068  -6.1876  -3.3908   8.5442  -1.1383   1.8270  -2.7536   3.6476
+#>   -1.0132  -9.6756   5.4367   6.5859 -12.0758  10.8135   6.3074  10.0008
+#>   10.4877   6.8412 -10.6524   5.2664  -6.0304  13.2375  14.4764  -8.9619
+#>  -14.3012  -3.0017   7.3253  11.1903   2.3234  -0.9351   3.1019   7.6333
+#>   -1.2189   5.0491  -8.8319  11.9791  -4.0255  -3.7533   8.2891   1.5896
+#>    3.2000 -17.0815  -2.4434  21.5138   5.3506   1.7681  -6.3518   3.1665
+#>   10.5045   4.0431   3.2837  -4.8931   0.2146 -18.7508   0.4636  11.0103
+#>    5.1954  -5.9280  10.3381  -1.5656   8.5219   8.1736   5.4672  -7.4633
+#>   -1.3095   6.5339   2.8158   8.4017  -0.4449  -2.2498   4.4189   4.1448
+#>  -11.5086   6.3872  -2.0754   2.7955  10.8612  -3.0908   9.1844   4.0402
+#>    0.1051   8.6908  -6.5760  -7.4659   1.4877  -2.4164   7.9129   1.2005
+#>    1.7190   9.7489   1.8291  -1.0358  -1.2305  -1.3863  11.3050  -1.2380
+#>   -1.7161   3.0360 -11.3789   4.4664   6.4385   3.0301 -14.2979  -5.4705
+#>    2.9439  17.2244  -4.9991  -3.0013  -0.6943 -13.1395   8.5538   4.6308
+#>   -9.8433  12.4677  -0.9130   1.2825  11.8843   4.1148  11.1564 -13.1213
+#>   -0.5187  -3.1152   8.9776  18.5254  -4.7923 -10.7471  -3.8255  13.0633
+#>   -0.5349  -9.1966 -10.8779 -12.6568  -3.5921   8.6177  -2.7212   0.2301
+#>   -8.2529   8.1280  11.6623   1.6168  12.7484  -6.3634 -17.0857   2.3086
+#>   10.1484  -0.6663   1.4103  -4.3130   2.1070   2.2680   1.4857 -12.4895
+#>  -10.5109  -3.9579   1.6315 -12.2888   5.8335   2.4822   1.2882  -4.2714
+#>   -0.1297  -3.2154   6.7121   3.0783  -5.2159  -7.5626  -4.8793   9.0379
+#>    6.5821   5.2232   7.6865   0.0642  -1.0540   9.7343 -22.4976   4.9506
+#>    7.2325  -3.1604   0.0343  -1.8626  -4.0984   3.8431  -3.3217 -12.1327
+#>   17.0317  -0.4552   5.2271 -13.4809   4.1506  -7.0243 -15.5145  -9.5525
+#>   12.2009  -0.8925  -1.5349   3.6809   0.1501  -2.6208 -10.8274  -5.6294
+#> 
+#> Columns 49 to 54   5.6963   0.2506  -3.0955   3.1177  -2.1769   3.9404
+#>   20.9385   7.7665   9.5588 -10.6093  -3.1436  -5.8950
+#>   -4.0900   4.9580   6.5708  -2.9171   6.7623   2.6823
+#>    6.6758  11.5514  -6.7011  -1.8267  -4.8228  -1.8819
+#>  -20.4042  14.7817   0.7493  -0.6215   1.7704  -0.8799
+#>    5.3647   7.5883  -8.9134   4.2283   0.4137   3.4573
+#>    3.6155   2.2602   1.3205   0.3387   6.4984 -11.8713
+#>   -0.9361   1.8455  -0.3475  15.1761   0.7070   3.1944
+#>   -0.4948  12.8991   6.2295   3.0410   0.5066   5.1099
+#>  -10.6016  -2.2949  -2.8080  -7.7852  -0.8843  -1.0471
+#>    2.8862  -5.6388   6.6788   6.9915  -3.7869  -0.1016
+#>    4.5676  -0.0893  -9.7924  -4.9659   3.6476   6.3014
+#>  -10.6180   4.4348  -4.9740   2.8333  -2.2137  -0.9116
+#>    3.0230  -1.5213   4.1895   1.4238  -7.1200  -6.4478
+#>    1.0273  -5.2753  -1.0815  -1.6767   4.4602  -8.7587
+#>   -8.7541   4.8772  -1.2043  -2.4257   9.8312   0.6178
+#>  -11.3903 -10.3645 -11.6155  -1.1508  -3.5049  -5.7102
+#>   -1.5347   6.3216   9.7323  -5.6061   0.3648  -1.7503
+#>   -5.4678   2.9957 -11.1503  -1.4187  -1.8333  -0.3659
+#>   -5.2660  -7.7109   0.7309   4.6312   6.5262   3.0843
+#>    6.5456 -14.1992  -4.3068  -0.1588  11.3763  -2.4150
+#>   -1.8872  -6.4028   7.5852   5.2557   1.2525   7.5619
+#>   -8.3606  -6.9282  -5.4472  -3.9413   2.6334  -0.0307
+#>    8.5096  -9.3000   0.5980   0.5252   3.0727  -5.8492
+#>    5.6290   2.9428   5.3831  -7.0399  -0.9795   0.8958
+#>   -6.1621  -4.6670  -7.7470  -8.0638  -3.9178  -0.0234
+#>   -5.3416   0.9509 -13.4397  10.7798   6.4156  -1.1138
+#>    6.0930   9.6360  -1.9067   7.3904   2.2579   2.0919
+#>   -0.7342   6.0397   1.0002  -0.3908  -0.7458   1.4206
+#>   -2.8761  -3.2436   5.7291   0.2252  -2.9766   5.8760
+#>  -11.7262   4.5826 -21.3365  -3.3397  -0.9450   0.7249
+#>  -12.0078  -4.0095  -3.3915  -4.8664   1.3195  -7.1477
+#>    1.0076 -14.7269   2.4294  10.5905   3.6087  -4.5968
 #> 
 #> (16,.,.) = 
-#>  Columns 1 to 8   5.9169   1.1962  10.7501  -8.9790   4.1484  -0.2881  -2.9131   8.8346
-#>    1.6393  -2.6889   5.0957  -4.3603 -10.1045   4.5340  -2.5791  -9.9538
-#>    2.0981   1.8680  -2.1006  13.5482  -2.9988  -9.8013   2.6953  -3.9434
-#>   -6.9594  14.0226  -6.9573  13.5674  -4.3679  -6.6665  -3.0626 -10.0226
-#>   -4.8158  17.7432  -5.9582   0.1271   5.2878  -2.4874   4.2246   7.6162
-#>   -1.8618   3.9992 -14.8552  -0.6717   2.1653  -0.0528  -0.5305   9.8745
-#>   -6.0365   5.1183   3.4952  -4.1085   2.3846   7.8327   4.9367  -2.7905
-#>    1.3103  -4.7215  -2.1793   4.6908  -3.7660   5.4065  -9.4307  -6.7514
-#>    4.4123   0.1263   6.5382 -10.5020   3.3511   4.1023   9.8486  -0.5677
-#>   -1.5796   0.2654  -6.1811  12.6362  -3.4534   5.1137   0.9683   4.5397
-#>    6.5069   5.2308  -1.0415   3.2129   3.6340  -3.6598   2.7702  -4.7992
-#>    1.8092   2.6591  -5.5465   0.3925   6.2483  -0.5837   4.6502  -8.0858
-#>    6.4339  -4.2291  -5.4463   1.3123  12.0333  -0.3098   3.8704  -5.4030
-#>    6.8779  -5.5842   3.2166  17.6905  -1.6929  -5.4125   4.0909   6.6283
-#>   -4.6292   0.9189 -11.1377  -4.3370  -5.0335  11.1261   0.1276   8.0493
-#>   -2.5658  -3.2492  -2.2783   0.5461  12.7220   2.3526  -4.2149 -18.9606
-#>   -4.9198   7.3656  -4.8668  -0.3271  -0.8615   0.2618  14.6728   2.8823
-#>    0.3484   0.3916  -8.9037   6.6532  -2.4009   6.8033   1.5034 -17.2913
-#>   -1.1485   5.0058   4.2632  -2.1971   3.7979  10.3216   7.6086   1.4941
-#>    0.4886   3.0998  -2.3149   6.9763  -3.5498 -11.9070   0.7731   2.6125
-#>   -5.4350  -4.2100   6.8747 -11.3444   3.5116  -6.8341   3.3729   9.3017
-#>   -0.1810   0.0186  11.3952  -3.4121   6.9950   3.7474  -9.6391   4.4926
-#>   -5.7175  13.6596 -11.6647   1.7630  -8.6601   3.9700   8.8271 -19.6870
-#>   16.2903   2.8260  23.8470  -2.6987  14.3097   5.0003 -10.3122   4.4237
-#>    0.7487  -4.2665   9.0854  -0.7235 -13.1581  12.4358 -19.0049 -16.0640
-#>    0.0781   1.3698   9.2325   4.6129   5.4748  -4.3969   0.6132   9.7189
-#>    5.8890  -1.4452   5.4483 -10.7550 -18.6493  -0.3483   2.3382   5.1563
-#>   -0.0916   5.1133  -2.3725  -7.2628 -11.0122  -9.0826   1.5038   6.7757
-#>   -1.1501   0.0571  -6.8683  -0.1019  -5.7275  -0.2904  -3.8972  -6.5371
-#>    1.7825   3.4269 -11.7473   3.5808   7.4465   5.7079  -0.9744 -11.0135
-#>    3.0592   1.6905   3.4647  -1.9238 -10.8315   8.9985  -2.4843  -8.3443
-#>    0.2737   1.7902  -6.4242   2.6640 -10.9324 -10.8223  11.0115   4.6460
-#>    1.6822   3.0623   0.2343   2.7980  -4.0485   7.8199   1.1451  12.8511
-#> 
-#> Columns 9 to 16 -13.7071  -0.7653   5.5538  -2.4353  -5.7447  -0.6709  -7.2778   6.2663
-#>   14.0684   8.3597  -5.1964  -0.3258   6.5183 -12.6385   1.4381 -19.2791
-#>   -4.4823  -5.3254   3.3574   7.8482   6.4927  19.5868   1.6706   5.6892
-#>   11.6002  -4.3351   4.4546  -8.9352  -0.6136  -0.0973   8.0068  -8.3215
-#>   -2.1117   7.8508   4.9740  -4.2024   8.0122  -4.6648  -3.8557   6.6858
-#>   19.8712   5.1868 -11.2870   0.4251   6.9703 -12.9883 -13.2475  18.2691
-#>   -9.1562  10.4813  -1.2635  11.8747  -4.2531  -1.0797  11.2160 -19.6530
-#>   -0.0567  -4.9667   9.7458   2.3701  10.9538   1.9237  -1.9627  13.9311
-#>    1.3140   8.7365  -0.1949  -8.5719  -7.5008  -2.7805  -0.9558  -5.4660
-#>   -6.1256  -3.5703  -2.3028  -9.0828  -8.1802   3.4247 -11.0404  14.6100
-#>  -12.9374 -12.2777  -3.5171  -5.9202  13.1994   8.6371  -5.0024  -0.5268
-#>   12.8851   3.0432   2.3660   5.1235   5.7813  -8.8750  -0.5470   4.2925
-#>   -2.6762  -1.8033  -3.3926 -17.6865  -2.9754  -8.5596  -1.3519 -10.5077
-#>    7.7198  -7.3484   3.8967   2.3075   7.3826 -11.7406   6.7200   3.9621
-#>    6.3456  -5.4186  -5.6147 -19.3007  -6.5337  -8.0445  -1.3140  11.4247
-#>   -3.4923   8.3719   1.3613  -0.5927  -7.4595   4.1947  12.5511   2.8711
-#>    2.5924  -1.3840  -6.6778  -1.6072   6.5471  11.2356  -6.4557   3.2157
-#>    6.1776   5.3566  11.3179   0.2979   3.6626  14.9784   0.3117 -15.7688
-#>  -11.2680   6.5108   7.6255   2.8750 -15.6441 -11.5801   4.3952  -6.3325
-#>   -5.5521  -5.5296   5.4242   2.3838   2.0523   0.7012 -18.1893  -0.5384
-#>   14.5744  -0.8031  -4.6487  10.3956   5.4341  -4.0979  -0.9086  -0.3972
-#>  -11.0830 -22.4040 -12.0306 -11.2804  -2.2865   3.0656 -12.6764  -6.4913
-#>  -16.6999   4.3738   4.0502 -10.0046   3.9659   2.0011   9.7486  -4.4500
-#>   -8.8754  -5.8829  -2.1642  10.0776 -10.7525 -10.7539  -0.5335 -16.6962
-#>   -3.3995 -13.1827  -8.8088   7.4763  -1.9271  11.0060  17.0620   9.4130
-#>    3.0052  -0.0882  -7.0467  -5.8017 -14.0206 -15.3782  -9.5878   8.9883
-#>   -3.8261  -5.8923  14.9247  11.1826   5.4076   6.7611  10.0125  -4.7802
-#>   -4.5243  -8.8796  -3.0152   7.7899  -9.3244   3.0303 -14.7213   3.7061
-#>   13.4022  -0.5678  -2.1548   1.3116  -4.6149  -1.8015  13.2173  -2.9844
-#>    6.0882 -17.9818   0.5556 -23.7100   0.1930  -4.3171   1.8997  -3.6025
-#>   -7.7793   9.4382   3.9473  -2.6181   0.2232   8.3356  -3.1850  -7.8183
-#>   -4.7888  -1.6125  -8.2620  -6.1865  -4.4570  -9.0457   1.7380  -9.7770
-#>   -2.7259 -10.7252  -7.6792   2.6634  -2.2631   3.8497 -15.9251 -20.6218
-#> 
-#> Columns 17 to 24  -7.8796  17.9775  -9.3992  -4.8710 -10.0097  -0.4248  12.5003 -13.9830
-#>   10.9893   3.6070   5.4122  -4.1925 -10.8265  11.4295   4.7736   3.5215
-#>   -9.8660   6.7317   8.8891  -2.7798  -4.1303  -5.8500   2.5645   3.4559
-#>   12.4319  -7.9728  -2.8910  -1.3465  -2.0578  -2.5140  -7.0054   8.4268
-#>   -0.9489  -7.8873  -3.4151   0.7461   1.9786  -4.1699   3.1521   5.1590
-#>   -7.7628  14.5598  -3.7605  -2.4563  -4.8887   4.3739  -1.3184  12.0368
-#>   -3.3868  -9.5283  23.5325  12.5333  -6.4736  -6.5903   7.1718  10.8270
-#>   12.0811   0.8471   3.1682   7.7866 -11.2046  -0.6210  -5.9061   6.1771
-#>   -2.5386   6.6288 -21.1735  -7.4060   8.6651  16.5632  -3.3783 -14.9110
-#>    6.4171  12.3525   3.2848  -7.7431   6.7203  13.0827   2.9858   3.2090
-#>  -14.8683  -2.8080  -0.1957  10.3795  -7.0103  15.6995 -15.7298  -8.2846
-#>   -0.5186  -0.5380  -1.4989  -1.5238  -1.6552  -0.1317   3.3847 -10.4952
-#>    1.0079  -0.7870  -3.5615   3.2061  10.0451  17.6399   3.1742  -9.9340
-#>    8.9299  -8.4056 -10.5260  -3.7712 -14.4148   0.4000 -15.8502  13.8139
-#>   -4.4611  -6.0424  -8.9023  10.5212   4.2197   7.3905 -12.1776  11.8185
-#>   -9.9123  -1.9639 -10.0037  -0.6602  -2.5114   6.8629   1.1878  -6.3813
-#>   -0.0282  -2.9304  -9.4680  -8.3135  -6.3681  -4.3604  -7.0652  -3.8044
-#>    4.8215  22.9106   7.5484  -2.5532  -2.5809   3.1510  -2.8192  -4.3745
-#>  -13.7029 -13.7980  -6.9890  16.0019  -1.9355  -5.9001   9.0478  20.1010
-#>    3.2395   0.1785   3.6520  19.3297   5.5748  -2.6324   0.0679  -6.5968
-#>    8.1041  -7.6415   4.9666  -0.1479   6.9535  -1.0444  -1.1585  13.3160
-#>  -10.1763  -5.5170   0.5665   1.8729  -0.9565  -3.9006  -0.9908  -0.9861
-#>  -22.9678   2.1149   1.5818   9.4493   1.6239   2.5172  -7.3676   5.2019
-#>  -15.0124   3.9553   4.8910  -3.0632   4.0516  -6.1032  -3.0397 -16.0125
-#>    4.1737  -6.5068  -0.9279   4.9228   5.0661   7.6071  -5.4581   6.6090
-#>  -15.5276  15.1106   2.8998 -10.0532  15.5078 -12.6153  10.8250 -12.8329
-#>  -14.1448  -0.5968  25.9646  -1.1691   6.4707   4.0210  -4.2684  -3.9871
-#>    4.7639   3.5075  -4.7696  -0.6260  -0.1757  -1.9947  14.4427  11.0419
-#>    2.0681   9.3647  10.1557  -3.0895 -13.3740   0.8708   2.6143   5.3274
-#>    8.0578  -6.6474  -0.7361   4.9535  -6.1931   2.7753  -4.5427  11.5385
-#>  -11.6095   3.9669   5.9931  11.6033  -5.9513  -1.0784  -0.9202   2.6523
-#>   -0.9995   9.6205   4.9987  -3.6116   6.1728  -1.6928   6.5078   6.2526
-#>   -5.6990  10.3888  -4.3727  -2.2719  -4.1629  15.1052   5.0346   4.0389
-#> 
-#> Columns 25 to 32  -3.3179   8.9114  10.0572  -1.2587   2.0931  -2.0530   6.5594  10.9616
-#>   -4.7203  -7.3808  -6.6912 -12.0078 -11.0555   5.6575   7.7934  18.6705
-#>  -19.2785 -15.9866   1.3843  -0.8469  15.9078  -0.2410   7.6636   8.9358
-#>  -11.1760  14.2224  -3.0896  16.3962  -5.9672   8.0302 -13.1924  -1.9202
-#>  -11.5296  10.4649  -3.0178   2.9463   8.7748 -14.6963   4.4653   2.7594
-#>   -7.3800  -5.3960 -17.0513   0.9882   0.4144  -4.9462  18.2287  -5.4529
-#>   -4.0838 -15.5619  11.6511   8.8858 -12.9532 -13.0284  -0.6605   8.6867
-#>   -9.4081  -3.5767  -5.3829   2.1063   6.0295  -9.8064  17.7653  -5.9768
-#>  -12.7177   4.9938  -7.4952 -13.0969   8.9979  -0.0998  22.3145   1.7401
-#>   -5.8592   4.4302  -6.8574   3.3468 -11.9368  -0.0754  11.5460   0.0574
-#>   -4.4656   5.4609   3.4491 -10.8961   7.8371   3.5985   7.8025   2.8597
-#>   -3.9979  -0.6659  -5.5074  15.7577   5.8280   6.6209  15.0434  -1.5492
-#>   10.2756   9.7925   1.1668  -2.0181  -2.6645   5.3276  -1.1349  -4.2664
-#>    1.6781  -0.7389  -0.0576   0.9104   0.7526  -4.0417 -13.5008   0.9477
-#>    9.2704   6.1267   2.2287  14.4398 -15.2803   8.1811 -15.1956  -4.7375
-#>   -0.8798   2.0092  -5.3028  -3.4708  -1.6346   7.8313   4.4540  -8.5716
-#>    6.6299  13.0193  -4.3130   6.2526   1.2605  -7.0203   4.4134  -8.3241
-#>   -4.6752   5.8241  -1.0570   6.9613  -6.6649  -9.4870  15.5721  -8.3716
-#>    2.1473 -12.8502 -18.3775 -20.7149   5.0487  -0.8780  -9.4605   4.9170
-#>    7.3915   0.3985   4.3123  -3.5218  -0.3456  -9.1704  -0.8021   1.1542
-#>    2.5691  -6.5108   3.8852  -2.2915   9.4971  -0.9335   8.3368 -17.9244
-#>    8.7376   4.6082   6.3856   0.9325   0.8904  -2.0705  -6.0855   1.2072
-#>   -4.9679   9.4473  -8.5908  14.6577   9.2367   2.4137  -5.8935  -3.3195
-#>   -8.3799 -11.7817   8.2112  -1.3504  -3.6656  14.2825  -1.3165   6.0722
-#>    1.7320 -12.4078  11.2184   0.9507  -8.4417  12.5083   9.1634  12.5494
-#>   13.9281   3.4773  13.7172  13.0891  -9.7119  -8.2638  -8.7622  -8.8073
-#>    6.4454   5.8153   5.1188   1.5204 -21.2378  -1.3159 -18.2051   5.7055
-#>   -4.9967  -9.7521  -2.4479  -0.7929   5.7172  -4.6572  -1.0606  10.8816
-#>   -2.7617   4.1111  -7.0416   0.6337   0.8852   2.1034  -5.1787   8.5571
-#>    8.7010  12.4941  -5.7907  16.1933   0.8232  -8.7499 -21.4545  14.7660
-#>    3.5851   8.8012  -3.2526   0.5185  -1.6775  -6.9595 -13.1621   6.2687
-#>   -7.7303 -11.4324  19.5842  10.0752   1.3555   1.9197   8.3356   4.5247
-#>   -8.8562  -2.9946   6.5604  -1.4950  -7.3620  13.2178  17.4251  11.9080
-#> 
-#> Columns 33 to 40  11.3978 -11.5616   0.6751   5.4931 -21.9684   2.6750   8.7489  24.5304
-#>   -6.9687 -10.7086 -13.1778 -13.8945   8.1555   6.2153  16.2339 -10.6723
-#>   -8.1479  -2.2703   4.3881  -1.6490  -1.7727   1.6611  19.1424  -9.4395
-#>    1.6611   6.1769  -3.1931  -0.8321   6.1536   5.8515  -5.7873   4.9654
-#>   12.7029  -4.0359   3.3536   7.6909 -10.7435  21.4226   5.7366  34.2568
-#>    9.1381 -17.1925  -5.0425  14.0283  11.5891  -0.8054   6.9017  -3.6643
-#>    1.6606   3.6179 -16.8114 -10.9507   3.6880 -12.8685  -8.8030 -13.1678
-#>    0.9630   3.8960   1.9926   5.5745   6.0234  -7.7944   2.0300  -8.8346
-#>   11.5074   6.9423   6.5628  -7.9312  -0.3811   2.8890   3.3496 -13.8868
-#>   -9.8659  -9.3607  -5.8141  -3.7807  -1.1790   7.4838   8.1174  15.1448
-#>   -4.9651   9.1740  14.4638   3.6277   9.6858   8.8757   4.3408 -11.4244
-#>    8.6441  -1.5692   8.1847   0.1239   8.9716  -2.4765 -10.7623   7.3977
-#>  -11.0943 -16.6178   2.2971 -21.6193  -2.5390 -10.3741   9.8438  -5.6642
-#>   -9.2277  14.6166  -0.9204  14.9913  10.7844  13.5524   2.8325  -8.9513
-#>   -2.6473  -5.8741   4.6406  -5.3104   4.9784  -7.5659   7.1071  -9.0259
-#>   -1.1382   0.5198  -2.9474   5.7955   6.4414  -5.7807   3.7976 -12.1651
-#>    6.7978  -6.5910   1.2208   6.1146   7.8964  16.9533   8.0562  -5.8046
-#>    4.1896  14.8452  -4.5841  -7.3017  -0.0823   4.4017   4.8627  -7.9858
-#>    7.3247  18.5870   7.4510  -3.6479 -25.6386  -0.6795  -4.1227 -13.5621
-#>  -10.5895 -10.6946   8.3915  -3.0894  -2.1166  -8.1934   3.9229  11.6619
-#>    3.3745   3.2772 -12.0464   0.5004  -2.8965  -5.3103 -15.6017  10.8435
-#>   10.7255   3.9382   5.0526   6.5678  -8.1970 -12.1692  -4.3047   2.8854
-#>   -5.4066   0.8166  -0.8165 -12.6045  -2.5846   7.5421  19.8140  -4.3701
-#>    7.6552   7.8124  14.6502  -2.3297 -11.4268 -10.0371   1.9409  12.4794
-#>    1.4078   9.8272   7.0404  -1.8239  10.8952  11.4932   1.9102 -10.4790
-#>    9.4703  -6.6227  12.2637  -6.4715 -15.9110 -14.3271  -7.6908  14.2522
-#>   -0.8424  -4.3798   9.7508  -1.0919  -3.3694 -15.9257  12.9108   2.7339
-#>    1.0147  -4.9658  -6.1954   5.5696  -5.3329  -1.1068   4.5829  -5.8869
-#>    9.7037  -5.5950 -16.5860   7.1283  -6.5315  -2.6857   2.3856   8.0047
-#>   14.0389   2.8653   0.3209   4.5985  11.4294  -2.1647  10.7592 -13.9907
-#>   14.3837  19.9179  -1.5585 -13.3698  -8.6395  -1.7041   3.9278   2.5171
-#>  -13.9244  -0.2977  -3.2740   4.1151  -4.9771  11.6901  -6.4768   6.6685
-#>    5.1153  -0.3700 -17.0048   8.4165  -6.7035  -6.2654  -1.5954   9.6956
-#> 
-#> Columns 41 to 48 -22.1942  -7.2350  -2.4917 -11.1559  -3.0166 -15.0017  -1.1812  -3.4713
-#>    0.2388   8.1962  -9.7739   9.0305   8.3463   2.1590  -5.6769   5.0629
-#>   16.7023  -3.2802   0.8704   2.1747  14.1589  -8.2684  -3.2724   7.1870
-#>    4.6840  -7.5805   9.2810   2.9401  -2.8086   6.2953  -9.3358  -1.4327
-#>   -0.5781  10.5564  19.4567  -8.2992   1.6499   1.0847   2.9731  10.7564
-#>    9.4773  -0.5292  16.9066  -6.3917 -11.6539  15.5057   4.0474   7.7430
-#>   15.5874   1.3924 -16.0389   4.0388   5.7586   4.8510  -7.7798  -2.4974
-#>   21.3204 -14.2573   3.3693   5.9481 -10.7813  -0.9151  -5.0470 -10.1517
-#>    5.6014  -5.1494  19.4992   6.3122  -8.5826  -4.0738   2.6674   7.9877
-#>    0.5577   6.1221  16.2732  -1.8582  -3.3096  -1.8466   8.7034  20.5187
-#>    3.1797  10.5174 -15.2399  -2.6417   6.3028   1.2196  18.4807  -4.0511
-#>    3.0401  -8.5193  -1.1578   0.3379  -1.0156  -7.5664 -18.8893  -6.9766
-#>   -8.8674  11.4856  -8.3224  -7.0834   7.2036   6.2827  18.5061   9.4553
-#>   -0.7081   3.2447 -13.5620  -3.5952   5.2871  -7.9405 -12.0735   1.1528
-#>   15.1621  -0.6657  17.3880  -4.1401  -5.4776   1.6688  -5.8632   9.7832
-#>   -4.7534  14.7511  -7.6977  -1.0605   6.7663   8.0379  -1.9791   2.8543
-#>   11.4958   0.2522 -19.5168 -14.8940  -3.2251   9.1275   9.5365   5.7808
-#>   16.8602  -8.5958   1.7279   0.3534 -15.7748   1.2065  -5.9608  -1.8861
-#>  -11.9294  23.8705  10.0658  -9.9190  11.2422  13.1915   2.9278  -8.3528
-#>   20.0953  -0.7472  17.5934  -3.3260  18.8082  -8.5177   0.2490  -6.8630
-#>  -12.7300  -2.4564  -1.6036  -1.8352  -7.7443  -7.1272 -13.3813  -3.6956
-#>    8.1264 -13.6377  10.9397  -3.0560   6.5067   2.8503  -4.2379 -10.5363
-#>    0.5117  11.3553  -9.0868  -2.7140   1.1550   5.6160  11.9133  14.7809
-#>   -3.9240   5.4078 -13.7294  -4.3688   6.5080  -3.5070  -1.7219  -4.4902
-#>   12.5034 -12.8467   2.1763   5.1926   3.1938  -0.7747 -20.0967  -9.0441
-#>   -4.2370   4.1992   7.6872  -5.2593  -1.7053 -11.8188  -0.1682  -4.0788
-#>   -5.0200 -27.1574  12.7201  20.7038   0.9286 -12.9077   0.3077  10.2574
-#>   -6.0969   0.4821  11.8856   5.4467   0.9379   3.4836  -5.4542   6.9657
-#>  -18.5719  -0.9974  -5.4086  15.3446  -2.6354  -2.4166 -15.3805   5.1253
-#>    6.2883 -29.3754  -1.2569  -1.7976  -6.8801  -0.9421   3.6779   3.4648
-#>   14.4022   7.4876  -4.4341  -0.7004   5.4160  -1.7521  -5.0956 -13.7839
-#>    7.8083   2.2676  -7.3100   7.3198  -1.0796  -7.9814   2.6069  -0.7932
-#>   -2.4366 -11.5112   3.4781  12.4499  -3.4112 -10.5143 -14.6968   5.0345
-#> 
-#> Columns 49 to 54  -8.9649  15.0480  -3.5127  -1.3708  -6.2487   0.3050
-#>   14.9347   5.8413   1.5031  -1.6481  -4.4734  -2.3108
-#>   -6.8708  -4.6939  13.0038  -4.7202   2.7074  -7.4918
-#>   -1.4283  11.7403   9.1098  -4.2097   5.4451  -4.1161
-#>    7.9117  21.6279  -2.7317  -0.8931  -9.2567  -3.0612
-#>   17.0366 -33.7473   4.8260   0.1720  -1.2427  -4.2173
-#>    9.5700 -15.3509  12.6820  -0.3305   0.5376  -6.5525
-#>  -13.2608 -18.3225  18.4981  14.1568  -3.1979 -10.7866
-#>    8.7527 -11.6705 -10.1354 -10.2437   4.1236   5.4367
-#>    7.5527  -4.3760   9.8673  -0.5702   4.7787  -5.6126
-#>    1.2647 -21.6524 -21.0066   7.6477   2.8037   3.0143
-#>   -8.7994 -14.5644 -19.7484   6.1574  -0.9049  -1.3891
-#>   -4.8802   0.4612  -3.7767   2.4743   1.4301   0.2956
-#>    7.3854  10.7632   8.3053   1.8451   6.9022  -4.2529
-#>   -3.2762  -6.7379   8.6647   5.6959  -1.1013  -0.0037
-#>    0.3988  -6.0855  12.6957   1.2420  -4.7745   3.7277
-#>   12.0424 -22.9109   0.8896   5.6065   4.0492  -0.3889
-#>  -12.7498  -0.4841  11.0614  -5.0944  14.2974  -8.2520
-#>    3.4900   2.0673  -2.8354   1.1162  -1.4959  -4.3088
-#>   -7.9860   7.1940  -1.3905   5.9913  -1.7375  -5.5433
-#>    1.8201   5.1878   9.1053  -6.9162  -5.2749  -1.3472
-#>  -13.8737 -16.1515   4.4713  11.9625  -2.8959  -2.3175
-#>    7.7018  -2.5956   0.8244   3.0606   5.3660  -4.8304
-#>  -20.7461  -7.6867  12.4397  -6.9317  -7.0717   6.5692
-#>   -3.1832  -9.8930  -9.0189   2.7933  -0.3160  -0.1822
-#>   -2.9797  16.1053  -1.1040   4.7778  -8.6608   3.9642
-#>   -7.8118   1.3081   0.3643 -13.3631  -4.6473   1.9124
-#>   16.6642   3.7141 -11.1340 -17.0547   4.8123   2.1086
-#>    1.4257   8.5336   7.0028 -12.1989  -3.7266   4.4054
-#>  -13.9857 -10.3642   0.7977   5.1235  10.0793  -1.8005
-#>  -11.1381  18.8368   9.4772   8.2808   1.2748  -3.9059
-#>    5.8320  -3.8253  -8.4454  -7.3230   2.7275   3.6790
-#>   -0.2858  -1.3838  -1.1742 -20.6285   5.9709   7.5458
+#>  Columns 1 to 8   5.2756   1.7547   6.6615  -4.6317 -11.0351   4.6979  -0.3336  12.0969
+#>   -8.4552  -2.7863  -2.5687   7.5584  14.7468 -10.8910  18.6608   6.5969
+#>   -5.1925   7.5049  -3.4704   7.0544  -8.9914  -8.3004  15.1310 -12.3357
+#>   -1.1411  -6.0144  -2.0602  -4.1445  -3.4500  -4.4563  16.2231  12.0519
+#>    7.7178  -3.0893  -2.8235  -6.4274  -8.1859  26.6533   1.2059   0.3104
+#>    1.5923 -10.1808  -2.9224  -7.9397   1.6827  16.1166  -9.2551  12.5632
+#>   -2.6303  -7.3634   7.1459 -10.0291   3.6116   4.1022   3.6122   8.8857
+#>    3.1622   0.5190  15.0729  -5.9795  -2.1665  16.2795  13.3101   2.7286
+#>    5.5378   5.0749   2.1329  -3.5275   2.5768   2.9050   2.9379   2.5825
+#>    3.8436  -1.4010   3.5313  11.2961 -13.4705 -18.4719   0.8731  -8.6201
+#>   -5.1079   0.5902  10.3610  -0.1259  -0.6092  -0.3629  16.7746  -2.3444
+#>    0.0413  -6.9749   3.9347  -9.8101  -8.9197  -8.8230  -2.9649  -9.1024
+#>    3.7957  -8.6419   0.0118 -12.8990  -3.1335   8.5876  -0.2939   2.3774
+#>   -2.1789   5.7850   6.0346  -2.0728   6.2211   0.4319  -8.2255  18.9112
+#>   -7.7170  -6.1099  -1.3654  13.0622   6.8478  -3.4010  -5.4624   3.1738
+#>   -7.1405  -4.9243   4.6307  13.6795  -9.1733  -2.8841   7.6890   4.3012
+#>   -2.4381  -6.4208  -1.1564  -0.8427 -14.2324   0.8812   2.6017  -1.5269
+#>   -5.5222  -6.3645  -1.7536  22.5823 -13.4884 -12.7068  -7.2487  22.9923
+#>  -10.0144  -6.4853 -15.9476   4.4563   1.2097  -0.5564   9.2309   1.4572
+#>    4.0753   0.6627   8.7961  -6.3962   1.5498   5.9692  -9.9231   1.2858
+#>    1.2143   1.5773   6.6916  -3.1724  -8.4606  -8.0125  25.8460 -12.1257
+#>    4.2672 -17.0935   2.0135  19.9261   9.4984  -7.5729   5.1595  -4.6295
+#>    6.5616   5.6412 -13.1349   3.5440   0.4855  -3.9691 -14.9047  -5.1175
+#>   -1.6898   5.4509  19.7283   6.9923  -5.5815 -10.8268  15.0298  -6.0301
+#>   -0.5550   8.4818   0.2669  -8.1374  -2.4088  -5.0113   8.1721   1.9246
+#>    4.0182   6.4205  -0.3107   8.1392 -11.3114  -8.3182  18.0963  -9.7825
+#>    0.2734   7.0354  -0.6524 -13.5626  14.5923  11.9680   1.2434   9.3070
+#>   -3.8056  -5.2942   6.0883   7.5276   4.8486  -1.1728   4.0150   6.6492
+#>    1.4906   0.7824  -7.3681  11.2375  -5.6878 -14.5941  12.9500  -0.1145
+#>    1.5652  -1.1036  -2.0194   7.8666 -11.4598  -5.0640  -2.6630   7.8504
+#>    0.9509  -3.8416   2.4783  -0.5952 -20.7763   5.2880 -14.7230   5.2904
+#>   -2.1371  13.4726 -14.8625  -3.5884  -6.7279  12.7785  -5.6060   5.2959
+#>   -1.6139   3.2251 -13.8866 -13.3582  13.3213  -3.4479  11.4082   4.1389
+#> 
+#> Columns 9 to 16  -5.7304   1.0103   5.8575  -0.0605   5.0028  -4.5229  -5.3523  -1.9468
+#>    6.3223   3.2229   4.6141   5.6089  -6.0990  -6.5460 -16.9111  -4.8647
+#>    3.0358   1.6391   9.6789  -3.6553  11.9386  -0.4901   5.4186   0.9924
+#>   -7.8927   4.0743   7.0767  18.4033   8.8516  -8.0430 -11.5070   3.5952
+#>    1.8210  -4.5351  12.3444   7.1112  13.6045 -13.3215  -2.5869   2.1400
+#>  -15.2553   3.0540   1.0591  -4.6158  -1.8773   6.0072   5.9049 -11.5138
+#>    8.6981   9.6180   1.8021  11.7182  -0.8657  16.7797 -19.3308 -10.6778
+#>    5.8178   2.5498 -13.3592 -15.0191  -5.4614 -15.2058  -4.8563  -0.1500
+#>   -6.3362   0.2004   1.9199  -4.4868   0.3165 -13.1603   4.2994  -2.3995
+#>   -0.8427  -2.1965  -2.4171  -8.7800  12.6301   3.9171   1.3621   2.9480
+#>    2.1233   1.0991 -14.8571   0.4214 -14.3135  12.0101  -3.3511 -24.4492
+#>   -7.3505 -13.1537  11.8116  -5.8900   6.6713  -5.9084  25.6507   4.0086
+#>    2.4995  12.2153  -4.9295  -1.6695   9.2545  -8.2995  -3.3051 -11.0737
+#>    1.9268   2.9528  -4.8150   1.7671  32.9636 -21.0868  -5.9075  -0.3272
+#>    9.2840   5.8526   8.2257  -4.3659   1.3838  -5.5580  10.4893  -6.5285
+#>    9.4716   1.0466  -2.3091   2.0697  -0.4807  11.8881  -7.1691   3.6942
+#>   -2.6656   9.8087  -8.4430 -15.8668   2.8323  -3.1499   3.0797  -0.2514
+#>  -10.9524   4.5483  -4.3438 -11.9182  18.9662   5.8849  -7.9422 -18.0211
+#>   17.4253  18.2292   5.2624  -7.4562   6.4601 -11.2950   0.7170  -8.4622
+#>   -9.6193   0.6903   1.9112  -0.8364  -1.7324  -2.6348   0.6072 -18.0377
+#>  -11.3179  -4.8949  -9.0830  -4.9424   8.9319  12.5477   9.1676  -0.6539
+#>   -0.5279  -7.8971   1.8857  -6.4948 -12.6447  13.8687  -5.3077   0.3727
+#>  -10.4233  -0.6892  -2.8241   7.6385   9.2530   7.2591   7.7877   2.2405
+#>   -9.8340  -2.5524   6.6764   2.0074  -6.1819  13.9545  16.0812   8.3872
+#>   -3.1945   3.7308   0.2755  -2.7634  -2.0122   9.0720 -13.1779  -2.0101
+#>  -15.7492  -9.8427  -1.9868   7.1928   8.1297  18.2289   2.5699   9.7198
+#>    9.1292   5.4583   0.4262  -3.8870  -0.3989 -25.9822  -1.8671   0.1439
+#>   -4.2841  12.0578   7.4115  -1.3739   0.9352  -7.4396  -9.2268   0.9252
+#>   -7.1374   3.7755   2.6228   6.6387   7.2836  -5.1350  -5.6117  -6.1560
+#>   -6.7697  -5.5184   1.8841   4.6353 -14.6008   3.6154   8.1505 -17.1586
+#>   -4.9002   1.8613  -4.3523  -5.4893  10.2204  -6.3039  14.3135   4.9045
+#>   -0.8154  10.8824   0.1450   7.3946   7.5411  -2.2142  -4.3591  16.2671
+#>   -6.2743  11.4999  -1.3266   7.2196   5.8977   1.2253   2.5984  -3.3130
+#> 
+#> Columns 17 to 24   0.1109  -8.6602   4.1653   9.9289  -2.8564  16.6534   8.1101   4.4451
+#>    0.9047   3.2154   3.7040  -9.7789   0.2520 -16.1604  -5.5157 -19.8182
+#>    2.4664  11.2283   1.5133  -8.6573   0.4796  -7.3835  -0.9546  -3.5618
+#>  -10.9444  -3.3607 -14.2992  -7.1980  -3.0000   0.7098  -0.2666   2.2843
+#>   -7.6134   9.6128  -2.1862   1.9244  15.3964   9.4247   3.8489  18.8211
+#>   -7.0254  11.2304   6.9857   5.2021  -6.8423   3.9968 -12.0987 -13.4685
+#>  -12.9116  -6.9782  -4.7683  -8.5014  10.8463  -1.0870  -4.5421   0.6890
+#>  -22.3120   2.9633   4.8133  -0.4045  11.2195  15.4009   0.1240  -2.2965
+#>    6.2753   5.9701  -2.5405   0.9651   2.6084  -7.2175   5.5234  -0.6114
+#>   10.8033  13.4333   7.4037   5.2561  17.5797  -6.7208  -3.8077  10.9466
+#>    0.7625   4.0083   1.6502   0.7978   2.5485  -2.0261  -3.7659   0.2436
+#>    4.8823   3.7094  13.0514  -6.3162   2.8484  -3.3460   1.4774  -2.0317
+#>    8.1490   4.1746   4.9700 -13.2374  12.3449   9.5299  -1.6022  12.8237
+#>   -9.6539  22.7185   2.0802 -17.1319   6.8095  -6.3835  -2.9193  -6.1267
+#>  -15.9983 -12.2554  -9.9446 -18.7150  -1.0571  -6.6002   1.9996 -11.0283
+#>   -1.0477   0.2512 -12.7843  -9.4633   2.3716 -14.1197  -0.7984  -7.9425
+#>   -5.9936   9.1579  10.7186   3.8784  -0.4395  -5.5418  20.8995  -1.0326
+#>   -5.0643  10.4046 -24.2997 -26.7654  21.8561   2.0324 -10.0300  -5.5872
+#>   14.1897  -3.7722 -11.7379  -4.4245 -13.1528  -7.1254   0.9065  -9.4944
+#>    9.7989 -22.3986   7.1816   2.1629  -3.1439   8.9054   7.9801  -5.7809
+#>   -7.3011  10.6445  15.4557  -2.4307  11.8996 -21.2262   8.4361 -19.3996
+#>    2.7022 -10.6509   2.3958   5.3890  10.9339   1.9430   5.5174   8.5673
+#>   -0.7941  16.4490  12.1735   6.4868   0.6859   6.3813   7.3958  -9.0892
+#>    1.8388   5.1728  12.7578   1.3988   8.4592  -7.9833   4.2787  -5.4130
+#>   -9.0848  -6.4597  -9.0238  -1.4956  11.2817   9.4407  -2.7686  15.5198
+#>    6.0515   4.1566  -1.0304   3.7152   0.4336  13.3752  -2.2339   1.8877
+#>  -22.5849  -1.9260   7.2711  -5.7071  -1.6980   1.2614   8.5001   9.4663
+#>  -16.9608 -28.3583  -6.1349   0.0221  -3.0540  -1.9245   0.7631 -12.7320
+#>   15.4369 -19.8765  -7.6738  -2.9603   3.5349   0.6490  -3.5738  -8.5207
+#>   18.5094  -5.8799  -8.7084  13.3065 -12.8286   7.0230   0.1117 -14.1954
+#>   -8.4163   4.1708  -8.8344  16.7151   5.7089  -0.7503   7.7101  -9.3620
+#>  -12.2627  -2.6774  -0.9829  -1.2696   0.2368  -6.6867   1.6201  -8.1525
+#>   -1.5377   7.5412  13.3149 -19.6364   1.2173 -15.9384   6.3028 -10.5030
+#> 
+#> Columns 25 to 32   7.1455  -1.0906  -2.7605  -0.3507  10.5780   4.1830   4.5996   1.7428
+#>    3.6134  -8.3677   3.0470  -4.5174  27.7372 -19.6549  -3.9806 -21.1256
+#>   -0.8752  11.5483  -5.3463   5.2390   7.5473   1.4855  17.2177  -1.7172
+#>    3.9694   9.8566   5.8868  -4.9664   4.0228   8.7470 -14.0383  10.2980
+#>  -14.8703  13.0404 -18.9169  10.2354   6.5883  -2.5261   9.8096   8.6856
+#>   -8.9858  -4.8288 -29.5893  23.5058 -13.8050   8.3925   5.4768  12.7193
+#>   -1.7898 -15.4314  -0.3950 -18.1937  10.4765   3.2355 -15.7613   3.0058
+#>  -10.5070   4.1645  -2.1300   1.5021   4.1172  -9.5280  20.0038 -23.7300
+#>    0.8192   3.8243  -1.8633  20.9778  -4.7873  -4.7055   8.2949 -11.0779
+#>  -21.6078   5.3028   4.4726 -12.8169  -2.9467  -2.2130  -3.7131  -6.5314
+#>    7.4495   7.1566 -11.4765 -14.4477  17.2949   6.1652   1.4114   5.5598
+#>   -0.8195   9.1307  -6.2796  -1.0628 -11.5630   3.4110  13.2530 -10.2064
+#>  -15.0065  -1.3195  -0.0967   7.2141  -6.7762  12.2057 -11.2859  -0.8617
+#>  -19.3545  16.9331  -8.0291  10.5120   4.1357  -1.9985   9.7107 -33.3601
+#>   23.4512  -0.6348  -8.7729 -15.9981  -6.2032  -8.8835   3.5345  -2.1418
+#>   -1.5216 -15.0219   0.0789  -1.7791  -4.5648  -0.2017  -1.6768   3.5868
+#>   -1.7076  -5.5204  -8.1446   1.9945  -0.7330 -11.8539   0.3281  -7.9996
+#>    2.7804   9.0645   0.0402  -6.8113   9.6398   9.3978 -11.1483   6.6381
+#>    5.7593 -14.7045  -2.2387   6.7050  -3.6270  -3.3686  -3.1270  -5.6681
+#>   12.5126 -16.0731   6.5076   4.1613 -18.1303  10.4273  15.7300  12.6138
+#>   -1.3093   5.4387 -15.1621  -8.3894   7.9490   9.0548 -10.6498  -4.1357
+#>    4.8561  -4.9120   4.1151  -0.9482   5.3171   0.6698   0.4033  -2.0774
+#>    8.8815 -10.4062   6.0685 -13.0138  -6.7070   6.9667  16.0388   4.2778
+#>   -0.7046   5.3964  -7.3500 -11.9831  13.2234  -6.0727   4.5220   2.2326
+#>   -5.3754  -3.7180  -3.9801  -4.4031   3.3587   2.2696  -9.9712  -2.3492
+#>    6.2067   4.7716   0.7327   6.2060 -11.9394  -6.4664  -9.2583   7.6398
+#>  -14.8866  -0.4833  -8.6024  -5.7619   1.6164  -0.5714   2.3140 -14.4776
+#>    8.2720   3.0408   6.0909  13.6438   2.1994   5.6075   6.0553  10.2403
+#>    8.3371  -6.4873   2.7770   7.7559 -21.3662  31.6905 -13.0160  -7.5158
+#>   22.1024  -6.1697 -16.2355   8.0592  -4.7968  10.2041   9.6756  16.9020
+#>    2.1754  -1.6632 -16.7546  -5.1437  -2.9404   5.8497   8.3028   2.7146
+#>    7.1739   2.7593 -15.4200   5.7264 -19.3383 -20.0260  18.7217   4.6537
+#>   -6.3425   0.0260  -1.8281  -9.1379   2.8205  15.9186  -2.1441  13.4606
+#> 
+#> Columns 33 to 40   6.8411  -4.4396  -9.5836  -1.6676   6.9374  14.1813  11.5965   9.2761
+#>   -9.3780   5.6008   2.0125   6.8668  -3.4065   2.7283 -17.4808 -17.3281
+#>   -3.5577  -1.7638 -11.5944  -2.3791   1.5909  -0.9679  -0.1730   4.8436
+#>    2.9281  10.0167  -6.1779 -15.2507   5.7254   4.8623  10.4327   6.4091
+#>   16.3262   0.8307 -18.4928  13.7244  -0.3390   2.3932   7.1833  -5.6180
+#>    5.4358  -9.5330  -6.5301  14.0511  -1.7974  -3.0830  -8.5173 -11.6941
+#>   -0.8204  11.9848  10.6886  -7.7296 -10.2907  -9.2758  11.0179  -2.0856
+#>    1.9375 -19.4346  -4.5586   3.9863 -12.2739   3.3930   6.9249  -1.0322
+#>   11.1694   4.4905  -1.8620   6.5131  20.0568  10.0477   2.8180   8.0744
+#>   -5.2887  12.7394  12.3157  -5.5464   1.6756  -3.8787   5.5408 -14.1235
+#>   -0.4279   2.5755  -0.0170 -20.2724 -13.7843  15.1414   3.4993 -14.2596
+#>    2.0926  -6.7771  -4.2083   7.2270  -2.0337   1.8369  -8.8234   0.9679
+#>  -11.5875 -11.0893   5.8020  -5.4750   5.8504  -2.8166   4.0331  -4.4042
+#>   -3.5533  -5.2732   3.6338   5.3809  -6.3057   6.1845   5.8185  -3.7466
+#>   13.9148  -5.9032  -3.1771 -14.6675 -17.9047  -7.0205  -7.5716  -8.0607
+#>   -0.0368   5.3509   0.1135   8.1883  -6.2960  -5.8944   2.2877   5.0143
+#>    7.9204   1.6694   2.4287   3.8599 -10.1610  -6.7682   3.0148  -1.4434
+#>   -0.4123   7.4901   8.6897 -21.7944 -13.1354  -0.7698   4.3561 -10.5247
+#>   -1.6998  -9.8662  14.5214  18.2987   2.7539 -15.8786  -0.8604  11.9122
+#>   12.1845  -7.1130   4.7102  -5.6439  -4.5554 -10.8677   0.7087   8.4045
+#>  -16.4750   1.0094  -8.3482  16.7520   9.9351   7.1005   7.3992   3.0023
+#>   -5.8115  26.9794  -3.1757  14.0404  -4.5152   3.0581   7.1578   8.3886
+#>   -2.6003   4.8403   1.8387 -13.1631   0.2252  15.2221  10.0423  -0.5418
+#>    1.0098   0.1739   7.2735  10.8776  -6.1047  -1.8527   2.8180  -2.4202
+#>  -22.3656  -1.9155  -9.6280 -16.1306   1.2218   9.2338  -4.9751   2.4195
+#>    3.2826  10.7070  -4.0028   9.0551  22.1263   2.1284  -0.9929   7.2130
+#>   -6.6908  -8.4050   7.3130  -8.8912 -10.0687   8.0647   5.3002  12.4514
+#>    7.5722   4.7857 -14.6610   3.8883 -13.8927  -6.2390   3.6901   2.1713
+#>   -5.4662   0.6774  16.0048  -8.5734   6.1506 -10.2157  -0.5454   1.5779
+#>    1.0933   0.9105  -4.0711  -3.8430   5.7126  -1.4640 -16.7412 -13.0553
+#>  -12.5979 -17.3075   0.1075   1.2570  -4.8064  -2.8653   7.6689  -4.2270
+#>   11.8769 -14.2723 -22.5438  -4.0313  -2.4251  -0.0567 -12.0291  -2.0781
+#>  -21.3470   0.0947  14.8395   1.5358  -1.8869  -0.2724  -0.1991  -4.5065
+#> 
+#> Columns 41 to 48   6.9446  -4.2671   3.4144  -1.1094   5.2336   1.9632 -16.7952 -18.1477
+#>    9.6741 -16.3215  -6.5841  -5.6629   3.5887   2.3931   0.7793   3.2301
+#>   -5.8765   0.6689  13.0392  -7.3758   4.0077   0.1969  -4.1479  -7.8417
+#>    2.4437  21.6355  -0.6811   4.3060  -1.1801   7.3260  -4.1952 -12.1952
+#>    3.3129  -2.8117  16.2947  -5.0317  -2.8167  10.5863  13.1083 -13.7058
+#>   -1.5049 -10.6149   1.1306  -0.9964  -8.6222  11.5668 -11.7854  -5.0032
+#>   -5.3137  -3.5947 -10.3030   1.4657   0.1443  -1.2825   7.0504   3.9803
+#>   -4.3435  -3.6132 -15.7955   4.1468  -2.7513  -0.3367  -0.1255  -7.0169
+#>    8.5116   6.4778   1.1936   0.5842  -0.2371  -2.0299  -8.6984   8.5404
+#>  -12.2470  -2.3939  -1.7271  -5.3625   3.8095   3.1663   2.8408   5.2890
+#>   -0.2210  10.5781   2.6809   7.9563   6.2104   1.6067   7.1071  -6.9584
+#>    8.2769   2.9826   8.8886  -7.6564  24.0268 -10.4096   1.1162   4.4026
+#>   -2.1162   2.8869  -1.1564 -11.1091   5.8721  -1.7154   8.7926 -13.7035
+#>    4.7409   6.4705   2.7221   2.0685  -3.5677   7.9069  -6.5497   3.9448
+#>    2.6934   0.8747   6.5580  -9.8086   3.8814  -8.3940 -15.2200  -3.0584
+#>   -5.0661  -2.5881   3.7141  -9.0621   1.3558   3.8149 -11.0696  -4.8240
+#>   -0.1446  -6.4048  -2.8131   9.9504   1.2759  -5.2146  -6.3272  -3.6930
+#>    0.1263   6.3730   3.8088  -1.7857  -3.4236  21.0228   3.1908  -7.5723
+#>   -0.4359   1.1442   5.9393   3.1697  -4.6568  -5.0381   0.3295   0.1785
+#>   -5.6850  -7.3113  -5.6905  -8.5304   3.4065 -12.5474  -4.2248  -3.9635
+#>   10.6694   1.2094  -0.2402  -0.6552   1.1061  -7.0099   6.4595   6.0198
+#>    3.4160  -5.6929  -0.2469  -2.6480  -5.4434  -1.6890   3.9987 -13.5543
+#>    6.4329   2.1033   2.5955   6.5692 -11.0004   4.3492  -0.2325  -2.4060
+#>   -4.9823   0.9858  -3.5069   3.2410   4.9803  -6.7506  -4.1168   4.7269
+#>    4.4581   5.0462  -5.1053  -5.5269  -3.4229  -0.7907  -0.7029   2.3380
+#>    4.8148  -3.2190   2.4329   1.2624   6.6487  -0.9443  -6.4351   7.2845
+#>  -14.3486   3.3816  -1.9723  -3.9340  -2.8140   2.5117  -9.7417 -10.1351
+#>    8.1523   3.6266  -5.9597  -0.3466  14.7332   5.0281   2.0042  -0.7068
+#>    1.5586   3.6073  -7.7473 -25.5572  10.1104   4.5242  -5.0179   4.7243
+#>    8.7372 -20.9357  12.0967  -3.2205  -2.7543   1.6430  -3.6255  -4.6444
+#>    3.1438   1.6858  -7.3364   2.0136  -5.4578   7.2369  -4.5787  -2.8689
+#>  -10.7882  -1.9402   7.2337   4.8458  -3.6868  -6.1800 -14.5916   9.2025
+#>    6.0829   0.8040   8.5610   2.7991 -10.8768   0.4372  -3.2278  -5.7635
+#> 
+#> Columns 49 to 54 -13.7861   4.1895  -2.6849   5.5715   4.6134  -8.8529
+#>    3.9196  -1.0141  -4.3942  -9.2992  -7.6787  -7.5954
+#>    1.9036   4.0315  -6.0885   8.4513  12.0977  -8.4275
+#>    2.5213   2.3490  -2.5723 -10.0099 -11.1267  -0.9692
+#>   -1.2945  -0.2695  -4.9368 -10.3326   2.8373   2.8081
+#>   -4.4903  -4.6993  -0.7000  -2.2604  -0.7307   3.6957
+#>    7.8396  -9.3960   1.6068  -2.4263  -9.6085  -5.5385
+#>    8.2954   4.4205   3.6761   3.6611   3.0474   4.6097
+#>   22.8393  14.4712  10.6769   3.7294  -5.8846  -7.9642
+#>   12.2628 -18.3305 -15.0729   5.8586  13.6415  -4.5394
+#>    1.3358  -1.9427  -7.2661   3.4302 -11.0954  -0.8750
+#>   26.2816   7.4058 -17.6920   5.9520  -3.1000  -3.3631
+#>   -4.4457   7.8442 -13.7301   5.5825   3.2497  -4.7904
+#>    3.0219 -12.1137   5.8340  -4.9455  -5.6589  -2.7322
+#>   -1.6002   9.9409  -3.7162  -1.8972   1.5393  -2.9746
+#>   -5.6452   3.6341  -6.8602   1.8428   1.0427  -3.9592
+#>   -8.7532   2.1036   3.1808  -4.1851   8.4547  -5.4933
+#>  -13.1717   8.5755 -15.8391 -11.1131   6.9142   6.7456
+#>   -6.1324   8.8025  -9.4276  -5.5035  -3.4948  -2.1023
+#>   -9.5285   5.5477  -7.6595   3.9417   2.6315   3.9414
+#>   11.4401  -2.6202   0.4147  13.7419   0.3354  -3.5892
+#>   -0.3527  -0.6917   6.2912  -5.7992  17.0025   5.5500
+#>   -6.5020   2.7133  -0.0885   3.5808  15.8258  -1.8593
+#>   12.5222 -10.9913   5.9336 -10.6480  -4.6576  -0.4921
+#>   17.6329 -15.9664  -1.0999   9.4738  -2.5659  -2.3957
+#>   -2.9495  -1.7847  -2.6418  -8.1787  -0.0291   7.0303
+#>    0.4696   1.8555   2.1434  13.2853   8.1429  -7.6976
+#>    4.8279   0.7118  -1.3883  -1.3308   0.9165  -0.1007
+#>    7.5085  17.3070 -17.9927   1.4501 -15.0000   3.7068
+#>   -6.4306  -7.2700 -13.2596   9.3895  -4.2451   2.0024
+#>  -11.0030 -14.6823  -3.8624  12.9594  11.3709  -4.8381
+#>  -14.5862  -9.1310   7.3428  -3.2828  13.3119  -5.4210
+#>   -8.0792  10.3616   1.0423  -1.0622   6.3049  -8.3263
 #> 
 #> (17,.,.) = 
-#>  Columns 1 to 8  -0.0036  -3.3190  -7.9587   3.2906 -13.0091   4.0434  11.5652 -12.1427
-#>    0.1164   0.8379   4.9234  -1.3137   0.0675  -5.0679  -4.4452  -4.7952
-#>    2.5500  -3.7011   0.9437   1.4541   1.8920 -14.1025   0.3019  14.6240
-#>   -1.2880   4.0459   1.9433   3.9558   2.5960   2.6825  -9.3180  14.3707
-#>    1.4861  -3.1195  -1.5166  -4.0141  -7.8198  -5.3168  16.8417  13.6179
-#>   -2.4961  -1.8708  -3.0573  -7.6208  14.6806  10.5994   3.2349 -19.1036
-#>    1.1798   0.3461   9.7883  -3.9783   3.7118  -5.1532 -10.9065  17.8243
-#>    3.5643  -8.1262   0.6550  16.2425   7.3746   0.6471  -4.3637  16.9330
-#>    8.2370   0.4671   8.6061  -3.5247  -2.3181   0.9235  -2.5817  -4.7410
-#>    1.2592   0.1341  -8.5860 -10.3599   1.3681   3.0343  -3.2627  -4.4536
-#>    2.4246  -4.6030  -0.9959   1.6058  -1.8115  10.3210  11.3543 -14.6947
-#>    1.3383  -1.8282  -1.9980  -7.7775   0.9999  10.5046   7.7484  -1.0406
-#>    6.5106  -5.1481  -8.9645   2.7458  13.4778  -1.9987  -8.3724  -6.8411
-#>   -6.7584  -5.6272  -3.1131  18.0075  -6.0840 -12.9692   4.0645   0.7947
-#>   -4.2316   5.9853   7.8798  -3.3417 -11.0788  -4.6102   7.5990  -4.8012
-#>    2.0482   2.4839   2.4645  -0.5146   7.5531  -6.1914  -5.2242   5.2656
-#>    2.3681  -6.2423  -1.4921  -1.4588  13.6384  11.2711   6.0663   0.3827
-#>    1.7283   0.8490  -4.8233  -8.1522   1.8019  -3.3521   1.7968  13.4382
-#>    1.9979  13.1230   7.3525 -13.3938  -4.5078  -1.8459 -14.6914 -14.1974
-#>   -1.2166  -5.0405   1.4720   3.4205 -11.9439 -16.7012   5.8988   9.3834
-#>    2.9653  -4.7849  11.0924  -3.6218   6.1173  -2.4609 -12.5736  11.4327
-#>   -3.2606  12.5382   9.5843  -9.3089   4.5541   2.0092 -20.0066  -9.3231
-#>    0.5372   6.2045  -6.6611 -15.5596  -8.4444   7.8671  -0.7634  -4.9848
-#>    1.0054   0.0214  -4.3680   3.8120  -2.7707  -7.4050  -8.1512  -9.1003
-#>   -7.9268  -6.0573   9.4077   1.9014   0.7067   1.0413  14.1705 -11.5789
-#>   -3.5318   8.0966   9.8679  -7.0404  -7.6394   3.9163  -2.4689   0.5888
-#>  -11.6240   2.5042  14.4123   2.6386 -19.1348   4.2073  11.3132   4.3702
-#>   -4.9533   1.3656   2.4042  -2.3242  -2.4680  -8.1332  13.3980  -0.8918
-#>   -7.9514   6.3341   5.5485   2.2275   1.4123  -1.7811   3.2591   3.3837
-#>   -6.7042  -2.2874 -10.2507  20.4313   9.5276   8.1279  -1.0739  12.8549
-#>    2.1646   6.3275  -8.4244   7.2597  -2.9179 -12.6174  12.2693  -3.4996
-#>   -2.9582   1.8840  -1.1082   4.9597  -8.9087   5.5940 -13.2627  15.1192
-#>    3.8620  15.6053 -14.6896   8.3630   5.0803 -12.6752  -7.6925   2.4458
-#> 
-#> Columns 9 to 16  18.2032 -19.6725   5.0508 -16.0836   9.1070  -3.1893  -5.8347  -3.0840
-#>   -5.6912  -0.4025   4.0813  -1.5855  -9.3055 -12.0094 -11.9593  -5.9454
-#>    6.7649  18.8722  -8.4775  10.3950  -4.3235  -0.8341  13.1874   3.2388
-#>   -8.8735  16.5639  -1.0106   5.3047  10.7215  -8.9410  15.9507   2.6645
-#>   -0.4064  15.6966   1.1777   7.8093  18.6075   0.4738   1.0298 -12.1063
-#>   -2.6083  22.5461 -18.7206  15.4192 -32.0458   7.3132  -8.8752 -11.7511
-#>  -13.7117 -11.5101  -0.8774   1.9782  -4.6365  11.1330  -1.2761   7.9712
-#>    0.2302  13.4904   1.5463  -1.8252  -9.0678   9.8361  -8.8496   1.8389
-#>    5.2399  -2.1986 -10.1448  -6.6668 -12.4374   9.5085  -8.5832   3.4156
-#>    1.6309  11.2047   5.7111   5.6381 -10.8640  -0.8878   3.1294  -7.3463
-#>    0.7705   9.0933   0.9354  -3.5888 -14.0555 -11.2154  15.2315  -3.7600
-#>   -1.7876   8.2053   1.9111  -0.1420   7.0392  -7.9765   4.7696   2.9816
-#>   -0.0192 -12.5377  -9.9169   2.0928  12.4499   2.4241  -1.6729  -4.4325
-#>    8.1069  11.8041   0.5192   7.5075  -3.9413  -1.4742   8.3041   6.3053
-#>  -13.3357   2.6357   6.9307   9.0956  -4.7934  -2.9089   8.3363 -13.9510
-#>    7.2659 -13.8078  -1.1089 -15.7769   9.4236  -5.1112  -6.0908   2.2405
-#>    7.9741  -7.2104  -7.6181  20.0668 -10.3272  -1.1638  11.3504 -12.6043
-#>   -9.1118   9.9215  -0.0853  15.8825  -8.3135  -1.1377   3.6858   0.1344
-#>    8.6729   1.6374   1.3439  -9.6331   1.6315  -8.2283  -9.1082   4.5495
-#>   -5.4776   6.7521  -3.4657  10.1412  11.8968  14.7285  -1.7380 -11.2804
-#>   -2.5355  -4.1345   2.2051 -18.1531   5.0060  -6.3627   4.9419   4.7983
-#>    9.2168  -8.3501   1.9289  11.9154  -9.8096  11.2362   9.2350  -8.4550
-#>    4.9740  12.1595  -3.6848  10.5441   7.1024  -6.7502  10.8825  -5.2812
-#>   21.5238 -15.0916  12.0990  -2.5791  12.4567   1.5288  -7.3818  16.1056
-#>   -0.0058   2.3107  13.5580   7.0735   4.1138  10.0373  -8.5884   9.2899
-#>   -1.8384  -0.6716  -4.3395   4.3135  13.5270   2.1079   2.9250 -11.1521
-#>   -0.6385 -10.9262  -4.9343   5.1412 -14.9500 -13.1119  13.7721   2.5448
-#>    2.3601   5.2188   0.3091   0.2517  -6.2330  -9.7139   7.5875  -0.9963
-#>    1.1930   7.7077 -11.2370   3.9249   7.5846  -2.9404   1.1477  -1.9224
-#>   -2.4618   3.0866  -5.5732  13.2863 -11.5555   8.1338  -1.0781  -8.0933
-#>    4.2506  -6.1249  -2.8451   6.1572  13.4412   4.0567  -1.8001  -1.1474
-#>  -16.7791  13.2047   4.3998   5.3425  -3.4872   4.9933  11.7567  -7.1929
-#>   -4.3003  -4.2831  10.9765   5.8159  -5.9722  -1.2655  -5.2305  18.9444
-#> 
-#> Columns 17 to 24 -12.1367   1.4567  -1.6269   8.3249  -1.6047   3.3990 -11.6582  -5.8878
-#>   -2.3760   0.4142   9.0234 -13.6288  -8.3573  -6.6818   7.0287  -2.7305
-#>   -9.1737   0.7451  20.8649  17.2731 -19.0187   9.8993   7.9144   1.6249
-#>    9.1767  -5.3467  -4.6508  -9.8663   1.7865  -8.4075   5.7143  -0.6771
-#>   10.6411 -19.1595   0.1464  -0.1775  -1.1914 -10.3426  -1.2584  -7.9563
-#>  -16.1971 -13.8179  -9.4703  20.5316  -9.9628 -20.8515   9.6431 -10.2868
-#>   -9.6250  10.1539  14.2932 -11.5516  -4.8499  -2.0416  12.4956   8.9299
-#>  -12.3655   0.1540   9.0695  -9.6254  -7.8712 -19.9817  20.3502   4.4879
-#>    1.8061  -8.6229  -6.4227  17.4155   4.0062   4.4123 -12.7788 -12.0072
-#>   -4.9458  -5.6366  -4.4784   0.7285  -4.5531 -13.3600  -4.7004 -15.0519
-#>   -0.2497  -0.2428  -8.2563   3.6055 -14.8665  -2.6090 -15.8119   5.3822
-#>    4.3693  -6.9149 -12.2015   4.5604  -0.3401   5.8007 -17.0388   0.4493
-#>   15.0418  -0.5247   5.2963   3.6107  -8.5075 -11.3623 -12.6868   1.0413
-#>   -4.0378   8.6373   2.0527  -0.9846  -0.3666  13.2075   6.5006   9.9071
-#>   -0.7308   7.5805  -3.0739  -0.0865 -14.7430 -19.0587  12.9800 -11.4091
-#>   -6.2874  16.6758   7.1346  -7.2085  -4.7202   7.4124   3.7254   5.7929
-#>  -11.5627   6.5587  -4.1144  19.9498  -6.9683 -10.0468  -7.7720  15.8065
-#>  -12.7454   8.7868  12.2793 -10.7621  -4.4990   2.5450  -0.2208 -17.3568
-#>    1.5817   1.2042   8.9885  -2.1994   4.8149  15.1747   2.5702  -2.2010
-#>    3.3618   3.3082  -4.8614  -7.8732 -11.8926  -7.8178  -5.5999  -2.6310
-#>   -7.0714  -4.9754   6.3195  -3.3233   5.6727  17.1982   2.4234   7.0407
-#>   -0.0079   4.2773  -2.5152  -5.1012  -6.6415   7.7634  -1.7209  15.0072
-#>    0.7598  -0.0868  11.9345  15.8469   4.9130  -2.6162  11.1826  -5.4076
-#>    3.9734   2.6924  -6.5429   4.9290  -8.8703  13.0501   1.0853  -1.9093
-#>    0.0759 -10.3995 -20.7333  -6.7558  -3.4664  -5.0556  -3.4500  -2.6451
-#>   11.9588  -0.9773  -5.5220  -9.4147   9.2083   2.0006  10.9240 -26.0863
-#>   -8.0222  -7.1364  -6.2283  -4.9439 -18.3233 -12.8074   0.2920   3.7361
-#>  -14.7121   1.6619  -5.7169   9.3542   3.5858   1.4770  -0.7576  -9.8484
-#>    1.3114  -0.9283  18.1555  -3.2016   4.1839  -9.9645   2.9604  -3.2908
-#>   -5.9759  -8.9707  -0.6552   9.0356  -7.6296 -30.9177  -8.4567   9.6805
-#>   -7.4921  -3.0522  -4.5990  -1.9557   0.6606   3.0873  -0.2393  -6.4692
-#>   -1.3348  -6.8173  -5.2836  -9.4862  13.9301  -0.0991   6.0113 -13.2417
-#>    4.5449   5.1664 -15.3847   1.6871   7.4429  -1.8639 -16.2640   4.9284
-#> 
-#> Columns 25 to 32 -14.1407  14.2955   3.8695  -6.0790  -5.2760   6.6175   3.1503  -5.2982
-#>    2.2688 -11.4986  -2.2534   2.4166  13.8815  -4.4936 -12.1703  -4.5053
-#>   -1.4144  -5.8853  19.2525  14.6247  19.1070  11.7330   5.7779  -4.3484
-#>   -3.9388  -1.7344 -10.0980   9.1879  -8.3052  11.6017 -14.8255   3.6860
-#>  -10.8255   7.7143  13.6104  -3.6169  -5.8611  -6.1461  10.0281  -2.9510
-#>    3.6030 -19.8067   4.5132  -1.7495 -11.6521 -12.9786  19.7649   3.0201
-#>    5.1040 -18.6112  10.5612   5.6103  -2.6788   0.9387   1.4030   1.6843
-#>   -1.2084 -16.5771  15.1098   9.0082   0.5725   0.1737  -1.0725 -11.1100
-#>    8.9186  -5.9483  -3.8864  -6.2679  17.5105   7.6420   1.4482 -30.5893
-#>   -5.3877  -5.3120   1.5567  -4.8999 -12.9138   5.5266  -4.2428  12.0746
-#>    4.3041  -1.7397  -6.7371   0.3417  -0.7745  -5.9680  -4.7485  -9.4796
-#>   -1.8727 -14.7426 -10.1967   8.3783  -5.9343  -5.3843  10.4344  -4.4727
-#>    1.0782   4.0079   0.5302 -19.9129  13.8336  -5.9290   2.6223 -10.6001
-#>    3.2687  -4.3255  -7.6249  -5.0333   3.5369   2.8440   1.9130  13.2635
-#>    0.9298  -3.1962   4.1605  -0.2121 -19.9007  -2.3541  -9.4520   1.5965
-#>   -9.8351  13.3726  -3.0548   1.2052  -6.0823   8.8489 -14.4292   4.4013
-#>   13.7231  23.7698  -9.7120   5.3621 -10.5620 -14.4767  -2.1569   3.0922
-#>   17.7819 -11.9477   8.1932 -11.3599   5.3066   3.3444   5.0477   1.9190
-#>  -11.2310 -17.4655 -21.1093 -16.8335  11.1529  14.9433  -1.9176   0.8984
-#>   -8.4986   1.0614   5.4820  -7.7777  -7.8456  -2.3261   6.5574   0.4245
-#>   -5.7616  -5.6309  18.0811  10.8869   4.5236  -8.1555 -11.5226  -2.2178
-#>   -6.5431   0.4128  -2.1879   8.4302  -3.1771  -6.8854  -6.7822 -16.3649
-#>    3.8306   9.3733   2.6196  13.6446   6.9131   2.6113 -12.4660   7.1945
-#>  -20.6794  -3.3141 -15.1161 -15.1085   6.8808  24.0385  13.2143  -3.2077
-#>    9.8282 -19.6173 -10.8193  -0.2638   8.3347  14.2335  -5.1957   0.3102
-#>   -3.0323   9.2271   1.8307   0.7336  -7.8705  -2.3976  -3.2060   3.4770
-#>    3.7052   3.8880   4.7929  -8.8234  -3.5462   5.8435  11.7265   5.0594
-#>   11.1393  -4.2118   3.1103  -1.0103   5.0755   5.7154   5.7432   5.4446
-#>   -2.5627  11.9907   5.3111   3.7991   6.3624  26.5475  -4.0819   5.1281
-#>    4.9443   1.2031 -10.6381   1.1163 -11.6207  -7.0394   7.4165  15.7235
-#>  -14.2614   3.5230  -7.0514   4.8836  11.7337  -6.1559  -7.5084  -3.4756
-#>    0.3203   9.2310   4.0731   2.0453  -3.5192   7.8414 -11.4932  -3.4787
-#>   -1.9585  11.0028  -7.8681  -5.2722  -4.7085  -0.3444  -8.8240   2.5753
-#> 
-#> Columns 33 to 40 -16.2086   3.6009   4.1534  -4.0031   0.7665   6.8490   7.3541   8.2326
-#>  -11.6117   0.8177   2.1269   3.1290   4.0927  12.8487  -2.8535   9.8357
-#>  -20.3408   2.3152   4.2274   8.3990  -4.7943 -18.4804  21.1509  -7.6883
-#>    0.1038  12.0059 -14.0866   7.1420  -2.2766   2.9557  -9.2178  -4.7261
-#>    4.3252   7.8233 -10.1021   0.0185   6.5683  -8.0528  -3.2560   0.2315
-#>   -8.5887 -11.0928 -14.6416   3.7763   4.4483 -10.3446   1.0966   1.6011
-#>   11.9414  -6.7054  -2.7639  -4.9038  -6.6649   3.8405   5.4326   0.9827
-#>  -19.0930  -8.3548  13.6712  -4.4705  -7.1463   8.9296  -1.0585  20.3146
-#>   16.2528  -3.6523   0.9896 -21.1536   2.3888   4.4423  -6.8638   6.4089
-#>   -1.6519   6.3646   3.1551   9.5345  -4.8651   5.2906  18.3544  -2.8488
-#>    2.6591   4.9864 -21.2580 -10.1801   6.1868   3.7901   9.3343 -12.3642
-#>   -1.5676  -9.3029 -11.8483  -2.9516  -1.4575  -6.7623   6.5269  -3.6121
-#>   -2.2296  -7.6398   6.7909  -5.8588   6.1594  12.8406  -4.1241   9.5473
-#>   11.0382  10.7851  -4.9545  11.5578  -4.3485 -11.3629  -4.7165  -5.0466
-#>    2.5669  -4.2435   8.9381   0.1753   8.1956  -0.5499   2.4431  21.6045
-#>    4.7957 -14.3638  -1.0203   5.1330  -5.1298  -4.5777   4.2896  -5.9634
-#>   24.2212 -21.3843  -6.0477  10.0261   4.5586   2.9284   4.3612  -9.6834
-#>    3.6108   2.4465   4.1788 -12.8152 -10.0255  19.3243   9.6198   7.5142
-#>   -8.3931  -8.3866 -14.1537  -3.7318  -8.6589 -18.7726   8.6055  17.5604
-#>    0.3024 -15.7799  -0.8323   5.2878   1.8991  -9.5312   3.1859   2.5497
-#>   -1.8618  12.1423  -3.1689  -2.1018   6.6899  -7.0607  -4.2293   0.3692
-#>    6.0903 -19.3583   0.6555  -4.0463   1.7142   1.0252   5.1535  -2.7416
-#>    2.6232  16.2810  -4.7329  12.3208  -6.1677   5.3871   1.9002  -0.7168
-#>  -10.6743 -21.4390   1.2288   1.0133   1.3441  -7.4375  -8.6747  -3.8495
-#>   -2.5638   5.9309   4.8542  -4.5298  23.0909  -7.1586  -0.3635   1.0118
-#>   -7.4935   9.6163  -7.2168   7.3669 -16.4921   3.3259  -2.4198  10.2347
-#>    4.2367  -0.5784  16.5164   4.3086  -7.5247   1.8854   3.8563  -7.8103
-#>   -9.3610  -8.2013  -2.5560  12.9635  -3.8509 -10.7876   1.4986  -1.3885
-#>  -14.0475  -2.9250  -2.8980  14.5107  -5.2680  -4.4482  -5.0714  14.8558
-#>   -6.7825 -16.8933   9.7776   1.8856   4.4857  17.3968  -0.1486  13.6344
-#>   -4.0811   1.1356  -6.8292 -15.4074  11.5754  -3.7018   8.3617  -1.3083
-#>  -11.2235   4.8559   3.5214  23.2757  -6.6173   3.2491  -0.4854   9.0660
-#>  -13.1656  -2.8782   9.3092 -10.9921  10.8435   1.7970  18.7206  -7.0084
-#> 
-#> Columns 41 to 48  -0.4794  -4.7200   2.1935  -2.2066   4.2906  -7.6101  10.5657   6.5193
-#>  -10.3014 -12.9307   1.8835   2.5713   2.2012   2.0539 -13.8011   7.5363
-#>  -17.2547 -12.5349 -20.9318  -4.4617  -0.9685  -0.3390   5.2662  -6.5757
-#>   -3.1898   5.6737  -7.1999  14.6965  -5.5819  -2.1300   0.7077  -3.1309
-#>  -18.0966   1.1532 -11.4858   7.5103  -4.0690   8.0907  -7.2170  -7.7530
-#>    7.6633   1.8808   5.7849   7.3424  -3.0240  -9.6336  -4.3158  -4.6648
-#>   -8.0560   1.5696   3.1842 -11.2185   7.8985   8.3951  -3.0128 -21.7698
-#>  -10.0482  14.3059   5.0131  -2.6909   9.0579   1.8615  20.8154   9.7676
-#>   -1.6295   4.7432  -4.8663  -3.7719   1.1519  -3.5064  15.8951   2.9470
-#>   -5.0773  -6.4014   0.0620  10.7953  -1.3257   3.5800  -8.8108   3.2778
-#>   -7.8998   4.0090  13.3705 -15.3735  -3.4188   3.6468  -1.2002   8.7656
-#>   -2.2991   7.6728  -7.0008  19.0875 -10.4308  -5.2981  -6.5476 -10.0805
-#>   -7.6098  -5.1771   5.7833   4.8127  -7.2318  -4.9819 -16.0469   1.4642
-#>   -1.1119   2.1865   5.0103   0.7753 -13.7961  -3.5765  11.4363  11.7848
-#>    5.3285   0.6544  -4.3014  -3.7732  -6.6757  -4.6680   9.2389  -3.0111
-#>   14.9243 -18.9852   1.5592   5.8168   3.2338   7.5046  -9.2199  -1.9998
-#>    5.8316 -16.0814  24.0642   1.9585  -3.5264  -6.1538  -9.1210  -2.4992
-#>  -13.5651  -5.3863  -7.1816  17.6731 -13.0657   7.3824   1.5581  -5.1607
-#>   13.1493   4.8888  -5.5545   0.1581  -0.9223  11.4728   1.0332  -0.4469
-#>    1.6263   3.3358   0.6984  -6.3469  14.6469  -9.9027  -5.6818   1.4462
-#>    7.5497   7.6437 -17.7139   3.6806 -12.6042   4.5378   7.5226 -16.3628
-#>   15.6895 -10.2874  11.3661  -2.3743  -5.0022   2.3989   6.9662   1.3720
-#>  -14.7766  -5.7658  -9.8210  -3.8484   9.2184  -5.0199 -10.5909   4.3750
-#>   -3.3873   3.1920  10.2297   8.2557  -5.2131 -17.5189   8.5251  12.7148
-#>   -4.4102  13.5598 -20.2984 -13.7089  -0.8403   3.0563  10.2671  -2.3559
-#>   15.3842   5.0043  14.6081   9.0073   4.0625  -3.2320  -2.1856  -2.8255
-#>   -6.7683   7.2567   4.1482  -2.9104  -3.1228  -1.1391  10.5654   6.2337
-#>    4.1170   8.3056   0.6752   6.1380  -1.4899   6.1811   7.6111  -5.8355
-#>   10.8976 -17.5127 -13.1384   4.9627 -13.8710  14.4422  -2.8103   3.5432
-#>    7.4061   7.1341  -0.5488   2.8806   0.9175  -2.6211  14.1834  31.8526
-#>   12.3116   0.2204   1.3383 -13.2747  -2.8086  10.5885  -3.7188   6.7941
-#>    0.7298  13.8047  -3.9216  -1.4572   7.6106   1.1530   8.8140 -11.3650
-#>   -5.5953 -14.6633   2.4963   2.2599  -9.8951   4.3039  -9.2876  -5.9474
-#> 
-#> Columns 49 to 54  -7.3718   0.1724  -6.6997   9.3962  -6.5077  -3.1607
-#>   23.4490  -5.6180   1.4565  -7.3384  -1.8619   4.1763
-#>   -0.4912   2.9085  -4.5126  -6.7124   5.1127  -3.9138
-#>   -6.0853   3.5843   3.1338  -1.4118   0.0586  -5.0726
-#>   -0.1282   6.5454  -0.7282   3.4190   2.0427   5.7622
-#>    4.9669  -6.5563 -12.0750  -7.4261   1.5339   1.5126
-#>    8.9537   5.2251  -4.8157  -0.5032   3.1482  -3.5577
-#>    0.7154   0.0666  -7.2519   2.2708   5.7407  -4.2322
-#>    0.7558  -4.7962   7.9415  11.3416  -3.6383   4.6629
-#>   -3.2890  -0.2584   6.8779  -1.2153   3.1597  -6.3844
-#>   -8.5711  10.4096   9.5683  -2.4923 -11.0658 -11.7025
-#>    2.7361   6.0141   1.2901  -1.3441   6.5208  -3.1641
-#>   -6.9538  -8.9816   1.3693   2.0008  -4.1646   2.1112
-#>    0.4658  10.2723   5.8848   5.8259  13.0186  -2.1762
-#>  -11.7348  10.4812  -4.3369   7.0428  11.4794  -2.2028
-#>    7.4112 -19.0299   7.3612   1.7996  -5.1023   3.9962
-#>    4.9686   9.4521   8.7250  -0.0540   1.7540  11.1828
-#>   -8.2419 -10.3812   8.1248   3.6823  12.9724  -3.7185
-#>    2.8024 -12.8766   3.1866  10.2768  -4.1207   5.6507
-#>   -1.8880  14.1398   4.2122 -11.5026  11.6045  -3.2251
-#>   17.1062  -8.0658  -5.3075   0.5834  -0.4006  -2.4423
-#>    8.6359   3.7877   3.2643   3.3035  -3.9406   0.0275
-#>   -6.1192  17.6782   7.8204  -6.9942   0.3734  -7.6414
-#>   11.0851   6.6772   3.3842  12.7506   5.1887   7.7000
-#>   14.8332  12.8735   2.1239   3.1732   5.5896   2.0718
-#>    7.2465  -5.4545   3.8073  -0.9183   7.1958  -0.9900
-#>   -5.3976   8.9492   1.4537   1.1187   2.3930  -5.6091
-#>   -4.3078   4.0652  -8.3012 -19.9772  -1.4070  -4.8790
-#>    1.6937 -16.0303   0.0988  -2.5211  -4.9906   9.1394
-#>    0.4467  17.8078  17.1774  -6.6270  12.9747   0.3961
-#>  -11.5709  12.6492  13.1732   3.6568  -3.5198   0.5396
-#>   12.2177   7.3196  -6.3694  -1.8782   3.0797  -0.6957
-#>   -2.2324  14.6047  -0.8001  13.8972  -4.3510   5.3483
+#>  Columns 1 to 8   5.2062   6.4065   8.5312   9.0955  -0.5235   0.3085  -7.8041 -10.1075
+#>   -1.5281   2.3107   6.2066   7.8368  -7.5168   7.6761  -6.8371   5.9547
+#>    3.3820   1.8783  12.0787 -10.0834  -7.5565  -8.6900   5.9631 -20.5098
+#>    2.3642   8.4941   1.8612  17.1973   7.5288  -4.4782  -3.4536  -9.3324
+#>   -2.5016  -5.9783   4.4389  -3.1082   1.1414   4.3949   2.8199  -6.6375
+#>    0.9962  -0.3967  10.1941   2.3201   2.8284   2.5462  -3.6083   0.8569
+#>    2.3564   0.7161  -7.4071   3.0146   2.6186  17.3825  -4.5904  -5.4011
+#>    0.5117   1.0361  -1.5090   1.4770  -4.2030   4.0998  -7.5226   5.9214
+#>   -1.7923   5.7535  -4.9701 -10.4625 -14.1787  -2.8550   3.1688  -9.5902
+#>    2.8345  -1.5235  -9.4966  11.9819  -6.2584  -7.3193  -5.0261 -11.1585
+#>    2.2643   5.8170   5.2043   9.2719   9.3628   6.2426  -9.1843   1.3245
+#>   -7.1762 -12.2680   5.5735 -11.8904  -7.1599  14.8536  17.6881  -9.8516
+#>    5.5518   3.2217  -4.4778   3.8995  -1.8664  -6.9647  -3.9305 -12.9324
+#>   -6.4909  -4.9552   5.2168 -12.6810   7.6878   5.8827   8.7034   7.9903
+#>   -5.6575  -5.1057  -1.0492  -3.9127  -0.9752  -0.0680   1.8378  -1.0330
+#>    2.4254   2.5319  -1.5565  -3.2438   9.6880  -0.3314  -3.3405  -4.7011
+#>    3.2410   2.1115 -14.9651   7.7893   4.1532   6.7871  18.3936  -3.1358
+#>    2.9924  -2.4656  -8.2656  -0.5360   9.2659   4.5416   0.6314  -2.9858
+#>    0.6287   6.7512   1.7734  18.0947   2.9011   0.4293   0.8328   3.5282
+#>   -0.0651   7.3371   5.6683   8.2378 -11.9909  -7.3594   0.4883  -8.6583
+#>    1.9222  -1.0131  -1.8941  11.5151   9.9498   6.7965   6.2610   0.0717
+#>   -3.1166  -0.3244 -13.7111  -1.4455  12.2203  -5.0392 -15.4664   3.8544
+#>   -3.6694   6.8850  -3.1157  -6.1977   9.5380   2.6029  -9.8562   1.5450
+#>   -2.2759  -3.8652  -6.8997  -5.9262  -5.3554  11.0629   2.9481  -0.5143
+#>    3.2514   8.2135   3.4933 -14.9007   0.9373   4.1942  -6.1680  -3.1610
+#>   -3.7257  -1.1718  -6.9096  -6.4412   9.2312  14.1220  -3.1979   1.1137
+#>   -1.5789  -0.4715  13.0554 -13.8019   2.7488  -2.5198  -0.7968  -0.7388
+#>    0.2977  -2.6954   2.8029  -0.7476   6.0299   8.5365   6.1323  19.9033
+#>    0.5072   0.5749  -0.9289   6.4329   7.7126  -2.6064   0.6026  -7.7032
+#>    1.9559   4.3685  -2.1914  15.9204 -20.1938  -1.2114  14.5099  -1.6412
+#>    5.4583   6.1685  -2.4450  16.8150  -9.8069   0.1982  11.3293   3.0131
+#>   -5.3711   5.6225   3.8660  -3.3717  16.2821  -8.9134  -2.9562   9.2144
+#>    5.1504   9.4042   0.5806  12.4228   2.5611  -9.9295   3.8668  -5.2253
+#> 
+#> Columns 9 to 16  -0.9677  -4.0097 -12.4059  -2.3359 -14.1406  -6.4539 -12.1545 -15.3925
+#>    7.0265  10.1461  -3.4154  -9.9663   6.8054  -1.3017   2.3787  12.3173
+#>    1.8448   3.4694   6.8664   5.6583  -6.4041  -9.7351  -1.2019  -2.7786
+#>   -9.0938  -4.1331   4.8760   0.4087 -12.8925 -12.6656 -16.5059 -14.6703
+#>   15.2378  -2.5167   0.2577  11.3278   3.3438   4.8032  -1.1242 -11.0190
+#>  -11.8968  -4.8267  -0.4278   8.6420  -6.6961  -0.6619  -5.6707 -10.5583
+#>   -4.3568  10.2016   3.6984   6.0754  -7.8074   6.3275   4.1293   6.0634
+#>    8.5500  -6.6387  11.6132  -6.8210   9.6192   8.8854  16.5629  -7.0220
+#>   -0.0838  -3.3344   5.2469   1.4617  -1.5493   4.2835  -8.1487   1.0005
+#>   10.5035   0.0507 -12.5861  -1.0949  -9.5833   2.2038   7.5619   2.7563
+#>    2.5321  10.7052   6.9507 -21.0274  11.1643   9.0992  -6.5149  -4.4616
+#>    6.3098   1.4958   0.0077  -0.0203   3.2615   5.5996   1.9291   2.2002
+#>    5.2948  -3.5760  -7.8087  -1.9050  -4.6672   8.0447  -4.9779   2.2689
+#>    1.2328 -11.3895  -7.5358  -2.3623  -6.8337  13.2612  -8.3084 -13.1817
+#>   -4.9721  -5.0257   1.6596   2.4616  -8.9933  -4.1106  -5.1344  -2.3345
+#>  -14.0951  -6.7244  -9.7946   4.3367   1.0985   3.3463   2.5564  -2.8044
+#>   -2.6770 -12.4474  -6.2746   6.7057  -1.1132   7.2519   3.3884   6.2520
+#>   -3.4401  22.3057  -7.1662  -2.1130 -12.6090   8.8895  -1.2511   1.5363
+#>    7.6137 -19.1470  -4.9358 -15.8546   3.5287  10.2997  17.6449   6.7492
+#>   -9.6267   2.9538  -1.4326   2.1347  -6.3685  -2.0564  -6.6023  -2.6144
+#>    3.4578   0.2958   0.3596  13.4159  -5.9611  11.7165  -2.2672  -0.9468
+#>  -11.2246   4.9543  -9.7973   7.4378   3.5873   6.2133   4.7434  -7.4909
+#>   -8.2853   3.8656   0.3439   2.6212  -8.3324  -2.7554  -2.5187   0.6864
+#>   -5.3623  13.4094  14.6258  -6.0378  -8.6302  -2.5292  -5.9406   3.4581
+#>    6.6980   0.5924  -3.1794  -9.1969  -3.8435  -0.3109  -2.3617   8.1715
+#>    4.3023   8.2797   8.9166  -0.1207   1.8869  -7.4730   3.3733  -0.0388
+#>    6.3221 -30.4470  -9.1873   9.8814   4.1910   8.1789   5.3645  -4.7537
+#>    7.9247   0.0846   1.1110   8.2288   5.7250   3.3094   5.2440   6.2563
+#>    5.4151  -7.0976  -5.6453 -10.9289   4.8744  -7.9637 -11.4142  -5.0917
+#>    2.4489   4.9953  10.6033 -11.6046   1.4299  -5.6220 -11.4937  11.7032
+#>    0.3344  -7.6370   0.5923   1.4171 -16.2069   2.5125   3.3490  -2.7550
+#>   -1.4463  -7.6959  -3.7580  11.3231   0.8858 -12.9351  -2.0833   2.8690
+#>  -13.3702  -2.0348  -1.6581  17.3827 -12.9511   0.6218 -17.0761   8.6773
+#> 
+#> Columns 17 to 24  -3.4340  -1.4332   9.4683   1.1172  -7.7836   6.6448  -3.4124   2.6377
+#>    4.0018  -5.1031   5.5315  -6.1011 -10.1176 -11.9185 -11.5298  -1.6543
+#>    0.7412   2.4470   4.4561  -5.6521  12.8072   5.4284   0.3308  -2.1896
+#>  -10.1075  19.2759   5.2872  -0.6025   0.0830 -18.6056   0.5928  -2.7637
+#>   19.0132  10.5841   6.0043   5.0118 -13.0968  21.0139 -14.1146   0.3770
+#>    8.0410  -6.4152   2.6467 -11.1163   6.9361  18.3679   1.2554  -7.2941
+#>   -4.6484   0.5479   1.3951   4.6272   7.0520   0.1075   2.0469  -1.8648
+#>    2.6453  -0.5733 -10.7167   5.9196   6.8765  -3.3049   1.6349   2.3652
+#>   12.6852   0.6612   1.1141  12.5043  10.3867  -2.4188  -5.2136  -1.9053
+#>   -4.3614  -0.3980  -0.4479   9.9437 -10.4470   5.8754   0.4783  -7.1779
+#>   -4.5928  -5.1068  -0.8685   0.9190  -1.8553   5.7822  12.7401  -1.2883
+#>    6.0113 -11.7025   5.0655   1.0502  -7.1784  18.2597   1.6481  16.7653
+#>    3.7718  -5.8672   1.0738  -1.9323   1.6446  -9.6286  -3.3639  -1.8432
+#>    2.5879 -10.5316   6.0032  17.6337  12.6403 -16.1492 -19.9456  -7.3339
+#>   -5.1003 -15.7109  -1.1631  -6.8531  12.4065   1.9177  -3.2176  -3.7601
+#>  -11.3250  -6.8002  -6.7891  -5.3432   0.1282   8.5800 -10.0017  -6.0924
+#>  -21.4015 -10.3819  -3.8519  -2.2009  -0.5347   4.8379   5.5032   5.9228
+#>   -2.9474 -11.2088   3.1387  -1.4097   1.0569 -14.3865  12.8763  -8.5439
+#>   -4.6242  10.5840 -15.1057   1.7397  17.9535 -20.1512  -2.3121 -14.5795
+#>   -0.7881  -6.0030  -3.5365  -4.5937  11.8125   1.2383   2.1917   4.4664
+#>   -7.8666   1.7594  10.4907  -0.8218   5.3324  -3.6404   5.5090  -4.6110
+#>   -6.9657 -14.1624   4.2566  -8.2208  -4.1070  -1.1306  -8.9193  12.0872
+#>  -12.3010   0.8195  10.7051  -3.4484   5.1781   1.5721   4.4617 -15.5562
+#>   -5.9939  -7.1084  -3.1787   6.5885  -3.8283   0.1379  -0.8186   6.0172
+#>   -3.6418   3.7688   0.7896   0.0369  -0.9160   5.1858  -9.1146 -13.5730
+#>  -12.7688   2.0074   3.9355   2.5086  -8.9581   6.8594   3.2289 -11.0553
+#>   -7.4703   9.3158  -1.5761  -5.1712  -5.0044  -3.6980   2.8445  11.5953
+#>    0.5107   4.8479  12.5108  -3.4621  -5.8938  12.0158 -11.9714  18.2226
+#>   -6.4609   1.8136  12.2727  13.1547   4.4119 -31.4823   0.9838  -1.9326
+#>   -4.1312   5.7892  -9.2721 -10.4745  -6.1841  19.9672   3.9965 -16.4372
+#>  -11.2938   8.0320  -6.3455   2.7606  -2.5874  12.2807 -13.0615  -9.6145
+#>  -14.3744  20.2807   0.1696 -18.3391   8.4987  19.6241 -14.5685 -22.7530
+#>   -6.0744   3.9201   3.4267  -4.2923   9.8226 -13.1160  14.3907 -14.1232
+#> 
+#> Columns 25 to 32 -25.6559 -10.9156  -9.3639  -2.1209  14.7189  -5.0703  12.8279  -8.1154
+#>    3.0320  11.0460   9.8138  -9.1326   8.1151 -10.1611   9.1126  -6.8986
+#>   -9.6741  12.1914  -7.0153   6.3812  -8.3031   1.5720  -7.1641 -15.9364
+#>   -9.0767 -12.0473 -16.0389 -10.9423 -11.2371 -14.7245  -1.0813 -10.2350
+#>  -15.6762  -9.1777   8.5985   7.7974   0.0979  -6.0800  11.8735  -0.9774
+#>   -6.1632   3.1626   2.9883   7.2371   2.0970   2.5618  10.7349  -5.8495
+#>    2.8342   4.3450   1.4687  -5.3153   7.0235  -4.8326   4.1890   3.2969
+#>    5.7836   2.5935  13.0898  11.6800  13.7342  15.2812  13.2999   3.4133
+#>   10.2866   8.7331  -4.8855  -9.4812 -10.5664   5.2894  -9.9995  -9.0635
+#>    1.4083  -7.5989 -16.7569  17.7992   6.5029  10.4945   7.0004   2.5671
+#>   -2.1015 -11.2026  15.4402  -3.0975  11.5574 -12.2456   3.7944  -0.9630
+#>    9.6743   8.1705 -12.7869   0.0080  -0.9624   1.2023  -6.8988 -14.4147
+#>   -8.7400   0.4795   2.4549  -0.0471  -7.8906   4.6204  -8.4670  12.1793
+#>   -4.4415   4.8317  -5.7580   6.7529  -3.4655  -9.1483 -10.0194 -16.1485
+#>  -17.8224  -9.6450  -8.6455 -11.4746 -15.5416 -10.2722  -4.1666  -2.5482
+#>   10.6338   0.0640   2.8336 -15.3082 -13.1138 -13.8507   0.9046  -1.2515
+#>   -2.7058  -8.5468   3.7900  -8.5088  -1.6693  -5.6441   7.3983  -3.3417
+#>   19.7399 -14.5031   2.0907  -3.6008  -9.4239   1.6132  -9.7886  17.4081
+#>   20.2215  -0.2101  -7.6222  -2.9692 -25.8327 -13.0773 -13.4568  -3.6684
+#>  -12.5797  -1.8838   3.4676 -10.5341  17.4419  10.1659   4.0989   1.4775
+#>   13.3531  -6.1383   2.3657  12.2830   5.1661  -0.0675   4.1757   1.4087
+#>   -3.7723 -17.3552   2.3568  -7.6880  -5.5097  -1.6227  10.8565  16.7836
+#>   -7.2957  -8.9424   9.9576 -11.6376  13.0173   4.0345 -13.3331   5.6424
+#>    8.6461 -11.3035   5.7422   0.3513   8.9917   5.0631  11.9125   3.8531
+#>   -8.9531   9.6955  -5.8416  11.5748  11.6782   8.6377   0.4979  -1.2785
+#>   -1.1237 -10.1375 -11.2824   7.5102  -1.2664   9.0790  -0.0468   6.8004
+#>  -13.3994  10.5269  -5.1302  -1.3119  -4.4761  -1.2386  -1.7679 -12.8146
+#>    8.5100   8.3615  16.6133  -5.8301  10.6464 -11.0602  13.0278 -10.1821
+#>   20.3466   5.8746 -17.3077  14.5867   1.2178   6.6536  -1.0953   1.4276
+#>   -1.5457  -5.3937   9.0299  -1.6478  12.1792  -3.2151   2.7575 -11.6832
+#>    4.5568 -10.8305  11.1900   3.3307   9.5425  -0.0017  14.8007   5.9503
+#>  -28.9684  -1.7698   5.5603   6.9113  -1.7502   4.8231  -7.1587  -3.1486
+#>  -11.5267  -0.1977  -2.8475  -8.7871  -2.1866  -9.6414  -5.4352  -4.4665
+#> 
+#> Columns 33 to 40   7.9492  -5.0804  -5.7993  -7.9770   3.7569  -8.4412  -3.8155  12.0096
+#>   -5.3788  18.9755 -11.2479  -1.8086  -0.8534  -7.1297  -4.9246  -8.0938
+#>   -0.1581  18.0097 -13.9512  -6.0909  -5.0746  -1.2351  11.2417  -5.4303
+#>   -2.9308  -2.5272   2.9170  -2.8434   2.2266   1.8755   4.3224  10.6988
+#>   13.6307  -5.3490   5.2155  16.0001  -7.0303  -6.2270   8.0629 -10.3844
+#>    5.8387 -12.4334  17.5634 -11.5684   0.4949 -16.0601  -7.4916  17.3772
+#>  -16.8662   0.9686  -8.7218   5.2073   2.6688  13.1830   3.0184  -4.8660
+#>    2.1112  -5.1226  -0.2730   7.3787 -16.1218  -7.2231   9.9759  11.2552
+#>   -5.4150   0.5366   5.5534   2.7030  19.7924  10.9628   8.9917  17.9985
+#>   -2.5858   8.1095  -7.0435  -9.2342   0.7987   1.6674   5.6875  -7.1116
+#>    0.3922   5.8502  10.8145   4.1114 -18.7439   6.8103  -2.5674   1.7185
+#>    1.7563  10.4029  -1.4483   4.8017  -6.0081   8.6435   6.5205  -0.8656
+#>   -3.9743   4.2120 -10.8495  -0.8868  -0.4211  -8.7428  12.1068   0.6119
+#>    2.7847  -2.8586  -6.3869  -3.9169 -22.2621  13.9679   3.6501  11.3068
+#>  -18.6067   4.6314  -7.7752  -5.1656 -12.7367 -13.8206   3.7056 -25.1201
+#>   -8.5392 -11.8004  -6.7347   6.9775  -0.3826   0.9470   8.6821   0.4177
+#>   -6.1266   8.8447  -6.9791   2.5319 -16.2844  -5.4277  14.4982   5.5883
+#>  -15.7056  10.6798  -4.1240  -4.1178  -0.2579   5.5221  -6.0502  -0.3089
+#>  -15.6076 -11.4628  -6.4459   4.3462  -4.9908   1.3151   2.8178  20.8673
+#>   -6.6252   0.4098  -5.4148   5.6463   4.0706  -4.8986 -15.1973   4.6258
+#>   -5.6373  10.1663 -14.6556   6.9960  10.2837  20.8380  17.3981   5.2066
+#>  -14.3733  -5.5633  -1.6637   3.5819   8.4398 -19.9616  19.8552   8.0458
+#>    1.3051   4.6582  -9.1313  -3.9248  16.2773   9.9309   0.4038  -9.2242
+#>  -10.8030  12.6749  -0.5136   2.2521 -10.2717  10.3002   0.7334 -20.7239
+#>   16.6504   5.3920 -22.9436 -14.0093   3.5895  11.1150  -3.3734 -10.8219
+#>    6.8710  13.4822 -23.3474 -12.1038  22.5145  -6.8989  -0.1700  -9.2741
+#>   -3.1169 -18.7922  -2.6409   0.4745  -8.8956  13.7294  -0.0943   7.7534
+#>    2.9541  -5.6867   2.5941  15.8580   9.7454 -12.2776  -9.5374   2.3370
+#>    1.5138 -10.5095   4.6716  -8.5460  11.0888 -14.5055   7.9629   5.4367
+#>    1.9638  14.9499   1.5870 -20.9975   0.0808   2.4397  -7.2864 -20.8043
+#>    8.3696 -10.7314  -3.8182  -3.0129 -12.0980   2.4990 -11.1520   9.2976
+#>   13.3530   0.1603 -17.3399   1.4224 -17.2003  -7.8135 -24.7462  -4.6054
+#>  -10.4999   6.7724  -1.3415   1.7718   3.6337   5.9713   4.8873   4.6049
+#> 
+#> Columns 41 to 48 -10.7290   1.2266  -1.1279  -6.7309   4.8564 -19.1697 -10.8422  -9.4680
+#>   15.3706 -12.0921 -15.1571   3.3368   3.0903  17.8897   7.6483   9.4141
+#>    8.6412   5.1572  -0.8060  10.5385   4.0186   5.9347  -0.0330  -7.5679
+#>    7.0214 -10.1595 -11.4584   1.3118   5.1355  -1.6815  -1.5747 -12.3343
+#>  -19.1103  -7.9682   8.5581   0.0851   1.8505  -6.6875 -21.2344 -12.5088
+#>  -18.5591  -2.7133   9.7430   1.5973  10.2906   0.3215  -1.6015   7.8144
+#>   -1.5293  -4.7240  -3.8865 -15.0194 -12.8862   4.5046  12.2952   5.5256
+#>   -1.4769  -0.9524  -2.0169   1.6460   4.8768  -9.9220   3.0602  12.2695
+#>   -3.2681   1.0740 -11.6155 -11.1632   2.6573   0.5347   0.8439  11.7267
+#>  -14.7730   3.6324   4.2335   8.8489  23.4191  18.0735  13.1651  -2.1606
+#>   13.6308  -3.6316  -8.3127  -4.5406 -12.0217   4.9650   6.1042  -4.3197
+#>   -9.1802   1.6441   1.9902 -10.0347  -5.0455  -5.2543   8.4804  -1.5737
+#>    2.3652   3.5311   1.4964  15.6320  11.5806  -2.2735  14.3417   2.5839
+#>   -3.7575  -2.8811  -0.9925  -4.3629  17.2919  -0.0197  -4.9680  -3.5100
+#>    7.2565  -0.0398  -2.7936  -6.1423 -14.8843  -9.1030   9.9438  10.3331
+#>    3.2569   4.8552  -1.1905  -7.5572 -14.9308   0.2999   7.4230  -1.0064
+#>   -8.8221  -1.0738  -0.8882 -17.1510 -11.1800   0.2144  -0.6876  -5.8947
+#>   12.2692 -13.4749  -3.1823   5.0263   4.2603 -15.4658   2.4992 -10.1891
+#>    0.7860  -2.0814  -2.9499 -13.4518  -6.8299   2.7675  -3.3247  -3.0892
+#>   -6.5321  -5.4376   6.9406  -2.3071   0.5853  -5.4339  -2.2470   1.5908
+#>    1.0329  10.0856  -4.5039  -5.2718  -0.3540  -1.6527   0.3171   2.4836
+#>  -11.9147   6.7385   5.7380  -5.0723  -3.0284  -9.4464   4.8818   3.0832
+#>   12.7580  -2.9179   1.7291   7.3266   0.2459 -13.2164   0.9082   3.2840
+#>   -7.1649  -1.0791  -4.0308  -7.5664   0.1214  -0.6316   0.7790  -6.8957
+#>   20.6936  12.1571  -3.3921  17.5928   7.0366  11.0281   6.9955   4.5702
+#>   -1.1660  -1.6073 -16.1980  11.4725   6.6002  -6.8282  -9.1363 -12.4136
+#>   -5.3623   3.8805   6.7446 -13.4904 -12.3133 -15.7244  -1.2280   4.4612
+#>   -6.5582   9.1700  12.1526  -1.7667 -10.2811 -24.1948 -10.7069  -2.4161
+#>  -14.2601   0.8724   6.2914  17.0546   9.1718   6.6296   2.0580   3.9653
+#>  -13.2472   6.2988   6.0759   2.9227  -6.9672  -9.0773 -12.7429 -18.0649
+#>    0.5279  -0.3957  27.3752  10.9115   9.9392  -4.9250  -3.0218  -8.9675
+#>    3.2111  -2.3804  19.1284  11.3197  -4.8310   6.6147  -9.8923   4.6418
+#>    0.2088   2.7633   1.1299   5.4723  -7.2777  -4.1593   0.9491  -1.3457
+#> 
+#> Columns 49 to 54   2.2773  -7.0427  -7.2601   5.2576  10.1473  -1.4825
+#>    2.0728   5.8440 -11.9129   8.8585  -3.8773  -7.8997
+#>   13.9054 -20.6030   5.8407   3.9196  -3.7404   6.9391
+#>    0.7607   4.4037  -0.6187  -4.4399   4.3271   3.9021
+#>   -6.7017  -2.3270   1.4333  -7.1526   5.4069  -2.5627
+#>   -5.7247 -13.7080 -10.0205   4.2689  14.5773 -13.5699
+#>  -17.6388   3.7523  -5.9033  -3.1553   2.3426  -6.7776
+#>    6.0180  -5.3101   0.0396  -9.6001  -1.2713  -6.8409
+#>    8.2616   2.0049   1.1839  -3.0684   8.2004  -2.0936
+#>  -12.4783   6.6496   8.7333  -0.1603 -12.2373   5.5264
+#>  -13.9707  -2.3604   5.3351 -12.4312   4.4919  -3.4858
+#>   -5.3698   0.9314  12.7246  16.1495   0.8187   0.5749
+#>   -4.9155   2.3534   8.6875  -4.7722  -2.2764  -1.1309
+#>  -19.2869  -3.8281  18.3395  -4.6993   8.7467  -8.4019
+#>    4.2668   6.5840  -0.1524   6.5347   5.8189  -1.9036
+#>    3.4862   2.1957   7.6857   3.4270  -6.4757  -4.2581
+#>   -6.3883  -5.9588  -8.9869   6.3761   1.9361  -7.1823
+#>   -7.9681  -7.3008  20.0452  -0.3360  -0.2440  -1.5120
+#>    9.3153   8.3198   6.5897  11.9121 -13.1154  -2.8477
+#>    5.9208  -9.8848  -4.9459  -5.5302   1.2124  -4.2260
+#>   -2.0859   3.9335  -7.6611   5.3094  -2.8403  -5.4787
+#>   -3.2523  16.8035   0.8463   1.3927  -6.3697  -2.4045
+#>   -9.5948 -11.6076   7.8068   3.9335   1.9341  -0.8143
+#>  -11.5494 -11.6516  -3.0629   5.4208   3.1919  -3.6417
+#>    7.8636   3.7975   9.1254  10.4197  -6.0210   1.5177
+#>  -16.3998  -8.0413 -10.2509  14.4855  -1.9143  -1.0065
+#>    2.2125   8.8223   8.0995  -4.1518  -2.7697   1.3032
+#>   13.8695  -5.3156  -8.4131   4.9752   0.0873  -5.5614
+#>   -2.7581   2.6294   7.1795   4.6050 -12.8226  -2.5074
+#>    2.1836 -11.2572  -8.0921  12.9866   7.1034 -10.8439
+#>    7.0694  -8.4061  -0.5035   0.9225   2.2821  -2.9494
+#>    9.9549  -5.9032  -4.1427   1.1756   3.0637  -1.2262
+#>   -0.3056  -1.8644  -9.1585  15.9833  -4.6379  -3.4776
 #> 
 #> (18,.,.) = 
-#>  Columns 1 to 8  -4.3911   5.4973   1.8484   1.9810  -5.6260  -0.5801 -15.8868   6.4607
-#>   -5.0212  -5.5476  -7.6658  -1.4139  -0.4396   2.2836  -5.9576  -3.4828
-#>   -5.4325  -0.4088   2.7157  -8.4452   1.3914  -0.2093  11.4034  -4.1957
-#>   -0.7190  -5.5509  -2.5918 -12.2629  18.8802   5.8711   1.6982   2.9475
-#>    0.4850  -0.5469  -7.6596  -6.5053   8.6592  13.8677 -21.4914   5.5691
-#>   -1.4620  -6.3371  -3.4570 -11.5751   7.5142  -8.2088  -6.2354 -10.5874
-#>    3.2021  -4.3906  -7.1871   6.7673  10.5294  -7.6768  -3.2767  -2.1408
-#>    5.6072   1.9384   3.5318   2.1610  -5.8641   2.2806   9.2333 -28.8971
-#>  -13.9020   7.9718  -2.5898  -4.9725  -1.6510  -4.6305 -11.0958   1.3548
-#>   -2.8662  -1.9706  -2.5360  -8.9197 -11.1579   2.4577  11.8483 -11.4991
-#>   -4.1481  -2.0496   9.5735  -1.7490 -13.7465  -1.5257   5.2256  -3.2159
-#>    4.4538   2.4108 -11.2044  -3.1763  -8.6365   4.4801   8.9262  -7.8778
-#>   -5.8780   0.8098  -5.3211   2.7571 -13.6454  -0.8512  -0.6691  23.8480
-#>    2.6878   0.7378   0.9408  -2.5093   5.4975  -2.4101  10.2283  -5.4472
-#>   -3.9325   1.6233  -8.9639  -7.7578   5.8783   2.2209  -9.6604  -4.5376
-#>   -0.4879   1.8802  -7.2441  -4.4474  -0.9615   6.2233  10.8690 -14.4598
-#>   -0.7505   5.6795   7.6239 -14.6193  -3.4399  14.3743  -3.5018  -0.3198
-#>    0.3104   9.1646  -9.0805  -7.8947  -5.8951   6.6030  17.4737  -4.6253
-#>   -0.9207   7.8309  -8.4844   0.3523 -14.6567  -8.1897  -1.8762   0.6771
-#>   -0.2594  -6.4661   2.0367  -5.6595  13.3032 -10.9836  -9.3047   7.9829
-#>    0.5242  -3.2823   0.8238   8.1593  -0.9138  -7.1425 -15.7148   2.3371
-#>   -5.6616   8.2627  -0.1257   6.7354   5.7825  -9.0814  10.6219  -0.4017
-#>   -0.0744  -2.6918   8.6814 -16.0938  13.5337   4.3540  -6.6646   9.3643
-#>  -16.0383  11.2291   1.4746  11.9791  -1.7396  -6.4750  -7.7020  10.6767
-#>   -3.9449  -7.1955   2.8869   4.3371   7.5388  -6.7228   3.2023   5.3479
-#>   -0.9995   1.3863   4.3533   4.3314  12.2726 -15.1262  -0.1044  -1.0410
-#>   -5.1356   3.6454   7.3923   1.7739   0.4303   6.9459  -2.3443  -5.2192
-#>    2.5240   0.3547  -3.5044   4.3585   7.6539   3.9435  -0.9854   1.7310
-#>   -5.0543   3.4230   0.4311  -1.0042  11.0358  -8.9949   3.6246  18.5413
-#>    6.2018   1.1566   0.8363  -9.6308   8.2723  10.8877  11.3666 -13.1488
-#>   -5.3042   4.6787  -2.5811  -0.9704  -1.7178  -2.1207  -6.6445   6.3538
-#>   -1.3022  -8.0480   8.6952   1.9588   3.4376 -23.2013   0.1420   5.0201
-#>   -1.6073  -7.1981   0.7532  -1.0488 -11.1509  -5.6928  18.6251   1.7090
-#> 
-#> Columns 9 to 16   7.6927   1.4596  13.0779   5.9595   0.2990   5.2401  -9.1685  -4.2340
-#>  -19.2284  -8.5529  -1.4149  11.4336  10.2557  -4.8607 -16.2310   3.1786
-#>    5.2186   0.3449  -5.2681  -1.7545   6.1023   1.9320   2.4020   7.8770
-#>   -9.2335   9.5286  -9.9603  -1.4513  -8.5965  12.4769   2.8153   6.8837
-#>    1.7237 -10.9426   4.0296  -7.5040   1.0439  12.2765  11.8449  -5.4733
-#>   -8.3496 -20.7904  19.8178 -11.1970   4.5073  31.3651 -11.1798 -22.0608
-#>    5.2126   3.1247 -12.3427  -2.4787   3.8732 -15.9318   6.2172   2.2279
-#>   11.4959 -15.5201   4.8571   5.7709   6.6421 -19.3220   8.6169 -13.8630
-#>   -6.1861  -8.2190  11.6082  -3.3027  12.0702 -15.4140  -8.6867  -8.4738
-#>  -10.8348   2.5051 -12.9579  -0.2584  -6.0464   3.2162   3.6795  -5.0257
-#>   11.7900  -7.5695   7.3021   8.0940  -0.3820  -3.9853  -7.1611   1.7104
-#>   -3.5033   4.5399   8.8900  -1.7946   7.6678   3.3881  13.1164  11.0010
-#>  -15.3460   4.2485  -2.2991  -9.0732 -13.1693  -5.2471   1.1429   3.8441
-#>   -5.8969   2.7695   5.1643   1.2429  10.3890  -2.7055  -6.0126  16.3083
-#>    8.5507  -1.9368  -6.1374   1.8580 -17.3128   9.9511   5.4091  -5.9266
-#>   -5.6076   2.7743  -1.6566  11.0053 -11.9033 -10.1063   9.6658   6.6356
-#>   -9.3610  -7.9023   0.7382   2.5932   3.6247   9.8894  -2.4512  -8.8385
-#>    8.9837  -6.6969 -10.0175   4.2795  13.5702  -6.6152  17.8442  -2.8948
-#>  -15.7365 -10.9606  -4.4099 -13.5879   1.5271  -2.8249 -17.4113   4.3666
-#>  -17.7783  -4.8231  -1.6511  -9.8570  -1.3040  10.0096   5.7238   5.7183
-#>    6.9739  16.3830   1.5270   4.4500  -2.1711  -5.9195   0.3354  -5.8155
-#>   -8.6017   2.4775  -6.2702  -2.7926   4.4337   1.5416   7.3396   9.1336
-#>    9.5212   2.5359   2.9281 -11.4843  -7.7582   4.5839  -7.2885  -9.0561
-#>  -17.8258  13.1264   0.3094   1.1877  21.5266  -2.0017  -9.3248   1.4888
-#>    4.2441   2.4671   1.9099   3.5147  19.1303   0.3357   1.9493   0.9496
-#>    1.1641  -8.2187  -1.0420  -9.8006 -15.3996  12.6898 -12.9848   1.7504
-#>    5.8278   8.0116 -16.8496  -6.5744  -1.8002   2.4645   7.1946   2.5312
-#>   -6.0108  -4.0062  -7.3233  -2.1383  15.2241   4.4813 -12.1431  -2.0238
-#>    0.3557   4.6276  -8.4960   0.1130  11.3615   2.8329  -9.3739   2.4401
-#>    5.9013   1.5485  -7.0356   0.5447   1.4222   0.1700   6.2146  -8.5471
-#>   -1.7002   3.5084 -14.1795   7.6799  -6.8140  13.4071  14.9627  -9.0716
-#>   15.5815  -4.9196   4.5648 -13.9866  -3.6228  -2.3111 -12.6678  -5.1569
-#>   -3.9958   7.5002  -8.8081   8.3486  11.7740  -7.7243  19.9508  -6.5645
-#> 
-#> Columns 17 to 24   1.1347   2.5347  -2.8868  -3.3789  -5.0710   8.2973 -12.6579  16.1731
-#>  -11.5549  -4.9196  -7.8892  -5.2357  -6.8317  -5.2695  -6.6647  -5.2959
-#>    2.1417 -11.1165   5.8018  -9.2976  -4.1183  -7.0978  -5.2528 -15.3420
-#>    2.0741  -5.3868   0.3100   0.1789  25.6531 -13.9063  17.0234 -10.6949
-#>    5.3593  16.3898   1.4933  15.7945   2.9275 -12.2839  18.2287   1.2301
-#>   19.4208   6.8820  -1.1850  -5.9584  -0.1833 -13.4068  30.5236 -17.4262
-#>   -5.4856  -7.0090   2.3754  -5.4628  -8.2741  -9.6361  -3.6279  -5.0407
-#>   -6.5071   3.6426 -22.6544  -4.3219  13.8968   8.3234 -15.4233  -7.8661
-#>   -3.1956   9.5999 -14.0066   7.7170 -12.2087 -14.1807  -3.1584   6.2416
-#>    3.7422   4.2694   7.8953  -1.7984   1.1673   4.2236  15.3837 -13.6411
-#>   14.2686  -2.6712 -18.7982  -5.5391  -2.4245   4.4291  -3.1279  -3.5791
-#>    9.1189   0.0225 -11.5063  -5.8858  -5.8044   1.0564  23.4499  -4.9465
-#>    1.7194  10.7878 -15.6375   9.9320  -2.6833  21.7980  -1.9116  -0.0044
-#>    3.9943 -17.6837   9.9010  -0.0358   6.4139  -3.3084   0.7509 -13.5968
-#>   -5.7291  12.0397  -1.5095  -5.5217  -5.2337   6.6550  14.6906   0.7526
-#>   -5.1364 -18.5721   4.7661 -10.3700   6.6646  -7.4164 -13.3842  -5.5470
-#>   -1.0711   6.4061   5.4447  -6.3443  -1.3465 -15.8051  25.1032  -5.0133
-#>   -6.1450  15.4360  -8.0648  -7.5083  -6.3784 -12.4713   3.3846  -5.0549
-#>    4.9954   3.8794   6.9410  -0.0043  -1.6452 -11.6989  -7.0418  -4.5863
-#>    6.1630  -2.5573   1.2800   4.9611  -8.7608  -5.2573   4.6300   2.7334
-#>   -4.4718  -5.3688  -1.9845   5.1600   0.4501  -7.0452 -16.2698   5.8914
-#>    0.2358  -0.7003   5.2938   9.5373  10.0164  14.2044   3.2263   3.9779
-#>   -3.0353   0.2633   8.0138 -12.7800  -3.9198  -3.9936  17.1907  -3.8080
-#>   -2.8725  -9.3989  -8.8084  -7.4599  -4.0630  18.1741  -5.8362   3.5222
-#>   -3.4204 -19.2696   3.7916   8.3952   2.4625  10.1270   1.0981   4.5824
-#>   -8.6086  -2.0380  13.7854   7.8583   7.4181   2.7249  -1.5048  -2.7189
-#>   -4.1792  -1.9535  -0.2705  -0.0452 -15.2096   3.8035  -8.7664   5.4589
-#>   -0.6465  -2.6124   8.4671  -2.1038  -4.6120  -9.2987  16.5657  -7.5394
-#>    4.7889 -10.3359   1.2910  -3.1306   8.0027 -12.9061   9.8752  -2.3037
-#>   -1.2102   9.4415   4.3168  -4.2345   9.6880  19.6927  29.5038   6.3476
-#>  -13.8122  -3.7279  -6.0201   7.7620  -3.1934  -0.1139 -16.0485   8.4671
-#>   -5.2801   9.7130   7.0902 -12.6219  18.6469   1.0631  10.1957  10.0229
-#>   13.3858  -6.0519   5.5020  -3.9310   5.2673  -0.1442   2.0037  17.0299
-#> 
-#> Columns 25 to 32  -3.8738   3.9220 -12.5185   6.4561   0.0799   5.5301   1.6065   5.2487
-#>    1.4690  -6.9025  -2.4601  -4.2432   4.1212   1.4848   0.4622   1.9091
-#>   -0.8780   2.4436  -8.1781 -10.4016  -8.1332   8.3900   4.3642   9.5500
-#>   -1.2560  -3.2221  -0.0284   6.5983   1.0221  -5.9329   5.6549   7.3524
-#>    0.7555  -4.4902 -11.1791 -23.5908   7.3431  -8.1840   8.0723   0.2110
-#>    1.8144  -1.8614  -1.3185  -4.0768   6.4284  -3.2070 -11.3596   4.9718
-#>   -0.1717   1.2119 -11.3041  -4.1853  15.7426  -1.5315  12.3409 -11.2353
-#>   -2.0192  -1.1751  10.6557  -5.3755  -8.8156   0.9094 -11.1758  -7.5134
-#>    9.2353  -9.6842  -9.6485  -2.3479   9.3271  -5.3431  10.0440   8.1175
-#>   -4.5267 -11.8635   6.1133  -2.1379   6.6027   1.5822  -5.5570  -7.7038
-#>  -11.3767  -6.1185  -0.4371  -6.2906  -8.4045  -3.0140  -5.9909   9.3692
-#>    5.3097  17.9685  -5.6408   2.5726   0.1373  -2.0362  -4.4878  13.1866
-#>   -0.2003 -18.0279   2.8840   7.6310   5.8091 -16.0159   2.7507  -9.0756
-#>   -4.3898   6.3232   9.0166   0.3971 -13.0539   9.7434  -0.7697  14.1789
-#>    4.1869  -5.1755   2.1565  -3.0330   4.2686  -5.8388  -1.7538 -22.2728
-#>  -13.1864  -5.5980   2.3033   1.6994  -5.2467   4.6051  -3.7793   2.0549
-#>   -2.9787 -10.3587  17.2497 -18.6068   2.2088  -2.4777  -5.9609  -0.2677
-#>    6.8232  -0.2212  -1.2915  -2.6751   8.7935   0.6610  -1.5027   7.6642
-#>    0.4888   3.7794   8.7645   1.5360  -6.1666  -8.0935  -0.3406   3.8793
-#>    2.7377  -5.2790   0.5362  -1.7392  -6.0426   4.2360  12.0210 -16.1900
-#>   -2.1322   2.6928  -3.7036  -4.7929  -3.1372  -2.6002 -12.2420   8.5769
-#>    2.5564  -7.6670   9.2444  -8.3447  -1.8659 -14.2068  -6.3827 -13.5205
-#>   -8.6856  -5.2431 -15.0486  -4.4206  11.4039  -5.5736   4.4667  13.0206
-#>   -8.1077  -0.0292  -2.1540   1.7868  -6.3482  -1.5963  13.8036  11.8692
-#>    7.5198   6.7072   7.6203  -0.7738 -11.0714   6.2842  -9.9921  -5.9641
-#>  -13.2923  17.4194 -21.3287   2.7288   5.8553  -2.0353  -6.1919   3.6219
-#>    7.8293  10.1106   8.7589  -6.9789   9.7184  10.9510   7.1282  -3.0372
-#>    1.8077   0.6001   3.8597  -9.2054 -12.7888  12.6369   1.9466 -11.6437
-#>    7.4352  -3.4234   4.3904   7.3170  -8.2280  11.0989   4.0127  -4.6733
-#>    3.7689  -1.8453  18.5053  -8.0752  -7.2611   6.8307 -10.3481  -6.0274
-#>    4.2616  -8.9127  -6.4516   1.1904  -7.8745 -11.4709  15.2262  -2.8091
-#>  -11.1368  12.1557 -11.0857   6.0072  -4.2969   8.1991  -7.0327  -9.7476
-#>   -1.2019  -2.4516  -1.3659  -0.5371 -10.7445   3.9925 -10.0558 -12.4318
-#> 
-#> Columns 33 to 40  -8.5350   0.0875   9.6567 -15.5151  12.1594   0.2972  -6.5993  -4.7356
-#>   -5.1730 -10.0375  -5.3178   8.7592   3.4126  -6.6100  -0.4462   2.7282
-#>   19.0838   6.3240   1.1039  -7.6161 -12.7377   5.3932 -16.2631  10.2438
-#>    4.3135  -3.5741  -5.6618  -5.5723  -6.1734  -9.6130   2.0577   5.0947
-#>   -1.6806   0.4366  -7.4311  -0.9253  -9.2197  -4.2929  -7.8661  -4.0315
-#>   -6.7793   6.6410  -2.3139  -6.6337  -5.3094   0.3094  -5.8473  -0.0352
-#>   -1.0434   1.2943  -9.3600   5.2054   4.6067  10.3721   0.2044  -1.7094
-#>   -2.7223  -0.6669   2.0470   9.0565  -3.1020   3.7929  -4.7129  -4.6957
-#>    1.7211   3.1714  -3.0869   5.9535  -1.7719   6.4213  -1.8118   2.8516
-#>   18.6640  -4.9960   2.3894  -0.6455   0.7528 -16.8513 -22.7559  -6.7013
-#>   -1.3260  21.0229  11.0511  11.1625 -12.5773  -8.3422   1.8465   5.4793
-#>    3.0718   3.7418   1.0446  11.1606  -5.0686 -10.1680   1.5955 -10.0100
-#>  -10.1795   2.2387   1.0678   2.0980   0.5267  -4.3053  -2.7337   4.6885
-#>   -1.4100  -0.6188  -1.5873  -9.6573 -17.2998  -1.8995   6.9750   6.5807
-#>   -2.1986 -12.3286  -0.7730  -2.9716   6.0537  19.9523   4.4846 -20.3818
-#>    2.2540 -10.7448   6.4450   5.5551 -14.0252 -12.9895  10.7741   0.6383
-#>   -3.2762  -6.2054 -12.4799  -2.6375 -16.2244   4.1103  15.7387   0.7658
-#>    2.2558  -1.1088   8.7188   8.7920 -14.1842  -0.3710  -2.6653 -16.4752
-#>    9.9595   0.9145  -7.1987   0.1316  15.5304 -11.5121 -17.1075 -18.9906
-#>    0.7257  11.0916  -5.9431 -12.6732  10.7616   7.0296 -18.1772   0.6022
-#>   -8.4137  -3.2743   4.7066  -0.6106  -1.2597   6.7818  -0.1954   8.4503
-#>    2.2677   2.4595  -2.5608   7.4922  19.8550   7.0307 -10.3801  -6.9346
-#>    3.2337  -5.8513 -10.3497   3.4862  -2.5652  -1.0337  -8.7844  -4.6261
-#>   17.0239  -3.5383  19.6143   2.4119   1.9370 -10.6873  -7.2749   7.7795
-#>   15.5725   4.1913   0.5852  14.9739   7.9995  16.0967  -4.3813   2.8909
-#>   -6.8186  -3.1106  -4.5133 -11.0466  13.4662  -5.2009   0.3870   0.9202
-#>   -0.6736 -15.3876   9.1537  -6.8619  -6.4745  16.5738   6.1942   0.9360
-#>   -3.0737  -8.4914  -6.8561  -9.2497   2.5473   5.0432   9.7131  10.9237
-#>   -7.1638 -11.7686  -5.0851   2.8317   4.6247   6.7577   9.9449   7.3291
-#>  -18.6639 -18.1522  -1.8617  -2.1253   2.2573  12.0296  19.0306  -1.1478
-#>    3.9248  -7.9585  -0.6073  -8.6901  -2.2986   4.8878   9.5865  -7.5159
-#>   -6.4216  -1.9025  -1.6467  -9.5660   0.5932  -0.9738  -7.3584   0.2400
-#>   -1.2851   0.1969  19.3340  12.5870  -6.2235 -10.5510 -13.2139  -0.5947
-#> 
-#> Columns 41 to 48   2.0407   2.9510   2.1377  -3.1482  -3.5517  -1.6021  14.0215   5.6081
-#>   -2.8634  -1.2369   5.2037   9.4677  -2.8090   8.5956   7.9898  -0.5333
-#>   -0.7450  -0.5756  31.8037  -5.6067   4.3648  -9.8439   9.4060   0.9765
-#>    5.5924   4.6208  -4.7390   1.3896   5.1584   8.9603 -19.1332  11.0267
-#>   -7.9844  -1.0559  11.0491   1.2988  -5.2304  -8.4710  -5.0629   5.1838
-#>   -6.3647  -8.2324  -8.2835   9.3915   6.6549   2.4155 -17.9305   5.7850
-#>   -7.2929  -3.4562  10.0331  -1.7906  -5.8117  -7.5337   3.8928  17.7967
-#>    0.0798   9.0012   8.6507   9.0891 -11.2270  -6.5867   3.6516  18.7555
-#>   -6.5055  -3.0581   0.1773  -7.4135   8.0395  -6.7013 -16.4906   0.7733
-#>    6.1016   2.0575   2.5795   0.0249   8.2600   5.5781  -1.7872   6.3839
-#>   16.5275  -0.9233   7.0861  -4.6934  -7.0417   2.9589  -4.6347   3.9824
-#>   -0.4278   8.0586  -5.4852   2.8071  14.5673  11.0391   1.6705  14.5835
-#>   -2.8442 -12.2410   2.2064 -10.4224  -3.4541   0.6471   8.3707  -9.5317
-#>    5.1208  13.7774 -21.4315  16.4837   4.1118  -1.6831  -6.3328  -3.8604
-#>   -3.4582  -5.1599  11.4076  21.8845   8.7755  -3.6688  -0.1227  22.9978
-#>   -4.8443  19.5697  -7.3775  -2.9987  -1.4183   2.7953  -1.1646  -5.3746
-#>   10.0052  -6.8885  -7.1253  11.0419   9.9484  -7.6375  -7.7870  10.2694
-#>   -1.6002  23.8070  -6.2378  -9.8259   5.4546  11.1698  10.0522   5.9604
-#>  -19.6452   2.8931  -2.6409 -10.8057  -8.9000  -0.9451   1.7805   0.4884
-#>   -0.8447  -8.7494  13.6706   9.4764   7.5104  -7.8797  16.1753  10.0951
-#>    1.0271  -1.0085   5.8542  -2.4994   3.6851   1.7053   1.4021  -0.8466
-#>  -13.5364   5.9332   9.0672 -12.8885   0.8499 -11.0641   3.9007  16.8223
-#>    7.2468  13.2686  21.9044   3.8828  -4.1238   3.4955 -10.0625   6.3312
-#>   -7.9251  -1.5882   8.9027 -10.5558 -13.2154 -10.4163  10.8657 -12.6363
-#>    4.4245  -8.0527   0.3378  19.5951  -3.7173 -10.0441   1.2527  -1.2587
-#>   -6.4083   0.5048  -2.7162  -9.1202  -9.5379  -1.4137  11.4220 -13.2740
-#>   -2.2673  -4.0698  17.3785   1.1086  -5.3697   2.8517  16.9722  15.4926
-#>    4.7849   0.3172 -14.7810  12.6112   5.7173  -9.8020  -8.7283  -3.6317
-#>  -12.4094   8.4149  -1.3401   4.7318  -2.2228  -6.4433  -2.3998  12.2818
-#>   -1.0780   1.9622 -18.8152   7.4658  -4.2106 -13.1520 -15.2373  22.1706
-#>   -8.8288  -0.7321  15.0267  -6.7328   2.3793  -3.1096   7.7510 -11.4905
-#>   12.4244   2.2718  -4.3188  13.4401   3.7834   2.9043   3.9654  -4.6028
-#>    3.2978  -2.8197  -1.1388   1.0209   6.0064   8.2707  11.6162  -5.2744
-#> 
-#> Columns 49 to 54  15.6366  -2.3198 -10.2642   1.5620   8.4289  -4.5042
-#>  -13.2333  16.8606   5.2800   3.8640   1.8557  -1.4473
-#>   -8.5576  -8.3046  -3.4812   0.9038  -0.0236  -3.5858
-#>   -4.5940   3.1661   4.2015   8.0996  -4.4305  -1.9093
-#>  -14.5974  -7.1334  -1.0637   3.5828   1.2339   2.5546
-#>   15.3339  -4.6737   7.5107 -15.1962  10.1448  -1.9309
-#>   -1.2980  -5.4444   1.6974   6.4495  -4.8496  -8.1780
-#>   10.3719 -23.1087   9.7942   6.6577   2.8608  -4.1214
-#>   16.3583  -0.6505  -6.5720  -1.6248   1.3788  -0.4460
-#>   -0.9532  -7.5573  12.7045  10.1062   2.0182  -1.4121
-#>    1.0502  -1.7704  -5.8809   6.1531   0.9003  -0.1029
-#>    4.9284  -3.1047   6.9632  -4.9418  -7.1467  -0.6355
-#>  -10.0474  11.3646  -8.2997   0.2088   4.3690   0.6673
-#>   -6.8664  13.9372   4.2527  -3.8616  -2.4935   0.8499
-#>   -2.9825  -6.4267  -2.6215   1.2597   4.8453   5.8249
-#>  -12.8910   2.5260  14.1257   1.6857  -5.3704   4.9627
-#>    3.3596  -1.9308   1.8196   6.9653   3.4050   7.2779
-#>    9.6907  -0.6020  12.7136  -3.0734   2.4047  -0.6476
-#>   -0.0868  -5.7963  -6.1393   5.7635   3.1106  -5.9301
-#>    3.9145  -6.2789  -2.0478   0.5966   2.0519  -1.6803
-#>   -1.7202   1.0289  -2.8785  -9.7041  -9.5608   3.8213
-#>   -2.7978 -21.7646  -8.9645  14.0012  -1.5626   0.9232
-#>   -6.1989  -0.9096  -6.5068   3.1514   6.2610  -1.1243
-#>  -13.9085   2.9254  -2.3032   1.3641  -5.4963  -8.0123
-#>   -2.2217   3.5599  -0.3479  -0.7171   4.3949  -8.4972
-#>    6.1102  -6.4740   6.4523 -12.9943   0.6849   0.2070
-#>    0.6102   6.2528   8.6411   1.7952  -4.9242   1.0878
-#>   -0.0187  -8.1872   8.5016  -5.4746  -3.8439  -3.6778
-#>   -9.5806   1.2556  -1.5308   0.2019   2.0435   1.0044
-#>    3.9516  -3.5034   0.2421   7.7355  11.8162   5.8535
-#>    0.2947  13.0730 -19.5379  -0.3044   5.2665   7.0298
-#>    9.6607  -5.9342  12.9901  -2.8921  -7.3424   0.0711
-#>  -10.5470   6.4672  13.2806   1.4521   0.7254  -5.5319
+#>  Columns 1 to 8  -0.6943 -11.7297 -19.8109 -17.8715  -3.1018  -1.6216  -6.1713   7.0175
+#>    1.2637  -5.5946   1.9734   0.6969  -4.4829  21.6387  -4.2255  19.2646
+#>   -1.5830   6.0621  12.1829 -12.1584   1.3138   2.0291  -1.6666   1.3453
+#>    4.7081  -1.7967  -1.6231  -4.6924  -5.8023   0.1627   5.7965  -8.3263
+#>    4.8833  -5.1227 -11.7820  -6.5734 -20.2747  -0.1523 -14.7940   2.5937
+#>   -1.6729  -7.6201   7.1983   3.7689   0.6869  -3.6706   2.7649  11.3126
+#>    2.9467  11.5288   0.5804  -0.6815 -15.2222   0.2143  -0.9897  -0.7493
+#>    0.0487  -8.7858   4.8289 -22.1639 -19.0966 -17.4730 -14.4899   5.5767
+#>   -1.6500  -0.3760  -6.2824  -1.1997 -10.1115   1.7025  -0.8467  -2.7430
+#>    3.1746  13.3972   2.6368  14.6530  -9.9063  14.6099 -15.3711 -11.4837
+#>   -2.3207  -2.9667   2.3969  -8.9530   1.3322 -13.4430  -2.2326  -2.1133
+#>    5.0351  -3.1128   4.6477  20.7669  22.2164  22.5177   1.3325   4.6321
+#>    4.1186  10.3644   3.6980   9.9454  -1.7817   0.2860  -7.5642  -7.7186
+#>    1.7704   4.8310  -5.5803  -8.2606  -7.3151 -14.7874  -6.8896  -1.6936
+#>   -3.4143  -2.9708   3.3480   5.9815  -3.1605  12.1714   8.4230  16.6094
+#>    1.3821   8.0293  13.5909   6.7996   7.1770 -11.5062   1.7293  -5.9990
+#>   -1.4155  -0.1367   6.0215   9.5220 -14.9440  10.9706  -6.4762  -6.5018
+#>    0.2713  12.5095   8.9550  -7.6216 -11.0048 -10.5175  14.7178 -14.4070
+#>    0.0928  -1.1508  15.2024  10.5827   8.2966  12.1700   0.0713  -5.2117
+#>   -5.6411  -2.6629  -6.7772  -1.9683  -6.1702  -5.1844  -7.6235  -1.0473
+#>    5.1876   2.1848   0.7629   1.5598 -17.7521  -2.2956  -8.2912  -1.3584
+#>    6.0448  -3.0509   4.2316   4.0943 -17.1256   1.2761   1.2891  -6.8882
+#>    1.4241   3.3625  -3.1053  -7.0194   1.2046  -4.3439   6.1482 -10.1996
+#>   -6.9858  -3.9343   1.1994  -3.8249  -1.0314  13.4154   7.5780  -0.9521
+#>   -2.1753   9.3451  -3.8307  -0.4245  12.4384   5.0704 -10.5902 -19.9977
+#>   -1.2625   2.0943 -11.0664 -10.9072  -2.8737   9.1838  -6.2670  -4.7347
+#>    1.3522  -2.2314  -6.1485 -10.1211   4.9181 -14.2759  -4.2897  -8.3166
+#>    6.8338 -15.5991  -9.0361  -7.6642  -4.8751  -3.5506  -9.2338  -3.5385
+#>    5.3268   1.8028  -1.1131  -1.5406   2.7674   6.4356   3.8217 -11.3108
+#>   -7.9179  -7.7759  -4.7621   2.4999 -14.1889  16.0488   1.1483  -8.4646
+#>   -0.4503   0.6640   1.7990   8.5521  -3.0257  -3.9707  -7.6669  -8.4911
+#>   -9.1814   2.3031 -14.0849  -1.3711   4.9259 -15.8364   5.5219  12.1400
+#>   -5.2072   6.7106   8.2145   3.6960 -11.0155  -6.5847   5.2929  -1.9771
+#> 
+#> Columns 9 to 16  -0.2085 -17.7203   6.9680  11.7706  -6.4290   4.5609 -12.3673  -6.2474
+#>   -2.1783   0.9402  11.8875  -6.0537  12.6271  -0.9372   1.2834  -5.1291
+#>    6.9743  -3.1526   3.1583   6.9085  -2.7171   8.1336   4.5249  -8.2805
+#>   19.8336  -2.6956   3.8896   8.2024  -2.8023   1.4369 -21.6870 -15.9530
+#>   -6.3177   0.6611  10.1995  -8.8367 -18.8627  -4.1257   4.8174  -6.8275
+#>    3.0856  -4.7208  14.3747  16.8146 -11.1565  26.8793   3.4444 -11.6118
+#>   -2.2330  -1.2387   9.7785   5.4645  -0.2280   2.3692  -1.9889  -2.9757
+#>   -1.3426 -12.3795  -8.1321  -9.2347   8.0537   3.2193   1.0646  13.8183
+#>   13.4733  -1.1225   7.8080  -3.7626 -12.8206   4.2175  -5.1426   6.2702
+#>   14.1561   7.5268   1.6482  -8.3289   8.5740  12.9041   2.1424  -4.4302
+#>   -3.8716  -1.5857  11.5844   7.2413  11.3246  -7.5156  -3.2773  -4.4696
+#>  -10.6152   0.0301  -0.0632   0.1916  12.7462  -9.1572  -8.4740  -3.3215
+#>    1.1052   6.1220  11.3024  -0.8637  -6.3029  10.7655   2.6314   1.2299
+#>  -14.0506  -0.9264   9.8830   1.5366  -0.4005  12.3184 -15.1565  -3.3964
+#>   -8.3041  -2.0620   3.4451   0.4774  11.3717 -11.3828  -9.9329  -8.4014
+#>    7.6627  21.6430   9.6255  -1.2751  -6.8021  17.6907  -7.4235  -0.0468
+#>   10.2767  -3.3592  17.5942   8.9685   7.9768  -3.9261   5.0782   6.3163
+#>    9.3883  12.9818 -14.0936   9.2934 -11.9957  23.8184  -9.5113 -23.0765
+#>    8.3766   8.0653  19.6231   0.5976  -9.0933   4.1783   5.5905 -16.3117
+#>    3.8622  -6.3793   0.4563  -0.3446  -3.6082   1.6331   8.3198  -0.6253
+#>   14.3392 -15.3463  -8.4942  10.0521   2.2562  -6.5267 -16.0519  10.3532
+#>    4.0017   5.5630  -3.4266   5.9563  -7.5467  -1.5756   3.9646   0.2592
+#>    6.3461  11.1385 -10.5328  15.8490  -8.2805  -6.3062   4.7586   9.7775
+#>   -4.2591 -13.8001  -0.0379   5.3841  14.2784 -17.9311   2.7991   2.5867
+#>   -5.7767  -0.6882  -1.5613 -10.9829   7.8961  -4.9369  -5.8701  -0.7507
+#>    5.0183 -10.7408  -0.1752  -1.6686   9.6445  -7.2490  -7.5411   1.3869
+#>   -1.4334  13.7027   2.1856  -1.1775  -4.2999 -10.8275   9.1272   2.4816
+#>   -3.1155 -14.5365 -13.3983   1.9740  -5.6428  -0.4146  -2.1260   2.4407
+#>  -10.8399  -9.6009  -1.1107  -2.5755   8.7914   9.5045 -10.0667   0.7754
+#>   -3.1308 -20.8805  31.0240  -8.6923  -1.8422   3.0008  -4.1554   0.5273
+#>    9.6524 -16.6468  -8.2139   9.6052  -1.4911   8.3558  -4.5143  -2.8026
+#>    0.8688   1.1877   5.1967   1.2700  -2.3589  -3.3697   9.9622   0.3795
+#>   13.7129   5.9230   5.9550  13.3223  -5.4269  -2.0488  -6.0408   1.6363
+#> 
+#> Columns 17 to 24   9.9841   7.4051  -5.4774   5.4655  12.8064  -0.3276  -2.4372   9.2858
+#>    8.1535  -1.4907  14.4492  -4.0077  15.8888 -11.0130  11.5421 -14.8288
+#>    7.6276  -1.5935   1.5369  -0.3802  -4.4202  -4.6816   2.8409   3.8529
+#>    3.3394  -8.8562  -7.4803   0.0609   0.0779   5.1738  11.2288   5.7781
+#>  -11.2799  -0.2456  -7.1385  17.5001  12.3590   5.5588  11.4625   4.9288
+#>  -15.8009   3.6199  -7.8953  24.6365  12.5110   7.3805   4.7441   8.6363
+#>    3.7035  -8.1959  -1.1975 -13.0497   3.5089   0.9590  19.4003  18.6206
+#>   -4.6876  22.0236  13.4174   7.9197  -3.0797   0.9036   2.1096  -9.7126
+#>   -7.2211  -1.1826 -10.2896  -2.5285 -14.1303   2.3528   8.3646   5.4984
+#>    3.6114  13.3496  -0.6921  -2.6715   1.7045 -10.7906  -5.6524  -8.8471
+#>  -10.6224  -8.0436   4.6234   0.1423   6.2833  -0.9170   6.8136   1.7302
+#>  -17.9536   2.5027 -10.1077  -0.3824 -17.0680 -13.9117   2.8445  -4.0350
+#>   -5.0261  -0.1825  -6.6372   6.7096  -4.7771  -1.3963  -4.1694  -2.0248
+#>    1.7750   3.3133   8.3620   6.5019   7.7299   7.8319   6.5405   2.1397
+#>   -1.2157 -23.9608 -15.6523  -4.2013   2.8778  -0.2913   7.7776   5.4285
+#>   -5.8463  -5.0958   0.5109   5.3433 -10.7702  -0.0089  10.8228   4.9703
+#>   -7.8639  -8.5939   0.1666 -12.7026  -4.1873 -10.0299 -13.2304   4.7837
+#>  -10.0848  14.0785   4.3467   9.9640   1.8178  -6.4756  -4.9856   5.7538
+#>   -1.4450  -0.2188 -11.5968  -3.3136  -4.4837  -6.7709  11.8512 -10.7862
+#>   11.6271  -5.3994   4.0734   0.8112  -4.4228  13.3920   1.2578  15.7194
+#>   13.5841   5.8131  -8.7279 -10.4675   5.0931 -13.4010  15.8498  11.5048
+#>    6.1375   0.2089  13.9665  -9.8636   1.8155  -1.8648 -15.8568 -14.0856
+#>  -12.2706   3.4789   8.0192 -16.1278  -4.2455   0.5700   5.4249  13.3981
+#>    3.8624 -13.6549  -8.2070   4.4581   7.9761   8.5087   0.9004   1.0381
+#>   13.3910   2.2407  15.0027   6.5939  -4.9817  -5.6574  -6.7505   4.9883
+#>    7.2875  -4.6972  -5.4647  -4.2520 -14.6316  -6.0051   4.0616   2.8983
+#>   -7.4033   3.8761  -4.3224   7.4854  -0.8088   6.3289   0.4113  13.4652
+#>   11.4750   2.3820  -3.3070   4.3407  -4.3791  -1.2249  -3.3734   7.5285
+#>   16.4850   5.0340 -12.2983  -3.8812   1.5803  -2.6512   2.0305  -1.4490
+#>    0.8871   2.4346 -24.7953   9.7044   7.9937  -6.9829  -3.7912   0.0028
+#>   -6.7067   9.4630 -13.7946  11.0594  -7.6773  -3.6778  -5.3839  -0.0313
+#>   -2.5180 -10.7711   2.5268 -15.7344   3.0335   4.9066   1.5910  16.8586
+#>   -6.3701 -16.7405  -0.3658  -7.6082  15.7316   5.9246   0.0710  18.4202
+#> 
+#> Columns 25 to 32 -12.4924 -13.9386   5.3801   1.9533   1.8343  -5.5427  -6.0740  11.9692
+#>   -7.4786 -13.7394  14.2841  -4.0213   0.4788  -1.0037  15.6994   9.5437
+#>   -3.3687   7.2376  -0.5511   3.3196  -4.7483 -11.6931  -4.3725  -2.6839
+#>   -6.8991  -4.2236 -14.5938  10.6822   0.5059   4.8129   1.2190  -2.3443
+#>   -3.5351 -14.6734  -4.6409   5.2077   1.7140  -1.4250  -5.5963   3.2164
+#>   -7.2577 -14.4673 -15.1203  -6.8119  -7.0486  -9.0583  -2.8600   3.8979
+#>    4.1563   6.1172 -11.5901  -1.1599   9.4513   0.3673  -4.7681   3.0133
+#>   15.3027  -6.7064   7.5268  -6.6253   9.7704   4.5595  14.7593  -5.9744
+#>    9.5468   4.0438   3.6617   3.8005  -3.8920  -5.9028  10.6563   1.1908
+#>   -2.4248  16.3710   4.4801  13.1811   9.8501   7.4075  -9.1692 -13.7154
+#>   19.2488  -6.3371   2.0719  -8.0576   6.2853   4.9698  -3.7756   0.1722
+#>   -8.2789   3.9989   6.4186  -4.1124 -10.6450   6.0232  10.1185   0.5724
+#>  -14.9448   6.4501  -5.9246  -0.9967  -6.6727   6.0222  -2.9239   7.1939
+#>   -9.1822   9.7263   0.6503   9.8614  -3.9117  -0.5857  23.0976  -8.2061
+#>   -3.2678   0.9722  -7.8012  -4.9839  -5.8058  -8.1955 -12.5700  -5.8367
+#>   16.3260  -6.9330   2.9484 -11.7137  -3.4506   2.0883  -7.6949   1.1805
+#>    4.7742   9.0419  -8.2699  -3.2370  -2.4196   8.8271  -5.9554 -16.6552
+#>   -0.7793   1.4161 -10.9983  -8.4753   7.3569  15.8702  -7.2797  -7.2277
+#>   13.1009  11.8405   3.0866   4.0939  -6.3774   4.9618   7.5836  -7.9561
+#>   -1.2617   2.3014 -21.6267  -1.0531  -2.4203  -0.1569  -9.9339   5.3545
+#>   10.4250  12.6715   2.1675  11.6370  -3.3146  11.1795  -5.7525  -4.9723
+#>   -6.2057  -7.0479  12.3756   1.2099  12.7864  18.5670  -6.6470  -2.4877
+#>    9.4214 -12.5478   2.9603   6.0830  -4.4291  -7.2537  -6.5186 -10.3807
+#>   -4.4477   1.1599  -1.0153  -8.6561  -5.7503  -2.8120  -4.7871 -13.8161
+#>  -10.0472  -1.5990  -5.3590  13.4239  15.2395   2.7747  12.9785  13.0879
+#>   -4.9381  -5.2128  -6.7875   9.4383   4.7720   9.4286  -0.3002  -7.3394
+#>  -12.5935   2.1243   2.2790   3.6599   4.6004  -4.8816  12.9005  -4.3094
+#>   13.1197  -2.2674   2.9963   0.8996  -1.7416   3.2874   7.5521  12.8102
+#>    1.5030   3.4196  -8.1728   3.4395 -13.9593  18.6215   1.7676   6.2154
+#>    5.7199  -1.0655  -1.0215   0.4555  -8.7670  -2.9984 -23.7500   9.6391
+#>    0.0883 -11.4441  -4.1828  14.0759  -8.5707 -14.8937  -8.7314   1.0714
+#>   -3.4293  -5.6978  -8.4774   3.1523 -12.4606 -20.4160 -13.7440  -5.0757
+#>    3.0508   4.3126 -14.6681  -1.8550 -12.7661   2.1600 -10.6789  -4.6630
+#> 
+#> Columns 33 to 40  -8.9554 -14.5013   0.6054  -5.8190  -1.4334  10.7517   9.6013  -0.1070
+#>  -10.4074  -8.5697  -9.7644  10.2820  11.4430   1.3164 -19.6196   2.3149
+#>   -4.4762   0.6259  -4.1491  10.1084   8.5566   2.8234  -2.0995   7.3509
+#>   -1.7968  -8.2735   1.4772   3.2515  -5.0901  -0.0443 -12.3752   1.1087
+#>   15.0824   2.6325  -2.3707   5.0981   1.6012   0.2394  12.7248   5.9542
+#>    2.3918  -7.7244  -0.4085  -1.7616  -1.6698  11.9080   5.3033   2.2969
+#>   -9.9263   7.6323  -5.7591   2.0003   8.4513   2.3243   1.2843  -3.0821
+#>    2.9641  -2.6295  -7.7144   3.4335  -0.9605  16.3155  11.7670 -12.5938
+#>    3.5488 -14.2483   4.6517  14.7637  13.2793  -1.1110   1.3821  -0.3804
+#>   -2.5041  -8.4909  -5.8024 -10.4037   2.5426   3.6872  10.8499   6.2814
+#>    3.9631  -7.4547 -10.5730  -9.3411   1.5350  -0.8418  -5.3848  -5.5719
+#>  -23.4962   7.8277   1.2841   1.7089  -6.6692 -20.3650 -20.4044   2.0223
+#>   -8.1212  -7.7868   1.0049 -11.7738  -0.5577   3.1284   8.0836   5.3553
+#>    1.4814  -5.0421  -5.4116  11.9177   9.1392   0.8603   3.9767 -11.0868
+#>    4.6231   9.2437  -2.6831 -10.9876  -6.7121   9.2350 -18.8191  -0.9715
+#>   -9.8373  -3.2559  -2.3032  -4.5612  -2.7924   0.2588   8.8858   1.7649
+#>   -0.7487 -13.0943   3.3256  -5.7844 -11.6853  -2.6758   0.8129  -5.3919
+#>    3.4169  -2.2824  -5.0868  -8.6401   3.5563   8.8348 -12.2568  -8.0357
+#>    8.4339  -9.8513  17.4138  -2.0076  -6.0231  -5.6696   2.8648  -7.6884
+#>   -3.2783  -3.3848  -0.9662 -19.3254   1.5167   9.2626   9.8224   9.6015
+#>   -2.0517   5.0160  -3.1997   7.1047  -8.3877   8.5859   4.5483   2.5926
+#>    7.4822  -1.5455 -16.9269   8.8366 -18.0318   1.5934  10.3386  12.9113
+#>    8.6641  -2.1338 -14.8348  14.0025  -3.1614   9.5194  -6.6382   2.9387
+#>    0.8064   1.1796   4.4789   1.4288  -7.3881   2.5189  -2.7048   3.8731
+#>    0.9824  -3.2064 -13.0805  -4.2816   7.2986  -4.9678  -8.5971  10.0907
+#>   -0.5998  -1.9650   0.5499   1.1543  -0.8881  -5.6264  -0.9134   7.5820
+#>    5.0291   2.3153   0.8026 -10.7897   9.6179  -4.4469  14.4409  -3.1396
+#>    3.4095  16.8697   7.2437   7.5075  -6.3582   5.0839   0.8750 -13.5392
+#>  -11.7284   2.2721  18.0969  -6.0961 -10.9177 -12.8979  -3.2199   9.9099
+#>    0.0326 -18.2042  12.2909 -11.5698 -12.4102  11.8029 -23.5936  11.7020
+#>    9.9583 -17.2390  -2.7436  -7.6730   0.9455  12.1800  -6.1141   3.9890
+#>   15.4247  -0.6704  -1.6215   1.0959  11.4966   2.0526   4.0590  -7.6983
+#>    8.1723   2.8213   5.4104 -11.4786   3.2855  11.7383   2.4266  -0.3940
+#> 
+#> Columns 41 to 48   3.2757  10.8145   1.8187 -10.8773   0.0613   5.9120 -20.7639  -7.6829
+#>   -5.8169 -15.1133  -9.6044  13.6763   7.8400 -12.5860   1.7253  -7.5364
+#>    9.6021  -4.4161   7.2538 -10.3463  -9.9258   5.3735 -13.9486  -1.5164
+#>   17.5350   1.6708  -1.4872   3.4978 -21.1603 -12.5518  -6.7924  -4.3215
+#>    0.0957   7.6806 -16.4572  -8.8855   2.8795   5.3116  -5.4549  -2.6387
+#>   14.4676   4.3018  -5.3326  -3.8244   7.9262   1.6860  -7.2135   0.3964
+#>    0.5103  -1.8801  -3.0475  -2.3776  17.5428  -1.3414  10.0828  -4.3307
+#>   -4.5380  -2.9757  -5.9528  -8.7044  22.2579   7.8321   2.6347   8.8544
+#>   -0.2866  -2.2502  15.8532  -6.3859  -1.0899  -3.2782 -16.0715  -2.1797
+#>   -2.2209  -0.7420   8.3737  10.0736   9.8639  -8.7984 -15.6570   2.5879
+#>   12.0457   4.2703  -5.1882   7.5619  10.1244  -8.3739  19.5678   3.9155
+#>   -8.2974  12.9329   8.0896  -4.2640  -8.2841  -5.6907  -2.9056  13.6863
+#>    5.2255  -2.3657   2.7089  -9.1566   4.3666   2.0922   0.1060  -0.3940
+#>    2.0557   5.7824  -4.0109   9.2817  21.0503 -22.0207   0.8276   8.6519
+#>    3.8673   7.9139  -3.2026  12.0452  -0.0251  -7.4712  19.3353  -9.8834
+#>    7.4787   3.2177  -1.7692   7.5804  -0.4800  25.0913   8.6167 -11.9164
+#>    2.2891   4.7743  11.3459  -0.0053   4.7265   1.0612  14.5198  -1.1703
+#>   10.6246 -19.2143  -5.0525   7.3475  -1.0796  -4.3981  -1.6431  -6.9889
+#>    9.7104  -8.1107  -3.4126  -4.1727   1.9839  26.8486   0.6193  19.7643
+#>  -10.7407   6.8807  13.6166  -9.6841   6.0132  13.3368  -5.9151 -18.3632
+#>    6.9898  -7.7316 -13.1621  -9.9887   7.6353  -5.5938  12.0758  -6.8949
+#>  -11.1150  -7.8397   0.2596  -3.7319   8.7702   0.8530   3.0326   8.8942
+#>    1.4253   5.1044  -2.3931   0.2922  -0.8858 -14.6207   1.5947 -10.1076
+#>    6.5030  -4.5554  -1.5744   9.7471   0.7615 -11.9855  15.4412  -7.1916
+#>    2.7969  -3.4683  -7.6129  -1.7743   8.2984  -8.6413  -4.9321  -3.2166
+#>   -1.4983  -4.7405   9.3071  -6.3806  -4.9376   2.7848  -0.3833 -11.3562
+#>    5.1440  -4.8126  -6.5556   7.4010  11.8370  24.1226   2.9931   4.0632
+#>  -14.5159  10.4019 -10.7014 -12.2669  -7.8915   4.9762  -8.2486   9.3089
+#>    6.4645  10.5710   2.8239  -4.9620  -5.3707   4.9703  -0.6751   5.5845
+#>   15.0005  -0.9998  -7.2795  -3.3259  -7.8754   2.7780  21.2901 -14.5734
+#>   24.5584  -8.3442 -12.7770  11.3651 -10.8127  -4.3726   5.9924  -4.9559
+#>    8.8961  10.3320  -9.1711   3.1170  -5.2894 -10.5718  16.2240 -22.4172
+#>   26.0771  -7.0022 -15.3138  -5.7192   0.2452   4.3132   2.0631 -18.1102
+#> 
+#> Columns 49 to 54  -2.8295   4.3922  20.7197  -0.0240   8.6751  -1.6035
+#>   16.3829   2.7459   4.2050 -14.4186  -1.4939   1.6178
+#>    3.9324  -9.5108   7.4369  -1.6881  -4.5359  -6.8948
+#>  -14.7896  -4.1422   9.6311   5.1452  19.3832   0.8723
+#>  -11.2585   1.6993  16.2487  14.5141  -2.5438   1.0223
+#>  -21.5838  15.8320 -10.3487  -4.8617  -3.5866  -1.3017
+#>   -0.3889 -12.2566   1.2446  -8.4364  14.7537   7.8892
+#>  -15.1715  20.0868  -1.8932   9.7009  -7.2752   1.7632
+#>   -8.2849   6.3488 -13.4587   6.3709  -6.2838  -4.1846
+#>  -11.8349  -7.7772   7.5496   8.5381  -7.6831  -4.4465
+#>  -33.7483  -2.5239  17.8613   3.9449  -0.1833   0.1565
+#>   17.9800 -15.4024   0.6699   8.7572 -10.7733  -8.2805
+#>   -0.3194  -0.6577   2.6579  -1.6968 -10.8007   5.7856
+#>   -3.9182   5.8878 -10.9374   0.0723   3.4909   5.3152
+#>   21.3063  -9.3108   3.2918  -4.3441   9.3519   5.3003
+#>    1.1076 -19.6701  -5.4674  -6.2837  -1.6270  -0.4506
+#>    7.8793 -15.8570  -8.6042  -0.9513   3.5009   8.1138
+#>   -1.9548  -6.8843   8.3953  12.1991   4.0054   4.4473
+#>    2.8620  -0.4685 -23.6100 -11.0123  -3.9941   1.8789
+#>   11.4245 -11.0546  10.2274   2.2243  12.8753   0.8180
+#>   -0.8705  -5.9181  10.5114  -8.3738 -11.2249  -0.5612
+#>  -14.9168 -17.3760  -3.5284  10.0062  -8.0880  -1.8089
+#>   -5.6122 -16.4036  10.7873   6.8826  -6.3163  -0.5449
+#>   -2.2350  -7.1392   0.7758   2.0823   5.4589  -2.7004
+#>    2.2204   5.7967  14.4527   4.0159   1.5278  -2.7988
+#>   10.6012   4.1860  -9.5941  15.1213  -1.4634   0.8367
+#>   -6.5743  -0.4198  11.2743  -2.1883  -7.1866   5.1508
+#>   25.7816  -9.5695  10.0629  -0.5985   5.0684   4.5676
+#>    4.5558  -8.9279  -5.6411   2.4653  11.3544   4.8136
+#>   -8.0721  13.1690   9.5670   1.8495  14.7423  -2.3996
+#>    4.6414   5.7775   9.1238  -1.9057   3.1172  -1.0679
+#>   15.4091   5.9839  11.1199  -5.9117  -6.0987   6.2805
+#>   -4.1258  -9.6158  -0.6696 -14.0358   4.9354   7.6087
 #> 
 #> (19,.,.) = 
-#>  Columns 1 to 8   1.0767   5.5533  -4.7036   9.9412  -1.1217  -2.2419   1.8039  -5.7265
-#>   -8.6722  -5.3671   9.6119  -5.1046   0.2392   1.5390  -1.9327 -11.1522
-#>   -1.5390  -9.6345  -7.3554  16.1637  -0.7063  -2.6086  -5.6490  13.4125
-#>   -1.3161   6.6821   9.2491   9.2681  -1.5992 -10.0013  -5.6260 -14.3100
-#>    4.1478   1.8199 -13.8644   9.2892 -11.3088 -17.8347  12.9773   1.0151
-#>    4.2774   0.4376 -12.1486  -1.4250 -11.3629   1.0010  14.5349   2.7159
-#>   -9.5957 -10.0090   9.3964 -11.6175   9.9439   1.5080  -4.2625 -14.0303
-#>    1.8417  -3.3999   3.4323  -9.7912  13.9317  -0.4858  -2.4816   3.5579
-#>   -1.1186   3.8074 -10.4497   0.0204   0.5021  14.5979  12.0954   9.1226
-#>   -0.0734   2.5938  -1.4849   3.2009 -11.6996   4.1094  -8.8224  16.3809
-#>    0.7290   0.7407 -13.0315   9.0456   7.0214  18.5311 -14.6049  21.0846
-#>    6.7994   5.7353   9.3056   5.6686  -1.1127  -3.9791  -8.0180  -9.2956
-#>   -4.5172  -9.8966  -1.6582   2.0723  -8.5300   8.9774  -4.6183  -8.2331
-#>   -1.9031   5.4573   3.7323   9.5858  -2.3931  -3.7597 -14.8737 -11.7221
-#>   -1.2665   7.6661  -8.5460   7.0667   0.4723 -13.6319  -6.8802  10.5505
-#>   -1.7393  -4.5569   8.2047  -5.7268  -5.8031   6.0560  -1.6962  -2.7832
-#>   -2.9019  -9.2061 -13.3871   3.5014   0.2425   4.3444   2.0498   3.8161
-#>   -2.4064   0.5608  10.4820  -0.3968  -4.0915   9.8314  -9.2122  -0.0182
-#>   -4.4806   0.9583   1.7148   6.4891  -3.7685  12.5118  11.5523   1.7418
-#>   -4.1795   4.3758   2.3889   3.8275   5.5741  -2.8142   5.4183 -10.0492
-#>   -2.5554  -4.5916  -0.9749  -7.9303  -7.7465  -1.9804  -3.6414  -1.7364
-#>   -0.2133  -3.2456  -1.4034  14.3690  17.6698   8.4782  -5.1666  18.6391
-#>   -2.6984   1.6757  -1.8009   9.5193  -5.9498   1.4157   9.3605  -1.7934
-#>    2.0156   3.8572   0.4427  14.9490   8.4395  -0.4469  -6.9477 -14.0243
-#>    5.4003  21.3595   0.1373   1.2464   6.0059 -10.9409  -4.1660  -4.8872
-#>    4.0334 -10.4847   5.6587  -4.9414   3.0759  -4.8128  12.8768  -1.0747
-#>    1.5269  -4.5568  -2.0269   5.0052  -1.8830 -16.4411 -14.7579   6.1195
-#>    1.8340   9.0677 -12.6624   1.7791  -0.0315  -4.7311   8.5713   5.6756
-#>    2.8952   3.3245 -10.5208  -1.0060  -3.9409  -7.1061  -1.5845  -4.5052
-#>    9.5655   2.2102 -10.4304   5.0988  13.1694  -2.3234 -10.7082  -4.1711
-#>   -3.5340   9.0905   5.9303  12.0301 -16.5272   4.6653  -3.9841   3.0749
-#>    3.4951  -6.8877   7.5635  -1.8781   4.6857   2.5944  13.6946  -0.5487
-#>   -2.0527   3.1539  -6.7138   6.3938  -1.3217  13.6770  -8.9408   6.8614
-#> 
-#> Columns 9 to 16 -14.0036  -6.9333  -7.5914  -7.3397  21.1018 -20.7922  -7.4453  -2.9514
-#>   15.3105   2.2971   0.2982   4.3908  -0.2667  -6.4674 -13.3505  -1.4617
-#>   -6.1115   2.5714  -7.5683  11.4539   1.3155   3.3049 -21.5070  19.7832
-#>    4.9959   1.4983   9.3627   3.7017 -10.2146  26.5324  -2.2526   5.2295
-#>  -12.8752  19.7129  -0.4196  -7.5762  -2.0572  13.2759  -4.4405  -9.2029
-#>   10.0756   2.7735 -23.5013  12.2906  -5.4782  21.6938 -17.0761  -4.0252
-#>   14.6526   6.0746   4.7952  13.9198  -3.3412  16.1222 -16.5424  -3.8929
-#>  -12.3913  -2.0690 -13.4521   7.9808   6.5945 -14.5495  -2.6231  -0.5771
-#>   -9.7536   9.1040   9.0998   4.7534  -6.1598   8.4980  -7.8844  -9.6756
-#>   -0.1418 -16.9912  -0.5980  16.0907   0.1257   8.7807   2.0439  -1.3475
-#>   -3.1518  -2.1738  -6.6052   6.8677   1.5873 -14.2726   1.7766 -10.5044
-#>   -2.8683  -5.1831 -10.5308 -18.5447 -12.5690  -1.3005  -3.5881 -11.1699
-#>    3.7412  16.8748   3.9050   9.9851  15.3700  -2.9613  -5.7636  10.6608
-#>   -4.2591 -12.5602  -3.5333   3.8479   8.8281  -7.0624   6.9942   5.4780
-#>    8.9278  -4.0972   0.0137  -4.8261  -4.0591   3.0358  11.8793   4.4076
-#>    8.2928   4.9787   6.3531   4.0125  -0.8324  -8.5832   0.3094   7.7585
-#>    7.5487  -6.4172 -12.2168  14.1711 -11.3847  16.0068  -8.3022 -10.8496
-#>  -10.7014  -9.5926  -0.8300  -0.6476   3.2910   5.1632  -0.4048   2.1139
-#>   -5.1289   5.3220   8.1721   0.3316  -0.0183  -1.2238  11.9751   4.5372
-#>   -0.4399   7.5658  -5.5506  -0.7805 -14.4911  -0.9694  -9.7239  15.7855
-#>    6.4175   4.7623  -4.1142  -0.3659  -2.9082   0.1668  -7.6900   3.3325
-#>   12.2541  -4.5770  -4.3425  -3.8493  -7.1113 -17.3472  -5.5664  -3.4450
-#>   -7.3949   2.0393  -7.3935   0.2332  -7.9718  13.4079  -3.7224  -2.4832
-#>   -4.1343 -18.4931  -1.6467 -11.0502  -0.3584 -12.0707  -9.2867   4.7150
-#>    2.5687 -21.6220   4.2097  -8.2748 -15.1219  -3.1143 -11.4862 -14.7020
-#>    3.6943   0.4919   7.0880   8.1900   2.3499   4.6997  11.1992  16.1555
-#>   11.3095   2.7934  11.0052   9.1714   1.3916  -6.2684   4.4728 -10.0944
-#>    4.7105  -5.1521   2.2129  -2.0156   1.4167   3.6537  -3.3794   0.4796
-#>    5.8399   3.3100   8.1257   4.9947  11.2183   4.3283  -7.2079   8.7814
-#>   -7.1103 -10.6905   7.1750   1.3918   1.9821   9.5506  -4.6820 -24.4189
-#>   -8.6839   9.8079  -4.0095 -13.7002  16.0389  -5.6545   7.1707  -1.1640
-#>    4.9739  -0.7280 -13.5759   0.1589  -8.2647  -3.7519   9.5167 -11.3627
-#>    4.5451   0.6956 -10.7291  -7.4561  -4.6505  -7.3631  -4.7628 -28.0731
-#> 
-#> Columns 17 to 24  -3.8539  -2.0230  -4.8859 -11.9436 -22.1095  -5.6628  -5.5931 -13.9555
-#>    9.4386 -11.0877  -7.7920   1.4645   9.1822  -4.6735   3.8736  13.9348
-#>    1.1258   3.4778   3.1203  -1.8828  16.5443  14.8341  -2.4198  13.1858
-#>   -1.4624   3.6322   7.5670  24.5240  18.2129   0.3216  -0.8468  -6.3034
-#>    5.6501  -0.1130   7.7071  24.8891   7.6820   5.8292  -2.0203  -7.4934
-#>    7.5183  12.9281   9.2345  12.9773 -14.5725  15.2097  -5.0126   6.9257
-#>   10.3476  -9.5865   4.2492  -0.4222  -1.4051 -22.3373  21.0602   0.2441
-#>   13.8562  -4.1246 -11.0882   6.7276   1.1437  14.5107   9.8191  -8.3293
-#>   22.9158 -10.9553   1.8614  -6.1393 -14.1027   3.3065  -4.3564  15.2460
-#>   -2.5361   5.8835  10.0634  10.9973   1.2540  11.9511 -12.0903  -2.0412
-#>    7.4133   5.8439   7.5822  -8.0505  11.4378  -2.9357  -7.5842  -0.7917
-#>   -1.2279   1.6481  -0.9516   6.5344   5.2117   4.4362  -8.1969   0.8317
-#>    8.7589 -13.4882   5.3295  -5.6887  -3.2925   5.1488   5.2311  11.4647
-#>  -14.5725   0.6747  -9.5241  -7.4326  10.4742  -2.8029  -6.1621   0.9784
-#>   -2.6426 -23.0778   0.8946  12.7232   1.5908  -0.3250 -11.8063 -12.0918
-#>    4.6674 -16.3255   0.2027   5.4052  10.1676 -15.8522  -3.5531  -9.6944
-#>   -6.6342   5.1806 -11.5974  -1.7326  -1.8143 -11.4650  -5.1670  12.2587
-#>   -1.6934 -12.3489   0.2702   3.2438  -8.1793 -12.0089 -19.8978  12.2621
-#>  -11.0990  -2.8589  -5.5567 -11.1047  10.5503  -6.1740   0.0674   1.6390
-#>   -6.9366   5.6578   6.1890   1.4972   9.6568  24.4663   0.9538  15.1331
-#>   -4.2098  -9.1040 -11.4043  -5.8215  -4.2459 -18.2579  12.9748  -2.4634
-#>  -16.1518  -5.9413  -5.0830  -8.7746   6.3358   1.4073  15.6339  -5.8825
-#>    5.7514   2.2376   8.7201   6.7791  -7.1000  -1.2431  -6.1248  14.9285
-#>   18.2804   4.7080   8.9722   3.6387  -0.8141   5.1707   8.3539   7.4838
-#>   -6.5839  12.5141  -3.7244  -6.3505  10.4050  -3.5125 -13.8111  19.2087
-#>    3.7694   3.4359  -3.1777   4.5373 -13.3302  10.7501  -3.6951 -17.2893
-#>   -3.1303   0.0702   2.6647  -9.0818  -6.5239  -7.3000 -10.3534   4.1345
-#>   -1.6505  30.5225  14.7744  -2.7572   1.8382  22.8058 -15.6121   2.9228
-#>    6.1693  -2.7620  -0.2159  -3.6507  -9.1278  13.1196  10.4311   0.6940
-#>  -15.8986   3.7884 -11.8322 -23.4437 -10.0117  12.9315  -7.4701   2.8522
-#>   -5.2591 -14.5160  12.0403  11.5505  -9.5525 -14.6422   1.0221  14.5150
-#>   -2.2123  23.6488  -8.6307  -2.5041  -1.4410   1.0480  -1.4282  -8.2588
-#>  -10.4613  -1.3295   1.1473   6.4935  -6.8058  -4.4212  -4.8149   1.6436
-#> 
-#> Columns 25 to 32  -9.6950   5.8274  -2.7130   7.8220   0.2628  -8.4575  -0.8611  -2.0891
-#>   -0.6262  -7.6070  10.3993   2.3214  -4.4756 -17.3426  -5.3815   3.6312
-#>   -3.7904  -8.9074  11.7960 -15.4559   2.9198  -3.6039 -18.5744   2.5687
-#>    1.0892   5.1254   1.9500  -0.2883  -5.9760  -1.8075  12.4245   1.2843
-#>   -3.4794 -13.5702  10.2551 -18.4022   4.0738   6.1874   4.3489  10.5939
-#>  -13.8473  -8.3958  20.2520  -2.2035  11.2639 -12.5962  10.4556 -23.7736
-#>    5.4967  -0.0896   1.8114   5.0066   9.4052   3.1489 -13.4869  21.5895
-#>  -14.4500   2.4206   5.2329  -3.3601 -14.6554 -11.6877  -4.2139  10.0467
-#>   -4.6730  -7.9645   8.5485  12.7164   0.3668  -3.0959  -2.8246  -2.3478
-#>   -2.8326  19.0015   2.2847   1.0802  -2.5495  -7.1782   5.1540  -5.5282
-#>   -7.0435 -18.7726  -0.9276   2.9793   7.8034  12.0036  15.0883  -8.5713
-#>  -10.3140  10.2727   9.5996   8.0028  -9.0517  -2.4783  -1.7422  -0.5073
-#>   12.4743  10.3130   3.6449   3.9975   1.7386   4.4202   1.0105   3.8690
-#>  -26.0719  -2.5164 -14.8735 -10.9506  -7.5253   1.9838   8.8550  -7.7213
-#>    3.2727   2.7772  15.2995  -7.4337 -21.3232 -17.8804  11.6360   0.8503
-#>    0.1637  11.9308  -3.0914  -3.8390   5.9435  -6.4313  -0.5033   2.3434
-#>  -19.7685 -25.8448 -22.1217  10.0720   6.8253  -7.4429   3.5742   8.5442
-#>  -12.8470  -3.7712  -0.5459   9.1840   0.1786 -23.6156 -13.7719   7.9354
-#>   14.0543   3.6764   8.9798   2.1178   8.6580  11.0799  -3.9781 -10.0693
-#>   -6.5107  16.1062   1.6875  -5.3284  -9.7218  -9.3557  -3.5293   0.6473
-#>   11.4312  -2.4190  -6.2085  -5.4153  -8.2941  10.3688  -9.1526  17.0744
-#>   -8.0095   4.4340   9.9769   8.8788 -12.9031   7.2673  14.6156   8.1925
-#>    3.3411 -13.2837  -0.9161  -9.1904  11.4688  -3.5172  11.5384  -8.2214
-#>    0.6083  11.5753 -15.9066  14.4976   1.0073  19.7245  -9.7316  -5.6104
-#>    5.7921  14.3940  -1.5066  -1.9038   3.0477   5.1525  -6.0519 -14.2574
-#>    5.7377   0.6755   0.4734  -2.9928   1.3565   4.9964  10.1169  -1.9241
-#>   11.6454  11.2528  -1.9511  11.1411  -7.6800   0.4682   3.3893   7.9758
-#>   -8.2559   4.2834   8.2124   7.4970   0.2958   4.1579  -6.5515 -10.6424
-#>   -0.0748 -10.6938  20.9447  -6.5912   3.3622 -14.7842  -8.1799  -8.8367
-#>  -29.9325   2.2621  -1.3697  21.0621   1.0445  -4.1829  10.6821 -12.7333
-#>   10.7188  -6.6811   2.9166 -17.8202  15.5555  -5.1319  -6.5339  -5.7573
-#>   17.2279  -6.4359 -11.4349   4.5974   9.9781  -0.8728 -17.6340  -4.9185
-#>    9.8801   8.1995   7.8229  -4.0455   6.4705  -8.6501  -6.0530  14.8059
-#> 
-#> Columns 33 to 40 -16.8134   0.9345   6.0130   3.2657   4.0334  12.5669  -3.8526  -7.4433
-#>    4.8040  -7.5728  -8.6687  -4.9897  10.4568  -0.3818   2.9275   7.0264
-#>   17.1487  17.1811   1.8131  -1.6268   3.2504  -1.6103   2.1148   4.8444
-#>    4.3901 -14.4819   4.4570  -3.3131  -4.5546   9.1669  -4.3040   5.5706
-#>   -5.8960  -3.2672   8.1288  -3.7381  -2.1924  22.5451   2.3667   9.7284
-#>  -18.2572   5.4731  11.3168  13.8111 -14.0473   0.3453  14.3634   3.4614
-#>   11.5613 -18.0741   2.7858   6.7078   4.9347  -8.2687  12.4922  -6.4614
-#>   -5.2524  -8.8967   4.0937  -0.9864 -10.7034  18.0523  -4.1173   2.9440
-#>   -7.9853  -3.3826  -1.6270  -9.9387  -0.3615   0.6658  -3.2412 -16.5801
-#>  -20.2413  -3.5176  13.3472  -8.2069   1.5136   7.9762  -8.3682  10.7648
-#>   -3.7595 -11.8931  -1.3305  -8.0434 -15.8344  -6.0567   1.9021  -2.7385
-#>   -5.2238 -10.1297  -4.3264   1.0212   0.9195  -4.5202 -14.3401  -1.9053
-#>    1.1637  19.7997  -1.2302  -3.6157  10.0791  -6.5270  -0.1198   0.9702
-#>   21.7248  -9.0051   0.7113  -6.1913  15.0238  15.0909  -7.3654  10.6735
-#>    7.9912  -5.0314  10.6472   5.0058 -11.3351   3.3964  -3.5761  12.9362
-#>   -3.6125   4.4261   6.5276   1.2179   9.4282  -2.5485 -10.9319  -1.7716
-#>   13.3769   3.6996   2.7557   6.6103  -3.1640   5.6600  15.0018   2.5374
-#>    4.6061  -6.1245   1.1185   4.9069   3.9026  -9.1939 -12.4780  17.3670
-#>   -3.2972   4.5357  -3.6831 -12.5809   5.1991   7.6571  -7.7334 -16.0696
-#>    4.4995  -3.4061 -18.2221   0.1657 -17.2780  18.2298   4.7350  -1.9094
-#>   -4.8294   9.7842  11.0807   7.1513  14.7856 -11.5080  11.0651  -5.7143
-#>    1.3501  -6.8370 -11.7194  -5.1865 -18.3552  -2.8589  14.2047 -23.2365
-#>    7.9918   1.8035   3.4218   3.9900   5.4208   6.7179  -4.7383  31.7702
-#>   -9.9291  -6.0875 -19.0885 -27.4261  11.9650   0.2927   4.1253 -10.5055
-#>   -5.5741 -25.3679  -2.6794  -6.6270  -4.9728  -0.6329 -14.6550 -16.3996
-#>   -8.0340  20.9211 -18.7240   3.0184   0.9541   8.7990  -0.4756   2.7910
-#>    9.2895  -9.3025  -8.1933   0.8929   4.0431  -3.9183  -4.1492  -4.6099
-#>    1.2121   1.7955  -3.7963   7.0246  -2.8241   7.2822  10.2972   6.1966
-#>    9.2500  21.6554  10.0764  -3.8195   1.0120  -0.5537  -0.8079 -14.6708
-#>    7.1617 -10.3057   3.1645   3.6164 -22.4676  10.5733  -3.8868  -2.0448
-#>    1.6206  -5.9916 -17.1144   5.1716   5.7639 -15.1969   4.0864   4.3495
-#>    5.8028  -2.6665   2.5533   8.6684   2.9800   7.9136   2.4781  10.7375
-#>   11.7868 -12.8855  -3.9318   0.7253  -8.3808 -15.1319  -0.2693  10.1590
-#> 
-#> Columns 41 to 48 -16.5444  -4.6287   2.0314  -1.7905  -4.3152 -15.1268  -3.5579   8.1316
-#>    8.2578  12.1657  16.9449  -4.7423  10.7516  -5.3044 -13.8603  -8.9339
-#>    5.1447   3.9740  11.7261   1.3244   3.9746   7.0768  -6.8797   4.1232
-#>    7.0068  -9.2230   0.2787   0.7377   7.8000   0.7086  16.1432  -8.6581
-#>   18.8627   6.4956  16.8406  17.1304   3.2918   3.1755  -3.4407   4.3053
-#>   -4.4693  -1.4799   6.3924   1.7607  -1.3521  -4.0490 -14.7393  -5.2026
-#>    1.0006   1.6647 -13.1156  12.6615   8.3567  -6.5948 -15.2573   7.6170
-#>   -3.1967   4.9423  -4.7073  11.9696   9.9641  -9.7513  -0.7074   6.3584
-#>   -0.4424   8.1667  -5.0007   0.7535  -9.9669 -12.6861 -11.3693  22.1647
-#>   11.3647  -1.3075   7.4077   0.7178  -2.3886  -5.3439   3.9297  12.7206
-#>   -2.3143   2.2302  -7.5687 -31.6458  -2.5112  18.9413   4.5091   9.2022
-#>   -6.8183  -8.6902 -19.0367  -7.5001 -12.1462  -5.3157  -3.3004  -7.1680
-#>   -6.7658   2.4393   6.3413  -8.4221  -5.2463  10.0096   4.4987   4.1712
-#>   -7.3988 -14.1535  11.3764  -4.7804  18.2793  12.4318  11.0025 -13.3344
-#>    4.3526   2.2977  11.8006  -1.2596 -24.6957   3.3288   7.7652  13.5084
-#>   32.4653   8.8435   9.7412   8.9345  -2.9832  -1.6264   4.4315  12.2145
-#>   18.7072  -0.1537   7.3249   1.2747   6.8130  15.6451  -5.6066   1.3597
-#>   -1.5957  -5.7835  -7.2907  -4.1485  -2.1856  -5.6710   6.6938   0.1075
-#>    2.2760   2.9900  -7.1284  -5.2640   0.3314   5.4664  -5.5271   3.6673
-#>    1.9755  -3.5697   5.8237   4.3192   2.5900   1.1106  -3.2682  -3.8377
-#>    1.5259  -1.8319   7.3504   1.0233   8.1935  -9.2719  18.0310  -7.7718
-#>    3.9253  -0.7988 -11.2892  15.1197  -1.6806  11.3325   3.1717  15.6728
-#>   16.1292  10.9454  15.4373 -15.8961   0.9757  -9.1515   8.7367  13.7797
-#>  -19.1597  -8.2421 -16.0136  -7.3302 -11.1742  -0.3500  -1.6897  -6.1324
-#>  -18.3563   7.7518  -7.3949  -7.3812  -2.3338 -13.3431  -7.0959 -10.0065
-#>  -11.7076  -5.5947  -0.5833  -1.9684  -8.2818   1.4432  12.8440  -6.4148
-#>  -14.6965 -15.2207 -16.3022   1.4870 -10.0094   0.8704  -8.3260 -19.1158
-#>    0.3915   0.7392  13.2677   7.1700  -0.7309  -7.1851 -14.4830 -18.1360
-#>    2.5043   2.7492  12.0607   4.7828   6.7693  -7.3077  -5.7348 -11.0223
-#>   -7.0619 -29.3620  -5.8536  13.0872   7.5924  -0.6150  19.7299  -5.6692
-#>  -11.1114   4.6274   8.7869   6.7250  15.6530  23.9817  -3.5366   6.9809
-#>   -5.1315   8.5302  10.0534  -6.3178 -12.8647 -10.8650  13.3111 -11.5817
-#>    4.6134  12.7501 -11.3046   3.7991  -6.4595   5.7412  -5.2063   1.9086
-#> 
-#> Columns 49 to 54  -5.3282  -3.2266 -24.8687 -10.5104  -4.4439   0.3163
-#>   10.6467  -5.4864   2.7542  -0.7171   0.6800   2.0922
-#>   11.7002  10.2993   6.8480   0.6578  17.3164   6.8616
-#>    3.6185 -18.4867   4.6401   4.8705   8.8435  -0.9595
-#>   15.3718   6.8593  14.3551  -5.4822   5.1366   6.1417
-#>    3.1160   9.3470  -6.9606  -0.0294  -3.2393  -4.2674
-#>   15.7196  -1.8938  -7.7399   0.0166   2.4686   3.5718
-#>    6.9156   4.8775  -7.8181   1.5310  -2.6594  -0.0888
-#>    3.7727   7.0841   3.0279  -0.1965  -8.6360  -1.8248
-#>   -8.9784 -22.4677  -8.8660   0.3609   9.7901  -3.4502
-#>   -4.3600 -10.9410  -5.0836  -3.6610  -5.5090  -5.2339
-#>    9.3056 -11.7218 -16.2098  -2.7711  -2.8203  -7.1593
-#>   -3.8441  -9.5755   2.5076   1.8440   6.0984   3.8825
-#>   -3.3463   6.1568  11.1819  16.0915   8.8665  -5.5455
-#>    0.6073   0.7520  -7.7763  -8.8121  -3.8470   1.5911
-#>  -11.1212   5.0537   2.1586   9.1666  -4.0692  -2.6149
-#>   -4.3383  20.6349   7.5382   1.8032  -7.1276  -2.8183
-#>    6.8676  -6.5512 -13.4673  -7.1652   2.2067  -3.7488
-#>    7.9024  13.9121  -0.7281   0.6378  -1.9312  -0.9425
-#>    1.3689   8.0473  18.6780 -11.5955   9.1147  -0.9635
-#>   14.7714  -2.3066   3.0062  11.6449   2.5655  -0.1592
-#>    1.5034   1.5028  -8.2844   1.8364   0.1551  -0.7391
-#>    3.1903  -9.6047   8.1067   1.0414  14.3937   0.3468
-#>  -16.7015  10.6327   1.9860   5.6200  -2.7926   4.6682
-#>  -15.6331 -11.5799  -9.7693  -5.3290  -3.0923   0.8328
-#>   13.5332   2.7533  10.4474  -6.1639   1.1230  -7.9291
-#>  -13.7635  -8.4313  -3.9145  -5.8836  -5.1160   1.8951
-#>   -0.4143  18.4338   8.0957  -5.2611  -2.2945  -2.4783
-#>   -4.6864   9.1464  19.1237  -1.0431  -8.9225   2.5674
-#>  -14.4443  -4.0471  -0.9250 -16.8284 -11.6586  -4.4408
-#>   -4.8694   3.6092  10.6813   0.2885   4.8268   5.6979
-#>   12.0734   2.5061  -1.6331   1.3433   4.1259  -4.5298
-#>    0.4723   3.8604 -15.6149   2.0348  -7.0461   7.8540
+#>  Columns 1 to 8  -0.6585   9.8826   1.4030 -11.7249  -2.5811  -6.9256  -0.2827  -5.6463
+#>    0.1650  -0.9250  -7.3263   5.7255  20.3290  16.4450   5.1317  -0.3027
+#>   -3.0503   2.2478  -2.3873   7.3440   2.1027  -9.6104 -18.6459   2.7329
+#>   -1.6699   2.7374   6.5905  -0.8758   2.9866  -3.3036  -9.0732  11.1067
+#>    2.5126  -8.3576  -4.3203  -2.3904   2.5905  -3.0552  10.2008   3.5784
+#>   -6.1066  -9.3117 -14.3303 -10.4691  -5.8768   6.6407  -3.0724  -1.1505
+#>   -5.8966  -0.7295   4.0729   4.7547  -3.7514   5.5806   6.0984  -6.0869
+#>   -0.0661   7.7294   7.3693   5.9577  -4.7820   0.8416   0.1636 -12.4664
+#>   -1.4011  -7.8906   0.1955   0.9014  -3.4799 -25.4973  -8.7293  -0.9653
+#>   -2.1498  -5.1125  -0.9872  -9.8244   6.0989  -1.5911   4.2375  -8.2248
+#>    0.0196   5.9959   0.7984  -1.3006   2.6809  10.8185  -6.3292 -15.5907
+#>    4.7008  -8.0456  -6.5674   8.6466   5.6945  -7.0601  -0.9332   3.3819
+#>   -0.9763   2.1652  -0.4644   1.2342   7.8279   3.4226  -2.8396   0.6060
+#>    5.3351  -5.6966   0.6555  12.2491  11.7811  -3.3733  -7.5998  -4.3169
+#>    7.4829   8.3227   5.7174   1.9248  -1.0290   1.4823   7.8087   7.7931
+#>   -3.9558  -1.1283   9.0225   9.8182   2.3755  -3.9436  -0.7533  -7.7746
+#>    0.2957   3.3435  12.7207  -4.4201   3.8301  -5.3327  -6.6411 -14.3132
+#>   -0.5431   2.8346   8.6470   1.6883   7.7011  16.6742  -0.8749   1.6114
+#>   -3.2364  -5.1738   6.5949   4.2149   6.6641   4.4238   1.2473   3.6537
+#>   -0.3931  -7.0275  -3.2776  -0.1583   2.9636  -1.4412   1.4360  -4.1485
+#>    4.1846  11.3994   0.0978 -15.9557 -15.6705  -3.0953  -8.8132   3.4283
+#>   -0.0567   9.4752   8.0310   2.5737   0.2140   7.0641   6.5740  -0.1967
+#>   -2.2564  -4.1701  -0.9821 -12.4637  -5.1985  -2.5128 -10.4689  -8.4280
+#>    1.0629   1.8494   5.9899  -9.4499 -13.7456  -4.2967  10.3306   2.2490
+#>   -3.5610  -2.6600   1.6836  10.8043   8.6932   6.9998  -0.2618   0.5577
+#>    2.4975   4.1212  -2.3321  -3.9119  -8.5979  -0.4280   1.9751  -0.5522
+#>    4.6049   5.1911  10.3075   0.9192   4.9468  -3.5057   3.0299  -0.6479
+#>   -5.2388  -1.2334   8.2371   2.6726   9.1956   7.0493   2.6240   0.3927
+#>   -5.1976  -2.7467  -2.1800   0.0677  -5.9041  -7.7093  -2.2795  10.3765
+#>   -0.4761  -0.7456  -5.1437  -2.2301  12.5085  -6.5408  -2.4305   5.8645
+#>   -3.4447  -4.6770   8.7570 -16.7938   0.7921  -6.2499  -9.9334  -3.1894
+#>   -0.2361  -2.5222  -3.6469  -9.9391   1.0013  -4.6817  -3.0360  -7.3333
+#>   -3.0991  -0.9720  -3.4605 -21.3730  -1.2274   4.6351  -4.7941  -1.8650
+#> 
+#> Columns 9 to 16  17.0764   5.8430   8.9233  -1.1620 -14.1773 -11.3817 -15.1978   7.5053
+#>   -3.4096   8.3395  -4.9624 -11.3267   1.9752   9.8892   4.4464  12.5635
+#>   -0.7508   7.3893   5.2803  -7.6950   3.4215   1.5168   4.2154  -2.7620
+#>   14.7705  16.0933   7.3837 -17.3485  -9.5440  -8.8536  -6.7643   0.4151
+#>    9.2540  -0.4406  -1.8635   5.1248   2.0445  -6.1785  -8.1137  11.8449
+#>   -0.7375   0.6059  -3.2835   7.8062   4.8646 -11.8888  -5.1828  22.3903
+#>   -0.5122  -0.4262   1.7144  -0.1126  13.4943   8.0834  -6.9894   5.0771
+#>   -2.1991  -8.1310   2.5913  -4.3688   9.3261  17.9060 -24.1810   7.4151
+#>   10.6987  11.0966   1.7284  -5.2203   0.6270  12.3438 -10.8410  12.5950
+#>   -6.1397 -15.9450   4.3618  -2.1385   1.5842   2.9679  -5.9682   1.8272
+#>    0.4473   8.8776   5.9637   9.0792   1.9436  -1.9682  -1.4089   9.4347
+#>   -2.7729  -3.7891  19.5924 -17.5691  -7.6583  -4.0795   2.0968   8.5380
+#>    8.7640 -11.7137  10.3386  -5.7720  -0.2722   5.8701  -7.6923  12.3095
+#>   -7.7306  15.5506   6.8464   6.5464   1.0749  -4.6382  -3.2192  21.5630
+#>   -2.4014   6.8278   2.1954  -5.4732 -14.2277  -6.1357  10.7854  -0.2827
+#>    2.8345   2.4622   7.9324  15.2339  -2.5405  12.8163   9.3821  -8.4103
+#>    0.9343  -9.9945   1.4696   6.1561   2.6929  -0.2515  -5.4584   1.5540
+#>   -6.4884  13.8360  -7.6828  10.1019 -12.2709  12.1233  -7.7072 -21.8447
+#>    1.3176   1.5931 -14.7742   1.1585   1.5179  23.7545   9.8173  -8.3933
+#>   -2.8098   8.7542   6.3600  -0.5930   0.9488  -2.6780  -7.2417  -9.2113
+#>  -10.5671  -0.1901   0.2076   2.1970  10.1435  -6.7166  -8.7641  -7.4586
+#>   -5.5138  -4.6460 -10.3325   4.9752  -3.2165   3.5890  -6.1464   0.5999
+#>   -5.0317   5.9133   3.6240  14.2959 -14.0091   2.2582  -9.2788 -10.8581
+#>    1.1499   1.0356   7.4818  -0.3367   6.0681  -2.0430  14.2815  -5.7766
+#>   -0.8230  -1.8076  -2.2688 -17.6456  11.6994 -12.3278  -2.9590  -0.2083
+#>    2.3176  11.2455  10.2384 -11.4388   5.5013  -5.4900  -8.0858  -9.6185
+#>   -3.8191  -4.9966  18.0780  -2.4338   2.3540   1.6048  -9.6348   1.2277
+#>   -1.2965  -8.7914   8.3066   4.6738  12.5374  -2.2019  -0.0928   2.9176
+#>    4.3275  -7.6179   3.7792   1.4985  -2.4665  -9.1225  14.3049  -3.5420
+#>    5.5015 -14.7975   3.1378   1.0079  -1.3136  -9.9615   7.3445  -3.1338
+#>   11.7060 -17.1671   0.7704   8.7745  -6.2961  -2.3564   0.4598  -3.6809
+#>   16.3385   3.8710  -9.4886  10.0640  -1.2299 -13.2768  11.2196  -2.4243
+#>    7.3597   0.8981   0.5580   3.6674   3.2407  -6.7594   3.3683  -9.1721
+#> 
+#> Columns 17 to 24  12.3716   0.3837  10.9611   5.0459  -2.7379  -5.6940  -8.2364 -10.0896
+#>   -5.5260  17.9999   6.1926   2.3621 -11.2591  10.1728   9.3540   9.5865
+#>   15.7756  -5.4608   5.8634   7.0200   1.5367 -18.7260   0.4461  -5.2404
+#>    9.8541  -8.2423   8.8742  -3.5748   7.2042 -11.2182  -3.1723   6.6835
+#>    3.4857  -5.9538   2.6302 -11.9920  -1.5768 -11.1749   1.0269   6.6369
+#>   19.3770   1.3367  18.3553  14.1196  -5.5469  -2.7597  -2.1055  -5.3372
+#>    2.7199   1.6571   3.2364  -7.4958   1.6000   1.9315   2.9589   8.8029
+#>  -14.7438  35.2956  -6.2668   4.0513   6.3596  -8.6667   3.8587  -3.1671
+#>   -7.2888  -4.4561  -1.6586   2.2003 -18.8753   2.6387   7.1313  -3.0279
+#>   -6.2671  -4.4149   6.2316   3.1299  15.8317  12.3411 -12.2143   0.4030
+#>  -17.1368   1.2511  14.9939  16.3784  -1.7329 -12.3537   3.1450   6.1910
+#>  -17.1984 -19.1974  -1.4921   3.0762   0.7544   3.6702   5.1287   1.0856
+#>   15.2964  -6.5933   7.6373   3.7420   7.8779  -3.5939  -4.2540  -1.5932
+#>  -14.3014  -5.8670  -3.9555  -2.3297   1.4709  -2.6299  -3.3988 -15.9106
+#>    0.9698  -0.7359  -1.8761  14.7952  15.2837   9.9436   5.8233   9.8523
+#>   -7.1648   2.7458   6.0455  -0.5011  -3.5236   7.8465   0.3871   9.4305
+#>    2.5477   3.0310  -5.1516 -12.1554  14.4463   3.8393   3.4572   5.8848
+#>    2.7395 -13.1965  -2.1219  13.6616 -14.9050 -15.7851  -1.4292  22.1225
+#>    1.2172   1.7590 -12.6351   1.8396 -12.6134   4.4054  20.6590   7.7989
+#>   12.4026  -7.7903  12.7742   9.3675   3.1696  10.7111   4.7836  -7.6805
+#>   -3.9905  -5.2888   4.4187  -7.0884   3.4783  -7.1178 -12.4988  -3.3764
+#>    4.5872  -6.9516  -1.1587 -17.7174  -0.9773  15.6822 -17.0617  -0.0740
+#>    0.3508 -13.9338 -18.0642  15.0680  -5.4266  -2.7523  -9.4221   7.6032
+#>   -5.6482   9.7174  -8.2347 -10.9364  18.7895  14.0610  -3.6170 -11.6634
+#>    7.8077  -6.4071  16.5274   1.1633  -1.1429  -1.6385  -2.0166 -12.1178
+#>    8.8312  -0.9600  -6.0248  -4.4254   0.8864   9.2633  -2.6882  -2.7071
+#>   14.5306   2.2454 -15.7828   0.4146  15.6573  -8.9497  -3.4040   2.7653
+#>   -0.2983 -16.9829  -4.1195  -7.9388  -5.4294   4.0565   0.5919   3.5288
+#>    4.3897  -4.8480  16.0940  -4.9574  12.1835  22.9355  -5.1749  -7.5077
+#>   -9.2620  -7.7825  25.3690   2.7733  -8.4929  13.8857   6.5900 -11.8566
+#>    1.4553   8.9283   7.9397  21.4509   5.8687  -7.0294   1.1019  -4.3770
+#>   -2.7059  19.3818  17.2716  10.3299   7.9694   5.0580   0.6178   8.5162
+#>   18.8328  -4.1314  -0.0858  -3.2976   8.5809  -4.5226 -10.8117   3.5330
+#> 
+#> Columns 25 to 32 -14.6839   6.8461   8.1797   2.9323  -3.3079   1.0147  -1.2211  -7.4734
+#>    7.0771  12.1087  12.3633   9.1535  -7.6124   3.2287 -12.4478  -0.0069
+#>   -1.9076   0.3062  -0.8460  -7.1716  -5.9524  -0.5816 -13.8654   9.3998
+#>   -1.1433  -4.0499   6.0653   7.0734  -4.0855  -3.4087  -8.1162  -2.8009
+#>  -12.2015 -10.7412  14.5730  -0.6075   3.9057  17.0001  -2.7814   6.7763
+#>   -6.1382   3.5829  17.4019  -0.0395   2.6327   2.5508 -15.2277  -7.0339
+#>    2.7591   4.2329   2.0129   6.1050   5.7073  12.4767   3.0138   1.3023
+#>   -1.6469   3.0729  20.4677   4.8603  19.0591   0.6883  13.9318  -6.5538
+#>   -1.2853   9.5133  13.8790  -2.7390  -4.1806 -13.8094   6.3727  -0.0213
+#>   -0.9865  -2.0827  -4.9538  -4.4485   0.8715   7.5893 -10.6855   0.3418
+#>   23.9912   2.2103   6.8849   4.3127  -9.0539  -7.7329  -8.6874  -9.3279
+#>    2.0184  16.9708   8.1070  -9.1243 -11.0595 -12.4074   5.4987 -17.4810
+#>   -8.8934  -3.0940  -2.3081   1.3716  -3.8921   2.1954  11.7256   9.3779
+#>    5.9283   4.8621  13.3068 -17.0110   2.5952   7.7034   0.4248  -0.2420
+#>   -7.6045   2.8049  -9.3567  -0.5433  11.7936  15.3341  -0.7353   7.3319
+#>   -5.5023  -6.9329 -15.4190 -16.7040   1.2402   0.0872   8.3702   0.7609
+#>    0.4774   6.1620   5.7706 -14.1197  -5.0692  17.9640   2.9275   6.2174
+#>   12.7974  -6.9163  -7.4520   8.1369   4.3131   4.3910  -1.4471  -3.3956
+#>    3.7648  -1.6795   5.0141 -14.7830  -6.4952 -12.8173   6.8626   6.1952
+#>   -8.6048  -6.0142 -10.9879   8.9913  -5.0566  -7.9528  11.2173  11.9002
+#>   -2.3514   8.6348   1.9311  -4.7748  -3.4838   5.2479 -21.6047 -14.6047
+#>  -13.9327  -0.3495 -10.7567  12.5530  -3.5812  10.8072   2.5655   2.7480
+#>    8.6673  -3.4642 -26.0022   2.1550 -10.6572  -7.9585   1.0572  -3.8994
+#>   -9.0354  -0.4745 -10.0054   0.1008   1.3596  11.8869 -18.4773  -7.1295
+#>    0.9810  -0.4039   2.4569  17.4938   1.0490   6.2991  -7.8893  12.1776
+#>   -8.6968  -7.8995 -12.1378  -4.6789   7.5475  -2.4331  -6.3865   5.4573
+#>   -3.0174  -4.1122  11.9619  -3.9539  11.7905   9.4868  19.5192   6.4434
+#>    1.5298  22.3209  11.0158  12.4615  -8.4496   6.2791   7.0195  -7.3815
+#>   -1.8864  -4.9000  11.1343   2.2848   0.8357   1.0630   5.2691  -1.8301
+#>    9.5066   2.8672 -10.2872  -3.2455   8.1272  11.1888 -22.8100  -0.5767
+#>  -22.7715  -6.5251   2.7178   0.9808   9.8882  -0.0530   2.2730  -3.9347
+#>    5.3799 -10.3769  -8.7074 -11.6425  19.0864  10.0179  -2.9931   6.1525
+#>    0.9106  -4.2660  -9.4658   4.3267  -7.8953  12.4605 -19.6896   0.3364
+#> 
+#> Columns 33 to 40  -0.3796   4.3497  -6.3414 -15.4790   7.4071  12.6665  -2.7184   2.4283
+#>    2.7128  -3.8754   4.7812   9.5654   7.3676   5.0271  -2.8875   7.1535
+#>   -2.2233   0.7749  12.3845 -14.3747   3.7607  -3.1506  -1.5266   9.8238
+#>  -14.0021  -1.2939   4.8742  -2.6410  -7.0068  -5.7241   4.8954   6.5269
+#>  -12.8333   3.9485  -1.8470  12.1138   0.8843   5.7001  -2.2891 -14.8813
+#>  -16.9078  -1.6815  -7.7031  -2.1090   6.2136  12.4303  -4.7568  -2.2713
+#>   -0.7305  -0.7113  -6.7354   3.7713   9.2904   1.7057   6.3839  -2.7437
+#>   11.3646   4.2795  -1.3488   6.3319   2.6274  15.4925  -4.3177  12.4847
+#>  -10.5841  -9.9281  13.8982   5.4759 -10.0459   3.3208   9.5562  -6.3237
+#>   -2.7735  -4.3544   5.0805  -2.5470  -8.5915  -6.4103   3.6065  -8.5134
+#>    1.8425   5.5700 -23.2685  -2.4631  18.8629  -2.0304 -14.0650   3.0244
+#>   -6.5492   0.3404  11.5178   2.0990 -14.4096   9.1546  -1.4287 -11.5420
+#>   -1.1521  -6.1942  -8.9768  -0.4030   5.1833 -22.7510  -8.1499  12.0629
+#>   -3.4294   3.3127  13.4552  25.0106   2.5155 -12.0142   2.4186 -11.3317
+#>   10.8930  -0.7558   0.7617  -5.3487  11.3760   9.4106   1.4221   0.4910
+#>    2.3136  -6.2310  -3.3220  -3.1695  -7.1624 -12.1209  -9.9109  -2.1805
+#>   -8.2842  -5.9906  -3.6156 -10.9326   0.7163  -6.6026 -17.2435 -14.2433
+#>    7.1166 -17.0526   5.6994  -2.8829  13.0573  -1.2222   1.8053  12.8678
+#>    0.4565  -6.5211   5.5655  -0.2669  -4.9020  -5.3036 -18.1904   0.3326
+#>   -0.5590  -1.7494   2.6337 -12.4070   1.0603  -6.7488  10.2757  -3.2534
+#>    6.3244   5.4070 -12.0361 -19.4551  -2.2169   7.3486  -4.0652  -0.4261
+#>   -2.9591  -2.8364   2.1820  -4.0785  -5.8038   7.8457 -15.4939   1.3535
+#>    6.9466  -5.9346  -0.8241  -8.8413  -1.5481  -8.5602  -8.9101  -8.2495
+#>    7.0841  -0.4892  -3.1260 -23.4276   4.5827  18.2278 -15.5246  -5.9589
+#>    3.9757   0.5016   2.5670   6.0558  20.3464  -2.1646   3.0153   2.6730
+#>    6.6056  -1.3289   1.6741  -8.6122  -1.4567   4.5469 -11.8557  -1.1949
+#>    6.9510   0.3596  -6.5264   5.9946   4.0785   0.7225  -4.2921   1.7292
+#>   -8.8113   5.7748  16.1156  -1.9505   2.9988  20.6561   2.5375   1.3187
+#>  -17.2576  10.5056   4.4396  -3.2395 -12.2402 -13.9491   3.3513   4.3628
+#>    5.2828   5.8323  -2.1133 -14.3629   1.9819   2.3627   3.2801  -7.6097
+#>   -0.3246  -8.6066  -5.1149 -18.0291  -4.2166   1.3238  -0.3825  -5.5382
+#>    7.1872  10.7332   1.0616  -5.7285  12.3867  -8.0469   2.3091  -4.9136
+#>    1.4546 -10.9034 -13.5010 -12.3129   6.6715 -12.9542  -4.7032   8.3032
+#> 
+#> Columns 41 to 48  10.0628  10.3230  10.9151  -3.8171  -6.2244  -4.4165  -8.0065 -10.0503
+#>   -0.4867  -9.2430   5.7187   5.5422 -14.9284 -10.8488   5.5976  -3.3869
+#>  -12.1743  -5.1474  10.6593  -4.4454 -21.1598   2.7410   4.2188  -3.6996
+#>    1.5159   9.4710   3.7640   1.5259   0.2589  -5.4336  11.1809   3.3088
+#>   26.8123 -15.9038  -5.3623   6.9913   9.8407  -1.4732   0.1598  -1.3244
+#>    6.3979 -11.5956  -9.6217 -19.9339 -22.7870  -7.3380  -7.0088  -3.0605
+#>   -0.0878   5.2290  -0.0313  -5.2206   0.3272   5.9445  -3.0802   4.3588
+#>   13.6341   4.5430  -1.3493  11.5789   2.6397   2.8362  -4.8600 -10.0980
+#>   -3.5060  -0.7775   6.5990  -3.0528  -8.0371  -1.9193   6.7200   0.3974
+#>   -1.1054  -2.5882   1.0700 -10.0701  -1.5345   2.9574  -0.1513  -9.1856
+#>    6.0967   1.3947   3.6074   3.4811  16.8579  -2.9246 -11.6604   7.4707
+#>   -2.3503   6.5394  11.1039   5.7431  -9.7938   5.0839   1.3991   9.2364
+#>    6.3953  -1.1190   8.2917  -6.7516  -6.5861   4.9368  -7.8025   5.3642
+#>    4.9937   1.3474   0.2836   8.0037   5.1593  -2.4403 -13.0951  12.4779
+#>   -3.2154 -19.7367  -8.5535   8.1844  -5.1560  -3.2137  -2.4954  -9.7998
+#>    3.4415  -1.6426   0.6285 -11.2886  -2.0018   4.2029   1.7292   4.2282
+#>   11.1582  -5.7000  -3.5032  -4.2132  -5.6515  -8.5600   4.1543  -5.9248
+#>    6.9363  -4.0137  14.7716  11.0608  -8.6587  17.2787   5.0551   7.1445
+#>   -3.9364 -15.0201 -10.8555  -5.8179  -0.0540  -2.9061  10.4499 -14.9126
+#>  -13.5480   2.4110   8.0315   2.1498   0.5293  11.1205  -9.4289  -8.9675
+#>   26.4785   9.3031  -9.0149  10.4413  -2.0480   8.3809   9.4102   1.8135
+#>    2.6971   0.5015 -18.6142   3.1587   0.8600   7.9833  12.2409  -7.7840
+#>  -12.6269   1.3774   6.6004   2.7139  -8.8343   4.2410   9.8433  -0.0475
+#>   15.0651 -12.2557  -1.6488   8.6864   1.8046   4.8422   4.9187   0.7652
+#>   -5.8416  23.2031  25.9997  -1.5593  -0.9083  -1.2811  -5.1272   8.5302
+#>   -1.0311   1.0817   3.2356  -7.1522 -13.7981   5.3841  14.2587  -4.0031
+#>    8.1744  -3.6218   4.2007  -1.5445   2.4377  -0.4216  -0.9016 -10.7751
+#>    6.2261   5.8193  16.8180   4.3904   8.9278   7.8632   4.6301  -6.8090
+#>   -4.0843   3.2028   9.4383  -0.8920   7.2703   1.9310  -9.1039  13.9812
+#>   -2.6016  -4.1952  10.6024   2.5199   2.4970 -13.2140   4.7213  -8.5451
+#>   14.1817   9.8274   8.8007  -6.8922  -2.2044 -14.2716   7.1534 -14.1860
+#>   -7.6821  -7.9864  -6.9343 -20.8698  -4.8451  -7.3192  -9.9142  -2.8159
+#>    6.0660 -19.4450  -9.7641   0.1634  -3.2923  -2.0917   2.9346   9.0170
+#> 
+#> Columns 49 to 54   5.8200   8.4084   9.3054  -9.8628  -0.4510   3.9276
+#>   -0.7372 -14.4453   7.4721  -3.9569  -0.1176 -10.6268
+#>    6.5721   7.3733   3.0376   0.2524   1.4956  -0.7129
+#>   -6.4613  -1.8132 -17.5323   0.7709  -1.6627   2.6338
+#>   13.6628  -1.4183  10.7630  -0.2926  14.4118   0.7544
+#>  -11.6941  12.3944  -1.3716   0.1419  -3.1795   5.9284
+#>  -21.3091  -8.7149   1.4729   9.7163   5.3108  -1.7244
+#>    8.8412  -3.0840   3.7965  10.6223   3.5092  -0.1732
+#>    2.2283  -3.2319  -7.4862  -0.9160  -4.5971   2.0654
+#>   -4.5742  -6.4256  11.2858   2.7449   1.7005  -5.0744
+#>    9.4259 -12.7133   1.1876   9.9136   1.8085  -1.6360
+#>   12.5822  -9.9034  -7.7651  -8.2844   5.7129  -1.6666
+#>    8.7622   0.5824  -5.6734  -0.0371  -1.8248  -1.9474
+#>    7.2880   5.1096  -1.4563  -9.1424   5.4070  -0.8980
+#>   -8.1181  -4.0841  -9.3682  -9.2077   2.4062  -3.9323
+#>   -7.2562  -0.7773 -11.0796   2.9141   1.4463   0.0563
+#>    4.4834   7.0276  -2.7986  -4.0180  -1.7774   1.0401
+#>  -16.4588  -1.5919  -9.5157  15.1540   0.3869  -3.1181
+#>    5.3706   6.3268 -17.8983  -3.6777  -4.0082  -1.0565
+#>   -0.5372  -1.0455   4.8401   3.0141   9.7704   3.6299
+#>  -11.8018  -1.2936 -10.1979   4.6638   4.7457   2.2066
+#>   -2.4877  -8.5786   7.9622   4.4728  -3.2413   3.6715
+#>   -7.2463   4.0733   4.5851  15.7668  -4.4075  -0.5109
+#>    9.5208   0.7263  -2.5272   0.2300   7.6229   0.3069
+#>    2.1348 -16.7072  -6.1046  -1.1163  -0.3200  -3.3069
+#>    4.1479  -4.0324  -8.4265   0.6642   0.4718   3.8026
+#>    2.0110   7.1490   1.3257  -2.5020  -0.8291   0.3372
+#>    0.3210   3.6987  -1.7593  -2.7245   6.8877   0.0815
+#>   -3.1185  -3.9933 -15.9710   0.6983   1.9976  -0.2487
+#>    2.0827   6.4108  -1.6085  -3.8503  -4.8357   4.8550
+#>   -4.6074   6.6271 -10.5125  -2.1807  -3.7528  -0.7343
+#>  -17.0510  14.7428   8.2607 -11.1247  -1.3343  -5.0686
+#>   -2.1579   2.9472 -11.7718   1.6321   0.1034  -0.0795
 #> 
 #> (20,.,.) = 
-#>  Columns 1 to 6  1.1531e+01  2.3331e+00  6.3103e-01 -1.0747e+01 -1.5133e+01  4.4643e+00
-#>  -2.2385e-01 -2.7180e-01 -6.3996e+00 -6.3756e+00  2.2921e+00  5.5969e+00
-#>   2.5445e+00 -3.3252e+00 -1.3379e+01 -2.0287e+00 -1.2538e+01 -2.3544e+00
-#>  -7.9704e+00 -2.9126e+00  4.3689e+00 -9.9908e+00  1.1437e+01  4.4595e+00
-#>   2.0039e+00 -1.3223e+00  3.4075e+00 -1.4926e+00  3.8605e+00 -1.1802e+01
-#>  -5.0724e-01  1.2890e+01  4.9779e+00  1.1625e+01 -2.1969e+01 -4.6666e+00
-#>  -1.3738e+01  4.1919e+00  9.3695e-01 -1.6508e-02  4.2423e+00 -3.7811e+00
-#>   5.0110e+00  2.4779e+00  4.7327e-01  4.6535e+00 -1.9481e+01 -6.0639e+00
-#>   1.1933e+00  3.6992e+00 -7.0678e+00 -8.9165e+00  2.4942e-01  6.7458e+00
-#>   5.3256e+00  1.0658e+01 -4.6348e+00 -8.8527e+00 -4.4973e+00 -7.1829e+00
-#>   8.4373e-01  7.9508e+00 -7.1050e+00  9.3471e+00 -9.7752e+00 -7.6119e+00
-#>   4.2762e+00 -3.7394e+00  1.2157e+01 -4.3657e+00  5.3120e+00 -5.2498e+00
-#>   4.2000e-01 -1.4188e+00 -3.5735e+00  6.4012e+00  1.5363e+00  6.0558e+00
-#>  -8.0048e-01 -6.7814e+00 -5.5306e+00  3.3690e+00 -1.9144e+00  1.9936e+01
-#>  -1.0017e+01  3.4138e+00 -7.7884e+00  6.4376e+00  1.3389e+01 -4.7419e+00
-#>   2.2364e-01 -3.2301e+00 -7.9332e+00 -1.2841e+01  4.2582e+00  1.8374e+00
-#>  -4.4040e+00  4.5913e+00 -6.1392e+00 -1.2316e+01  3.4954e+00 -1.1173e+01
-#>  -2.2540e+00  3.2320e+00 -5.9081e-01 -5.3758e+00  3.4184e+00 -5.9921e+00
-#>  -2.6445e+00  9.4472e-01  6.2066e+00  6.4064e+00  2.4575e+00  3.5394e+00
-#>   2.5899e-01 -2.1468e+00  1.3507e+00  6.7354e+00  5.0850e+00  3.9325e+00
-#>   7.8227e-02 -5.2020e+00  2.0918e+00 -4.1192e+00  2.2587e+00 -3.3492e+00
-#>  -3.0478e+00  1.3071e+01  4.8683e+00  6.8381e+00  3.9535e+00 -8.2438e+00
-#>  -3.8922e+00 -7.1828e+00 -1.1203e+01 -7.5899e+00 -1.4261e+00  1.1600e+00
-#>   7.7494e+00  7.2217e+00  7.2037e+00  5.6197e+00  6.8696e+00  3.1197e+00
-#>   2.1424e+00  5.3235e+00  1.3030e+00 -9.0769e+00  1.5741e+01  1.3477e+00
-#>   3.4640e+00 -1.1975e-01  6.6389e+00 -4.4436e+00  1.1040e+01 -1.5087e+01
-#>  -7.3618e+00 -6.3839e+00 -3.2193e+00  9.0840e+00  1.5088e+01  9.2531e+00
-#>  -1.0281e-01  1.0142e+00  1.4126e-01 -1.8113e+00  6.3071e+00 -1.0526e+00
-#>  -1.0200e+00  9.5171e-01 -4.2450e+00 -7.9963e+00  1.2206e+01  1.0523e+01
-#>   3.7093e+00  9.6528e-01  4.0630e+00  1.1129e+01 -3.9470e+00  5.5017e+00
-#>  -3.9856e+00  3.6659e+00  1.6750e+00  1.0511e+01 -8.8842e+00 -3.3586e+00
-#>   3.0463e-01  4.3400e-01 -2.9486e+00 -3.8748e+00 -8.4585e+00 -1.4113e+00
-#>   6.9056e-02  5.3960e+00 -2.7156e+00  7.2227e+00 -6.3782e+00 -1.2635e+00
-#> 
-#> Columns 7 to 12  7.9277e+00 -3.3597e+00 -1.2598e+01  6.1640e+00  8.6454e-01 -1.4289e+01
-#>  -1.8540e+00  4.6359e+00 -4.1865e+00 -5.1530e+00  2.3492e+00 -8.1270e+00
-#>   9.1591e+00 -9.1336e+00  2.2569e+00 -2.5140e+00  1.1702e+01 -9.2587e+00
-#>  -7.6002e+00  6.2050e+00 -1.3418e+01 -7.7016e+00  1.2713e+01 -2.0433e+00
-#>  -3.8421e+00  1.9191e+00 -4.5832e+00 -2.3329e+00  9.7887e+00 -2.1843e+01
-#>   6.0568e+00 -2.4789e+00  7.5743e+00 -9.3153e+00 -6.3102e+00  5.5473e+00
-#>   9.7806e-01  1.3823e+01 -3.1965e+00 -6.0133e-01 -3.1926e+00  1.0389e+00
-#>  -1.1084e+01  9.0128e-01 -8.2534e+00 -1.1053e+01  5.0405e+00  9.5454e-01
-#>  -1.9832e+01 -1.6358e+00  7.7623e+00 -1.1648e+01  1.3623e+01 -1.1985e+01
-#>   5.8391e+00  3.5531e+00 -3.1517e+00  1.0902e+01  6.9349e+00 -1.1707e+01
-#>  -1.2187e+01 -1.9061e+01  2.7532e+00 -8.7982e-01 -1.2811e+00 -1.1784e+01
-#>  -9.6197e+00  1.4408e-01 -3.8304e-01 -1.0293e+01 -4.2343e+00 -9.3015e+00
-#>   2.1816e+01 -1.0358e+01  4.4754e+00  4.4965e+00 -3.9147e+00 -8.8297e+00
-#>  -1.3955e+01  7.2483e+00 -2.5442e+01 -7.0836e+00  5.3737e+00 -1.0677e+01
-#>   7.2140e+00  2.3756e+00 -4.7037e+00 -4.8433e+00  1.1872e+01 -8.0447e+00
-#>  -1.3389e+00  1.1117e+01 -1.4307e+01  9.4055e+00  1.2201e+01 -1.6476e+01
-#>  -4.9345e+00  8.2168e+00 -3.9515e+00  4.4601e+00 -1.3724e-01 -1.4649e+01
-#>  -1.0054e+01  1.1957e+01 -9.0555e+00 -1.1360e+01  1.1955e+01 -1.2255e+01
-#>  -6.8150e+00  6.2035e+00 -7.0091e+00 -4.7487e+00  1.4248e+01 -1.3114e+00
-#>   1.1368e+01 -7.3924e+00  9.4632e+00 -9.4340e+00  4.1829e+00 -1.3339e+01
-#>  -1.4158e+00  1.2500e+01 -8.5006e+00  6.2598e+00 -2.9507e+00 -2.1564e-01
-#>   2.3398e+00 -2.4304e+00 -7.3535e-01  2.8813e-01  6.6898e+00  1.7336e+01
-#>  -3.1147e-01  1.6517e+00  1.2610e+01  7.1454e+00  9.4211e+00 -1.4088e+01
-#>  -7.4251e+00 -8.1059e+00 -2.5371e+01 -1.2935e+01 -1.6045e+01 -2.7882e+00
-#>  -1.8432e+01  7.2453e+00 -5.2321e+00 -1.3105e+01 -9.3911e-01 -1.2354e+00
-#>   9.3714e+00 -1.6181e+01  1.5188e+01  7.1519e+00 -1.1995e+01  2.7365e+01
-#>  -1.7714e-01 -1.3419e+01  6.6561e-01 -8.0800e+00 -1.1812e+01  1.2695e+01
-#>  -7.3460e+00  9.6275e+00  2.8878e+00  4.3720e+00  1.5972e+01  4.4419e+00
-#>  -6.1104e+00  1.5898e+00 -7.9875e+00  9.2666e-01  5.0628e+00  7.6020e+00
-#>  -5.2511e+00 -5.0651e+00  5.6202e-01 -1.8023e+01 -1.7160e+00  1.4579e+01
-#>   4.1947e-02 -1.7834e+01  2.7451e+00  1.2405e+00  1.3470e-01 -1.2129e+01
-#>  -5.9979e-01 -3.5876e+00  5.7801e+00  3.4267e+00 -6.0223e+00  2.1237e+01
-#>  -7.8867e+00  3.8303e+00 -4.2517e+00  1.7581e+00  2.2147e-01 -9.6697e+00
-#> 
-#> Columns 13 to 18 -1.3265e+01 -1.3388e-01 -6.2657e+00  1.8457e+01 -2.0316e+01 -8.9335e+00
-#>  -8.2219e+00  2.7945e+00 -1.0981e+01 -1.2172e+01 -4.2599e+00  6.7829e+00
-#>   1.5608e+01  8.9277e-02  3.2390e+00  7.8762e+00  1.2230e+01 -6.7688e+00
-#>   8.7358e+00  5.5304e+00 -5.5587e+00  3.9099e+00 -4.6915e+00 -1.2642e+00
-#>   1.0147e+01  3.6444e+00 -9.1277e+00  1.7335e+01  3.6767e-01  4.4335e+00
-#>   5.7939e+00 -1.8102e+01  1.1411e+01 -8.8106e+00  1.4680e+01  6.0197e+00
-#>   7.5897e+00 -4.5091e+00 -9.6216e+00  1.3621e+01  3.3132e+00 -2.2411e+01
-#>  -7.9518e+00  5.3377e+00 -8.7286e+00  1.1977e+01  1.1974e+01  4.4068e+00
-#>   5.2793e+00  1.2346e+00  1.2539e+01  2.2888e-01  1.6566e+00 -4.3003e+00
-#>   6.2172e+00 -1.6921e+01  4.8339e+00  4.0403e+00 -3.6163e+00  6.3318e+00
-#>   3.7147e+00  1.1907e+00  1.7518e+01  9.2517e+00  9.1858e+00  7.7552e+00
-#>  -1.0452e+01 -2.0280e+00  6.5720e+00 -1.0286e+01 -6.1103e+00  6.2985e-01
-#>   1.8934e+01  1.2379e+01  1.6522e+01 -8.6650e+00 -3.7289e+00  1.0112e+01
-#>   2.5255e+00 -1.0635e+01 -2.0998e+01 -9.0092e+00  1.6594e+01  1.1383e+00
-#>   1.7404e+01 -1.6339e+01 -1.1579e+01 -6.3186e+00 -1.3261e+01 -1.9673e+00
-#>  -9.4890e+00  3.6748e+00  6.6153e+00  7.0064e-01 -1.4821e+00 -5.0903e+00
-#>   1.5503e+01 -9.9915e+00  3.1989e-01 -4.1594e+00  6.4457e+00 -4.3074e+00
-#>   7.8736e-01 -8.5897e+00 -2.0359e+00 -1.3591e+01 -4.8745e+00 -2.1896e+01
-#>  -6.6362e+00 -9.8757e+00 -5.5616e+00  1.8116e+01  7.5799e+00 -1.7781e+00
-#>   8.3797e+00  6.0021e-01 -1.7778e+01  4.7960e+00 -5.5892e+00  1.2583e+00
-#>  -5.1661e+00  5.4751e+00 -8.1088e+00 -5.9602e-01  6.1550e+00  3.6812e+00
-#>   1.0024e+01 -1.3289e+01  4.1283e+00  1.8308e+00  1.7409e+00 -3.9152e+00
-#>   1.5095e+01  1.9088e+00  6.1760e+00  2.3120e+01 -1.4459e+01  1.5964e+00
-#>  -4.3251e+00  1.3200e+00  5.6500e+00  1.0793e+01  1.1772e+01  7.8633e+00
-#>   3.0207e+00  8.6253e+00  3.7148e-01 -8.2103e+00  2.1514e+00  3.3441e+00
-#>  -4.2380e+00  8.7565e+00  5.1626e+00  1.0956e+01 -1.1340e+01  1.2452e+01
-#>  -4.7337e+00 -1.4291e+01 -8.9942e+00 -1.3272e+01  2.6020e+00 -8.6399e+00
-#>  -2.9628e+00  1.7688e+00  9.4542e+00  1.3147e+01  7.1706e+00  3.1746e+00
-#>   6.3297e+00  1.0754e+00  2.7610e+00 -4.0563e+00 -7.0332e+00  1.6860e+00
-#>   2.2331e+00  4.8680e+00  1.0615e+00 -1.3488e+01  3.2870e+00  3.1170e+00
-#>   1.5144e+01 -8.8461e+00  3.1123e+00 -4.4244e-01 -4.8833e+00  4.7693e-01
-#>  -6.3397e+00  1.5592e+01  1.5269e+01  1.5849e+01 -6.6470e+00  2.9379e+00
-#>  -3.5580e+00  2.0140e+00  2.5515e+00 -1.6820e+01  5.1384e+00  9.3253e+00
-#> 
-#> Columns 19 to 24 -4.3144e+00  4.6143e+00  3.7621e+00  1.0236e+00  9.1765e+00  1.1586e+01
-#>  -3.2139e+00 -2.9037e+00  1.1098e+01 -4.6513e+00  1.6938e+00  4.7404e+00
-#>  -1.2711e+01  6.3123e+00 -4.1875e+00 -1.0495e+00 -1.1496e+01 -8.3215e+00
-#>   1.1090e+01  7.4233e-01 -3.5049e+00  4.8077e+00 -4.0711e+00 -1.1453e+01
-#>   1.6252e+01  8.5940e-01  1.1081e+01  4.6098e+00  2.5550e+00  3.6903e-02
-#>  -1.2179e+00  1.0425e+01 -3.1774e-01  1.9092e+01 -1.3186e+01  9.3608e+00
-#>   3.7483e-01  4.5561e+00  6.0095e-01  9.6300e+00 -2.3309e-02 -4.5975e+00
-#>  -1.2796e+01  2.8253e+00  4.3625e+00  7.1202e+00  3.4291e+00  8.9793e-01
-#>   7.0558e-01 -1.2783e+01 -1.1601e+01 -5.2674e+00  4.8640e+00 -8.5976e+00
-#>   1.7004e+00  1.4138e+01 -2.1057e+00 -2.3106e+00 -1.0992e+01 -2.4524e-01
-#>  -9.8371e+00 -1.7429e+00 -1.6777e+00 -7.0428e+00 -4.9677e+00 -1.0101e+01
-#>   8.7128e+00  1.9727e+00  7.3768e+00  6.1230e+00  5.9434e+00  8.9918e+00
-#>   1.5984e+00 -4.9178e+00  2.1932e+01 -5.4599e+00  1.1959e+01 -2.2573e+00
-#>  -6.9175e+00 -4.1474e+00 -2.1975e+00 -5.3939e+00 -2.2144e+00  7.2748e+00
-#>   1.0882e+01  2.0940e+01  3.1680e+00  3.3280e+00  4.6097e+00 -4.0240e+00
-#>   7.1740e+00  6.6605e+00  5.2177e+00 -8.3093e+00 -1.1249e+01  1.3216e+00
-#>   5.9196e+00 -4.6431e+00  2.4043e+00  1.3990e+00  2.1708e+00 -1.1351e+01
-#>  -9.4173e+00  7.0889e+00 -6.5662e+00  3.4494e+00 -1.8142e+01 -2.3038e-01
-#>   6.4866e+00 -4.0087e+00  6.9798e+00  1.2574e+00 -2.6856e+00  1.7630e+00
-#>  -6.4867e+00  8.2717e+00  3.6461e+00  6.8531e+00  8.2888e+00  1.5702e+01
-#>  -1.4982e+00  1.2453e+01  2.7548e+00  8.9527e+00 -1.0024e+00  4.8150e+00
-#>   1.5337e+01 -3.2413e+00  5.8117e+00  1.6994e+01  6.7664e+00 -1.1449e+01
-#>   4.7731e+00  1.0507e+01 -1.6246e+01 -1.7924e+01  7.1068e-01 -3.8325e+00
-#>   1.8137e+00 -7.0561e+00  7.9566e+00  4.3107e-01  1.0817e+01 -5.4474e+00
-#>  -4.6797e+00  1.4865e+00 -1.6564e+01  1.5662e+00 -3.3163e+00 -3.9403e+00
-#>   3.1976e+00  2.9680e+00 -7.3438e+00  1.4924e+01 -2.1774e+00  9.0913e+00
-#>  -3.2800e-01 -6.5956e+00  7.9274e+00  6.7508e+00  1.2148e+01 -9.3619e+00
-#>   4.5274e-01 -2.6912e+00 -7.2021e+00  4.2023e+00 -3.9267e+00  2.5256e+00
-#>   4.3481e+00 -1.2686e+01 -1.1956e+01  1.0051e+01  5.9468e+00 -2.1925e+00
-#>   2.0461e+00 -1.3921e+01 -5.5452e+00  5.2529e+00  2.0601e+01 -2.6521e+00
-#>  -4.6468e+00  1.4440e+01 -7.3033e-01 -9.9996e+00 -8.8389e+00 -1.5808e+00
-#>  -1.2480e+00  9.1248e+00 -2.8179e+00  1.0731e+01 -1.5468e+01  1.7226e+00
-#>   1.3306e+00  8.6867e+00  4.8440e+00  9.5960e+00 -1.1540e+01 -3.9837e+00
-#> 
-#> Columns 25 to 30  5.4699e+00  5.6998e+00 -3.4012e+00 -1.1067e+01  8.6554e+00  6.6413e+00
-#>   1.4069e+01 -9.7582e+00  3.2213e+00 -2.2708e+00 -2.1654e+01 -1.5022e+00
-#>  -3.8468e+00  1.0437e+01 -1.3451e+01 -1.7239e+01  1.4509e+00  7.5753e+00
-#>  -5.2113e+00  2.8510e+00 -6.9533e+00  9.5826e+00  1.6959e+00  4.5609e+00
-#>  -1.4356e+00  1.7240e+00 -2.1492e+01  6.4118e+00 -1.2253e+00 -2.2039e+00
-#>  -2.2775e+00  4.8589e+00 -4.2910e+00 -5.1652e-01 -1.4957e+01 -5.9976e+00
-#>   1.0456e+01  1.3280e+00 -3.5628e-01  2.1212e+00 -8.3177e+00 -3.2242e+00
-#>  -5.4305e-01 -1.2461e-02 -4.7155e+00 -6.2646e+00  3.0596e+00 -1.0798e+01
-#>   6.1351e+00 -6.4744e+00 -3.7152e+00 -7.4972e+00 -3.5417e+00 -6.1697e+00
-#>  -1.1345e+00  5.3915e+00 -1.0067e+01 -4.4847e+00 -5.1633e-01 -1.4962e+00
-#>  -1.7299e+01  7.8617e+00 -5.3225e+00 -5.7595e+00  9.3727e+00  2.8566e+00
-#>  -3.7784e+00  6.6955e+00  2.7104e+00  6.3596e+00  1.0636e+01  7.4172e+00
-#>   3.5782e+00 -2.2840e+01  1.7752e+00  5.2072e+00 -1.1504e+01 -2.6621e+00
-#>  -1.4476e+01  5.4797e+00 -1.4041e+00  3.9727e+00  6.1982e+00  3.7617e+00
-#>   5.4233e+00  1.8527e+00 -1.8342e-01 -6.8265e+00  1.1266e+01 -1.5702e+01
-#>   4.4169e+00 -1.5739e+01 -1.1726e+01  7.3365e+00 -7.8890e+00 -8.5225e+00
-#>   1.2108e+01 -3.1495e-01 -1.5868e+01  6.5639e+00 -8.2699e+00  1.5419e+00
-#>   1.1193e+01 -4.3754e-01 -5.9623e+00 -4.0880e+00  7.9017e+00  8.8718e+00
-#>  -9.3012e+00  1.3934e+00 -5.8818e+00 -1.1752e+01 -8.1547e+00 -5.0979e+00
-#>   1.4485e+00 -5.6431e+00  8.8961e+00 -4.3433e+00  1.0669e+01 -6.7620e+00
-#>   7.8333e+00  1.4045e+00 -1.4081e+01  6.5516e+00 -1.7546e+01 -6.2451e+00
-#>   8.1935e+00  3.1147e+00  2.5687e+00 -8.3399e+00 -3.0574e+00 -7.1151e+00
-#>   1.3414e+00  4.2013e+00 -1.7803e+01 -5.1214e+00 -5.5807e+00  1.5435e+01
-#>   2.8192e+00 -2.4385e+00  1.2972e+01 -3.1511e+00  1.5503e+00  9.6873e+00
-#>  -7.7992e+00  9.0596e+00  2.0192e+01 -7.8699e+00  7.2253e+00 -6.7786e+00
-#>  -2.9993e+00 -1.3195e+01  1.2465e+01  1.7358e+00 -3.1596e+00  3.3080e+00
-#>   6.0420e+00  1.3294e+01  1.7468e+01 -4.0664e+00  1.5949e+00  9.8797e+00
-#>  -3.8004e+00  1.5496e+01 -1.4409e+00 -8.1662e+00  1.1723e+01  1.1117e+00
-#>   3.1524e+00 -2.1490e+00 -1.3218e+00  1.4153e+01 -2.0530e+01  8.6345e+00
-#>  -9.4948e-01 -3.9658e-01  9.1029e+00 -5.6274e+00  1.4035e+01  9.4233e+00
-#>   5.4670e+00 -5.4394e+00  7.3929e+00 -1.1360e+01  2.4601e+00 -4.9093e+00
-#>   1.7102e+00 -4.0448e-01 -8.3279e+00 -4.1029e-03 -1.1221e+00 -1.2746e+01
-#>   1.0214e+00  9.5807e-01  1.8192e+00 -1.2437e+00 -1.7461e+01 -5.0998e+00
-#> 
-#> Columns 31 to 36  1.6438e+01 -1.3438e+01 -2.0719e+00 -1.0913e+01 -7.5714e+00  1.8081e-01
-#>  -8.8542e-01  2.0675e+00 -8.0859e+00  6.9595e+00 -8.2521e+00 -1.8931e+00
-#>   1.0092e+01  1.7633e+01  5.2209e+00 -1.6351e+01 -1.9979e+01  9.8331e+00
-#>  -6.9253e+00  1.1116e+01 -1.1913e+01 -4.0747e+00  4.2845e+00  2.2116e+00
-#>  -1.9262e+00  1.0957e+00 -4.9534e+00 -5.6227e+00 -1.0123e+00  1.1315e+01
-#>   4.2937e+00 -2.6627e+00 -1.2887e+01  4.3937e+00 -5.1109e-01  1.0921e+00
-#>  -7.9952e+00  1.4324e+01 -3.9196e+00 -2.7443e+00  6.3004e-01 -4.6276e+00
-#>   9.4714e+00 -4.8978e+00 -1.0007e-01 -2.0158e+01  1.6750e-02 -1.0488e+00
-#>   3.1494e-01 -6.2173e+00  6.5896e+00  2.9818e+00 -1.0756e+01 -4.3337e+00
-#>   1.3194e+01 -1.4014e+00  2.6727e-04 -1.6177e+00  2.1996e-01  1.5266e+00
-#>   1.9788e+00 -8.5207e+00 -3.9199e-01 -1.0973e+01 -1.4129e+01  1.7517e+00
-#>   9.3051e+00 -2.2612e+00 -7.2882e-01 -1.8655e+01 -1.1064e+01 -5.8109e+00
-#>  -1.1440e+01 -3.3624e+00 -3.3130e+00  9.9564e+00  1.2247e+01  7.4611e+00
-#>   1.4041e+01  8.0288e+00  4.4719e+00  6.5836e+00  5.6681e+00  4.5749e+00
-#>  -1.0175e+01 -2.4834e+00  1.7094e+00  6.6462e+00  9.4967e+00  5.3757e+00
-#>   4.1961e+00 -2.3064e+01 -1.0312e+01  5.6536e+00 -2.5044e+00 -4.9450e+00
-#>  -2.5085e+00 -4.3374e+00 -8.5296e-01  2.7185e+00 -5.6022e+00  6.4684e+00
-#>   1.0241e+01  9.5646e+00  6.8356e+00 -1.2182e+01  4.2921e+00  5.7431e+00
-#>   1.3466e+01  1.3720e-01  1.0346e+01  2.1120e+01 -5.1694e-01 -5.4295e+00
-#>   3.2274e+00  2.0233e+01 -1.4584e+00 -4.0477e-01  6.6957e+00 -4.2500e+00
-#>   4.9743e+00 -2.1034e-01 -1.1772e+01 -5.7535e+00  8.7506e+00 -7.1696e+00
-#>   9.0055e+00  8.2756e+00  5.8166e+00  4.2791e+00 -2.4567e+00 -6.4340e+00
-#>   3.1091e+00  2.3341e+00 -1.7254e+00  2.9272e-01 -1.0347e+01  6.9994e+00
-#>   7.0724e+00 -1.0171e+01  1.3105e+01  1.9351e+00 -1.1987e+01 -6.6013e+00
-#>  -1.6740e+01  1.5330e+00  1.0160e+00  4.8406e+00 -1.2461e+01 -3.5074e+00
-#>  -9.7528e+00  1.1982e-01  1.5609e+00  1.0209e+01  5.4214e+00 -5.4774e+00
-#>  -3.3251e+00 -4.8822e+00  4.5186e+00 -1.5024e+01 -1.3310e+01 -6.2242e+00
-#>  -2.7370e+00 -4.1817e+00 -3.3404e+00 -7.9251e+00 -7.3348e+00  1.0770e+01
-#>  -5.2601e+00 -7.2051e+00  5.9309e-01  1.5419e+01 -1.4580e+01 -6.6892e+00
-#>  -1.4980e+01 -7.9538e+00  1.0906e+01 -3.5722e+00  1.7001e+00  1.7724e+01
-#>  -7.8612e-01 -7.4339e+00 -1.1043e+00  2.8421e+00 -7.2769e-01  2.5934e+00
-#>   1.2884e+00  1.1799e+01  2.6829e+00 -1.6889e+00  1.7691e+01 -9.7053e+00
-#>   1.0168e+01  4.0578e+00  1.3561e+01 -4.0530e+00 -4.0119e+00 -9.7104e+00
-#> 
-#> Columns 37 to 42 -8.2735e+00  3.1403e+00 -1.1959e+01  1.1864e+00 -8.7998e+00 -2.9534e+00
-#>   9.1179e+00 -6.3446e+00  2.2182e+00 -1.5197e+00 -1.2004e+01 -1.0454e+01
-#>  -8.7442e+00 -3.2043e-02  1.6638e+00  5.4491e+00  1.7899e+00 -1.4846e+00
-#>  -1.8708e-01 -7.6960e+00 -5.4669e+00 -7.6985e+00 -7.7320e+00  1.1031e+01
-#>  -2.1632e+00 -7.0626e+00  1.4560e+00 -6.1482e+00  4.3042e-01  1.4981e+01
-#>   5.2970e+00 -1.5019e+01  2.8836e+00 -6.4169e+00  1.1324e+01 -1.0338e+01
-#>   9.4684e+00 -1.3241e+01  4.9726e-02 -2.9331e-01  1.3627e+00  2.9866e-01
-#>  -1.2529e+00 -3.2748e+00  1.9241e+00  9.2193e+00 -1.0986e+01 -2.7363e+01
-#>  -4.8738e+00 -7.7993e+00 -4.7592e+00 -2.0297e+01 -8.3224e+00 -2.2505e+01
-#>  -1.0972e+01 -2.9503e+00  1.1717e+01 -1.6971e+00 -5.3677e+00 -2.8579e+00
-#>  -8.9193e+00 -6.8548e+00 -1.2901e+01 -1.0337e+01  6.7866e+00  3.4333e-01
-#>  -1.5797e+01 -5.8071e+00 -1.9483e+01 -8.7037e+00  5.0029e-01 -1.0541e+01
-#>   3.2845e+00  4.1856e+00 -3.9064e+00 -3.7897e+00  2.1681e+00  7.0478e+00
-#>   1.4918e+00  6.1159e+00  4.3892e+00 -7.7357e+00 -8.0483e+00 -6.6913e+00
-#>  -2.9716e+00 -1.3224e+01  3.6103e+00  5.5090e+00  7.1380e-01  7.3106e+00
-#>  -3.1293e-01  6.9401e+00  7.3920e+00 -2.7220e+00  9.2464e-01  5.9456e+00
-#>   1.5518e+01  1.1428e+01  7.0763e+00 -2.1403e+00  9.3160e+00 -3.3511e+00
-#>  -1.1750e+01 -5.4353e+00  7.5489e-01  4.7460e+00  1.0737e+01 -6.3573e+00
-#>   9.4763e-01  2.6830e-01 -3.0907e+00 -1.5246e+01 -1.6409e+01 -2.3621e+01
-#>  -3.2955e+00  2.3546e+00  1.1154e+00 -2.8251e+00 -8.3575e+00  5.5872e+00
-#>  -2.2356e+00  1.0417e+01  6.6118e+00  1.0563e+01 -4.5935e+00  9.5886e+00
-#>   2.5937e+00 -9.0757e+00 -1.9578e+00 -6.8779e-02 -1.0149e+01 -1.8089e+01
-#>   5.6172e+00  2.3983e+01  5.6968e+00 -3.7683e+00  1.6258e+01  1.9859e+01
-#>  -6.4705e+00 -2.5923e+01 -2.7608e+01 -1.9793e+01 -2.2804e+01 -1.3724e+01
-#>  -3.4172e+00 -1.4107e+01 -9.9054e+00  4.4123e+00 -1.1000e+01  5.2239e+00
-#>   2.5845e+00  2.8300e-01 -3.3345e+00 -1.4225e+00 -9.2023e+00  1.4570e+01
-#>  -5.9657e+00 -2.1567e+01 -2.3819e+01  1.1180e+00 -1.8332e+00 -1.8851e+00
-#>   5.3897e+00  3.4841e+00  3.8876e+00  3.3017e+00  5.1111e+00  3.0648e+00
-#>   1.4952e+01 -5.3567e+00 -2.6438e+00  9.7077e+00  8.9029e+00  6.6048e+00
-#>   1.3372e+01 -5.9119e+00 -1.4201e+01 -9.9048e+00  4.3797e+00 -1.7422e+01
-#>  -3.7871e-01  2.2780e+00 -5.7702e+00 -2.5621e+00  1.3025e+01  1.0764e+01
-#>   3.2211e+00  4.6307e-01  1.1810e+01  5.7527e+00 -5.7293e+00  6.3445e+00
-#>  -2.5170e-01 -1.1096e+01 -1.0406e+01  5.4917e+00 -1.1964e+01 -5.6287e+00
-#> 
-#> Columns 43 to 48  1.0823e+01  1.0227e+01  5.2452e+00 -7.6024e+00  1.0346e+00 -7.9165e+00
-#>  -6.8778e+00 -8.0179e+00 -3.0957e+00 -5.3885e+00 -7.4486e+00 -5.7790e+00
-#>   7.6706e+00  2.3105e+01  7.8097e+00 -1.3647e+00 -1.2281e+01 -1.5098e+01
-#>   2.6246e+00  6.9635e+00 -1.8214e+01  5.7573e+00 -1.0707e+01  7.4098e+00
-#>   2.3787e+00  4.8133e+00 -4.4525e+00  5.3442e+00 -1.4970e+01 -3.1860e+00
-#>  -1.7354e+01  1.6283e+01  1.5564e+00  1.3106e+01 -6.1499e+00 -4.8105e+00
-#>   5.7187e+00 -1.2279e+01  7.5568e+00  1.6225e+00  6.1519e+00  2.5067e+00
-#>   5.5771e+00  7.8960e+00  3.8538e-01  1.5720e+01 -6.6727e+00 -1.1263e+01
-#>  -1.6229e+00 -3.2439e+00  3.1027e+00 -2.5326e-01 -8.8451e+00 -8.2672e-01
-#>   9.0183e+00  4.2075e+00 -1.4198e+01  6.5227e+00 -1.2879e+01 -8.1087e+00
-#>   6.6603e-01  3.8175e+00 -2.9692e+00 -1.5503e+00  1.2798e+00 -7.5683e+00
-#>   1.8157e+01  1.5004e+01 -2.7700e+00  1.1028e+01 -4.5021e+00  4.6181e+00
-#>   7.3547e+00 -5.4684e+00  3.7659e-01 -1.4494e+01  3.1610e-01  1.0328e-01
-#>  -5.1568e+00  1.1580e+01 -1.5136e+01 -1.8530e-01 -7.0306e+00 -5.5930e+00
-#>   7.5433e+00 -1.9105e+01 -6.2412e+00 -5.3192e+00 -3.9712e+00  1.0104e+01
-#>   1.3997e+01 -7.2630e+00 -1.5452e+01 -1.4429e+01 -2.8080e+00  2.6409e+00
-#>   4.5980e+00 -2.1559e+01 -1.2334e+01 -4.2134e-01  7.6498e-01  3.6497e+00
-#>   1.3544e+01  6.7151e+00 -1.9943e+00  8.4802e+00 -1.3441e+01  3.5801e+00
-#>  -2.1211e+01  7.9966e+00  5.1467e+00  1.9884e+00  1.4655e+00 -1.7772e+01
-#>  -6.7888e+00  9.6563e+00  2.9522e+00 -3.2235e+00  4.3726e+00 -6.4796e+00
-#>   1.1923e+01  1.0458e+01  1.1862e+01  6.3710e-01  4.6522e+00  3.3642e+00
-#>  -2.0412e-01  2.3011e+00  7.5140e+00 -6.2739e+00  1.2391e+01  6.0583e-01
-#>   8.2744e-01  4.1622e+00 -1.2777e+01 -6.6249e-01 -2.0946e+01 -5.7807e+00
-#>  -4.2178e+00 -9.3935e+00  1.4077e+01 -1.0468e+01  2.5169e+00 -1.2825e+01
-#>  -7.4545e+00 -1.7324e+01  1.0080e+01  4.3010e+00  6.2536e+00  5.2832e+00
-#>  -1.7112e+01  7.4615e+00 -5.2570e+00 -4.3795e+00  4.2586e+00  1.0613e+01
-#>  -4.2153e-01 -1.6174e+01 -3.5876e-02 -2.6960e+00  5.8841e+00  1.0271e+01
-#>  -1.0999e+01  9.6090e+00  4.4693e+00  3.4537e+00 -4.9400e+00 -5.7933e+00
-#>  -1.1098e+01  4.3504e+00  4.1122e+00  6.6750e+00 -1.3501e+01  5.7151e+00
-#>  -1.8164e+01 -7.8379e+00 -1.6745e+01 -2.6554e-01 -1.3939e+01 -4.3230e+00
-#>   4.1970e+00 -1.3924e+01  8.9203e+00 -1.8577e+00  3.2587e+00  3.2568e+00
-#>  -9.2823e+00  2.3534e+01 -8.9547e+00 -4.0144e+00  1.0853e+00 -1.2542e+01
-#>   6.3050e+00 -1.6179e+00  3.5026e+00  3.5845e+00 -2.6009e+00 -7.2738e+00
-#> 
-#> Columns 49 to 54 -1.2046e+01 -7.2920e+00  6.6618e+00  3.8642e+00  1.1839e-01 -1.1737e-02
-#>  -1.9295e+00 -1.2324e+01  4.3648e+00 -4.7373e-01 -2.2393e+00 -2.6499e+00
-#>  -4.9235e+00  9.2022e+00  8.5990e+00  5.1345e+00  2.7203e+00  1.3626e+00
-#>   1.1801e+01 -3.4244e+00  9.3624e+00 -2.5441e+00  4.0397e+00 -1.0988e-02
-#>  -7.1344e+00 -1.4162e+01 -1.0796e+01 -1.5963e+01  4.3136e-01  5.5208e-01
-#>  -2.7459e+01 -7.8029e+00 -4.7287e+00  7.1100e+00  8.4058e+00 -8.3266e+00
-#>   1.7347e+01 -2.7476e+00 -5.8147e+00  1.0619e+00 -2.6772e+00  3.0231e+00
-#>   7.3309e+00 -3.1074e+00  1.9109e+01  6.2488e+00 -6.4734e+00 -4.7125e-01
-#>  -4.8922e+00 -7.5932e+00 -2.2855e+00 -1.2168e+00 -1.4721e+00 -4.0499e+00
-#>  -1.8002e+01 -4.7983e+00  6.1407e+00 -6.8202e+00  3.7508e+00 -6.9611e-01
-#>   1.5803e+00  2.1600e+00  9.1819e+00  2.6416e-01  1.6545e+00 -6.8457e+00
-#>  -2.5037e+00 -3.5136e+00 -1.2750e+01  9.4648e+00  2.0263e+00 -7.7318e-01
-#>  -5.8539e+00  7.2161e-01  3.3986e+00  2.6831e-01  8.4629e+00  1.0556e+00
-#>  -8.1779e-01 -9.5382e+00  8.8504e+00 -7.6203e+00  1.3994e+00 -2.9230e+00
-#>  -3.5740e+00 -1.7280e+00  1.4569e+01 -1.1737e+01 -8.8975e+00  3.7210e+00
-#>   1.1795e+01  3.3146e+00  1.2242e+01  2.9391e+00 -1.8201e+01 -1.2430e+00
-#>  -1.8295e+01 -4.6633e+00  5.9964e+00 -4.3451e-01 -6.5197e+00  3.5632e+00
-#>   3.7127e-01  7.4454e+00  1.1682e+01  2.6667e-01 -1.9586e+00 -3.3837e+00
-#>  -1.0365e+00 -8.2232e+00 -8.4112e+00 -9.1053e+00 -3.6521e+00 -2.2473e+00
-#>  -2.5694e+00  4.3786e-01 -1.0536e+01 -8.8205e+00 -3.6897e+00  1.0798e+01
-#>   8.6545e+00 -5.0410e+00  7.0885e+00  1.8632e+00  6.6410e+00 -1.3708e+00
-#>  -5.7480e+00  8.0330e-01 -3.3123e+00  5.2791e+00 -5.4046e+00  4.0533e+00
-#>  -3.6363e+00 -8.6738e+00  3.7204e+00  1.1624e-02  1.8064e+01 -1.6268e-01
-#>  -2.2105e+01 -1.7262e+01 -1.0033e+01 -9.1466e+00 -5.0783e+00  6.1272e+00
-#>  -1.2115e+01 -2.3348e+00 -1.0168e+00  5.4170e+00 -1.1375e+00 -1.1276e+00
-#>  -1.1370e+01  9.9577e+00 -1.5178e+01 -3.5892e+00  2.8437e+00  9.4898e-01
-#>   1.3018e+01  1.0340e+01  2.0948e+00 -2.8574e+00  8.2862e-01  3.8906e+00
-#>  -1.7084e+00  2.7070e+00  3.6188e+00 -2.3062e+00  8.2864e+00  5.3085e+00
-#>   1.5275e+00  6.4327e+00  1.8934e+01  7.2108e+00  1.6752e+00  1.9821e+00
-#>  -1.6915e+00  8.5454e-01  7.1428e+00  1.0604e+01 -7.7169e-01  9.2157e-01
-#>  -1.8514e+00 -1.1039e+01 -4.7102e+00 -7.0633e-02  5.1042e+00  5.8931e-01
-#>  -7.9077e+00 -5.2208e+00 -4.4968e+00  7.9002e+00  1.5707e+01  6.7454e+00
-#>  -4.4708e-01 -2.1211e+00 -2.3933e+00 -2.8860e+00  2.2992e+00  4.1969e+00
+#>  Columns 1 to 8  -1.8085   6.8235  11.1442  -4.3852 -15.3604  -8.8875   1.1256   0.1523
+#>    3.2338  11.4704   0.3934 -14.2270  13.4444   2.4024   6.8763  -1.6765
+#>   -3.7818   9.8499  -9.2872   5.0741  -0.6256 -12.0439  -9.2977   3.9530
+#>    5.6605  -1.4260   7.0991   6.6795  -2.6111 -13.9174   1.7084   8.8847
+#>   -7.9131   2.2159  12.6250   0.0961  13.1972 -11.4249  -0.1778   4.9907
+#>    0.0938  -0.5793  10.3645   1.8925  11.3503  -3.1427   0.4214 -15.2126
+#>    3.3332  -2.4953  -0.5407   9.7266   7.8946  18.1418  -3.0654  18.2605
+#>    0.2715  -3.8937  -1.1274   7.2516  -0.6246 -10.3331  12.9356  -3.2030
+#>    1.6485   6.9216  -1.0674  -1.8517   0.0904   5.8350   1.1440  -3.3267
+#>   -5.6427 -11.8021  -4.9037   6.8329   5.0761   6.1332  -8.6573  -5.3008
+#>   -0.4524  -7.5455  10.2084   8.8555 -10.6587  -5.6035  17.9837  -9.9650
+#>   -7.2268  11.1057   2.6428  -1.7195  13.2818   0.4378   1.4844   2.4036
+#>   -6.8513  -7.4731  -2.7462   3.8581  -8.6282   5.4673   3.6392   6.9059
+#>   -3.7328   2.4122  -6.1795   4.3776   1.8762   9.3600  -2.5737   3.5618
+#>   -3.8572  11.2351  -4.3227  -4.8577 -12.3157  -1.9139  -2.5290  10.5374
+#>   -5.5685  -4.6600  -3.2141  -2.2996  20.0426   0.0587  -3.7593  11.4482
+#>   -2.3589  -1.4210 -10.3781  11.2984  -2.7009  14.6150   2.2226  -8.7667
+#>   -2.6438  -7.9484  -6.7505  19.6090  10.5052  -0.4875  -3.4537  18.9603
+#>    6.3091   6.8971  -4.9796  -4.3744   9.0554  -3.9599   9.5644  -1.5092
+#>   -0.9940   3.9283  -4.9377   1.1211 -12.0237   1.5106 -10.0866   0.0542
+#>   -0.8940  -5.6882   3.2082   3.7745   0.5697  -1.4983   5.5870   9.2724
+#>   -1.9254  -7.3987  -2.3184   2.9481  -1.0781 -18.3138  13.4581  -9.8867
+#>    5.2962 -13.5625  -5.7083  10.7356   0.0219   0.3040 -10.6505  10.0103
+#>   -1.3605   6.3681  -6.7055   8.3580  -0.0669  -3.7588   3.8232   5.6098
+#>    7.3196  -3.2715 -10.4852  -3.4072 -13.0453  -0.1817  -2.6775   4.2766
+#>    1.4467  -1.2051  -4.3364  -1.9571   7.8727 -11.4624  -8.6240   5.5867
+#>   -7.8125   2.9865  -5.7988  12.0364   3.4152  -7.1135   9.6319  11.9023
+#>    5.7813   6.9540   9.3736  -7.6431   5.5177  -5.2435   8.0511   3.0021
+#>   -0.2057  -1.1182   6.9689  -4.2778 -12.3012  -6.0379   0.3608   0.4296
+#>    2.8318   1.5916   7.1183 -19.4666  -8.1717   5.6651 -21.5762 -20.9961
+#>    0.3252 -13.7046   0.9702  -3.9895 -12.7617   3.0772  -9.7669   7.3512
+#>    4.8164   1.5928 -11.3728   9.7084 -20.2306   8.2180 -21.9845  -2.1740
+#>   -0.5004   0.1439   1.8745  11.2255  -2.9842  -6.1502  -1.9812  -0.8021
+#> 
+#> Columns 9 to 16 -10.1794 -14.9284 -17.3641   2.0294  -6.9438  -0.0645  14.2671  -2.3613
+#>  -10.8704  -7.2153  -7.5334   6.1847   1.3025 -16.6261  -2.6642  -7.1922
+#>   13.3906   2.5243  -2.6378  11.3990   2.6536   0.6407   2.1983   4.5764
+#>   -6.5675   1.6233  -5.1832  -8.1122  -8.8643  -4.9960   3.3150   4.6671
+#>  -12.4715   4.1802  -4.1456 -21.4683  -0.9824  -2.2127   5.6457   2.9535
+#>    2.0502   0.7811   1.9804  -1.8003  -2.4586   5.2659  -0.9761  -7.8882
+#>   -8.0298   0.6442   2.8820   5.6903  -5.4814 -11.2479  -4.6485 -11.4214
+#>   -1.6430  -4.9383  -9.5249   7.0498  -2.5464   0.4699   1.3884 -11.7889
+#>    8.4361   7.2545   3.7774  -5.6343  -9.9351   5.1677   7.9047   4.6966
+#>   -5.6658 -11.9736  13.2212   5.0623 -13.8838  -9.4784   3.6282  12.6086
+#>   -4.6914   7.3080  -6.2720  -0.5174 -10.6196  -3.2478  -9.8968  -9.6412
+#>    6.2494  15.0193   5.1235  10.0639  -5.8118  12.3543   4.3839 -16.2044
+#>   -5.7358   3.5501  -0.6512  -2.1734  -1.8810  -2.6743   8.3366  -2.7352
+#>   -3.9936   2.5910  11.1833  -8.1628 -15.2792  -1.3009  -3.0070  -3.0361
+#>   -9.4892   4.0590   5.0090  -7.3886  14.9358  -7.8874   1.0146  -1.7621
+#>   -2.1584  14.1129  -1.4494   7.7903   3.0989  -3.4798   8.6529   7.1713
+#>  -11.1146   4.1217  -3.8267 -13.7234  10.0567  -3.3526  -8.0599  -8.4550
+#>   -1.6442  -6.4007   1.9564   7.6880   5.2909 -24.5806   1.7764  20.3234
+#>    5.2582  -4.1678  -5.1555   5.3339   6.2349  -5.0789  -5.9958   1.3127
+#>    6.2296   3.3385  -3.7889   4.4874  -1.8305  -4.2832  19.3959   6.4340
+#>   -5.9719   3.7739   6.9392   3.1651   5.9951  11.3894 -10.9516   6.2830
+#>  -17.4815  -4.0967   3.5075  -7.3431  -0.9606   0.9446   0.2215   5.6110
+#>   -1.4048   5.0152  10.7015  -1.5667   9.9075  -4.0370  20.2363  26.3620
+#>   -2.9662   4.9210  -0.0204   6.3144  13.1104   3.0940  -9.7981  -1.6755
+#>    1.1846 -17.2423   0.6635  10.1996 -11.0985 -16.3306  -6.2983  10.5892
+#>  -10.9259 -20.2519   2.9075   3.5902   0.4626 -19.5359  -1.5157  23.6804
+#>   -8.5254  -0.4845  -4.3085  -1.1406 -10.9099  12.9873   7.2302 -12.9039
+#>   -9.8433  12.1345   1.3542  -1.1830 -11.8594  -4.6079   6.2281  -5.3346
+#>   19.6813 -10.2379   5.3029   4.3069  -0.9405  -9.8509  -4.2888  -7.2717
+#>   19.0780   1.2020 -21.4062  -7.8763  24.9200  -6.6737 -12.9860   9.6250
+#>   -9.8688   1.7647  -1.3504  -3.3562   2.8880   8.9753  10.0467   5.1882
+#>  -11.3345   6.3551   2.3746 -12.2455   1.3373   5.8598  16.3165  -3.8965
+#>    8.5505   5.9874  -7.7551   3.9953  11.5428   4.3301  -4.1456  -1.0553
+#> 
+#> Columns 17 to 24   3.4379   8.4352  12.1864  11.4839  -1.0812  -0.6587  11.6241  -2.1610
+#>    3.5834  -5.9094   7.1619  -9.0004   2.8066   1.2815  -3.2055 -18.7561
+#>   -3.4830   6.8322   8.4415  -1.6890  16.4708   1.9059  -2.7801  17.4356
+#>    1.9267   9.1798   5.2292  11.5135  15.1043   8.0399   9.3046   6.4772
+#>    0.2345  13.2745  -4.6980   9.0597  -4.3470  -0.9129  -6.4124   9.2362
+#>   -5.9683  12.7167  -8.7012  12.7688  -9.3868  -2.7994  -3.4714   4.3325
+#>   10.1765   0.9581   0.9913 -10.5012   5.9029   2.4469  20.0124  -1.4693
+#>    2.5257  -2.9225  12.2828   2.6812   3.4723 -18.7732  -0.9865  -2.4597
+#>   -6.6799   7.1511  11.3533   9.9620  12.0954   4.7766   8.4221   2.0568
+#>    0.6233  -6.0174 -12.5099   4.2834   4.6351   7.5994   0.9900 -16.6508
+#>   16.9863 -12.8674   3.8486  -6.7235   0.2406  -8.0315  -3.3391   0.0315
+#>  -13.4598  -3.4615  -4.5909 -17.8737  -9.8440   8.8483  -6.2322  -0.5780
+#>    4.4961  -3.0535   4.8150   5.2029  -7.6731  -5.4773  -0.5935   8.1613
+#>   16.1048  13.1719  -0.7813  13.9314  12.1535   0.6091   7.7643  -3.3097
+#>   -2.2050  -8.8802  -4.0884   1.1293   7.7253   7.7275   2.0325   7.9701
+#>    0.6568  -4.2595   0.6793 -14.9570   6.2110   2.2176   2.4834  20.1524
+#>    9.4107   0.3862  -5.3271 -11.7983 -11.8202 -15.5278   6.5015   7.7119
+#>    2.9367 -13.8717 -15.5943  -1.1245   6.1041  -4.5714   0.6443  20.2473
+#>   -5.6470   9.7853   4.0830  -3.9930  -1.9974  -8.0662   1.6419   9.8002
+#>   -0.2521  -2.0018   4.5139   1.1573   0.1829  -2.9886   2.5863  -0.8541
+#>    7.5024  -1.8956   4.5682  -5.8431  12.3655   0.2531  -2.7889  -2.4726
+#>   -1.7530   1.5935  -5.7318   5.3683 -10.2838   9.1896   0.4879  20.1177
+#>    2.4782  -0.4610   2.1775  -3.5302   9.6107  12.4782   3.2454  -0.9944
+#>   10.2677  -1.8757  -6.3894  -2.2962   4.8556  -7.3212  -6.0320   9.5188
+#>   -2.7605   3.6678  -1.8530  -2.0780   8.1630   6.7658   5.9929 -14.7745
+#>   -3.0390   0.4741  -6.7724  -2.5366   0.7335   3.6058   1.7393  -3.0921
+#>   -0.6922  12.0244   0.5601  -2.9409  -3.6292  -7.0280   2.4363  11.4067
+#>  -13.0394  -0.1910   1.2098   1.6614  -5.8048   6.6063   0.3493  -1.5076
+#>   -5.1819   1.9542   5.1423  13.0171  -4.8891  -4.5958   2.1498  -4.7827
+#>    0.0163   5.8201   4.3923 -14.4396 -10.1979  -0.6131   0.0601 -31.7168
+#>   -9.7506  -2.1386   5.7524  -0.7152   3.1771 -12.9438  -1.3517  -3.8864
+#>    5.4991   2.0661  -1.7861  10.3215  11.8130   1.1582   4.7063 -13.8469
+#>   13.7012   4.1644   9.8376   3.4762   5.3983  -6.1923   5.5819  19.3746
+#> 
+#> Columns 25 to 32 -10.2416  14.7264 -12.6604  -8.2676   4.4424 -19.3142  -8.1464 -11.3920
+#>   -2.1834  -6.8935  15.3232   1.2277   3.4624  -2.7000 -11.4923  13.4703
+#>  -15.8238  16.7675   2.6045  -0.7082  -5.0369   4.0627 -16.2359  -7.6680
+#>   -8.8415  -2.6171  -6.9167 -10.2169   2.3852  -3.7276  -8.1779 -21.2976
+#>  -13.8236  -6.7069   3.3380  -3.6913  -0.5309  -1.6683 -10.3296   3.6428
+#>   -0.3584  14.2790   5.2645  -2.6072  -5.9618   0.7734   5.3214 -13.7755
+#>  -10.0030 -24.8429   0.0549  -3.5409   5.6547  14.3611   5.5172  -1.4669
+#>   -7.3790  -6.3546  -3.1390  13.5413  11.5983  11.6355  -1.6879   6.5976
+#>   -3.4076  14.3032  -1.6495   7.9119   0.6767  12.3789   1.7472  -5.9689
+#>  -25.6925   2.2857   6.3914  -4.8627   9.1784   8.6958 -12.4188   8.5016
+#>   -0.7826   0.6261   4.9188  11.8967  16.4723   3.9813  -7.3106  -4.9731
+#>   21.7040  21.4698   0.7837   7.4448  -6.2250  16.1856   4.6194  -4.5464
+#>  -16.1303  -3.3117 -10.2965  -4.5425 -16.3345   4.9616  -5.9379   7.6910
+#>   -3.9478  12.4847  -1.7680  -8.5500  13.8150  -6.3223 -22.6779  -3.1781
+#>   13.6536  -5.5077  -9.0472   1.0408  -1.5763   0.1702  -3.6839   1.2109
+#>    0.3589  -0.4649  -4.9002 -17.8386  -1.3519   4.9333   9.8586  -6.8356
+#>   -0.6898  -1.8803  -1.8696 -13.0802   7.3337   8.0793  -1.6920   1.1841
+#>   -9.9161 -12.7632  16.5711  -5.5078   3.9504  14.7979  -0.0271   1.3653
+#>    0.2711  -2.6653  -1.1627 -18.3533 -17.6653  -2.5515  -0.5849  -3.9738
+#>   -3.6610 -15.2496  -0.0277  -0.8859  -8.2856   7.2251   6.9414  -3.6113
+#>   11.6710   7.2822  -8.5050  11.5796  19.4352   6.2796 -13.4865  11.6867
+#>   -1.7649  -2.8612 -15.2160  -4.4413  -8.1216  12.6129  18.1034   6.2739
+#>   -6.6626  -1.7059  10.3411   3.0628  -7.0777  -4.8876   0.6616   4.7759
+#>   18.5314 -15.7258  -3.0978   4.7971  13.4731   1.4660  -8.1811  19.7662
+#>   -4.4994  -1.6873  -8.5064  -5.7987   0.9318  -1.1732  -6.0563   7.7234
+#>   10.4725  -4.7629  -4.1846  11.6992  -4.1662  -7.0571  -5.9636  -0.2261
+#>   -8.4743   1.4216  -8.4977 -15.5294   6.3919  -6.3828  17.5120  -6.7123
+#>   -0.9549   8.3025   5.9612 -10.3707   5.9064  15.3804   9.5534   6.8740
+#>   -6.7103   3.9216 -20.5821  -7.7008  -3.1681   5.8599  -1.1451   7.4167
+#>   14.6057  11.7997 -11.0051  12.4682   3.9693  -6.5482  -1.8915   0.3585
+#>   -6.6026   3.1699   1.8563 -17.1516   0.4566  -9.4297   9.3337   5.1812
+#>   -6.8692   5.3250  10.1857  -3.2662   0.6716 -25.4983 -18.7460  -8.6142
+#>   -4.2331  -6.0863  -3.7910 -11.5121   8.6729 -12.9421  -8.5479  16.3405
+#> 
+#> Columns 33 to 40  -0.1569  -2.7266   9.2791  -0.5810  -8.5486   2.1007   5.0249  -4.4974
+#>  -17.1284  -5.1497   1.8101 -16.9630  -6.0321  -1.4432 -10.3474  -3.1184
+#>   -3.2402  17.3936   5.4339  -6.3686  -5.3584  -3.1360   4.2987  15.2203
+#>   -7.1489   8.9191  12.8711   4.6684  -6.7725 -10.1931  13.6875   5.3358
+#>   -2.6737   7.5119   6.0522 -11.3305  -9.8963   9.7640  -7.6690  11.4855
+#>   -8.4428   8.8333  -0.4129  -3.4478  -9.0430  -2.3889   1.6318  -4.2240
+#>  -16.8384  -7.0844   8.0303   3.8051  -2.0820 -11.8136  -1.6724  -3.6335
+#>    0.8212  -5.1141  -0.3663   0.3196   0.7888  -1.7771   5.2814   1.0386
+#>   11.1806  -0.9534  -1.2515  13.7424  -5.1325  -1.8527   2.8168  14.6277
+#>    7.6767  -4.4157   0.2736 -13.6328   8.7376   4.3707 -15.7661  -1.8361
+#>  -23.8295  15.3801  26.8475  -9.6708  -2.6077   8.6731 -12.2178   1.0008
+#>    9.4455  -2.0803  21.2394   4.5900   3.8484  17.1744 -17.3394  13.5123
+#>    3.6987  -1.7717   7.6256 -15.0688  -4.2955   9.2369 -15.0015   1.8412
+#>   -5.1464 -10.5151  -6.5049   5.7651  -5.9820  -1.9951  -2.6133   4.6113
+#>  -18.5323  -9.3310 -11.7947   1.6622  -5.2074   0.8505   1.1189 -11.8461
+#>  -10.7856   9.0138   2.0116  -6.0254  -7.5856  -3.6127  -7.5107   4.1595
+#>  -14.6936   1.0497  11.7719  -0.8666  11.2925  -1.7937 -12.3447  -2.1602
+#>  -17.4366   7.2441   6.1121  -3.1418  -4.5704  16.3396  14.8591  -5.4702
+#>   14.5764  -0.2693  -4.9854   6.5477 -12.2666  -5.2875  -3.9501   0.2375
+#>   20.4611 -14.2881  -4.1101  15.8526  -4.0581   4.1525  -7.3295  -1.5252
+#>    3.2099   8.6845  11.7543  -6.1204  11.4738  13.2616   6.8109   9.5679
+#>  -13.9970  -6.7169   7.1447  -7.0404  -2.9133   8.2108   5.3468  -1.1020
+#>   -9.1758  18.5591  14.3107  16.4954  14.1348  -3.4231  11.9152  -3.0018
+#>   -2.1620  -0.2339  -2.0764 -10.2497  -2.7732   6.1084  14.7679 -17.9348
+#>   -5.4329  -4.7309 -11.4260 -15.3764  10.0641  -9.1388  -0.5764   1.1177
+#>   11.0932  -9.1824  -1.3550   0.4423 -13.8164   6.2092  12.3749  -9.0443
+#>   -5.2818   6.7898 -10.2957  -3.5308   2.9238  -7.6418  -4.0772  -5.8566
+#>  -10.4743 -15.1906  10.0438   3.0422   8.8272   3.9424   6.1575   8.7274
+#>   16.2969  -2.8221   2.0969  -1.5984   0.6494  -7.9316  -2.5844  15.0276
+#>    5.7547  17.6778 -14.5500 -17.0432   2.0552  -5.0652   4.6477   2.8888
+#>   -4.4138   2.8106 -10.8570  -5.7065  11.7775  -0.4233   6.0246   0.7196
+#>  -12.5856  18.1345 -12.7985 -13.2050   6.7073  -6.8200  -7.4915  -3.0724
+#>    2.1405   4.6461   9.6643 -12.9346   8.3115  -0.7018  -6.4356   6.8158
+#> 
+#> Columns 41 to 48 -12.9826  -6.1457 -22.5488  -2.0832  -0.4162   9.0465   7.8768  -0.0694
+#>   -0.9580  -7.0457   2.5557  -9.2195   3.5493  -8.0014   5.3317 -14.8083
+#>    1.6561   5.0123  -4.6498   4.0050 -10.8499  14.4019   6.2230  -5.0632
+#>    8.5846  -4.8309 -15.6632  -4.6525  -6.3167  17.2654  -6.8944   0.4633
+#>   -5.9663  -4.1631 -14.6245   7.2168  -5.4054  -1.6174  13.9642  -8.5509
+#>    4.6117  -8.1391 -11.8904   4.0783 -16.7419   0.1631  -7.9859  -1.2737
+#>    6.5693  -2.6732   5.1538 -18.7892   2.5048 -11.5552   1.8862   4.1121
+#>    2.8661   1.9581 -18.6130   8.5455 -18.4324   7.4794 -15.1059  18.8131
+#>   -3.7367  -4.4750  -2.8802  10.2123 -17.2796   9.6597  -3.4403 -10.2159
+#>  -10.3463   3.4056  13.2073   1.2496  -7.6686  -9.6420   4.2977 -18.2493
+#>    7.3743   4.9233 -24.7237   1.1679 -10.1948   2.1863   3.6010  -3.3140
+#>   -2.1077   6.6198   4.1753  26.1887  -4.3479   7.6100   2.2583  -1.4093
+#>    9.0027   7.2566 -13.9227  10.5004  -8.0264   5.4405   3.4126  -0.6111
+#>   -1.7096  -2.5521   0.4205  -2.6405  -8.7542   0.6280   3.0397   4.6723
+#>   -8.2249 -13.6187   6.7401   1.5473   2.5867 -10.1461  -6.3527 -13.9548
+#>    1.5364   7.2244  -3.4340   0.9902  -6.0006   5.9815  -0.6316  -4.7893
+#>    1.6934  -1.1801   1.8450   2.8821  -5.2247  -3.5259   0.5963  -4.4809
+#>   15.3615  -0.2178  -1.8231   3.3698  12.2011   1.5852  17.7421 -13.3385
+#>  -10.7165  12.3857   5.7598  10.3255   2.9782  -3.5616   2.7659   4.2417
+#>    4.7488  -7.8035   0.2133 -13.8613   3.9308  -4.9753  -2.1151   3.2447
+#>    2.0527  16.0718   4.7682   1.0596  -3.4375   8.6130   3.9209  -8.1618
+#>   -2.6088  -4.4650   0.7012  15.9247  -8.6858   2.7749   9.4317 -10.2418
+#>   13.8988  13.9604  -4.1115  -2.0487   8.5215  -3.4302   4.5700 -10.3320
+#>  -14.6660  -3.9202   0.7864  -4.2224   3.1868  -5.1094   4.5952 -10.4710
+#>   -0.7723  -4.8941  10.8158  -9.3243  -6.1164   8.6143   1.8235  -1.1579
+#>   -6.3796  -6.1962  -3.8432 -14.4264  -0.6532   6.8874  -1.5109  -9.5585
+#>   -8.3360   5.8116 -20.3218   4.3709  -1.6870   0.2719  -1.1324  13.1253
+#>   -0.7276  10.8420  -8.8445   5.1642  -4.7909   5.8770   2.3446   3.1134
+#>   -3.4728  -8.4132  -9.3282  -2.4598 -13.9889  -0.1141 -17.7281   8.4579
+#>    0.7313 -19.1262   4.8611   4.3102 -14.6471  12.2990   9.9911 -10.5856
+#>   -4.0521   9.9625  -5.2854   2.2909  -3.1532   3.3688  -2.9038  -1.1516
+#>   -0.5690  -5.5379  -0.1454 -16.6919   1.0590 -11.4037 -17.8771  -8.3987
+#>   16.9248   7.9114  -9.7885  -4.4478  11.5209 -12.3277   3.0344  -4.4491
+#> 
+#> Columns 49 to 54  -2.4816 -10.0894   1.5627  -5.4510   2.0629  -7.8133
+#>    8.0295   7.2492   1.5337   0.4177   9.9281   2.2332
+#>    5.5653  11.4952 -10.0090   9.0028  -0.4650  -9.2711
+#>    1.5195  -7.7740  -4.9919  -6.7892  -2.5577  -4.0503
+#>   22.2933  -7.5280   4.2462  -3.8104 -10.4262   9.4113
+#>    2.1584  -0.8205  11.7842  -5.0073 -13.0770  10.9063
+#>    5.1162   3.6789   2.0313  -5.3068  -6.0775   4.7882
+#>    6.5513  -5.6222   4.8174 -10.5155  -6.2489   3.0581
+#>   -3.1880 -18.2021  -2.9476  -7.1455 -14.8756  -5.6792
+#>   16.5248  -1.6972  -4.7581   2.6545  19.2105  -0.7215
+#>    0.2193   9.9254  -4.3810  -6.0774  -1.6955   2.7366
+#>   11.6958  -9.3216   0.9710   6.0428  -0.2308  -3.2657
+#>   13.9625  12.0341   3.2408  -2.6272   4.7907  -3.3315
+#>    7.8446  -8.4412   2.0168  -2.4455 -13.0284   7.6630
+#>  -11.7375 -11.6168   9.6884  -6.0522  -4.5266  -1.8117
+#>    7.2731  11.9864  -7.5153  -3.1953  -1.0606   0.9127
+#>    7.0889  -6.3740   7.7219 -12.9289  -3.1015  -1.5893
+#>    0.6799  38.9608 -20.6200 -15.9873  -1.9256   7.7908
+#>   -4.6026   0.4663  -2.1743  -3.4974   5.8252   1.5821
+#>    0.1014 -11.5744   5.3243   4.4900   5.1452   3.3991
+#>   -1.5698 -10.0190 -14.3703  -1.4048  -4.0098   5.7948
+#>   14.9512 -10.6372   2.0850 -17.9989  -3.8559  -1.8780
+#>  -11.8236  -5.0441 -13.3233  -0.7043   5.6571  -3.6184
+#>    4.4885  -0.1081   5.8368  -7.9772  -3.0133   7.3391
+#>   10.4458  15.7382  -2.6193   5.8192   3.3003  -4.2809
+#>    1.2943   3.1749 -12.1381  -8.1458  -0.0987   4.7939
+#>    0.3299 -15.6021  -1.2116   8.8089  -6.6296  -9.7615
+#>   12.9355 -11.1910   1.6897  -8.2362  -6.7389  -2.0588
+#>    4.3795   2.6748  -8.3122  -3.4194   6.7437   4.1822
+#>    3.5644  -0.2029  -3.1704  -2.1312   0.7880   2.3878
+#>    4.3588  -3.4375   2.4982  -0.9298   4.9904  -6.5135
+#>    1.9467 -17.8350   2.4432   8.4665   2.7656   0.2217
+#>  -13.2103   7.5169  -7.0545 -10.5433   5.0430  -7.0124
 #> [ CPUFloatType{20,33,54} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,16 +227,16 @@
     
    Conv_transpose2d
    
     
 
-    
    torch_conv_transpose2d(
-  input,
-  weight,
-  bias = list(),
-  stride = 1L,
-  padding = 0L,
-  output_padding = 0L,
-  groups = 1L,
-  dilation = 1L
-)
    
+    
    torch_conv_transpose2d(
+  input,
+  weight,
+  bias = list(),
+  stride = 1L,
+  padding = 0L,
+  output_padding = 0L,
+  groups = 1L,
+  dilation = 1L
+)
    
 
     
    Arguments
    
     
    
 
 
     
    
@@ -253,69 +285,69 @@ composed of several input planes, sometimes also called "deconvolution".
    See nn_conv_transpose2d() for details and output shape.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
 # With square kernels and equal stride
-inputs = torch_randn(c(1, 4, 5, 5))
-weights = torch_randn(c(4, 8, 3, 3))
-nnf_conv_transpose2d(inputs, weights, padding=1)
-}
+inputs = torch_randn(c(1, 4, 5, 5))
+weights = torch_randn(c(4, 8, 3, 3))
+nnf_conv_transpose2d(inputs, weights, padding=1)
+}
 
    #> torch_tensor 
 #> (1,1,.,.) = 
-#>   -3.6222  -7.9970   2.8105   3.1026   1.7725
-#>   -4.7404  12.8775   1.0415  -0.8881   4.1451
-#>   -2.2161  -0.0100  -7.8568   2.5352   7.7713
-#>    1.3625  -3.1941  -6.9461   6.7935   2.9976
-#>   -3.9660   0.9707  12.1132  -7.1898  -5.3734
+#>  -4.1597 -0.1576  1.4483 -2.0094  2.9097
+#>  -1.8583  9.8337 -0.7585 -8.0211  1.4750
+#>  -0.1620  5.7865 -2.7381 -1.5447  2.8569
+#>   6.2899 -0.6621 -10.8934 -0.5446 -3.5256
+#>   3.3105 -1.7729  1.7700 -2.0344 -2.9281
 #> 
 #> (1,2,.,.) = 
-#>   5.5750  4.9565 -1.2831 -9.3766  2.7933
-#>   0.3677 -1.5841 -1.1948  2.0401  1.6757
-#>  -1.1554 -7.9480  2.0611  3.8100 -2.3715
-#>   3.6882  5.7345  7.8422 -10.5027  4.2258
-#>   6.4450 -2.5137 -14.2397  6.3799  2.2110
+#>    5.2262  11.0420  -9.6969  -4.1312   6.6328
+#>    8.9018   5.9441 -12.9078  -6.6986  -3.9976
+#>   -5.8637  -7.4986  -5.0125   3.2821  -1.8264
+#>   -1.0976   3.5361   6.9400   1.0611   0.6038
+#>    3.1511   7.2763  -6.7689   4.3118  -1.2124
 #> 
 #> (1,3,.,.) = 
-#>   2.3055  1.7193  1.5106  5.7209 -0.4044
-#>   5.4206 -5.3050 -1.7869  8.7362  4.7972
-#>  -1.1797 -8.2598  0.6797 -5.7008  2.3267
-#>   8.3444 -1.9313 -3.6077  1.1224  0.3162
-#>  -0.1740 -4.8076 -2.2376 -3.3701 -7.0435
+#>   2.7638 -6.3663 -1.5166  0.0480  3.5406
+#>  -5.4546 -11.2902 -1.3522 -2.4067  9.6663
+#>  -3.4808  1.0996  0.8911  1.4416  0.6622
+#>  -1.1227  6.4158 -2.2347  2.0933 -6.1268
+#>   0.1290 -3.2629  4.0837 -6.5931  3.2663
 #> 
 #> (1,4,.,.) = 
-#>  -2.1321 -0.6399 -0.5702  6.2185 -6.6191
-#>  -6.7085 -1.7849  0.0567  0.2547  4.4343
-#>  -0.1791 -13.0960 -1.1711 -11.7881  7.9123
-#>   1.6398 -8.7353  5.6190  2.7717  2.2932
-#>   6.8436  5.9709  0.8596 -5.4111 -4.1101
+#>  -0.3616 -1.2966  2.9143 -3.9220  4.6383
+#>   0.5495  4.0566 -3.7807 -2.0309 -8.2831
+#>  -1.4155  0.3986 -5.6448  3.5119 -1.9671
+#>   6.8860  1.2865  4.3564 -4.5815  4.5818
+#>   2.6446 -5.4075  1.3979 -6.4102 -0.1252
 #> 
 #> (1,5,.,.) = 
-#>    6.3795  12.9273   3.7923  -2.6331  -6.7760
-#>   12.3485  -7.3105   3.6371  -0.5407  -1.6971
-#>   -6.8453   8.1406   8.8767  -0.0699 -10.0042
-#>    0.8129  -1.6930   3.5113  -0.9711   2.3083
-#>   -1.1577  -2.1860  -1.9327   3.4738  -2.3536
+#>   2.6558 -0.9246 -3.8111  1.1757  3.2649
+#>   7.2648  4.3120 -5.3182 -12.6696 -3.2790
+#>  -3.4242 -5.5040 -0.7625  3.8862  9.9976
+#>   2.8658 -2.9160  1.8878  4.9650 -2.0246
+#>  -0.8867 -1.9310  4.2303 -5.3019  0.0027
 #> 
 #> (1,6,.,.) = 
-#>    2.8265  -4.2878 -10.9216  -1.4770  -3.4568
-#>   -8.9188  -7.0011  -0.9053  -2.3985  -0.2689
-#>   -0.8018  12.4512   0.5999  -5.5394   1.6234
-#>    4.3200 -10.4630  -4.2572   7.0144  -0.1696
-#>   -4.6365  -0.5886   7.6997  -3.6797   2.4974
+#>    3.0233  -6.9291  15.0609  -3.6230  -5.5245
+#>   -2.9845   3.6031   0.4262 -11.7900  -5.2817
+#>   -0.7342   9.5789  -1.9073   2.4465  -0.7759
+#>    2.6915   4.0482  -4.1381  -3.2260  -7.9044
+#>    7.0667  -0.0559   4.4528  -2.3568   5.1196
 #> 
 #> (1,7,.,.) = 
-#>  -2.3134  1.9207  6.9010 -2.2058  2.9683
-#>  -2.6449  5.9937 -0.7752 -2.3005 -1.7128
-#>   3.1190 -11.4434  2.3856  3.4017 -0.6764
-#>  -2.5913  6.5472 -0.4979  2.9762 -7.4267
-#>   1.9456  3.5234 -6.3143 -3.6686 -0.6943
+#>    0.3640  -3.3696  -4.5873   8.1249   0.1399
+#>    3.5094 -10.6056  11.0451   8.7865  -9.4152
+#>    2.7518  -0.7870  14.8498   0.1078   4.9419
+#>    1.5540   2.8918   4.9318 -20.0163   0.5801
+#>   -0.1355   1.0936  -6.8449   0.3927   1.4653
 #> 
 #> (1,8,.,.) = 
-#>   -0.3478   4.7669   7.3665   6.3556  -2.5469
-#>   14.4336   2.1104   2.8604   0.8508   7.2790
-#>   -9.4439  -5.6837  -8.8314  10.9166   0.5517
-#>   -8.0535   6.8200   4.4783  13.7326   3.1114
-#>   -0.0064   8.4882  -0.5751   1.8667  -4.2371
+#>    4.1818   9.4601   4.7066   1.2210 -11.6650
+#>    8.3462  10.2608 -10.2658  -6.3403  -2.5061
+#>    5.7553  -3.7399  -6.0003  -1.9122  -7.3839
+#>    5.2740  -4.6355  -7.1101   5.5363   1.2041
+#>    3.0118   3.4482   4.6497   2.5063  -3.3164
 #> [ CPUFloatType{1,8,5,5} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,16 +227,16 @@
     
    Conv_transpose3d
    
     
 
-    
    torch_conv_transpose3d(
-  input,
-  weight,
-  bias = list(),
-  stride = 1L,
-  padding = 0L,
-  output_padding = 0L,
-  groups = 1L,
-  dilation = 1L
-)
    
+    
    torch_conv_transpose3d(
+  input,
+  weight,
+  bias = list(),
+  stride = 1L,
+  padding = 0L,
+  output_padding = 0L,
+  groups = 1L,
+  dilation = 1L
+)
    
 
     
    Arguments
    
     
    
 
 
     
    
@@ -253,13 +285,13 @@ composed of several input planes, sometimes also called "deconvolution"
    See nn_conv_transpose3d() for details and output shape.
    Examples
    
-    
    if (torch_is_installed()) {
-if (FALSE) {
-inputs = torch_randn(c(20, 16, 50, 10, 20))
-weights = torch_randn(c(16, 33, 3, 3, 3))
-nnf_conv_transpose3d(inputs, weights)
-}
-}
+    
    if (torch_is_installed()) {
+if (FALSE) {
+inputs = torch_randn(c(20, 16, 50, 10, 20))
+weights = torch_randn(c(16, 33, 3, 3, 3))
+nnf_conv_transpose3d(inputs, weights)
+}
+}
 
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Cos
    
     
 
-    
    torch_cos(self)
    
+    
    torch_cos(self)
    
 
     
    Arguments
    
     
    
 
 
     
    
@@ -217,17 +249,17 @@
 $$
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(4))
-a
-torch_cos(a)
-}
+a = torch_randn(c(4))
+a
+torch_cos(a)
+}
 
    #> torch_tensor 
-#>  0.9950
-#> -0.6881
-#>  0.9608
-#>  0.4292
+#>  0.3112
+#>  0.0153
+#> -0.1457
+#>  0.8875
 #> [ CPUFloatType{4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Cosh
    
     
 
-    
    torch_cosh(self)
    
+    
    torch_cosh(self)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -218,17 +250,17 @@
 $$
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(4))
-a
-torch_cosh(a)
-}
+a = torch_randn(c(4))
+a
+torch_cosh(a)
+}
 
    #> torch_tensor 
-#>  1.6794
-#>  1.0414
-#>  1.1420
-#>  1.4987
+#>  3.1999
+#>  1.2990
+#>  2.7654
+#>  1.1329
 #> [ CPUFloatType{4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Cosine_similarity
    
     
 
-    
    torch_cosine_similarity(x1, x2, dim = 2L, eps = 0)
    
+    
    torch_cosine_similarity(x1, x2, dim = 2L, eps = 0)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -229,114 +261,114 @@
 $$
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-input1 = torch_randn(c(100, 128))
-input2 = torch_randn(c(100, 128))
-output = torch_cosine_similarity(input1, input2)
-output
-}
+input1 = torch_randn(c(100, 128))
+input2 = torch_randn(c(100, 128))
+output = torch_cosine_similarity(input1, input2)
+output
+}
 
    #> torch_tensor 
-#> -0.0839
-#> -0.0532
-#> -0.0722
-#>  0.0385
-#> -0.0419
-#>  0.0142
-#> -0.1544
-#>  0.0472
-#>  0.0008
-#> -0.0617
-#>  0.0141
-#> -0.1420
-#>  0.0335
-#>  0.0430
-#>  0.0615
-#>  0.0353
-#>  0.1243
-#>  0.0716
-#>  0.0977
-#> -0.0064
-#>  0.0749
-#> -0.0833
-#>  0.1121
-#>  0.0322
-#>  0.0634
-#> -0.0648
-#>  0.0626
-#> -0.0684
-#>  0.0730
-#> -0.0172
-#>  0.0471
-#>  0.1222
-#>  0.0414
-#>  0.0982
-#> -0.0227
-#> -0.0359
-#>  0.1331
-#>  0.0087
-#> -0.0901
-#> -0.0007
-#>  0.0342
-#> -0.0128
-#> -0.0778
-#>  0.0089
-#>  0.0861
-#>  0.0460
-#>  0.2530
-#> -0.0914
-#> -0.0440
-#> -0.0222
-#> -0.0150
-#>  0.0758
-#>  0.0366
-#>  0.0954
-#> -0.1439
-#> -0.0192
-#> -0.0154
-#> -0.2044
-#> -0.0923
-#>  0.0788
-#>  0.0914
-#>  0.1129
-#> -0.1281
-#> -0.0538
-#>  0.0407
-#> -0.0087
-#> -0.0040
-#>  0.0872
-#>  0.0249
-#> -0.0875
-#> -0.0190
-#> -0.0206
-#>  0.0033
-#> -0.2125
-#> -0.2117
-#>  0.0331
-#>  0.1047
-#> -0.0187
-#> -0.0631
-#> -0.0723
-#>  0.0119
-#> -0.0522
-#> -0.0242
-#>  0.1630
-#>  0.2203
-#> -0.0939
-#> -0.0853
-#> -0.0385
-#>  0.0749
-#> -0.0212
-#> -0.1387
-#> -0.1505
-#> -0.1320
-#>  0.0642
-#>  0.0524
-#> -0.0435
-#> -0.0708
-#>  0.0286
-#>  0.0018
+#> -0.0524
+#> -0.0031
+#>  0.0624
+#> -0.0047
+#>  0.0654
+#> -0.0487
+#>  0.0383
+#>  0.0094
+#> -0.0003
+#>  0.0799
+#>  0.0575
+#> -0.2452
+#>  0.0222
+#> -0.1215
+#> -0.0114
+#>  0.0025
+#> -0.0643
+#>  0.0510
+#>  0.0154
+#>  0.0035
+#> -0.1361
+#>  0.0832
 #>  0.0362
+#> -0.1286
+#>  0.0707
+#>  0.2782
+#>  0.0233
+#>  0.1162
+#>  0.1480
+#>  0.1364
+#>  0.0900
+#> -0.0424
+#>  0.0016
+#> -0.0590
+#> -0.0151
+#> -0.0540
+#>  0.0001
+#>  0.0757
+#> -0.1474
+#> -0.0219
+#> -0.0619
+#> -0.0699
+#> -0.0355
+#>  0.0544
+#> -0.0972
+#> -0.0169
+#> -0.0430
+#> -0.0224
+#>  0.0027
+#> -0.0083
+#>  0.1242
+#>  0.0050
+#> -0.1446
+#>  0.0612
+#> -0.1662
+#>  0.0265
+#> -0.1349
+#> -0.0373
+#>  0.1385
+#> -0.0781
+#>  0.0478
+#> -0.0592
+#> -0.0434
+#> -0.0869
+#> -0.0025
+#> -0.0563
+#>  0.0269
+#>  0.2477
+#> -0.0614
+#> -0.0770
+#> -0.0660
+#>  0.1701
+#> -0.0977
+#> -0.1226
+#> -0.0282
+#>  0.0571
+#> -0.0036
+#> -0.0479
+#>  0.0554
+#>  0.0151
+#> -0.0387
+#>  0.1160
+#>  0.0803
+#>  0.1881
+#> -0.0753
+#>  0.1063
+#> -0.1912
+#>  0.0516
+#> -0.1088
+#> -0.0408
+#> -0.1378
+#> -0.0315
+#> -0.0505
+#>  0.0735
+#> -0.1867
+#> -0.0145
+#> -0.0203
+#>  0.1713
+#> -0.0131
+#>  0.0257
 #> [ CPUFloatType{100} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Cross
    
     
 
-    
    torch_cross(self, other, dim = NULL)
    
+    
    torch_cross(self, other, dim = NULL)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -227,20 +259,20 @@ and other.
    
 size 3.
    Examples
    
-    
    if (torch_is_installed()) {
-
-a = torch_randn(c(4, 3))
-a
-b = torch_randn(c(4, 3))
-b
-torch_cross(a, b, dim=2)
-torch_cross(a, b)
-}
+    
    if (torch_is_installed()) {
+
+a = torch_randn(c(4, 3))
+a
+b = torch_randn(c(4, 3))
+b
+torch_cross(a, b, dim=2)
+torch_cross(a, b)
+}
 
    #> torch_tensor 
-#>  1.0051  1.1323 -1.5101
-#>  0.1088 -0.3737  1.1852
-#> -0.4150 -0.6857 -0.1986
-#> -1.1004 -2.0375  0.8405
+#>  0.5935 -0.0352  1.4618
+#> -0.0312 -0.5835  0.0514
+#>  0.3859  0.9580  0.3189
+#> -1.2798  1.7368  1.1011
 #> [ CPUFloatType{4,3} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Cummax
    
     
 
-    
    torch_cummax(self, dim)
    
+    
    torch_cummax(self, dim)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -223,38 +255,38 @@ location of each maximum value found in the dimension dim.
    
 $$
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(10))
-a
-torch_cummax(a, dim=1)
-}
+a = torch_randn(c(10))
+a
+torch_cummax(a, dim=1)
+}
 
    #> [[1]]
 #> torch_tensor 
-#> -1.3457
-#> -1.3457
-#> -1.2500
-#> -1.2500
-#>  0.6911
-#>  0.6911
-#>  0.6911
-#>  0.7823
-#>  0.7823
-#>  0.7892
+#> -0.9319
+#>  1.6240
+#>  1.6240
+#>  1.6240
+#>  1.6240
+#>  1.6240
+#>  2.1832
+#>  2.1832
+#>  2.1832
+#>  2.1832
 #> [ CPUFloatType{10} ]
 #> 
 #> [[2]]
 #> torch_tensor 
 #>  0
-#>  0
-#>  2
-#>  2
-#>  4
-#>  4
-#>  4
-#>  7
-#>  7
-#>  9
+#>  1
+#>  1
+#>  1
+#>  1
+#>  1
+#>  6
+#>  6
+#>  6
+#>  6
 #> [ CPULongType{10} ]
 #> 
    
   
    
diff --git a/reference/torch_cummin.html b/reference/torch_cummin.html
index e1892ecb0..5ed0acd1e 100644
--- a/reference/torch_cummin.html
+++ b/reference/torch_cummin.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Cummin
    
     
 
-    
    torch_cummin(self, dim)
    
+    
    torch_cummin(self, dim)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -223,24 +255,25 @@ location of each maximum value found in the dimension dim.
    
 $$
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(10))
-a
-torch_cummin(a, dim=1)
-}
+a = torch_randn(c(10))
+a
+torch_cummin(a, dim=1)
+}
 
    #> [[1]]
 #> torch_tensor 
-#> -1.4730
-#> -1.4730
-#> -1.4730
-#> -1.4730
-#> -1.4730
-#> -1.4730
-#> -1.4730
-#> -1.4730
-#> -1.4730
-#> -1.4730
+#> 0.01 *
+#> -4.5300
+#> -4.5300
+#> -4.5300
+#> -156.5067
+#> -156.5067
+#> -156.5067
+#> -156.5067
+#> -156.5067
+#> -156.5067
+#> -156.5067
 #> [ CPUFloatType{10} ]
 #> 
 #> [[2]]
@@ -248,13 +281,13 @@ $$
    
 #>  0
 #>  0
 #>  0
-#>  0
-#>  0
-#>  0
-#>  0
-#>  0
-#>  0
-#>  0
+#>  3
+#>  3
+#>  3
+#>  3
+#>  3
+#>  3
+#>  3
 #> [ CPULongType{10} ]
 #> 
    
   
    
diff --git a/reference/torch_cumprod.html b/reference/torch_cumprod.html
index 4ec85b4c9..1e07d69c6 100644
--- a/reference/torch_cumprod.html
+++ b/reference/torch_cumprod.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Cumprod
    
     
 
-    
    torch_cumprod(self, dim, dtype = NULL)
    
+    
    torch_cumprod(self, dim, dtype = NULL)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -228,23 +260,24 @@ a vector of size N, with elements.
    
 $$
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(10))
-a
-torch_cumprod(a, dim=1)
-}
+a = torch_randn(c(10))
+a
+torch_cumprod(a, dim=1)
+}
 
    #> torch_tensor 
-#> -0.2882
-#>  0.0701
-#>  0.1595
-#>  0.0911
-#>  0.0191
-#>  0.0297
-#> -0.0133
-#>  0.0059
-#> -0.0030
-#> -0.0012
+#> 0.01 *
+#> -1.5305
+#>  0.5353
+#>  1.1566
+#> -1.0608
+#> -1.0334
+#> -0.1878
+#> -0.1060
+#>  0.0316
+#> -0.0599
+#> -0.0588
 #> [ CPUFloatType{10} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Cumsum
    
     
 
-    
    torch_cumsum(self, dim, dtype = NULL)
    
+    
    torch_cumsum(self, dim, dtype = NULL)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -228,23 +260,23 @@ a vector of size N, with elements.
    
 $$
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(10))
-a
-torch_cumsum(a, dim=1)
-}
+a = torch_randn(c(10))
+a
+torch_cumsum(a, dim=1)
+}
 
    #> torch_tensor 
-#>  0.5764
-#>  0.8705
-#>  0.0415
-#>  0.2468
-#> -0.7231
-#> -2.5011
-#> -3.5835
-#> -2.3688
-#> -3.0899
-#> -2.3232
+#>  1.0341
+#>  0.3108
+#>  1.2103
+#>  0.9886
+#>  1.7014
+#>  2.0861
+#>  2.9594
+#>  3.5739
+#>  3.7172
+#>  1.8170
 #> [ CPUFloatType{10} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Det
    
     
 
-    
    torch_det(self)
    
+    
    torch_det(self)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -223,18 +255,18 @@ unstable in when `input` doesn't have distinct singular values. See
 
    Calculates determinant of a square matrix or batches of square matrices.
    Examples
    
-    
    if (torch_is_installed()) {
-
-A = torch_randn(c(3, 3))
-torch_det(A)
-A = torch_randn(c(3, 2, 2))
-A
-A$det()
-}
+    
    if (torch_is_installed()) {
+
+A = torch_randn(c(3, 3))
+torch_det(A)
+A = torch_randn(c(3, 2, 2))
+A
+A$det()
+}
 
    #> torch_tensor 
-#>  0.2158
-#>  1.0192
-#> -0.1403
+#>  3.5172
+#>  0.0857
+#> -2.0464
 #> [ CPUFloatType{3} ]
    
   
    
   
 
@@ -146,6 +158,9 @@ is or will be allocated." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ is or will be allocated." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,7 +229,7 @@ is or will be allocated." />
 is or will be allocated.
    
     
 
-    
    torch_device(type, index = NULL)
    
+    
    torch_device(type, index = NULL)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -219,18 +251,18 @@ with device 'cuda' is equivalent to 'cuda:X' where X i
 
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
 # Via string
-torch_device("cuda:1")
-torch_device("cpu")
-torch_device("cuda") # current cuda device
+torch_device("cuda:1")
+torch_device("cpu")
+torch_device("cuda") # current cuda device
 
 # Via string and device ordinal
-torch_device("cuda", 0)
-torch_device("cpu", 0)
+torch_device("cuda", 0)
+torch_device("cpu", 0)
 
-}
+}
 
    #> torch_device(type='cpu', index=0)
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Diag
    
     
 
-    
    torch_diag(self, diagonal = 0L)
    
+    
    torch_diag(self, diagonal = 0L)
    
 
     
    Arguments
    
     
    
diff --git a/reference/torch_diag_embed.html b/reference/torch_diag_embed.html
index 902ceb337..192081f1c 100644
--- a/reference/torch_diag_embed.html
+++ b/reference/torch_diag_embed.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
         torch
-        0.0.3
+        0.1.0
       
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
  • Datasets
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
-
  • Examples
    • 
+
  • 
+  
+  
+
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Diag_embed
    
 
-    
    torch_diag_embed(self, offset = 0L, dim1 = -2L, dim2 = -1L)
    
+    
    torch_diag_embed(self, offset = 0L, dim1 = -2L, dim2 = -1L)
    Arguments
    
 
       
     
     
     
   
 
 
     
 
     
     
    
@@ -244,24 +276,24 @@ the same arguments yields a matrix identical to input. However,
 need to be explicitly specified.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(2, 3))
-torch_diag_embed(a)
-torch_diag_embed(a, offset=1, dim1=1, dim2=3)
-}
+a = torch_randn(c(2, 3))
+torch_diag_embed(a)
+torch_diag_embed(a, offset=1, dim1=1, dim2=3)
+}
 
    #> torch_tensor 
 #> (1,.,.) = 
-#>   0.0000  0.3358  0.0000  0.0000
-#>   0.0000 -3.1130  0.0000  0.0000
+#>   0.0000 -0.7715  0.0000  0.0000
+#>   0.0000 -1.6565  0.0000  0.0000
 #> 
 #> (2,.,.) = 
-#>   0.0000  0.0000  0.4778  0.0000
-#>   0.0000  0.0000  0.6605  0.0000
+#>   0.0000  0.0000 -0.2706  0.0000
+#>   0.0000  0.0000 -0.1178  0.0000
 #> 
 #> (3,.,.) = 
-#>   0.0000  0.0000  0.0000 -0.0378
-#>   0.0000  0.0000  0.0000 -0.3726
+#>   0.0000  0.0000  0.0000 -0.4561
+#>   0.0000  0.0000  0.0000  1.5418
 #> 
 #> (4,.,.) = 
 #>   0  0  0  0
diff --git a/reference/torch_diagflat.html b/reference/torch_diagflat.html
index 308a4c347..5a0e08342 100644
--- a/reference/torch_diagflat.html
+++ b/reference/torch_diagflat.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Diagflat
    
     
    
 
-    
    torch_diagflat(self, offset = 0L)
    
+    
    torch_diagflat(self, offset = 0L)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -229,21 +261,21 @@ with the elements of input as the diagonal.
    Examples
    
-    
    if (torch_is_installed()) {
-
-a = torch_randn(c(3))
-a
-torch_diagflat(a)
-torch_diagflat(a, 1)
-a = torch_randn(c(2, 2))
-a
-torch_diagflat(a)
-}
+    
    if (torch_is_installed()) {
+
+a = torch_randn(c(3))
+a
+torch_diagflat(a)
+torch_diagflat(a, 1)
+a = torch_randn(c(2, 2))
+a
+torch_diagflat(a)
+}
 
    #> torch_tensor 
-#> -0.7924  0.0000  0.0000  0.0000
-#>  0.0000 -0.6579  0.0000  0.0000
-#>  0.0000  0.0000 -1.1050  0.0000
-#>  0.0000  0.0000  0.0000  0.5352
+#>  0.3237  0.0000  0.0000  0.0000
+#>  0.0000 -0.8425  0.0000  0.0000
+#>  0.0000  0.0000  0.3246  0.0000
+#>  0.0000  0.0000  0.0000  0.8017
 #> [ CPUFloatType{4,4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Diagonal
    
     
 
-    
    torch_diagonal(self, outdim, dim1 = 1L, dim2 = 2L, offset = 0L)
    
+    
    torch_diagonal(self, outdim, dim1 = 1L, dim2 = 2L, offset = 0L)
    
 
     
    Arguments
    
     
    
 
 
     
    
@@ -242,31 +274,31 @@ of the input. However, torch_diag_embed has different default
 dimensions, so those need to be explicitly specified.
    Examples
    
-    
    if (torch_is_installed()) {
-
-a = torch_randn(c(3, 3))
-a
-torch_diagonal(a, offset = 0)
-torch_diagonal(a, offset = 1)
-x = torch_randn(c(2, 5, 4, 2))
-torch_diagonal(x, offset=-1, dim1=1, dim2=2)
-}
+    
    if (torch_is_installed()) {
+
+a = torch_randn(c(3, 3))
+a
+torch_diagonal(a, offset = 0)
+torch_diagonal(a, offset = 1)
+x = torch_randn(c(2, 5, 4, 2))
+torch_diagonal(x, offset=-1, dim1=1, dim2=2)
+}
 
    #> torch_tensor 
 #> (1,.,.) = 
-#>   0.6147
-#>   0.7596
+#>   2.4176
+#>   1.8687
 #> 
 #> (2,.,.) = 
-#>  -0.9285
-#>  -0.0531
+#>   0.3707
+#>   0.0257
 #> 
 #> (3,.,.) = 
-#>   0.8622
-#>  -0.1970
+#>   2.5732
+#>  -0.6057
 #> 
 #> (4,.,.) = 
-#>  -1.2797
-#>   1.6829
+#>   0.2208
+#>  -0.7582
 #> [ CPUFloatType{4,2,1} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Digamma
    
     
 
-    
    torch_digamma(self)
    
+    
    torch_digamma(self)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -217,11 +249,11 @@
 $$
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_tensor(c(1, 0.5))
-torch_digamma(a)
-}
+a = torch_tensor(c(1, 0.5))
+torch_digamma(a)
+}
 
    #> torch_tensor 
 #> -0.5772
 #> -1.9635
diff --git a/reference/torch_dist.html b/reference/torch_dist.html
index cf47972c0..06b5b6cb3 100644
--- a/reference/torch_dist.html
+++ b/reference/torch_dist.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Dist
    
     
    
 
-    
    torch_dist(self, other, p = 2L)
    
+    
    torch_dist(self, other, p = 2L)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -224,19 +256,19 @@
 broadcastable .
    Examples
    
-    
    if (torch_is_installed()) {
-
-x = torch_randn(c(4))
-x
-y = torch_randn(c(4))
-y
-torch_dist(x, y, 3.5)
-torch_dist(x, y, 3)
-torch_dist(x, y, 0)
-torch_dist(x, y, 1)
-}
+    
    if (torch_is_installed()) {
+
+x = torch_randn(c(4))
+x
+y = torch_randn(c(4))
+y
+torch_dist(x, y, 3.5)
+torch_dist(x, y, 3)
+torch_dist(x, y, 0)
+torch_dist(x, y, 1)
+}
 
    #> torch_tensor 
-#> 2.48136
+#> 3.86412
 #> [ CPUFloatType{} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Div
    
     
 
-    
    torch_div(self, other)
    
+    
    torch_div(self, other)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -250,24 +282,24 @@ to the torch_dtype of the specified output tensor. Integral divisio
 by zero leads to undefined behavior.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(5))
-a
-torch_div(a, 0.5)
+a = torch_randn(c(5))
+a
+torch_div(a, 0.5)
 
 
-a = torch_randn(c(4, 4))
-a
-b = torch_randn(c(4))
-b
-torch_div(a, b)
-}
+a = torch_randn(c(4, 4))
+a
+b = torch_randn(c(4))
+b
+torch_div(a, b)
+}
 
    #> torch_tensor 
-#>  0.6647  0.7626 -0.4095  0.1570
-#>  0.7859 -0.3618  0.0104 -0.7726
-#>  0.3775 -0.4202  0.9625  0.0194
-#>  0.4176 -0.7826 -0.3309 -0.8345
+#>  2.9167 -0.6880  0.1183 -15.0537
+#>  0.9509  0.1741  0.0892  8.4831
+#>  1.2162  0.5509  0.0958 -2.2754
+#>  1.4318 -0.6574 -0.6130  6.9025
 #> [ CPUFloatType{4,4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Dot
    
     
 
-    
    torch_dot(self, tensor)
    
+    
    torch_dot(self, tensor)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -221,10 +253,10 @@
 
    Computes the dot product (inner product) of two tensors.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_dot(torch_tensor(c(2, 3)), torch_tensor(c(2, 1)))
-}
+torch_dot(torch_tensor(c(2, 3)), torch_tensor(c(2, 1)))
+}
 
    #> torch_tensor 
 #> 7
 #> [ CPUFloatType{} ]
    
diff --git a/reference/torch_dtype.html b/reference/torch_dtype.html
index ca6599a88..85c8f6ca9 100644
--- a/reference/torch_dtype.html
+++ b/reference/torch_dtype.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,41 +227,41 @@
     
    Returns the correspondent data type.
    
     
 
-    
    torch_float32()
+    
    torch_float32()
 
-torch_float()
+torch_float()
 
-torch_float64()
+torch_float64()
 
-torch_double()
+torch_double()
 
-torch_float16()
+torch_float16()
 
-torch_half()
+torch_half()
 
-torch_uint8()
+torch_uint8()
 
-torch_int8()
+torch_int8()
 
-torch_int16()
+torch_int16()
 
-torch_short()
+torch_short()
 
-torch_int32()
+torch_int32()
 
-torch_int()
+torch_int()
 
-torch_int64()
+torch_int64()
 
-torch_long()
+torch_long()
 
-torch_bool()
+torch_bool()
 
-torch_quint8()
+torch_quint8()
 
-torch_qint8()
+torch_qint8()
 
-torch_qint32()
    
+torch_qint32()
    
 
 
 
diff --git a/reference/torch_eig.html b/reference/torch_eig.html
index 66ca51941..4e18e8c25 100644
--- a/reference/torch_eig.html
+++ b/reference/torch_eig.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Eig
    
     
 
-    
    torch_eig(self, eigenvectors = FALSE)
    
+    
    torch_eig(self, eigenvectors = FALSE)
    
 
     
    Arguments
    
     
    
 
     
    
diff --git a/reference/torch_einsum.html b/reference/torch_einsum.html
index 7e8f13755..196d3f80a 100644
--- a/reference/torch_einsum.html
+++ b/reference/torch_einsum.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
         torch
-        0.0.3
+        0.1.0
       
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
  • Datasets
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
-
  • Examples
    • 
+
  • 
+  
+  
+
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Einsum
    
 
-    
    torch_einsum(equation, tensors)
    
+    
    torch_einsum(equation, tensors)
    Arguments
    
 
       
     
     
     
   
 
 
     
 
     
     
    
@@ -219,29 +251,29 @@
 Einstein summation convention.
    Examples
    
-    
    if (torch_is_installed()) {
-
-if (FALSE) {
-
-x = torch_randn(c(5))
-y = torch_randn(c(4))
-torch_einsum('i,j->ij', list(x, y))  # outer product
-A = torch_randn(c(3,5,4))
-l = torch_randn(c(2,5))
-r = torch_randn(c(2,4))
-torch_einsum('bn,anm,bm->ba', list(l, A, r)) # compare torch_nn$functional$bilinear
-As = torch_randn(c(3,2,5))
-Bs = torch_randn(c(3,5,4))
-torch_einsum('bij,bjk->bik', list(As, Bs)) # batch matrix multiplication
-A = torch_randn(c(3, 3))
-torch_einsum('ii->i', list(A)) # diagonal
-A = torch_randn(c(4, 3, 3))
-torch_einsum('...ii->...i', list(A)) # batch diagonal
-A = torch_randn(c(2, 3, 4, 5))
-torch_einsum('...ij->...ji', list(A))$shape # batch permute
-
-}
-}
+    
    if (torch_is_installed()) {
+
+if (FALSE) {
+
+x = torch_randn(c(5))
+y = torch_randn(c(4))
+torch_einsum('i,j->ij', list(x, y))  # outer product
+A = torch_randn(c(3,5,4))
+l = torch_randn(c(2,5))
+r = torch_randn(c(2,4))
+torch_einsum('bn,anm,bm->ba', list(l, A, r)) # compare torch_nn$functional$bilinear
+As = torch_randn(c(3,2,5))
+Bs = torch_randn(c(3,5,4))
+torch_einsum('bij,bjk->bik', list(As, Bs)) # batch matrix multiplication
+A = torch_randn(c(3, 3))
+torch_einsum('ii->i', list(A)) # diagonal
+A = torch_randn(c(4, 3, 3))
+torch_einsum('...ii->...i', list(A)) # batch diagonal
+A = torch_randn(c(2, 3, 4, 5))
+torch_einsum('...ij->...ji', list(A))$shape # batch permute
+
+}
+}
 
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,14 +227,14 @@
     
    Empty
    
     
 
-    
    torch_empty(
-  ...,
-  names = NULL,
-  dtype = NULL,
-  layout = torch_strided(),
-  device = NULL,
-  requires_grad = FALSE
-)
    
+    
    torch_empty(
+  ...,
+  names = NULL,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -242,13 +274,13 @@
 defined by the variable argument size.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_empty(c(2, 3))
-}
+torch_empty(c(2, 3))
+}
 
    #> torch_tensor 
-#>  1.9205e+31  1.8891e+31  6.3375e-10
-#>  1.8169e+31  4.4726e+21  8.4843e+26
+#>  4.4726e+21  8.4843e+26  2.7945e+20
+#>  5.5610e+31  4.2964e+24  8.4725e+11
 #> [ CPUFloatType{2,3} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,14 +227,14 @@
     
    Empty_like
    
     
 
-    
    torch_empty_like(
-  input,
-  dtype = NULL,
-  layout = torch_strided(),
-  device = NULL,
-  requires_grad = FALSE,
-  memory_format = torch_preserve_format()
-)
    
+    
    torch_empty_like(
+  input,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE,
+  memory_format = torch_preserve_format()
+)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -243,13 +275,13 @@
 torch_empty(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_empty(list(2,3), dtype = torch_int64())
-}
+torch_empty(list(2,3), dtype = torch_int64())
+}
 
    #> torch_tensor 
-#>  1.2885e+10  0.0000e+00  0.0000e+00
-#>  0.0000e+00  1.7180e+10  1.3700e+02
+#>  1.4062e+14  1.4062e+14  0.0000e+00
+#>  0.0000e+00  1.2885e+10  0.0000e+00
 #> [ CPULongType{2,3} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,15 +227,15 @@
     
    Empty_strided
    
     
 
-    
    torch_empty_strided(
-  size,
-  stride,
-  dtype = NULL,
-  layout = torch_strided(),
-  device = NULL,
-  requires_grad = FALSE,
-  pin_memory = FALSE
-)
    
+    
    torch_empty_strided(
+  size,
+  stride,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE,
+  pin_memory = FALSE
+)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -257,13 +289,13 @@ vectorized) may result in incorrect behavior. If you need to write to
 the tensors, please clone them first.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_empty_strided(list(2, 3), list(1, 2))
-a
-a$stride(1)
-a$size(1)
-}
+a = torch_empty_strided(list(2, 3), list(1, 2))
+a
+a$stride(1)
+a$size(1)
+}
 
    #> [1] 2
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Eq
    
     
 
-    
    torch_eq(self, other)
    
+    
    torch_eq(self, other)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -221,10 +253,10 @@ Must be a ByteTensor
    
 broadcastable  with the first argument.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_eq(torch_tensor(c(1,2,3,4)), torch_tensor(c(1, 3, 2, 4)))
-}
+torch_eq(torch_tensor(c(1,2,3,4)), torch_tensor(c(1, 3, 2, 4)))
+}
 
    #> torch_tensor 
 #>  1
 #>  0
diff --git a/reference/torch_equal.html b/reference/torch_equal.html
index f094458d9..1cbe65cce 100644
--- a/reference/torch_equal.html
+++ b/reference/torch_equal.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Equal
    
     
    
 
-    
    torch_equal(self, other)
    
+    
    torch_equal(self, other)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -218,10 +250,10 @@
 
    TRUE if two tensors have the same size and elements, FALSE otherwise.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_equal(torch_tensor(c(1, 2)), torch_tensor(c(1, 2)))
-}
+torch_equal(torch_tensor(c(1, 2)), torch_tensor(c(1, 2)))
+}
 
    #> [1] TRUE
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Erf
    
     
 
-    
    torch_erf(self)
    
+    
    torch_erf(self)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -217,10 +249,10 @@
 $$
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_erf(torch_tensor(c(0, -1., 10.)))
-}
+torch_erf(torch_tensor(c(0, -1., 10.)))
+}
 
    #> torch_tensor 
 #>  0.0000
 #> -0.8427
diff --git a/reference/torch_erfc.html b/reference/torch_erfc.html
index 8a4a96ccd..ba55d33d9 100644
--- a/reference/torch_erfc.html
+++ b/reference/torch_erfc.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Erfc
    
     
    
 
-    
    torch_erfc(self)
    
+    
    torch_erfc(self)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -218,10 +250,10 @@ The complementary error function is defined as follows:
    
 $$
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_erfc(torch_tensor(c(0, -1., 10.)))
-}
+torch_erfc(torch_tensor(c(0, -1., 10.)))
+}
 
    #> torch_tensor 
 #>  1.0000e+00
 #>  1.8427e+00
diff --git a/reference/torch_erfinv.html b/reference/torch_erfinv.html
index 03295dcb4..033f6d07d 100644
--- a/reference/torch_erfinv.html
+++ b/reference/torch_erfinv.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Erfinv
    
     
    
 
-    
    torch_erfinv(self)
    
+    
    torch_erfinv(self)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -218,10 +250,10 @@ The inverse error function is defined in the range \((-1, 1)\) as:
    
 $$
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_erfinv(torch_tensor(c(0, 0.5, -1.)))
-}
+torch_erfinv(torch_tensor(c(0, 0.5, -1.)))
+}
 
    #> torch_tensor 
 #>  0.0000
 #>  0.4769
diff --git a/reference/torch_exp.html b/reference/torch_exp.html
index e045862d1..45e0c295a 100644
--- a/reference/torch_exp.html
+++ b/reference/torch_exp.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Exp
    
     
    
 
-    
    torch_exp(self)
    
+    
    torch_exp(self)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -218,10 +250,10 @@ of the input tensor input.
    
 $$
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_exp(torch_tensor(c(0, log(2))))
-}
+torch_exp(torch_tensor(c(0, log(2))))
+}
 
    #> torch_tensor 
 #>  1
 #>  2
diff --git a/reference/torch_expm1.html b/reference/torch_expm1.html
index 7c09819e3..db8176f8f 100644
--- a/reference/torch_expm1.html
+++ b/reference/torch_expm1.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Expm1
    
     
    
 
-    
    torch_expm1(self)
    
+    
    torch_expm1(self)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -218,10 +250,10 @@ of input.
    
 $$
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_expm1(torch_tensor(c(0, log(2))))
-}
+torch_expm1(torch_tensor(c(0, log(2))))
+}
 
    #> torch_tensor 
 #>  0
 #>  1
diff --git a/reference/torch_eye.html b/reference/torch_eye.html
index 9bb24caef..c427e2441 100644
--- a/reference/torch_eye.html
+++ b/reference/torch_eye.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,14 +227,14 @@
     
    Eye
    
     
    
 
-    
    torch_eye(
-  n,
-  m = n,
-  dtype = NULL,
-  layout = torch_strided(),
-  device = NULL,
-  requires_grad = FALSE
-)
    
+    
    torch_eye(
+  n,
+  m = n,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -241,10 +273,10 @@
 
    Returns a 2-D tensor with ones on the diagonal and zeros elsewhere.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_eye(3)
-}
+torch_eye(3)
+}
 
    #> torch_tensor 
 #>  1  0  0
 #>  0  1  0
diff --git a/reference/torch_fft.html b/reference/torch_fft.html
index 78ddfe283..e2f898d40 100644
--- a/reference/torch_fft.html
+++ b/reference/torch_fft.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Fft
    
     
    
 
-    
    torch_fft(self, signal_ndim, normalized = FALSE)
    
+    
    torch_fft(self, signal_ndim, normalized = FALSE)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -256,333 +288,333 @@ shape of input.
    torch_backends.mkl.is_available to check if MKL is installed.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
 # unbatched 2D FFT
-x = torch_randn(c(4, 3, 2))
-torch_fft(x, 2)
+x = torch_randn(c(4, 3, 2))
+torch_fft(x, 2)
 # batched 1D FFT
-torch_fft(x, 1)
+torch_fft(x, 1)
 # arbitrary number of batch dimensions, 2D FFT
-x = torch_randn(c(3, 3, 5, 5, 2))
-torch_fft(x, 2)
+x = torch_randn(c(3, 3, 5, 5, 2))
+torch_fft(x, 2)
 
-}
+}
 
    #> torch_tensor 
 #> (1,1,1,.,.) = 
-#>   3.8970  3.1487
-#>   3.5089 -8.0428
-#>  -4.2355  4.7304
-#>   5.1422  8.4612
-#>  -0.2829  6.5526
+#>  -1.1481 -4.3297
+#>  -4.7188 -6.2804
+#>  -1.3097 -4.8588
+#>  -0.6476 -7.7830
+#>   5.8857  0.0997
 #> 
 #> (2,1,1,.,.) = 
-#>  -6.4914  0.7626
-#>  -9.6798 -6.4868
-#>   4.7413 -6.3207
-#>   3.1433  6.0861
-#>   1.4183  8.1368
+#>  -1.6040  2.3841
+#>   2.0651 -0.7709
+#>   3.9230  8.8587
+#>   5.2396 -3.9834
+#>   2.9333 -2.8714
 #> 
 #> (3,1,1,.,.) = 
-#>   4.1796 -0.8876
-#>  -2.7845 -0.2090
-#>   6.4877 -2.0553
-#>   1.6739 -1.4434
-#>   6.7934  3.7603
+#>   0.3662  1.1582
+#>   3.3350 -1.2566
+#>  -6.8803  1.0332
+#>   0.6336 -4.3311
+#>  -8.1302 -1.4290
 #> 
 #> (1,2,1,.,.) = 
-#>   -8.0135  -3.4585
-#>    3.2928   1.1881
-#>    1.0519   7.3132
-#>   -2.0995   0.7831
-#>   11.4499   0.4932
+#>   9.9370 -4.7862
+#>   1.7947  8.3546
+#>   7.8480 -4.3197
+#>  -4.5602 -4.6462
+#>   9.8942 -1.0242
 #> 
 #> (2,2,1,.,.) = 
-#>   2.4901  2.9753
-#>  -8.2115  4.1514
-#>   6.0281  1.2984
-#>  -7.5086  1.3346
-#>   3.6055 -0.8337
+#>   5.4458 -1.6464
+#>   0.9909 -0.2878
+#>  -3.9934  7.8341
+#>   0.6661  0.3599
+#>   9.6997 -2.0700
 #> 
 #> (3,2,1,.,.) = 
-#>    4.5424  -0.5391
-#>   10.2617  -3.2754
-#>   -1.8136   5.1917
-#>   -4.6990  -0.9120
-#>   -0.9396  -2.4928
+#>   -0.2640  13.3921
+#>   -3.9612   1.9535
+#>   -2.7264  -6.7090
+#>    6.3232  -2.0279
+#>    3.9858  -3.9658
 #> 
 #> (1,3,1,.,.) = 
-#>  -4.4424 -3.1498
-#>  -0.4156  0.1798
-#>  -13.2501 -4.7913
-#>  -0.4209  2.1745
-#>  -0.4077 -2.1715
+#>  -4.8588 -0.8276
+#>   4.9844 -1.4986
+#>  -2.8926 -1.1695
+#>   5.5408 -3.3529
+#>  -1.9728 -1.8652
 #> 
 #> (2,3,1,.,.) = 
-#>   1.8846  7.9342
-#>   3.5554 -4.6065
-#>   2.1314 -1.0630
-#>  -1.8522  5.3271
-#>  -4.2557 -6.4940
+#>   6.2058 -10.3060
+#>   2.3539  0.2381
+#>   2.5999  2.7840
+#>   4.4128 -0.1960
+#>  -1.7241 -1.4131
 #> 
 #> (3,3,1,.,.) = 
-#>  -5.2358 -6.1865
-#>  -13.5679  8.0294
-#>   5.8208 -5.6350
-#>   6.6610 -3.3221
-#>  -2.0237 -1.4322
+#>   3.4567 -0.4836
+#>   6.1658  2.3352
+#>   0.0856  0.4609
+#>   3.7644 -5.1061
+#>   1.0802 -4.8710
 #> 
 #> (1,1,2,.,.) = 
-#>   3.3358  4.7716
-#>   3.1996  3.2956
-#>   2.6306  7.2663
-#>  -2.2276 -8.0122
-#>   2.0598 -2.9997
+#>  -3.5388  6.0588
+#>  -3.2331 -3.7648
+#>  -3.3814 -2.3884
+#>   1.9866  0.6786
+#>   3.8671  0.4380
 #> 
 #> (2,1,2,.,.) = 
-#>   0.5116  0.5725
-#>   2.0175 -4.3590
-#>  -1.4288  0.5337
-#>   2.3421  0.0760
-#>  -7.5872 -0.3140
+#>  -2.7273 -7.3688
+#>   3.1946  1.7876
+#>   5.9637 -6.5781
+#>  -10.5504 -7.0482
+#>   9.0913 -10.8848
 #> 
 #> (3,1,2,.,.) = 
-#>   5.6348  1.3832
-#>   6.6151  0.4225
-#>   6.0133 -5.4770
-#>  -7.6286 -6.4727
-#>   3.6738  4.4512
+#>   1.7073  4.0997
+#>  -1.8936 -5.7653
+#>  -2.6932 -3.5705
+#>   2.1578  6.4609
+#>   0.3697  4.2655
 #> 
 #> (1,2,2,.,.) = 
-#>   2.8345 -7.9496
-#>  -1.2934  6.3027
-#>  -2.5656  3.4925
-#>  -4.2269  8.1202
-#>   7.0657  8.9951
+#>    3.2221  -3.5457
+#>   -5.6733  -1.1672
+#>   -6.0391  14.1264
+#>   -1.8346  -4.6253
+#>    1.4615   1.0004
 #> 
 #> (2,2,2,.,.) = 
-#>  -2.5830  2.6838
-#>   1.3960 -2.1490
-#>   3.6903 -4.3413
-#>  -4.8245  1.9661
-#>   2.4275 -0.8489
+#>   7.8673  2.4643
+#>   6.2591  2.9252
+#>  -0.3886 -0.8997
+#>   0.1365 -4.4832
+#>   3.5114  0.2806
 #> 
 #> (3,2,2,.,.) = 
-#>  -2.3136 -3.0029
-#>   6.4553  2.9853
-#>   1.0790  6.2816
-#>  -2.3066  3.1048
-#>   2.4425 -2.4980
+#>    2.5126  -0.0145
+#>    8.0643  -0.4143
+#>    6.9933   0.4218
+#>    7.4069   3.1231
+#>   10.0771   0.4146
 #> 
 #> (1,3,2,.,.) = 
-#>   5.0888 -1.9831
-#>  -3.3846 -3.1375
-#>   0.4764  0.9040
-#>  -0.2155 -3.9354
-#>  -0.2403  9.0833
+#>   8.7499  6.0461
+#>  -6.3412 -2.5804
+#>  -1.8398 -11.6373
+#>   0.7424 -0.4836
+#>  -3.3934  0.5697
 #> 
 #> (2,3,2,.,.) = 
-#>   3.2478  0.6904
-#>   4.8131 -4.7587
-#>   4.4327 -2.0460
-#>  -0.1583 -1.1435
-#>  -0.8185 -3.7916
+#>  -0.8282  2.5507
+#>   2.5071 -1.9104
+#>  -6.8926 -1.4344
+#>  -2.7887  0.8809
+#>   1.3705 -3.8546
 #> 
 #> (3,3,2,.,.) = 
-#>   -4.8215  -5.7823
-#>   -4.3197   2.5006
-#>   -7.6764  10.5907
-#>    5.7097   9.1375
-#>   -6.5421   5.3465
+#>  -1.3417 -2.2560
+#>  -0.9487 -2.3724
+#>  -8.4024  6.2669
+#>  -9.3354 -14.5017
+#>   3.7803 -3.7519
 #> 
 #> (1,1,3,.,.) = 
-#>   6.8857  1.6267
-#>   0.6538 -2.9130
-#>   4.2404 -5.3240
-#>   5.6476  5.1706
-#>   1.4154  2.3173
+#>  -1.9732 -8.0447
+#>  -4.0135  4.3342
+#>  -8.5510 -3.9217
+#>   1.7348  5.0812
+#>  -1.3394  5.5100
 #> 
 #> (2,1,3,.,.) = 
-#>  -0.5342 -1.8588
-#>  -6.3230 -2.3969
-#>  -2.6561 -1.8841
-#>   2.2272 -2.2146
-#>  -7.2355 -9.8852
+#>  -2.3784 -3.2535
+#>  -0.5325 -1.7479
+#>   0.5439  1.1196
+#>  -1.7045 -6.2396
+#>   1.8775 -5.5534
 #> 
 #> (3,1,3,.,.) = 
-#>    6.9817  -1.9044
-#>  -10.6065  -3.0032
-#>    6.2727  10.5124
-#>    2.0892  -0.2945
-#>    1.4965   3.6209
+#>  -7.0758 -4.6158
+#>  -4.4565  3.7113
+#>  -2.5085 -0.5332
+#>   2.2014 -1.8736
+#>  -6.4190  0.8344
 #> 
 #> (1,2,3,.,.) = 
-#>  -2.7138  0.5805
-#>   3.8351 -13.1060
-#>  -2.0923  8.4118
-#>   6.7002  2.8014
-#>  -2.0329 -5.4260
+#>  -1.6565  7.4865
+#>   7.9111  8.2888
+#>   1.7455 -7.7078
+#>  -8.7524  3.5801
+#>   3.2935  2.1932
 #> 
 #> (2,2,3,.,.) = 
-#>  -4.7038  4.4041
-#>   0.6079  6.9384
-#>  -2.4581 -2.0337
-#>  -2.4631  5.5419
-#>  -2.4616 -2.3076
+#>  -0.4705 -8.0645
+#>  -2.7541  5.7445
+#>   3.0258 -0.3652
+#>   4.6185 -9.1709
+#>  -3.3521  4.3578
 #> 
 #> (3,2,3,.,.) = 
-#>   1.0971  1.5684
-#>   4.6985  3.6660
-#>   1.7358  2.5220
-#>  -2.9059 -3.8360
-#>   8.5191 -0.6597
+#>  -9.7571  5.1088
+#>   0.5665  3.9230
+#>   0.8925 -0.7799
+#>  -2.2061  9.9910
+#>   0.5800  0.5737
 #> 
 #> (1,3,3,.,.) = 
-#>   1.2912 -4.1150
-#>   5.9200 -9.2185
-#>   2.7708 -11.2848
-#>  -5.2274 -9.0754
-#>  -5.9556 -8.3723
+#>   -7.3047   4.8323
+#>   -3.9106   3.1690
+#>   -6.3493  -0.1799
+#>    2.7435  13.2830
+#>    3.4891  -3.3635
 #> 
 #> (2,3,3,.,.) = 
-#>    3.6284  -9.7751
-#>   -6.4936   0.4896
-#>    1.0783   3.2584
-#>   -1.2375   4.0910
-#>    8.7281  11.1889
+#>   5.1295 -1.2111
+#>  -5.8640 -4.0844
+#>  -0.3176  2.4835
+#>  -6.5782 -4.5152
+#>  -0.8305  0.7476
 #> 
 #> (3,3,3,.,.) = 
-#>  -2.9392 -0.8457
-#>  -5.1092 -3.4291
-#>  -6.7226 -2.2762
-#>  -6.7354  0.7375
-#>  -3.6834 -10.3393
+#>  -5.4378 -5.3350
+#>  -2.3722 -11.4782
+#>   7.3945  0.4418
+#>   1.8523 -0.8628
+#>   1.4826 -7.1815
 #> 
 #> (1,1,4,.,.) = 
-#>   -0.4641   5.1642
-#>   -4.9807  -2.8183
-#>   -0.9600   1.1617
-#>   13.1615  -5.7607
-#>   -5.8192   6.1872
+#>   2.4687  5.1131
+#>   6.3423 -4.0122
+#>   4.2059  0.0545
+#>  -3.0227 -8.9330
+#>   7.3082  3.1296
 #> 
 #> (2,1,4,.,.) = 
-#>   -0.6880  12.9307
-#>   -8.0335  -1.1752
-#>    6.2135   2.0547
-#>    8.6658  -5.6305
-#>   -7.1765  -4.2632
+#>  -1.9773  4.5083
+#>  -2.9370  0.5696
+#>  -1.3116  0.0959
+#>   3.0593  2.6283
+#>   0.7708 -1.4834
 #> 
 #> (3,1,4,.,.) = 
-#>  -3.0057 -3.1364
-#>   4.9857  4.4488
-#>   1.3519  9.6340
-#>  -9.3729  4.8508
-#>   0.7863  3.1354
+#>  -0.4809  8.7561
+#>  -0.4841  3.7019
+#>  -2.3270  0.4256
+#>   3.7321 -10.0876
+#>  -2.0148  6.4611
 #> 
 #> (1,2,4,.,.) = 
-#>  -5.5472  0.7396
-#>   5.5568 -2.7190
-#>   2.1487  3.5182
-#>   2.2908  5.8565
-#>  -2.7511  0.1199
+#>   -2.6632   5.7187
+#>   11.6131   2.1020
+#>    1.8945   2.9998
+#>   -0.9931 -15.3454
+#>    1.6627   1.9919
 #> 
 #> (2,2,4,.,.) = 
-#>   0.1053 -9.7559
-#>   2.6366  2.9785
-#>   5.8546 -8.3557
-#>   2.3308 -0.3229
-#>  -0.1616 -2.8580
+#>  -0.7808 -2.8565
+#>   8.3442  0.4109
+#>   2.7123  0.6158
+#>   1.3526  5.0902
+#>   3.2664 -1.5515
 #> 
 #> (3,2,4,.,.) = 
-#>  -8.8217 -4.0197
-#>  -1.3547  2.0932
-#>   5.0738 -2.1672
-#>  -1.6119  7.2245
-#>  -1.4150 -0.6509
+#>    5.0697   0.5581
+#>  -11.7244   1.5131
+#>   -4.9459   3.7161
+#>   -3.6735   2.2271
+#>   10.1182  -0.3320
 #> 
 #> (1,3,4,.,.) = 
-#>  -1.2559  2.1416
-#>  -0.0231  4.1475
-#>  -3.8181  2.3299
-#>   3.7565  0.7653
-#>   1.8683 -3.8644
+#>   3.6115 -1.6901
+#>  -1.0491  6.2340
+#>   2.3156 -0.1393
+#>  -0.2285  1.8952
+#>  -0.8674 -1.8877
 #> 
 #> (2,3,4,.,.) = 
-#>    0.4839   6.4445
-#>    3.1534  -8.8380
-#>    3.7889   6.3242
-#>    9.0306  11.1551
-#>    5.7409  -1.1564
+#>   5.8264  6.5763
+#>  -5.1067  2.8225
+#>   4.7281  5.3284
+#>   3.8772 -8.7982
+#>   7.9865  3.5250
 #> 
 #> (3,3,4,.,.) = 
-#>  -2.2988  8.8577
-#>   4.7663 -5.2979
-#>  -4.7160  9.3649
-#>  -4.3578 -7.4509
-#>  -4.3874  2.7093
+#>    5.6288  -2.1604
+#>    5.2852  12.1708
+#>   -6.5885  -1.3408
+#>   -1.0228  -3.8949
+#>    4.9946   5.1332
 #> 
 #> (1,1,5,.,.) = 
-#>  -0.4679  1.3350
-#>  -1.4923 -5.4231
-#>  -2.3291  0.3742
-#>   2.6760 -0.3634
-#>  -3.0602  8.7696
+#>  -3.6772 -2.6791
+#>   9.3151  6.3421
+#>   1.0636 -2.4348
+#>   0.0648  0.2960
+#>  -9.0852 -0.3164
 #> 
 #> (2,1,5,.,.) = 
-#>  -2.1765  4.7492
-#>   7.2170  3.6409
-#>  -6.5629 -2.2038
-#>  -5.8527 -0.0878
-#>  -4.5430  0.7514
+#>  -6.7774  5.5203
+#>   8.8951  0.4216
+#>   1.4675 -0.9553
+#>  -4.2931  2.7152
+#>  -8.8658  0.0596
 #> 
 #> (3,1,5,.,.) = 
-#>   8.4563  6.2536
-#>  -7.6490 -0.3940
-#>  -2.2804  0.5806
-#>  -9.5464  4.4593
-#>  -5.9173 -3.0061
+#>   -0.8913  -3.6577
+#>   -2.9250   2.1499
+#>    1.9352  -0.6881
+#>   12.8338   2.7566
+#>   -8.3175   0.9609
 #> 
 #> (1,2,5,.,.) = 
-#>    5.1614  -5.6453
-#>    4.8296 -15.8688
-#>    1.8169  -0.8523
-#>   10.0722  -2.0120
-#>    0.8588   2.5174
+#>   -2.9347   0.3892
+#>   -1.4719  -6.7884
+#>    0.1891   8.8247
+#>   11.0228   8.0462
+#>   -3.6506   3.0815
 #> 
 #> (2,2,5,.,.) = 
-#>  -6.1863 -1.2811
-#>  -5.0314  3.6125
-#>  -6.2021 -2.8083
-#>  -0.3612 -3.9482
-#>  -3.0259  0.6619
+#>    2.6453  -9.5708
+#>   -1.4926   2.9505
+#>    9.6809  -4.5462
+#>   10.8256   6.3338
+#>    4.1002  -1.5479
 #> 
 #> (3,2,5,.,.) = 
-#>   5.1903  0.0372
-#>  -1.9769  1.8278
-#>   4.1834 -11.1886
-#>  -3.8679 -4.4589
-#>   2.2054 -3.4290
+#>  -1.0330  5.5573
+#>  -2.3059 -1.8269
+#>  -3.2600  6.0368
+#>   4.6868  2.6289
+#>   4.2373  0.1010
 #> 
 #> (1,3,5,.,.) = 
-#>  -9.9394  5.2093
-#>  -6.9233 -3.8417
-#>   7.3800 -2.2016
-#>   1.4532  4.9600
-#>  -2.6033 -3.4185
+#>   6.1920  5.8462
+#>   0.6780  4.9170
+#>   2.4530 -14.6991
+#>   8.8984  0.1436
+#>  -0.9811  3.2451
 #> 
 #> (2,3,5,.,.) = 
-#>  -6.9207  1.5530
-#>  -3.9120  0.2946
-#>  -0.9112  5.2904
-#>   3.2102 -2.0272
-#>   7.6431 -0.3506
+#>  -3.2560  0.5176
+#>   8.5553 -0.6800
+#>  -7.4328 -0.9606
+#>  -6.5130 -0.3419
+#>   2.8921 -2.0966
 #> 
 #> (3,3,5,.,.) = 
-#>   3.1340  6.7375
-#>   1.4610  3.8716
-#>   2.8714  3.5657
-#>  -4.2452 -3.6816
-#>   5.5423  2.7491
+#>   4.2309  0.3529
+#>   4.4683  8.9331
+#>   2.9729 -1.8606
+#>   1.4575  3.8359
+#>  -5.0445 -14.9692
 #> [ CPUFloatType{3,3,5,5,2} ]
    
   
    
   
 
@@ -146,6 +158,9 @@ floating point torch.dtype" />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ floating point torch.dtype" />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,7 +229,7 @@ floating point torch.dtype" />
 floating point torch.dtype
    
     
 
-    
    torch_finfo(dtype)
    
+    
    torch_finfo(dtype)
    
 
     
    Arguments
    
     
    
 
 
     
    
diff --git a/reference/torch_flatten.html b/reference/torch_flatten.html
index daccdc192..d719c3e86 100644
--- a/reference/torch_flatten.html
+++ b/reference/torch_flatten.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
         torch
-        0.0.3
+        0.1.0
       
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
  • Datasets
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
-
  • Examples
    • 
+
  • 
+  
+  
+
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Flatten
    
 
-    
    torch_flatten(self, dims, start_dim = 1L, end_dim = -1L, out_dim)
    
+    
    torch_flatten(self, dims, start_dim = 1L, end_dim = -1L, out_dim)
    Arguments
    
 
       
     
     
     
   
 
 
     
 
     
     
    
@@ -231,12 +263,12 @@ flatten
    Flattens a contiguous range of dims in a tensor.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-t = torch_tensor(matrix(c(1, 2), ncol = 2))
-torch_flatten(t)
-torch_flatten(t, start_dim=2)
-}
+t = torch_tensor(matrix(c(1, 2), ncol = 2))
+torch_flatten(t)
+torch_flatten(t, start_dim=2)
+}
 
    #> torch_tensor 
 #>  1  2
 #> [ CPUFloatType{1,2} ]
    
diff --git a/reference/torch_flip.html b/reference/torch_flip.html
index e1146db25..f49f22e1b 100644
--- a/reference/torch_flip.html
+++ b/reference/torch_flip.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Flip
    
     
 
-    
    torch_flip(self, dims)
    
+    
    torch_flip(self, dims)
    
 
     
    Arguments
    
     
    
 
 
     
    
@@ -218,12 +250,12 @@
 
    Reverse the order of a n-D tensor along given axis in dims.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-x = torch_arange(0, 8)$view(c(2, 2, 2))
-x
-torch_flip(x, c(1, 2))
-}
+x = torch_arange(0, 8)$view(c(2, 2, 2))
+x
+torch_flip(x, c(1, 2))
+}
 
    #> torch_tensor 
 #> (1,.,.) = 
 #>   6  7
diff --git a/reference/torch_floor.html b/reference/torch_floor.html
index ee0a1dac5..9f1a9a9fe 100644
--- a/reference/torch_floor.html
+++ b/reference/torch_floor.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Floor
    
     
    
 
-    
    torch_floor(self)
    
+    
    torch_floor(self)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -218,17 +250,17 @@ the largest integer less than or equal to each element.
    
 $$
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(4))
-a
-torch_floor(a)
-}
+a = torch_randn(c(4))
+a
+torch_floor(a)
+}
 
    #> torch_tensor 
-#>  0
-#> -1
 #> -1
+#> -2
 #> -1
+#>  0
 #> [ CPUFloatType{4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Floor_divide
    
     
 
-    
    torch_floor_divide(self, other)
    
+    
    torch_floor_divide(self, other)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -222,13 +254,13 @@ for type promotion and broadcasting rules.
    
 $$
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_tensor(c(4.0, 3.0))
-b = torch_tensor(c(2.0, 2.0))
-torch_floor_divide(a, b)
-torch_floor_divide(a, 1.4)
-}
+a = torch_tensor(c(4.0, 3.0))
+b = torch_tensor(c(2.0, 2.0))
+torch_floor_divide(a, b)
+torch_floor_divide(a, 1.4)
+}
 
    #> torch_tensor 
 #>  2
 #>  2
diff --git a/reference/torch_fmod.html b/reference/torch_fmod.html
index 9bd9cde04..b7a78a663 100644
--- a/reference/torch_fmod.html
+++ b/reference/torch_fmod.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Fmod
    
     
    
 
-    
    torch_fmod(self, other)
    
+    
    torch_fmod(self, other)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -222,11 +254,11 @@ numbers. The remainder has the same sign as the dividend input.
    other must be broadcastable .
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_fmod(torch_tensor(c(-3., -2, -1, 1, 2, 3)), 2)
-torch_fmod(torch_tensor(c(1., 2, 3, 4, 5)), 1.5)
-}
+torch_fmod(torch_tensor(c(-3., -2, -1, 1, 2, 3)), 2)
+torch_fmod(torch_tensor(c(1., 2, 3, 4, 5)), 1.5)
+}
 
    #> torch_tensor 
 #>  1.0000
 #>  0.5000
diff --git a/reference/torch_frac.html b/reference/torch_frac.html
index eafa181a0..ee1619de7 100644
--- a/reference/torch_frac.html
+++ b/reference/torch_frac.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Frac
    
     
    
 
-    
    torch_frac(self)
    
+    
    torch_frac(self)
    
 
     
    Arguments
    
     
    
 
 
     
    
@@ -217,10 +249,10 @@
 $$
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_frac(torch_tensor(c(1, 2.5, -3.2)))
-}
+torch_frac(torch_tensor(c(1, 2.5, -3.2)))
+}
 
    #> torch_tensor 
 #>  0.0000
 #>  0.5000
diff --git a/reference/torch_full.html b/reference/torch_full.html
index 6a26b0f00..d29d1832a 100644
--- a/reference/torch_full.html
+++ b/reference/torch_full.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
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+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,15 +227,15 @@
     
    Full
    
     
    
 
-    
    torch_full(
-  size,
-  fill_value,
-  names = NULL,
-  dtype = NULL,
-  layout = torch_strided(),
-  device = NULL,
-  requires_grad = FALSE
-)
    
+    
    torch_full(
+  size,
+  fill_value,
+  names = NULL,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -255,10 +287,10 @@ a bool fill_value will return a tensor of torch.bool dtype,
 and an integral fill_value will return a tensor of torch.long dtype.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_full(list(2, 3), 3.141592)
-}
+torch_full(list(2, 3), 3.141592)
+}
 
    #> torch_tensor 
 #>  3.1416  3.1416  3.1416
 #>  3.1416  3.1416  3.1416
diff --git a/reference/torch_full_like.html b/reference/torch_full_like.html
index 9113e685c..d042ed2bb 100644
--- a/reference/torch_full_like.html
+++ b/reference/torch_full_like.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,15 +227,15 @@
     
    Full_like
    
     
    
 
-    
    torch_full_like(
-  input,
-  fill_value,
-  dtype = NULL,
-  layout = torch_strided(),
-  device = NULL,
-  requires_grad = FALSE,
-  memory_format = torch_preserve_format()
-)
    
+    
    torch_full_like(
+  input,
+  fill_value,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE,
+  memory_format = torch_preserve_format()
+)
    
 
     
    Arguments
    
     
    
 
     
    
diff --git a/reference/torch_gather.html b/reference/torch_gather.html
index 153778c32..b8f014d62 100644
--- a/reference/torch_gather.html
+++ b/reference/torch_gather.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
         torch
-        0.0.3
+        0.1.0
       
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
  • Datasets
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
-
  • Examples
    • 
+
  • 
+  
+  
+
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Gather
    
 
-    
    torch_gather(self, dim, index, sparse_grad = FALSE)
    
+    
    torch_gather(self, dim, index, sparse_grad = FALSE)
    Arguments
    
 
       
     
     
     
   
 
 
     
 
     
     
    
@@ -224,9 +256,9 @@
 
 
 
    Gathers values along an axis specified by dim.
    
-
    For a 3-D tensor the output is specified by::
    out[i][j][k] = input[index[i][j][k]][j][k]  # if dim == 0
-out[i][j][k] = input[i][index[i][j][k]][k]  # if dim == 1
-out[i][j][k] = input[i][j][index[i][j][k]]  # if dim == 2
+
    For a 3-D tensor the output is specified by::
    out[i][j][k] = input[index[i][j][k]][j][k]  # if dim == 0
+out[i][j][k] = input[i][index[i][j][k]][k]  # if dim == 1
+out[i][j][k] = input[i][j][index[i][j][k]]  # if dim == 2
 
    
 
 
    If input is an n-dimensional tensor with size
@@ -236,11 +268,11 @@ size \((x_0, x_1, ..., x_{i-1}, y, x_{i+1}, ..., x_{n-1})\) where \(y \geq 1\)
 and out will have the same size as index.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-t = torch_tensor(matrix(c(1,2,3,4), ncol = 2, byrow = TRUE))
-torch_gather(t, 2, torch_tensor(matrix(c(1,1,2,1), ncol = 2, byrow=TRUE), dtype = torch_int64()))
-}
+t = torch_tensor(matrix(c(1,2,3,4), ncol = 2, byrow = TRUE))
+torch_gather(t, 2, torch_tensor(matrix(c(1,1,2,1), ncol = 2, byrow=TRUE), dtype = torch_int64()))
+}
 
    #> torch_tensor 
 #>  1  1
 #>  4  3
diff --git a/reference/torch_ge.html b/reference/torch_ge.html
index 5add98bfb..89ad9502a 100644
--- a/reference/torch_ge.html
+++ b/reference/torch_ge.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Ge
    
     
    
 
-    
    torch_ge(self, other)
    
+    
    torch_ge(self, other)
    
 
     
    Arguments
    
     
    
@@ -220,11 +252,11 @@
 broadcastable  with the first argument.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_ge(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)), 
-         torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE)))
-}
+torch_ge(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)), 
+         torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE)))
+}
 
    #> torch_tensor 
 #>  1  1
 #>  0  1
diff --git a/reference/torch_generator.html b/reference/torch_generator.html
index e205f8f22..e39927160 100644
--- a/reference/torch_generator.html
+++ b/reference/torch_generator.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -74,7 +86,7 @@ In-place random sampling functions." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -147,6 +159,9 @@ In-place random sampling functions." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -160,7 +175,24 @@ In-place random sampling functions." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -199,21 +231,21 @@ that produces pseudo random numbers. Used as a keyword argument in many
 In-place random sampling functions.
    
     
    
 
-    
    torch_generator()
    
+    
    torch_generator()
    
 
 
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
 # Via string
-generator <- torch_generator()
-generator$current_seed()
-generator$set_current_seed(1234567L)
-generator$current_seed()
+generator <- torch_generator()
+generator$current_seed()
+generator$set_current_seed(1234567L)
+generator$current_seed()
 
 
-}
+}
 
    #> integer64
 #> [1] 1234567
    
   
    
diff --git a/reference/torch_geqrf.html b/reference/torch_geqrf.html
index 506785d1a..2d1a24e0d 100644
--- a/reference/torch_geqrf.html
+++ b/reference/torch_geqrf.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Geqrf
    
     
 
-    
    torch_geqrf(self)
    
+    
    torch_geqrf(self)
    
 
     
    Arguments
    
     
    
 
     
    
diff --git a/reference/torch_ger.html b/reference/torch_ger.html
index 2a20545da..f4ecc3a90 100644
--- a/reference/torch_ger.html
+++ b/reference/torch_ger.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
         torch
-        0.0.3
+        0.1.0
       
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
  • Datasets
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
-
  • Examples
    • 
+
  • 
+  
+  
+
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Ger
    
 
-    
    torch_ger(self, vec2)
    
+    
    torch_ger(self, vec2)
    Arguments
    
 
       
     
     
     
   
 
 
     
 
     
     
    
@@ -223,12 +255,12 @@ If input is a vector of size \(n\) and vec2 is a vecto
 size \(m\), then out must be a matrix of size \((n \times m)\).
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-v1 = torch_arange(1., 5.)
-v2 = torch_arange(1., 4.)
-torch_ger(v1, v2)
-}
+v1 = torch_arange(1., 5.)
+v2 = torch_arange(1., 4.)
+torch_ger(v1, v2)
+}
 
    #> torch_tensor 
 #>   1   2   3
 #>   2   4   6
diff --git a/reference/torch_gt.html b/reference/torch_gt.html
index 98edc7761..8139c4ea7 100644
--- a/reference/torch_gt.html
+++ b/reference/torch_gt.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Gt
    
     
    
 
-    
    torch_gt(self, other)
    
+    
    torch_gt(self, other)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -220,11 +252,11 @@
 broadcastable  with the first argument.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_gt(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)), 
-         torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE)))
-}
+torch_gt(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)), 
+         torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE)))
+}
 
    #> torch_tensor 
 #>  0  1
 #>  0  0
diff --git a/reference/torch_hamming_window.html b/reference/torch_hamming_window.html
index bceab5f03..0ff2cfe0f 100644
--- a/reference/torch_hamming_window.html
+++ b/reference/torch_hamming_window.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,16 +227,16 @@
     
    Hamming_window
    
     
    
 
-    
    torch_hamming_window(
-  window_length,
-  periodic = TRUE,
-  alpha = 0.54,
-  beta = 0.46,
-  dtype = NULL,
-  layout = torch_strided(),
-  device = NULL,
-  requires_grad = FALSE
-)
    
+    
    torch_hamming_window(
+  window_length,
+  periodic = TRUE,
+  alpha = 0.54,
+  beta = 0.46,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
    
 
     
    Arguments
    
     
    
 
     
    
diff --git a/reference/torch_hann_window.html b/reference/torch_hann_window.html
index 2122ae2b5..efaffec3c 100644
--- a/reference/torch_hann_window.html
+++ b/reference/torch_hann_window.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
         torch
-        0.0.3
+        0.1.0
       
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
  • Datasets
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
-
  • Examples
    • 
+
  • 
+  
+  
+
  • 
   Reference
 
    • 
@@ -195,14 +227,14 @@
     
    Hann_window
    
 
-    
    torch_hann_window(
-  window_length,
-  periodic = TRUE,
-  dtype = NULL,
-  layout = torch_strided(),
-  device = NULL,
-  requires_grad = FALSE
-)
    
+    
    torch_hann_window(
+  window_length,
+  periodic = TRUE,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
    Arguments
    
 
       
     
     
     
   
 
 
     
 
     
     
    
diff --git a/reference/torch_histc.html b/reference/torch_histc.html
index 3fb1d585f..3a3d0221e 100644
--- a/reference/torch_histc.html
+++ b/reference/torch_histc.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
         torch
-        0.0.3
+        0.1.0
       
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
  • Datasets
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
-
  • Examples
    • 
+
  • 
+  
+  
+
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Histc
    
 
-    
    torch_histc(self, bins = 100L, min = 0L, max = 0L)
    
+    
    torch_histc(self, bins = 100L, min = 0L, max = 0L)
    Arguments
    
 
       
     
     
     
   
 
 
     
 
     
     
    
@@ -229,10 +261,10 @@
 maximum values of the data are used.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_histc(torch_tensor(c(1., 2, 1)), bins=4, min=0, max=3)
-}
+torch_histc(torch_tensor(c(1., 2, 1)), bins=4, min=0, max=3)
+}
 
    #> torch_tensor 
 #>  0
 #>  2
diff --git a/reference/torch_ifft.html b/reference/torch_ifft.html
index 833691b3a..855f0beb4 100644
--- a/reference/torch_ifft.html
+++ b/reference/torch_ifft.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Ifft
    
     
    
 
-    
    torch_ifft(self, signal_ndim, normalized = FALSE)
    
+    
    torch_ifft(self, signal_ndim, normalized = FALSE)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -255,28 +287,28 @@ shape of input.
    torch_backends.mkl.is_available to check if MKL is installed.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-x = torch_randn(c(3, 3, 2))
-x
-y = torch_fft(x, 2)
-torch_ifft(y, 2)  # recover x
-}
+x = torch_randn(c(3, 3, 2))
+x
+y = torch_fft(x, 2)
+torch_ifft(y, 2)  # recover x
+}
 
    #> torch_tensor 
 #> (1,.,.) = 
-#>  -1.1864  0.4079
-#>  -0.7702  0.2462
-#>   0.1156  1.5071
+#>  -0.4696  1.4304
+#>   0.3439 -0.8507
+#>  -0.2687 -0.8497
 #> 
 #> (2,.,.) = 
-#>  -1.1418 -0.3572
-#>  -0.6047  0.3412
-#>  -0.5722 -1.1495
+#>  -0.1440  0.7003
+#>   0.7445  0.9379
+#>   2.1761  1.1186
 #> 
 #> (3,.,.) = 
-#>   0.3688  0.8228
-#>  -0.6308  0.5165
-#>   1.4051  0.6681
+#>   0.8872  0.8706
+#>   0.8568 -0.9243
+#>  -1.4517  0.7946
 #> [ CPUFloatType{3,3,2} ]
    
   
    
   
 
@@ -146,6 +158,9 @@ type." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ type." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,7 +229,7 @@ type." />
 type.
    
     
 
-    
    torch_iinfo(dtype)
    
+    
    torch_iinfo(dtype)
    
 
     
    Arguments
    
     
    
 
 
     
    
diff --git a/reference/torch_imag.html b/reference/torch_imag.html
index 8f35c0bc1..ed55b6476 100644
--- a/reference/torch_imag.html
+++ b/reference/torch_imag.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
         torch
-        0.0.3
+        0.1.0
       
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
  • Datasets
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
-
  • Examples
    • 
+
  • 
+  
+  
+
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Imag
    
 
-    
    torch_imag(self)
    
+    
    torch_imag(self)
    Arguments
    
 
       
     
     
     
   
 
 
     
 
     
     
    
@@ -222,11 +254,11 @@
 $$
    Examples
    
-    
    if (torch_is_installed()) {
-if (FALSE) {
-torch_imag(torch_tensor(c(-1 + 1i, -2 + 2i, 3 - 3i)))
-}
-}
+    
    if (torch_is_installed()) {
+if (FALSE) {
+torch_imag(torch_tensor(c(-1 + 1i, -2 + 2i, 3 - 3i)))
+}
+}
 
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Index_select
    
     
 
-    
    torch_index_select(self, dim, index)
    
+    
    torch_index_select(self, dim, index)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -232,18 +264,18 @@ storage if necessary.
    
 of index; other dimensions have the same size as in the original tensor.
    Examples
    
-    
    if (torch_is_installed()) {
-
-x = torch_randn(c(3, 4))
-x
-indices = torch_tensor(c(1, 3), dtype = torch_int64())
-torch_index_select(x, 1, indices)
-torch_index_select(x, 2, indices)
-}
+    
    if (torch_is_installed()) {
+
+x = torch_randn(c(3, 4))
+x
+indices = torch_tensor(c(1, 3), dtype = torch_int64())
+torch_index_select(x, 1, indices)
+torch_index_select(x, 2, indices)
+}
 
    #> torch_tensor 
-#>  0.1471  0.1196
-#>  1.7271  0.1422
-#> -0.8387  0.1700
+#> -0.2085 -0.3586
+#>  1.8368 -0.7636
+#> -0.4655  0.7847
 #> [ CPUFloatType{3,2} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Inverse
    
     
 
-    
    torch_inverse(self)
    
+    
    torch_inverse(self)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -223,20 +255,20 @@ of 2D square tensors, in which case this function would return a tensor composed
 individual inverses.
    Examples
    
-    
    if (torch_is_installed()) {
-if (FALSE) {
-x = torch_rand(c(4, 4))
-y = torch_inverse(x)
-z = torch_mm(x, y)
-z
-torch_max(torch_abs(z - torch_eye(4))) # Max non-zero
+    
    if (torch_is_installed()) {
+if (FALSE) {
+x = torch_rand(c(4, 4))
+y = torch_inverse(x)
+z = torch_mm(x, y)
+z
+torch_max(torch_abs(z - torch_eye(4))) # Max non-zero
 # Batched inverse example
-x = torch_randn(c(2, 3, 4, 4))
-y = torch_inverse(x)
-z = torch_matmul(x, y)
-torch_max(torch_abs(z - torch_eye(4)$expand_as(x))) # Max non-zero
-}
-}
+x = torch_randn(c(2, 3, 4, 4))
+y = torch_inverse(x)
+z = torch_matmul(x, y)
+torch_max(torch_abs(z - torch_eye(4)$expand_as(x))) # Max non-zero
+}
+}
 
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,13 +227,13 @@
     
    Irfft
    
     
 
-    
    torch_irfft(
-  self,
-  signal_ndim,
-  normalized = FALSE,
-  onesided = TRUE,
-  signal_sizes = list()
-)
    
+    
    torch_irfft(
+  self,
+  signal_ndim,
+  normalized = FALSE,
+  onesided = TRUE,
+  signal_sizes = list()
+)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -285,20 +317,20 @@ certainly fail the check.
    torch_backends.mkl.is_available to check if MKL is installed.
    Examples
    
-    
    if (torch_is_installed()) {
-
-x = torch_randn(c(4, 4))
-torch_rfft(x, 2, onesided=TRUE)
-x = torch_randn(c(4, 5))
-torch_rfft(x, 2, onesided=TRUE)
-y = torch_rfft(x, 2, onesided=TRUE)
-torch_irfft(y, 2, onesided=TRUE, signal_sizes=c(4,5))  # recover x
-}
+    
    if (torch_is_installed()) {
+
+x = torch_randn(c(4, 4))
+torch_rfft(x, 2, onesided=TRUE)
+x = torch_randn(c(4, 5))
+torch_rfft(x, 2, onesided=TRUE)
+y = torch_rfft(x, 2, onesided=TRUE)
+torch_irfft(y, 2, onesided=TRUE, signal_sizes=c(4,5))  # recover x
+}
 
    #> torch_tensor 
-#> -1.2099  1.7469 -1.3247  0.0171  0.0024
-#>  0.4107  0.2902 -2.0005 -0.8215 -1.4046
-#> -1.2626  0.0016  2.6169 -1.2084 -0.1029
-#>  0.0418  1.5131  0.9458 -0.2751 -0.5556
+#> -0.8984 -0.0241  0.5814  0.0022 -1.5341
+#>  0.3579 -0.1102 -0.1934  1.5845 -1.6773
+#>  0.4437 -0.5878  0.5142  1.8715  1.0713
+#>  0.1601 -0.3049 -3.2821 -0.5589  0.7651
 #> [ CPUFloatType{4,5} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Is_complex
    
     
 
-    
    torch_is_complex(self)
    
+    
    torch_is_complex(self)
    
 
     
    Arguments
    
     
    
 
 
     
    
diff --git a/reference/torch_is_floating_point.html b/reference/torch_is_floating_point.html
index 30c95bb71..a6f5e8fdb 100644
--- a/reference/torch_is_floating_point.html
+++ b/reference/torch_is_floating_point.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
         torch
-        0.0.3
+        0.1.0
       
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
  • Datasets
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
-
  • Examples
    • 
+
  • 
+  
+  
+
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Is_floating_point
    
 
-    
    torch_is_floating_point(self)
    
+    
    torch_is_floating_point(self)
    Arguments
    
 
       
     
     
     
   
 
 
     
 
     
     
    
diff --git a/reference/torch_is_installed.html b/reference/torch_is_installed.html
index 2cf006e67..db94a69b0 100644
--- a/reference/torch_is_installed.html
+++ b/reference/torch_is_installed.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
         torch
-        0.0.3
+        0.1.0
       
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
  • Datasets
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
-
  • Examples
    • 
+
  • 
+  
+  
+
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Verifies if torch is installed
    
 
-    
    torch_is_installed()
    
+    
    torch_is_installed()
    
 
 
 
diff --git a/reference/torch_isfinite.html b/reference/torch_isfinite.html
index 593166800..be86eaecb 100644
--- a/reference/torch_isfinite.html
+++ b/reference/torch_isfinite.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
         torch
-        0.0.3
+        0.1.0
       
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
  • Datasets
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
-
  • Examples
    • 
+
  • 
+  
+  
+
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Isfinite
    
 
-    
    torch_isfinite(self)
    
+    
    torch_isfinite(self)
    Arguments
    
 
       
     
     
     
   
 
 
     
 
       
     
     
     
   
 
 
     
 
     
     
    
@@ -214,10 +246,10 @@
 
    Returns a new tensor with boolean elements representing if each element is Finite or not.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_isfinite(torch_tensor(c(1, Inf, 2, -Inf, NaN)))
-}
+torch_isfinite(torch_tensor(c(1, Inf, 2, -Inf, NaN)))
+}
 
    #> torch_tensor 
 #>  1
 #>  0
diff --git a/reference/torch_isinf.html b/reference/torch_isinf.html
index 6b703dfe6..ca7f6aed0 100644
--- a/reference/torch_isinf.html
+++ b/reference/torch_isinf.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Isinf
    
     
    
 
-    
    torch_isinf(self)
    
+    
    torch_isinf(self)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -214,10 +246,10 @@
 
    Returns a new tensor with boolean elements representing if each element is +/-INF or not.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_isinf(torch_tensor(c(1, Inf, 2, -Inf, NaN)))
-}
+torch_isinf(torch_tensor(c(1, Inf, 2, -Inf, NaN)))
+}
 
    #> torch_tensor 
 #>  0
 #>  1
diff --git a/reference/torch_isnan.html b/reference/torch_isnan.html
index a498c097c..6c41427f3 100644
--- a/reference/torch_isnan.html
+++ b/reference/torch_isnan.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Isnan
    
     
    
 
-    
    torch_isnan(self)
    
+    
    torch_isnan(self)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -214,10 +246,10 @@
 
    Returns a new tensor with boolean elements representing if each element is NaN or not.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_isnan(torch_tensor(c(1, NaN, 2)))
-}
+torch_isnan(torch_tensor(c(1, NaN, 2)))
+}
 
    #> torch_tensor 
 #>  0
 #>  1
diff --git a/reference/torch_kthvalue.html b/reference/torch_kthvalue.html
index 1a2abf400..67ad74584 100644
--- a/reference/torch_kthvalue.html
+++ b/reference/torch_kthvalue.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Kthvalue
    
     
    
 
-    
    torch_kthvalue(self, k, dim = -1L, keepdim = FALSE)
    
+    
    torch_kthvalue(self, k, dim = -1L, keepdim = FALSE)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -234,15 +266,15 @@ they are of size 1. Otherwise, dim is squeezed
 indices tensors having 1 fewer dimension than the input tensor.
    Examples
    
-    
    if (torch_is_installed()) {
-
-x = torch_arange(1., 6.)
-x
-torch_kthvalue(x, 4)
-x=torch_arange(1.,7.)$resize_(c(2,3))
-x
-torch_kthvalue(x, 2, 1, TRUE)
-}
+    
    if (torch_is_installed()) {
+
+x = torch_arange(1., 6.)
+x
+torch_kthvalue(x, 4)
+x=torch_arange(1.,7.)$resize_(c(2,3))
+x
+torch_kthvalue(x, 2, 1, TRUE)
+}
 
    #> [[1]]
 #> torch_tensor 
 #>  4  5  6
diff --git a/reference/torch_layout.html b/reference/torch_layout.html
index d55764961..510df0274 100644
--- a/reference/torch_layout.html
+++ b/reference/torch_layout.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,9 +227,9 @@
     
    Creates the corresponding layout
    
     
    
 
-    
    torch_strided()
+    
    torch_strided()
 
-torch_sparse_coo()
    
+torch_sparse_coo()
    
 
 
 
diff --git a/reference/torch_le.html b/reference/torch_le.html
index 9f5263505..fccc0a7de 100644
--- a/reference/torch_le.html
+++ b/reference/torch_le.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Le
    
     
 
-    
    torch_le(self, other)
    
+    
    torch_le(self, other)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -220,11 +252,11 @@
 broadcastable  with the first argument.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_le(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)), 
-         torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE)))
-}
+torch_le(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)), 
+         torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE)))
+}
 
    #> torch_tensor 
 #>  1  0
 #>  1  1
diff --git a/reference/torch_lerp.html b/reference/torch_lerp.html
index 59fb1fd74..fe68cb54c 100644
--- a/reference/torch_lerp.html
+++ b/reference/torch_lerp.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Lerp
    
     
    
 
-    
    torch_lerp(self, end, weight)
    
+    
    torch_lerp(self, end, weight)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -229,15 +261,15 @@ broadcastable . If weight is a tensor, then
 the shapes of weight, start, and end must be broadcastable .
    Examples
    
-    
    if (torch_is_installed()) {
-
-start = torch_arange(1., 5.)
-end = torch_empty(4)$fill_(10)
-start
-end
-torch_lerp(start, end, 0.5)
-torch_lerp(start, end, torch_full_like(start, 0.5))
-}
+    
    if (torch_is_installed()) {
+
+start = torch_arange(1., 5.)
+end = torch_empty(4)$fill_(10)
+start
+end
+torch_lerp(start, end, 0.5)
+torch_lerp(start, end, torch_full_like(start, 0.5))
+}
 
    #> torch_tensor 
 #>  5.5000
 #>  6.0000
diff --git a/reference/torch_lgamma.html b/reference/torch_lgamma.html
index a84df0c4f..16652d283 100644
--- a/reference/torch_lgamma.html
+++ b/reference/torch_lgamma.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Lgamma
    
     
    
 
-    
    torch_lgamma(self)
    
+    
    torch_lgamma(self)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -217,11 +249,11 @@
 $$
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_arange(0.5, 2, 0.5)
-torch_lgamma(a)
-}
+a = torch_arange(0.5, 2, 0.5)
+torch_lgamma(a)
+}
 
    #> torch_tensor 
 #>  0.5724
 #>  0.0000
diff --git a/reference/torch_linspace.html b/reference/torch_linspace.html
index 3b0cfa6d1..a003b5734 100644
--- a/reference/torch_linspace.html
+++ b/reference/torch_linspace.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,15 +227,15 @@
     
    Linspace
    
     
    
 
-    
    torch_linspace(
-  start,
-  end,
-  steps = 100,
-  dtype = NULL,
-  layout = torch_strided(),
-  device = NULL,
-  requires_grad = FALSE
-)
    
+    
    torch_linspace(
+  start,
+  end,
+  steps = 100,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -248,13 +280,13 @@ equally spaced points between start and end.
    The output tensor is 1-D of size steps.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_linspace(3, 10, steps=5)
-torch_linspace(-10, 10, steps=5)
-torch_linspace(start=-10, end=10, steps=5)
-torch_linspace(start=-10, end=10, steps=1)
-}
+torch_linspace(3, 10, steps=5)
+torch_linspace(-10, 10, steps=5)
+torch_linspace(start=-10, end=10, steps=5)
+torch_linspace(start=-10, end=10, steps=1)
+}
 
    #> torch_tensor 
 #> -10
 #> [ CPUFloatType{1} ]
    
diff --git a/reference/torch_load.html b/reference/torch_load.html
index a7506e056..2f8026161 100644
--- a/reference/torch_load.html
+++ b/reference/torch_load.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Loads a saved object
    
     
 
-    
    torch_load(path)
    
+    
    torch_load(path)
    
 
     
    Arguments
    
     
    
 
 
     
    
diff --git a/reference/torch_log.html b/reference/torch_log.html
index 0d30ca684..6a9b7fddf 100644
--- a/reference/torch_log.html
+++ b/reference/torch_log.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
         torch
-        0.0.3
+        0.1.0
       
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
  • Datasets
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
-
  • Examples
    • 
+
  • 
+  
+  
+
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Log
    
 
-    
    torch_log(self)
    
+    
    torch_log(self)
    Arguments
    
 
       
     
     
     
   
 
 
     
 
     
     
    
@@ -218,18 +250,18 @@ of input.
    
 $$
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(5))
-a
-torch_log(a)
-}
+a = torch_randn(c(5))
+a
+torch_log(a)
+}
 
    #> torch_tensor 
 #>     nan
-#>  0.8911
-#>  0.0003
-#>     nan
-#> -1.5295
+#> -0.3113
+#>  0.7617
+#> -0.4020
+#>  0.2561
 #> [ CPUFloatType{5} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Log10
    
     
 
-    
    torch_log10(self)
    
+    
    torch_log10(self)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -218,18 +250,18 @@ of input.
    
 $$
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_rand(5)
-a
-torch_log10(a)
-}
+a = torch_rand(5)
+a
+torch_log10(a)
+}
 
    #> torch_tensor 
-#> -0.2865
-#> -0.4904
-#> -0.9127
-#> -0.2747
-#> -0.2714
+#> -0.4762
+#> -0.4718
+#> -0.4183
+#> -0.2721
+#> -0.4976
 #> [ CPUFloatType{5} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Log1p
    
     
 
-    
    torch_log1p(self)
    
+    
    torch_log1p(self)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -221,18 +253,18 @@ values of input
    
 $$
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(5))
-a
-torch_log1p(a)
-}
+a = torch_randn(c(5))
+a
+torch_log1p(a)
+}
 
    #> torch_tensor 
-#>  0.6781
-#>  0.2969
-#>  0.6634
-#>  0.5704
-#> -0.1361
+#>  0.3740
+#> -0.0977
+#>  0.9810
+#>  0.4046
+#>     nan
 #> [ CPUFloatType{5} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Log2
    
     
 
-    
    torch_log2(self)
    
+    
    torch_log2(self)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -218,18 +250,18 @@ of input.
    
 $$
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_rand(5)
-a
-torch_log2(a)
-}
+a = torch_rand(5)
+a
+torch_log2(a)
+}
 
    #> torch_tensor 
-#> -0.6302
-#> -6.7372
-#> -0.0043
-#> -1.2060
-#> -1.4108
+#> -2.5997
+#> -2.8238
+#> -0.1515
+#> -0.2273
+#> -1.4200
 #> [ CPUFloatType{5} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Logdet
    
     
 
-    
    torch_logdet(self)
    
+    
    torch_logdet(self)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -227,17 +259,17 @@ be unstable in when `input` doesn't have distinct singular values. See
 
    Calculates log determinant of a square matrix or batches of square matrices.
    Examples
    
-    
    if (torch_is_installed()) {
-
-A = torch_randn(c(3, 3))
-torch_det(A)
-torch_logdet(A)
-A
-A$det()
-A$det()$log()
-}
+    
    if (torch_is_installed()) {
+
+A = torch_randn(c(3, 3))
+torch_det(A)
+torch_logdet(A)
+A
+A$det()
+A$det()$log()
+}
 
    #> torch_tensor 
-#> nan
+#> -1.84361
 #> [ CPUFloatType{} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Logical_and
    
     
 
-    
    torch_logical_and(self, other)
    
+    
    torch_logical_and(self, other)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -219,16 +251,16 @@
 treated as TRUE.
    Examples
    
-    
    if (torch_is_installed()) {
-
-torch_logical_and(torch_tensor(c(TRUE, FALSE, TRUE)), torch_tensor(c(TRUE, FALSE, FALSE)))
-a = torch_tensor(c(0, 1, 10, 0), dtype=torch_int8())
-b = torch_tensor(c(4, 0, 1, 0), dtype=torch_int8())
-torch_logical_and(a, b)
-if (FALSE) {
-torch_logical_and(a, b, out=torch_empty(4, dtype=torch_bool()))
-}
-}
+    
    if (torch_is_installed()) {
+
+torch_logical_and(torch_tensor(c(TRUE, FALSE, TRUE)), torch_tensor(c(TRUE, FALSE, FALSE)))
+a = torch_tensor(c(0, 1, 10, 0), dtype=torch_int8())
+b = torch_tensor(c(4, 0, 1, 0), dtype=torch_int8())
+torch_logical_and(a, b)
+if (FALSE) {
+torch_logical_and(a, b, out=torch_empty(4, dtype=torch_bool()))
+}
+}
 
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -214,12 +246,12 @@
 dtype. If the input tensor is not a bool tensor, zeros are treated as FALSE and non-zeros are treated as TRUE.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_logical_not(torch_tensor(c(TRUE, FALSE)))
-torch_logical_not(torch_tensor(c(0, 1, -10), dtype=torch_int8()))
-torch_logical_not(torch_tensor(c(0., 1.5, -10.), dtype=torch_double()))
-}
+torch_logical_not(torch_tensor(c(TRUE, FALSE)))
+torch_logical_not(torch_tensor(c(0, 1, -10), dtype=torch_int8()))
+torch_logical_not(torch_tensor(c(0., 1.5, -10.), dtype=torch_double()))
+}
 
    #> torch_tensor 
 #>  1
 #>  0
diff --git a/reference/torch_logical_or.html b/reference/torch_logical_or.html
index 6a7329686..fa4c0dc88 100644
--- a/reference/torch_logical_or.html
+++ b/reference/torch_logical_or.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Logical_or
    
     
    
 
-    
    torch_logical_or(self, other)
    
+    
    torch_logical_or(self, other)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -219,18 +251,18 @@
 treated as TRUE.
    Examples
    
-    
    if (torch_is_installed()) {
-
-torch_logical_or(torch_tensor(c(TRUE, FALSE, TRUE)), torch_tensor(c(TRUE, FALSE, FALSE)))
-a = torch_tensor(c(0, 1, 10, 0), dtype=torch_int8())
-b = torch_tensor(c(4, 0, 1, 0), dtype=torch_int8())
-torch_logical_or(a, b)
-if (FALSE) {
-torch_logical_or(a$double(), b$double())
-torch_logical_or(a$double(), b)
-torch_logical_or(a, b, out=torch_empty(4, dtype=torch_bool()))
-}
-}
+    
    if (torch_is_installed()) {
+
+torch_logical_or(torch_tensor(c(TRUE, FALSE, TRUE)), torch_tensor(c(TRUE, FALSE, FALSE)))
+a = torch_tensor(c(0, 1, 10, 0), dtype=torch_int8())
+b = torch_tensor(c(4, 0, 1, 0), dtype=torch_int8())
+torch_logical_or(a, b)
+if (FALSE) {
+torch_logical_or(a$double(), b$double())
+torch_logical_or(a$double(), b)
+torch_logical_or(a, b, out=torch_empty(4, dtype=torch_bool()))
+}
+}
 
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Logical_xor
    
     
 
-    
    torch_logical_xor(self, other)
    
+    
    torch_logical_xor(self, other)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -219,15 +251,15 @@
 treated as TRUE.
    Examples
    
-    
    if (torch_is_installed()) {
-
-torch_logical_xor(torch_tensor(c(TRUE, FALSE, TRUE)), torch_tensor(c(TRUE, FALSE, FALSE)))
-a = torch_tensor(c(0, 1, 10, 0), dtype=torch_int8())
-b = torch_tensor(c(4, 0, 1, 0), dtype=torch_int8())
-torch_logical_xor(a, b)
-torch_logical_xor(a$to(dtype=torch_double()), b$to(dtype=torch_double()))
-torch_logical_xor(a$to(dtype=torch_double()), b)
-}
+    
    if (torch_is_installed()) {
+
+torch_logical_xor(torch_tensor(c(TRUE, FALSE, TRUE)), torch_tensor(c(TRUE, FALSE, FALSE)))
+a = torch_tensor(c(0, 1, 10, 0), dtype=torch_int8())
+b = torch_tensor(c(4, 0, 1, 0), dtype=torch_int8())
+torch_logical_xor(a, b)
+torch_logical_xor(a$to(dtype=torch_double()), b$to(dtype=torch_double()))
+torch_logical_xor(a$to(dtype=torch_double()), b)
+}
 
    #> torch_tensor 
 #>  1
 #>  1
diff --git a/reference/torch_logspace.html b/reference/torch_logspace.html
index bdd37da52..2607088ca 100644
--- a/reference/torch_logspace.html
+++ b/reference/torch_logspace.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,16 +227,16 @@
     
    Logspace
    
     
    
 
-    
    torch_logspace(
-  start,
-  end,
-  steps = 100,
-  base = 10,
-  dtype = NULL,
-  layout = torch_strided(),
-  device = NULL,
-  requires_grad = FALSE
-)
    
+    
    torch_logspace(
+  start,
+  end,
+  steps = 100,
+  base = 10,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -254,13 +286,13 @@ logarithmically spaced with base base between
 
    The output tensor is 1-D of size steps.
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_logspace(start=-10, end=10, steps=5)
-torch_logspace(start=0.1, end=1.0, steps=5)
-torch_logspace(start=0.1, end=1.0, steps=1)
-torch_logspace(start=2, end=2, steps=1, base=2)
-}
+torch_logspace(start=-10, end=10, steps=5)
+torch_logspace(start=0.1, end=1.0, steps=5)
+torch_logspace(start=0.1, end=1.0, steps=1)
+torch_logspace(start=2, end=2, steps=1, base=2)
+}
 
    #> torch_tensor 
 #>  4
 #> [ CPUFloatType{1} ]
    
diff --git a/reference/torch_logsumexp.html b/reference/torch_logsumexp.html
index c5a19fbd1..efa99751e 100644
--- a/reference/torch_logsumexp.html
+++ b/reference/torch_logsumexp.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Logsumexp
    
     
 
-    
    torch_logsumexp(self, dim, keepdim = FALSE)
    
+    
    torch_logsumexp(self, dim, keepdim = FALSE)
    
 
     
    Arguments
    
     
    
 
     
    
@@ -232,15 +264,15 @@ Otherwise, dim is squeezed (see 
 output tensor having 1 (or len(dim)) fewer dimension(s).
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(3, 3))
-torch_logsumexp(a, 1)
-}
+a = torch_randn(c(3, 3))
+torch_logsumexp(a, 1)
+}
 
    #> torch_tensor 
-#>  1.7933
-#>  0.4143
-#>  0.9287
+#>  1.3929
+#>  1.0997
+#>  1.7192
 #> [ CPUFloatType{3} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Lstsq
    
     
 
-    
    torch_lstsq(self, A)
    
+    
    torch_lstsq(self, A)
    
 
     
    Arguments
    
     
    
@@ -242,25 +274,25 @@ for the solution in each column is given by the sum of squares of elements in th
 remaining \(m - n\) rows of that column.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-
-A = torch_tensor(rbind(
- c(1,1,1),
- c(2,3,4),
- c(3,5,2),
- c(4,2,5),
- c(5,4,3)
-))
-B = torch_tensor(rbind(
- c(-10, -3),
- c(12, 14),
- c(14, 12),
- c(16, 16),
- c(18, 16)
-))
-out = torch_lstsq(B, A)
-out[[1]]
-}
+    
    if (torch_is_installed()) {
+
+A = torch_tensor(rbind(
+ c(1,1,1),
+ c(2,3,4),
+ c(3,5,2),
+ c(4,2,5),
+ c(5,4,3)
+))
+B = torch_tensor(rbind(
+ c(-10, -3),
+ c(12, 14),
+ c(14, 12),
+ c(16, 16),
+ c(18, 16)
+))
+out = torch_lstsq(B, A)
+out[[1]]
+}
 
    #> torch_tensor 
 #>   2.0000   1.0000
 #>   1.0000   1.0000
diff --git a/reference/torch_lt.html b/reference/torch_lt.html
index ebda53acc..de77b5360 100644
--- a/reference/torch_lt.html
+++ b/reference/torch_lt.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Lt
    
     
    
 
-    
    torch_lt(self, other)
    
+    
    torch_lt(self, other)
    
 
     
    Arguments
    
     
    
@@ -220,11 +252,11 @@
 broadcastable  with the first argument.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_lt(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)), 
-         torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE)))
-}
+torch_lt(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)), 
+         torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE)))
+}
 
    #> torch_tensor 
 #>  0  0
 #>  1  0
diff --git a/reference/torch_lu.html b/reference/torch_lu.html
index c8eb4f82a..c97abd196 100644
--- a/reference/torch_lu.html
+++ b/reference/torch_lu.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -74,7 +86,7 @@ is set to True." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -147,6 +159,9 @@ is set to True." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -160,7 +175,24 @@ is set to True." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -199,7 +231,7 @@ tuple containing the LU factorization and pivots of A. Pivoting is done if pivot
 is set to True.
    
     
    
 
-    
    torch_lu(A, pivot = TRUE, get_infos = FALSE, out = NULL)
    
+    
    torch_lu(A, pivot = TRUE, get_infos = FALSE, out = NULL)
    
 
     
    Arguments
    
     
    
@@ -226,29 +258,29 @@ elements in the tuple are Tensor, IntTensor. Default: NULL
    
 
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-A = torch_randn(c(2, 3, 3))
-torch_lu(A)
+A = torch_randn(c(2, 3, 3))
+torch_lu(A)
 
-}
+}
 
    #> [[1]]
 #> torch_tensor 
 #> (1,.,.) = 
-#>   0.3564  0.0937 -0.2445
-#>   0.3677  1.8428 -0.7129
-#>  -0.8647  0.0045  0.4332
+#>   1.2816 -0.2739  1.3627
+#>   0.6032  0.2360 -0.9924
+#>   0.5229 -0.2575 -0.0504
 #> 
 #> (2,.,.) = 
-#>   1.5740 -0.5029 -0.3207
-#>   0.2765 -0.6417  0.4304
-#>   0.1308  0.1404  0.1848
+#>   2.2965  1.0492 -2.6765
+#>  -0.3373  2.0281 -1.0654
+#>  -0.1761  0.7415  1.4537
 #> [ CPUFloatType{2,3,3} ]
 #> 
 #> [[2]]
 #> torch_tensor 
-#>  3  3  3
-#>  3  2  3
+#>  1  2  3
+#>  2  2  3
 #> [ CPUIntType{2,3} ]
 #> 
    
   
    
diff --git a/reference/torch_lu_solve.html b/reference/torch_lu_solve.html
index 81d46bfbd..a334cb6a5 100644
--- a/reference/torch_lu_solve.html
+++ b/reference/torch_lu_solve.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Lu_solve
    
     
 
-    
    torch_lu_solve(self, LU_data, LU_pivots)
    
+    
    torch_lu_solve(self, LU_data, LU_pivots)
    
 
     
    Arguments
    
     
    
@@ -223,15 +255,15 @@
 LU factorization of A from torch_lu.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-A = torch_randn(c(2, 3, 3))
-b = torch_randn(c(2, 3, 1))
-out = torch_lu(A)
-x = torch_lu_solve(b, out[[1]], out[[2]])
-torch_norm(torch_bmm(A, x) - b)
-}
+    
    if (torch_is_installed()) {
+A = torch_randn(c(2, 3, 3))
+b = torch_randn(c(2, 3, 1))
+out = torch_lu(A)
+x = torch_lu_solve(b, out[[1]], out[[2]])
+torch_norm(torch_bmm(A, x) - b)
+}
 
    #> torch_tensor 
-#> 1.58402e-07
+#> 1.49012e-07
 #> [ CPUFloatType{} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Sets the seed for generating random numbers.
    
     
 
-    
    torch_manual_seed(seed)
    
+    
    torch_manual_seed(seed)
    
 
     
    Arguments
    
     
    
diff --git a/reference/torch_masked_select.html b/reference/torch_masked_select.html
index 6108bfdfd..d016f041b 100644
--- a/reference/torch_masked_select.html
+++ b/reference/torch_masked_select.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Masked_select
    
     
 
-    
    torch_masked_select(self, mask)
    
+    
    torch_masked_select(self, mask)
    
 
     
    Arguments
    
     
    
@@ -225,19 +257,19 @@ the boolean mask mask which is a BoolTensor.
    
 to match, but they must be broadcastable .
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-
-x = torch_randn(c(3, 4))
-x
-mask = x$ge(0.5)
-mask
-torch_masked_select(x, mask)
-}
+    
    if (torch_is_installed()) {
+
+x = torch_randn(c(3, 4))
+x
+mask = x$ge(0.5)
+mask
+torch_masked_select(x, mask)
+}
 
    #> torch_tensor 
-#>  1.2190
-#>  1.2591
-#>  2.0310
-#>  0.7883
+#>  0.9040
+#>  1.0121
+#>  1.2403
+#>  3.2352
 #> [ CPUFloatType{4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Matmul
    
     
 
-    
    torch_matmul(self, other)
    
+    
    torch_matmul(self, other)
    
 
     
    Arguments
    
     
    
@@ -243,79 +275,79 @@ tensor, out will be an \((j \times k \times n \times p)\) tensor.Examples
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
 # vector x vector
-tensor1 = torch_randn(c(3))
-tensor2 = torch_randn(c(3))
-torch_matmul(tensor1, tensor2)
+tensor1 = torch_randn(c(3))
+tensor2 = torch_randn(c(3))
+torch_matmul(tensor1, tensor2)
 # matrix x vector
-tensor1 = torch_randn(c(3, 4))
-tensor2 = torch_randn(c(4))
-torch_matmul(tensor1, tensor2)
+tensor1 = torch_randn(c(3, 4))
+tensor2 = torch_randn(c(4))
+torch_matmul(tensor1, tensor2)
 # batched matrix x broadcasted vector
-tensor1 = torch_randn(c(10, 3, 4))
-tensor2 = torch_randn(c(4))
-torch_matmul(tensor1, tensor2)
+tensor1 = torch_randn(c(10, 3, 4))
+tensor2 = torch_randn(c(4))
+torch_matmul(tensor1, tensor2)
 # batched matrix x batched matrix
-tensor1 = torch_randn(c(10, 3, 4))
-tensor2 = torch_randn(c(10, 4, 5))
-torch_matmul(tensor1, tensor2)
+tensor1 = torch_randn(c(10, 3, 4))
+tensor2 = torch_randn(c(10, 4, 5))
+torch_matmul(tensor1, tensor2)
 # batched matrix x broadcasted matrix
-tensor1 = torch_randn(c(10, 3, 4))
-tensor2 = torch_randn(c(4, 5))
-torch_matmul(tensor1, tensor2)
-}
+tensor1 = torch_randn(c(10, 3, 4))
+tensor2 = torch_randn(c(4, 5))
+torch_matmul(tensor1, tensor2)
+}
 
    #> torch_tensor 
 #> (1,.,.) = 
-#>  -0.2185  0.1251  0.5783 -0.7380  1.9290
-#>  -0.1489  0.9409  0.2506  0.7444 -0.3201
-#>   0.2165  2.5157  0.8195 -1.2013  2.7602
+#>   0.1457  0.5915 -0.2321  0.3854  1.0444
+#>   1.0695 -1.5871 -0.2751 -1.8681  2.0865
+#>   0.0864  1.0331  0.3989  1.9494 -1.9199
 #> 
 #> (2,.,.) = 
-#>  -0.6251  1.0054  1.5308  1.0346  1.7585
-#>   0.7581 -0.5255 -1.6358 -0.3028 -2.8103
-#>   1.9208  0.9674 -2.9281  1.9148 -7.9955
+#>   1.3647  0.0372  0.1681  1.9106 -0.5945
+#>  -0.4827  1.6374  0.5664  0.6887 -1.8527
+#>   0.0311 -1.1241  0.1378 -2.2851  0.2947
 #> 
 #> (3,.,.) = 
-#>  -0.1272  1.0480  0.5100 -0.5049  1.5373
-#>  -0.6784 -2.4129  0.4735  0.3082  0.6775
-#>  -0.9108 -1.5962  1.0820 -0.0427  2.2469
+#>   2.2523 -2.0509 -0.0099 -0.2129  0.8620
+#>  -0.9853  0.0842 -0.4192 -0.6732  1.1007
+#>  -2.6549  2.1404  0.0785 -1.7666 -0.1672
 #> 
 #> (4,.,.) = 
-#>   0.1000  1.6520  0.6610  1.2012 -0.2146
-#>  -0.9429 -1.2208  0.9296 -1.5397  3.7074
-#>   0.6559 -0.7159 -1.7710 -0.6774 -2.5811
+#>   2.8045 -1.6532  0.1686  2.0991 -0.4907
+#>   1.7490 -0.6220  0.1161  2.0473 -0.5601
+#>   0.5042  0.7286  0.2668  1.5151 -1.0157
 #> 
 #> (5,.,.) = 
-#>  -0.1291  1.6498  1.3190  0.7676  1.5068
-#>   0.1177 -0.1660 -0.6345  1.6803 -3.0527
-#>   0.5094 -2.0094 -1.7119  2.0194 -5.5159
+#>   0.3968  0.1744  0.3270 -0.2302 -0.5648
+#>   0.5085 -0.4784  0.4098 -0.3110 -1.0941
+#>   0.6001  0.1766 -0.1867  2.8172 -0.4761
 #> 
 #> (6,.,.) = 
-#>   0.0387  1.2627  0.4658 -0.2528  1.1118
-#>  -0.0531 -0.8584 -0.3116 -1.4711  1.1272
-#>   0.7294 -0.9226 -1.9783  1.3631 -5.2894
+#>  -2.1846  1.3429 -0.1652 -1.5079  0.4546
+#>   0.0389  0.1063 -0.1917  0.4759  0.4723
+#>   0.2003 -0.8489 -0.1358 -2.0153  1.3900
 #> 
 #> (7,.,.) = 
-#>  -0.7140 -1.2579  0.6561 -1.1249  2.6681
-#>   0.8946  1.1603 -1.5402  0.5578 -3.6267
-#>  -0.8431  0.8911  1.9896  0.9616  2.7199
+#>  -2.3057  1.1418  0.4197 -1.4893 -1.8241
+#>   2.2183 -2.3652 -0.2189 -0.6105  1.6256
+#>  -0.8210  2.7651 -0.0790  4.5377 -1.5942
 #> 
 #> (8,.,.) = 
-#>   0.7925  0.9144 -1.5220 -0.5466 -2.3076
-#>  -0.6783  0.0011  1.2162 -0.9383  3.4450
-#>   0.2356 -0.6907 -0.6703 -0.1563 -1.0982
+#>  -2.9265  1.9699 -0.0915 -0.0178 -0.9813
+#>   3.1908 -1.8613  0.2964  2.3269 -0.8748
+#>  -2.0344  1.4978 -0.1377 -1.9044  0.8209
 #> 
 #> (9,.,.) = 
-#>  -0.7889  0.4032  1.3563 -0.5223  3.2740
-#>   0.3636 -0.9006 -1.1470 -1.1717 -0.8283
-#>  -0.4663 -0.2173  0.5950  4.1339 -3.4726
+#>   0.4693 -0.6510  0.3761 -1.1013 -0.6223
+#>   4.6105 -4.3216 -0.0323  0.8181  0.9600
+#>   3.3824 -2.7861 -0.1797  1.7009  0.7962
 #> 
 #> (10,.,.) = 
-#>  -0.7729  1.7140  2.1228  2.9509  0.6895
-#>   0.4515 -0.0429 -1.0629 -2.8544  1.2142
-#>  -1.5890 -0.3097  2.8757  1.6369  3.7579
+#>   1.5995 -0.7023 -0.1898  1.2365  0.8378
+#>  -2.9803  3.7585  0.3185 -1.2573 -0.6455
+#>   2.5373 -4.5162 -0.3837 -2.9798  2.6712
 #> [ CPUFloatType{10,3,5} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Matrix_power
    
     
 
-    
    torch_matrix_power(self, n)
    
+    
    torch_matrix_power(self, n)
    
 
     
    Arguments
    
     
    
@@ -223,20 +255,21 @@ raised to the power n.  For a batch of matrices, the batched invers
 is returned.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(2, 2, 2))
-a
-torch_matrix_power(a, 3)
-}
+a = torch_randn(c(2, 2, 2))
+a
+torch_matrix_power(a, 3)
+}
 
    #> torch_tensor 
 #> (1,.,.) = 
-#>  -0.4133 -0.7320
-#>  -0.0927  0.0367
+#>   0.5965  0.6330
+#>  -0.4084 -0.2968
 #> 
 #> (2,.,.) = 
-#>  -1.2180  2.0019
-#>  -4.7379 -8.9109
+#>  0.01 *
+#>   0.6387 -0.4923
+#>    1.8252 -0.6598
 #> [ CPUFloatType{2,2,2} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Matrix_rank
    
     
 
-    
    torch_matrix_rank(self, tol, symmetric = FALSE)
    
+    
    torch_matrix_rank(self, tol, symmetric = FALSE)
    
 
     
    Arguments
    
     
    
@@ -230,11 +262,11 @@ singular values (or the eigenvalues when symmetric is TRUEinput.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_eye(10)
-torch_matrix_rank(a)
-}
+a = torch_eye(10)
+torch_matrix_rank(a)
+}
 
    #> torch_tensor 
 #> 10
 #> [ CPULongType{} ]
    
diff --git a/reference/torch_max.html b/reference/torch_max.html
index 265c01dd6..1a802ac21 100644
--- a/reference/torch_max.html
+++ b/reference/torch_max.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -266,29 +298,29 @@ but they must be broadcastable .
    
 $$
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(1, 3))
-a
-torch_max(a)
+a = torch_randn(c(1, 3))
+a
+torch_max(a)
 
 
-a = torch_randn(c(4, 4))
-a
-torch_max(a, dim = 1)
+a = torch_randn(c(4, 4))
+a
+torch_max(a, dim = 1)
 
 
-a = torch_randn(c(4))
-a
-b = torch_randn(c(4))
-b
-torch_max(a, other = b)
-}
+a = torch_randn(c(4))
+a
+b = torch_randn(c(4))
+b
+torch_max(a, other = b)
+}
 
    #> torch_tensor 
-#>  0.4118
-#> -0.1116
-#>  0.7360
-#>  0.9171
+#>  0.1607
+#>  0.8017
+#>  0.6057
+#>  1.4942
 #> [ CPUFloatType{4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Mean
    
     
 
-    
    torch_mean(self, dim, keepdim = FALSE, dtype = NULL)
    
+    
    torch_mean(self, dim, keepdim = FALSE, dtype = NULL)
    
 
     
    Arguments
    
     
    
@@ -238,20 +270,21 @@ Otherwise, dim is squeezed (see 
 output tensor having 1 (or len(dim)) fewer dimension(s).
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(1, 3))
-a
-torch_mean(a)
+a = torch_randn(c(1, 3))
+a
+torch_mean(a)
 
 
-a = torch_randn(c(4, 4))
-a
-torch_mean(a, 1)
-torch_mean(a, 1, TRUE)
-}
+a = torch_randn(c(4, 4))
+a
+torch_mean(a, 1)
+torch_mean(a, 1, TRUE)
+}
 
    #> torch_tensor 
-#>  0.1962  0.3079 -0.5549  0.3102
+#> 0.01 *
+#> -6.3939 -7.7447 -27.1052 -43.0190
 #> [ CPUFloatType{1,4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Median
    
     
 
-    
    torch_median(self, dim, keepdim = FALSE)
    
+    
    torch_median(self, dim, keepdim = FALSE)
    
 
     
    Arguments
    
     
    
@@ -235,33 +267,33 @@ Otherwise, dim is squeezed (see 
 the outputs tensor having 1 fewer dimension than input.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(1, 3))
-a
-torch_median(a)
+a = torch_randn(c(1, 3))
+a
+torch_median(a)
 
 
-a = torch_randn(c(4, 5))
-a
-torch_median(a, 1)
-}
+a = torch_randn(c(4, 5))
+a
+torch_median(a, 1)
+}
 
    #> [[1]]
 #> torch_tensor 
-#>  0.1832
-#> -0.8122
-#> -1.1052
-#> -1.2011
-#> -0.0244
+#> -0.7214
+#>  0.0035
+#> -0.7742
+#> -0.4327
+#>  0.4810
 #> [ CPUFloatType{5} ]
 #> 
 #> [[2]]
 #> torch_tensor 
-#>  3
-#>  0
-#>  3
-#>  0
 #>  1
+#>  2
+#>  2
+#>  0
+#>  2
 #> [ CPULongType{5} ]
 #> 
    
   
    
diff --git a/reference/torch_memory_format.html b/reference/torch_memory_format.html
index 5b23723e1..3408ebcd4 100644
--- a/reference/torch_memory_format.html
+++ b/reference/torch_memory_format.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,11 +227,11 @@
     
    Returns the correspondent memory format.
    
     
 
-    
    torch_contiguous_format()
+    
    torch_contiguous_format()
 
-torch_preserve_format()
+torch_preserve_format()
 
-torch_channels_last_format()
    
+torch_channels_last_format()
    
 
 
 
diff --git a/reference/torch_meshgrid.html b/reference/torch_meshgrid.html
index 5a4691b7e..f63260efe 100644
--- a/reference/torch_meshgrid.html
+++ b/reference/torch_meshgrid.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Meshgrid
    
     
 
-    
    torch_meshgrid(tensors)
    
+    
    torch_meshgrid(tensors)
    
 
     
    Arguments
    
     
    
@@ -217,13 +249,13 @@ vector, and create \(N\) N-dimensional grids, where the \(i\) th gr
 expanding the \(i\) th input over dimensions defined by other inputs.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-x = torch_tensor(c(1, 2, 3))
-y = torch_tensor(c(4, 5, 6))
-out = torch_meshgrid(list(x, y))
-out
-}
+x = torch_tensor(c(1, 2, 3))
+y = torch_tensor(c(4, 5, 6))
+out = torch_meshgrid(list(x, y))
+out
+}
 
    #> [[1]]
 #> torch_tensor 
 #>  1  1  1
diff --git a/reference/torch_min.html b/reference/torch_min.html
index f193a5f63..c289183f1 100644
--- a/reference/torch_min.html
+++ b/reference/torch_min.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -267,29 +299,29 @@ but they must be broadcastable .
    
 $$
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(1, 3))
-a
-torch_min(a)
+a = torch_randn(c(1, 3))
+a
+torch_min(a)
 
 
-a = torch_randn(c(4, 4))
-a
-torch_min(a, dim = 1)
+a = torch_randn(c(4, 4))
+a
+torch_min(a, dim = 1)
 
 
-a = torch_randn(c(4))
-a
-b = torch_randn(c(4))
-b
-torch_min(a, other = b)
-}
+a = torch_randn(c(4))
+a
+b = torch_randn(c(4))
+b
+torch_min(a, other = b)
+}
 
    #> torch_tensor 
-#> -0.5633
-#> -0.5903
-#> -2.0560
-#> -1.0671
+#>  0.9762
+#> -1.0571
+#>  0.2046
+#>  0.7020
 #> [ CPUFloatType{4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Mm
    
     
    
 
-    
    torch_mm(self, mat2)
    
+    
    torch_mm(self, mat2)
    
 
     
    Arguments
    
     
    
@@ -224,15 +256,15 @@ For broadcasting matrix products, see torch_ma
 \((m \times p)\) tensor, out will be a \((n \times p)\) tensor.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-mat1 = torch_randn(c(2, 3))
-mat2 = torch_randn(c(3, 3))
-torch_mm(mat1, mat2)
-}
+mat1 = torch_randn(c(2, 3))
+mat2 = torch_randn(c(3, 3))
+torch_mm(mat1, mat2)
+}
 
    #> torch_tensor 
-#>  0.1067 -2.8593  1.3572
-#> -2.8347  0.9182  1.1475
+#> -0.1280 -0.0578  0.9076
+#> -3.3546  0.1277  1.3863
 #> [ CPUFloatType{2,3} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Mode
    
     
 
-    
    torch_mode(self, dim = -1L, keepdim = FALSE)
    
+    
    torch_mode(self, dim = -1L, keepdim = FALSE)
    
 
     
    Arguments
    
     
    
@@ -233,20 +265,20 @@ Otherwise, dim is squeezed (see 
 in the output tensors having 1 fewer dimension than input.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randint(0, 50, size = list(5))
-a
-torch_mode(a, 1)
-}
+a = torch_randint(0, 50, size = list(5))
+a
+torch_mode(a, 1)
+}
 
    #> [[1]]
 #> torch_tensor 
-#> 34
+#> 1
 #> [ CPUFloatType{} ]
 #> 
 #> [[2]]
 #> torch_tensor 
-#> 2
+#> 4
 #> [ CPULongType{} ]
 #> 
    
   
    
diff --git a/reference/torch_mul.html b/reference/torch_mul.html
index 02940046f..eb081f050 100644
--- a/reference/torch_mul.html
+++ b/reference/torch_mul.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Mul
    
     
 
-    
    torch_mul(self, other)
    
+    
    torch_mul(self, other)
    
 
     
    Arguments
    
     
    
@@ -233,24 +265,24 @@ broadcastable .
    
 $$
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(3))
-a
-torch_mul(a, 100)
+a = torch_randn(c(3))
+a
+torch_mul(a, 100)
 
 
-a = torch_randn(c(4, 1))
-a
-b = torch_randn(c(1, 4))
-b
-torch_mul(a, b)
-}
+a = torch_randn(c(4, 1))
+a
+b = torch_randn(c(1, 4))
+b
+torch_mul(a, b)
+}
 
    #> torch_tensor 
-#>  0.9656  0.2508 -0.9725  0.1089
-#> -0.4285 -0.1113  0.4315 -0.0483
-#> -0.5136 -0.1334  0.5173 -0.0579
-#>  0.3787  0.0984 -0.3814  0.0427
+#> -0.1927 -0.8468  1.2574 -1.2014
+#> -0.0539 -0.2367  0.3515 -0.3359
+#> -0.0566 -0.2488  0.3694 -0.3530
+#> -0.0106 -0.0465  0.0690 -0.0659
 #> [ CPUFloatType{4,4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Multinomial
    
     
 
-    
    torch_multinomial(self, num_samples, replacement = FALSE, generator = NULL)
    
+    
    torch_multinomial(self, num_samples, replacement = FALSE, generator = NULL)
    
 
     
    Arguments
    
     
    
@@ -249,17 +281,17 @@ from the multinomial probability distribution located in the corresponding row
 of tensor input.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-weights = torch_tensor(c(0, 10, 3, 0), dtype=torch_float()) # create a tensor of weights
-torch_multinomial(weights, 2)
-torch_multinomial(weights, 4, replacement=TRUE)
-}
+weights = torch_tensor(c(0, 10, 3, 0), dtype=torch_float()) # create a tensor of weights
+torch_multinomial(weights, 2)
+torch_multinomial(weights, 4, replacement=TRUE)
+}
 
    #> torch_tensor 
+#>  2
 #>  1
 #>  1
 #>  2
-#>  1
 #> [ CPULongType{4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Mv
    
     
 
-    
    torch_mv(self, vec)
    
+    
    torch_mv(self, vec)
    
 
     
    Arguments
    
     
    
@@ -224,15 +256,15 @@
 size \(m\), out will be 1-D of size \(n\).
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-mat = torch_randn(c(2, 3))
-vec = torch_randn(c(3))
-torch_mv(mat, vec)
-}
+mat = torch_randn(c(2, 3))
+vec = torch_randn(c(3))
+torch_mv(mat, vec)
+}
 
    #> torch_tensor 
-#> -5.7310
-#>  1.3023
+#>  0.4328
+#>  1.0417
 #> [ CPUFloatType{2} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Mvlgamma
    
     
 
-    
    torch_mvlgamma(self, p)
    
+    
    torch_mvlgamma(self, p)
    
 
     
    Arguments
    
     
    
@@ -224,15 +256,15 @@ where \(C = \log(\pi) \times \frac{p (p - 1)}{4}\) and \(\Gamma(\cdot)\) is the
 
    All elements must be greater than \(\frac{p - 1}{2}\), otherwise an error would be thrown.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_empty(c(2, 3))$uniform_(1, 2)
-a
-torch_mvlgamma(a, 2)
-}
+a = torch_empty(c(2, 3))$uniform_(1, 2)
+a
+torch_mvlgamma(a, 2)
+}
 
    #> torch_tensor 
-#>  0.4040  0.4059  0.7450
-#>  0.3997  0.8720  0.4162
+#>  0.6115  0.3920  0.6409
+#>  0.8698  0.3909  0.4000
 #> [ CPUFloatType{2,3} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Narrow
    
     
 
-    
    torch_narrow(self, dim, start, length)
    
+    
    torch_narrow(self, dim, start, length)
    
 
     
    Arguments
    
     
    
@@ -228,12 +260,12 @@ dimension dim is input from start to start + len
 returned tensor and input tensor share the same underlying storage.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-x = torch_tensor(matrix(c(1:9), ncol = 3, byrow= TRUE))
-torch_narrow(x, 1, torch_tensor(0L)$sum(dim = 1), 2)
-torch_narrow(x, 2, torch_tensor(1L)$sum(dim = 1), 2)
-}
+x = torch_tensor(matrix(c(1:9), ncol = 3, byrow= TRUE))
+torch_narrow(x, 1, torch_tensor(0L)$sum(dim = 1), 2)
+torch_narrow(x, 2, torch_tensor(1L)$sum(dim = 1), 2)
+}
 
    #> torch_tensor 
 #>  2  3
 #>  5  6
diff --git a/reference/torch_ne.html b/reference/torch_ne.html
index 3696a1626..b3e7fb85f 100644
--- a/reference/torch_ne.html
+++ b/reference/torch_ne.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Ne
    
     
    
 
-    
    torch_ne(self, other)
    
+    
    torch_ne(self, other)
    
 
     
    Arguments
    
     
    
@@ -220,11 +252,11 @@
 broadcastable  with the first argument.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_ne(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)), 
-         torch_tensor(matrix(rep(c(1,4), each = 2), ncol = 2, byrow=TRUE)))
-}
+torch_ne(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)), 
+         torch_tensor(matrix(rep(c(1,4), each = 2), ncol = 2, byrow=TRUE)))
+}
 
    #> torch_tensor 
 #>  0  1
 #>  1  0
diff --git a/reference/torch_neg.html b/reference/torch_neg.html
index d8e7d5b45..15e0801de 100644
--- a/reference/torch_neg.html
+++ b/reference/torch_neg.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Neg
    
     
    
 
-    
    torch_neg(self)
    
+    
    torch_neg(self)
    
 
     
    Arguments
    
     
    
@@ -217,18 +249,18 @@
 $$
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(5))
-a
-torch_neg(a)
-}
+a = torch_randn(c(5))
+a
+torch_neg(a)
+}
 
    #> torch_tensor 
-#>  0.0573
-#>  0.4788
-#>  0.7503
-#> -0.4747
-#> -0.4677
+#>  2.2162
+#> -1.0400
+#>  0.3351
+#>  2.0381
+#>  0.5984
 #> [ CPUFloatType{5} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Nonzero
    
     
 
-    
    torch_nonzero(self)
    
+    
    torch_nonzero(self)
    
 
     
    Arguments
    
     
    
@@ -244,10 +276,10 @@ non-zero elements in the input tensor.
    
 value, it is treated as a one-dimensional tensor with one element.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_nonzero(torch_tensor(c(1, 1, 1, 0, 1)))
-}
+torch_nonzero(torch_tensor(c(1, 1, 1, 0, 1)))
+}
 
    #> torch_tensor 
 #>  0
 #>  1
diff --git a/reference/torch_norm.html b/reference/torch_norm.html
index 94a942e1f..f0c695f63 100644
--- a/reference/torch_norm.html
+++ b/reference/torch_norm.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Norm
    
     
    
 
-    
    torch_norm(self, p = 2L, dim, keepdim = FALSE, dtype)
    
+    
    torch_norm(self, p = 2L, dim, keepdim = FALSE, dtype)
    
 
     
    Arguments
    
     
    
@@ -231,16 +263,16 @@ Ignored if        dim = NULL and out = Returns the matrix norm or vector norm of a given tensor.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_arange(0, 9, dtype = torch_float())
-b = a$reshape(list(3, 3))
-torch_norm(a)
-torch_norm(b)
-torch_norm(a, Inf)
-torch_norm(b, Inf)
+a = torch_arange(0, 9, dtype = torch_float())
+b = a$reshape(list(3, 3))
+torch_norm(a)
+torch_norm(b)
+torch_norm(a, Inf)
+torch_norm(b, Inf)
 
-}
+}
 
    #> torch_tensor 
 #> 8
 #> [ CPUFloatType{} ]
    
diff --git a/reference/torch_normal.html b/reference/torch_normal.html
index 0387079e1..19f7a92fc 100644
--- a/reference/torch_normal.html
+++ b/reference/torch_normal.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Normal
    
     
 
-    
    torch_normal(mean, std = 1L, size, generator = NULL)
    
+    
    torch_normal(mean, std = 1L, size, generator = NULL)
    
 
     
    Arguments
    
     
    
@@ -258,21 +290,21 @@ all drawn elements.
    
 among all drawn elements. The resulting tensor has size given by size.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-if (FALSE) {
-torch_normal(mean=0, std=torch_arange(1, 0, -0.1))
+if (FALSE) {
+torch_normal(mean=0, std=torch_arange(1, 0, -0.1))
 
 
-torch_normal(mean=0.5, std=torch_arange(1., 6.))
+torch_normal(mean=0.5, std=torch_arange(1., 6.))
 
 
-torch_normal(mean=torch_arange(1., 6.))
+torch_normal(mean=torch_arange(1., 6.))
 
 
-torch_normal(2, 3, size=list(1, 4))
-}
-}
+torch_normal(2, 3, size=list(1, 4))
+}
+}
 
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,14 +227,14 @@
     
    Ones
    
     
 
-    
    torch_ones(
-  ...,
-  names = NULL,
-  dtype = NULL,
-  layout = torch_strided(),
-  device = NULL,
-  requires_grad = FALSE
-)
    
+    
    torch_ones(
+  ...,
+  names = NULL,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
    
 
     
    Arguments
    
     
    
@@ -242,11 +274,11 @@
 by the variable argument size.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_ones(c(2, 3))
-torch_ones(c(5))
-}
+torch_ones(c(2, 3))
+torch_ones(c(5))
+}
 
    #> torch_tensor 
 #>  1
 #>  1
diff --git a/reference/torch_ones_like.html b/reference/torch_ones_like.html
index 56cad96be..1b8da234b 100644
--- a/reference/torch_ones_like.html
+++ b/reference/torch_ones_like.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,14 +227,14 @@
     
    Ones_like
    
     
    
 
-    
    torch_ones_like(
-  input,
-  dtype = NULL,
-  layout = torch_strided(),
-  device = NULL,
-  requires_grad = FALSE,
-  memory_format = torch_preserve_format()
-)
    
+    
    torch_ones_like(
+  input,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE,
+  memory_format = torch_preserve_format()
+)
    
 
     
    Arguments
    
     
    
@@ -250,11 +282,11 @@ the old torch_ones_like(input, out=output) is equivalent to
 torch_ones(input.size(), out=output).
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-input = torch_empty(c(2, 3))
-torch_ones_like(input)
-}
+input = torch_empty(c(2, 3))
+torch_ones_like(input)
+}
 
    #> torch_tensor 
 #>  1  1  1
 #>  1  1  1
diff --git a/reference/torch_orgqr.html b/reference/torch_orgqr.html
index ab879c438..2622032eb 100644
--- a/reference/torch_orgqr.html
+++ b/reference/torch_orgqr.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Orgqr
    
     
    
 
-    
    torch_orgqr(self, input2)
    
+    
    torch_orgqr(self, input2)
    
 
     
    Arguments
    
     
    
diff --git a/reference/torch_ormqr.html b/reference/torch_ormqr.html
index 027e7c299..446c3f91c 100644
--- a/reference/torch_ormqr.html
+++ b/reference/torch_ormqr.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Ormqr
    
     
 
-    
    torch_ormqr(self, input2, input3, left = TRUE, transpose = FALSE)
    
+    
    torch_ormqr(self, input2, input3, left = TRUE, transpose = FALSE)
    
 
     
    Arguments
    
     
    
diff --git a/reference/torch_pdist.html b/reference/torch_pdist.html
index 32cabdc54..8287a7a88 100644
--- a/reference/torch_pdist.html
+++ b/reference/torch_pdist.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Pdist
    
     
 
-    
    torch_pdist(self, p = 2L)
    
+    
    torch_pdist(self, p = 2L)
    
 
     
    Arguments
    
     
    
diff --git a/reference/torch_pinverse.html b/reference/torch_pinverse.html
index 9e1f439af..3439b47ab 100644
--- a/reference/torch_pinverse.html
+++ b/reference/torch_pinverse.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Pinverse
    
     
 
-    
    torch_pinverse(self, rcond = 0)
    
+    
    torch_pinverse(self, rcond = 0)
    
 
     
    Arguments
    
     
    
@@ -232,26 +264,26 @@ See `~torch.svd` for more details.
 Please look at Moore-Penrose inverse_ for more details
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-input = torch_randn(c(3, 5))
-input
-torch_pinverse(input)
+input = torch_randn(c(3, 5))
+input
+torch_pinverse(input)
 # Batched pinverse example
-a = torch_randn(c(2,6,3))
-b = torch_pinverse(a)
-torch_matmul(b, a)
-}
+a = torch_randn(c(2,6,3))
+b = torch_pinverse(a)
+torch_matmul(b, a)
+}
 
    #> torch_tensor 
 #> (1,.,.) = 
-#>   1.0000e+00 -5.9605e-08  1.0431e-07
-#>   2.9802e-08  1.0000e+00  2.2352e-08
-#>   2.9802e-08  8.9407e-08  1.0000e+00
+#>   1.0000e+00  2.0862e-07  8.9407e-08
+#>  -5.2154e-08  1.0000e+00  2.2724e-07
+#>  -1.3411e-07  2.0117e-07  1.0000e+00
 #> 
 #> (2,.,.) = 
-#>   1.0000e+00 -5.9605e-08 -1.7136e-07
-#>  -7.4506e-09  1.0000e+00 -1.2666e-07
-#>  -8.1956e-08 -2.1607e-07  1.0000e+00
+#>   1.0000 -0.0000 -0.0000
+#>   0.0000  1.0000  0.0000
+#>  -0.0000  0.0000  1.0000
 #> [ CPUFloatType{2,3,3} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Pixel_shuffle
    
     
 
-    
    torch_pixel_shuffle(self, upscale_factor)
    
+    
    torch_pixel_shuffle(self, upscale_factor)
    
 
     
    Arguments
    
     
    
@@ -218,12 +250,12 @@ tensor of shape \((*, C, H \times r, W \times r)\).
    
 
    See ~torch.nn.PixelShuffle for details.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-input = torch_randn(c(1, 9, 4, 4))
-output = nnf_pixel_shuffle(input, 3)
-print(output$size())
-}
+input = torch_randn(c(1, 9, 4, 4))
+output = nnf_pixel_shuffle(input, 3)
+print(output$size())
+}
 
    #> [1]  1  1 12 12
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Poisson
    
     
 
-    
    torch_poisson(self, generator = NULL)
    
+    
    torch_poisson(self, generator = NULL)
    
 
     
    Arguments
    
     
    
@@ -223,16 +255,16 @@ element in input i.e.,
    
 $$
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-rates = torch_rand(c(4, 4)) * 5  # rate parameter between 0 and 5
-torch_poisson(rates)
-}
+rates = torch_rand(c(4, 4)) * 5  # rate parameter between 0 and 5
+torch_poisson(rates)
+}
 
    #> torch_tensor 
-#>  1  0  5  2
-#>  8  2  0  4
-#>  3  0  3  0
-#>  3  6  6  1
+#>  2  0  2  2
+#>  2  6  1  0
+#>  1  2  4  5
+#>  1  1  2  6
 #> [ CPUFloatType{4,4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Polygamma
    
     
 
-    
    torch_polygamma(n, self)
    
+    
    torch_polygamma(n, self)
    
 
     
    Arguments
    
     
    
@@ -228,12 +260,12 @@
 $$
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-if (FALSE) {
-a = torch_tensor(c(1, 0.5))
-torch_polygamma(1, a)
-}
-}
+    
    if (torch_is_installed()) {
+if (FALSE) {
+a = torch_tensor(c(1, 0.5))
+torch_polygamma(1, a)
+}
+}
 
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Pow
    
     
 
-    
    torch_pow(self, exponent)
    
+    
    torch_pow(self, exponent)
    
 
     
    Arguments
    
     
    
@@ -242,22 +274,22 @@ The returned tensor out is of the same shape as exponent
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-
-a = torch_randn(c(4))
-a
-torch_pow(a, 2)
-exp = torch_arange(1., 5.)
-a = torch_arange(1., 5.)
-a
-exp
-torch_pow(a, exp)
-
-
-exp = torch_arange(1., 5.)
-base = 2
-torch_pow(base, exp)
-}
+    
    if (torch_is_installed()) {
+
+a = torch_randn(c(4))
+a
+torch_pow(a, 2)
+exp = torch_arange(1., 5.)
+a = torch_arange(1., 5.)
+a
+exp
+torch_pow(a, exp)
+
+
+exp = torch_arange(1., 5.)
+base = 2
+torch_pow(base, exp)
+}
 
    #> torch_tensor 
 #>   2
 #>   4
diff --git a/reference/torch_prod.html b/reference/torch_prod.html
index e14b48c63..e94e0002f 100644
--- a/reference/torch_prod.html
+++ b/reference/torch_prod.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Prod
    
     
    
 
-    
    torch_prod(self, dim, keepdim = FALSE, dtype = NULL)
    
+    
    torch_prod(self, dim, keepdim = FALSE, dtype = NULL)
    
 
     
    Arguments
    
     
    
@@ -237,21 +269,20 @@ Otherwise, dim is squeezed (see 
 the output tensor having 1 fewer dimension than input.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(1, 3))
-a
-torch_prod(a)
+a = torch_randn(c(1, 3))
+a
+torch_prod(a)
 
 
-a = torch_randn(c(4, 2))
-a
-torch_prod(a, 1)
-}
+a = torch_randn(c(4, 2))
+a
+torch_prod(a, 1)
+}
 
    #> torch_tensor 
-#> 0.001 *
-#> -1.8251
-#> -120.0931
+#> -0.2616
+#> -0.0308
 #> [ CPUFloatType{2} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Promote_types
    
     
 
-    
    torch_promote_types(type1, type2)
    
+    
    torch_promote_types(type1, type2)
    
 
     
    Arguments
    
     
    
@@ -221,11 +253,11 @@ documentation  for more information on the type
 promotion logic.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_promote_types(torch_int32(), torch_float32())
-torch_promote_types(torch_uint8(), torch_long())
-}
+torch_promote_types(torch_int32(), torch_float32())
+torch_promote_types(torch_uint8(), torch_long())
+}
 
    #> torch_Long
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Qr
    
     
 
-    
    torch_qr(self, some = TRUE)
    
+    
    torch_qr(self, some = TRUE)
    
 
     
    Arguments
    
     
    
@@ -230,15 +262,15 @@ with \(Q\) being an orthogonal matrix or batch of orthogonal matrices and
 Otherwise, if some is FALSE, this function returns the complete QR factorization.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-
-a = torch_tensor(matrix(c(12., -51, 4, 6, 167, -68, -4, 24, -41), ncol = 3, byrow = TRUE))
-out = torch_qr(a)
-q = out[[1]]
-r = out[[2]]
-torch_mm(q, r)$round()
-torch_mm(q$t(), q)$round()
-}
+    
    if (torch_is_installed()) {
+
+a = torch_tensor(matrix(c(12., -51, 4, 6, 167, -68, -4, 24, -41), ncol = 3, byrow = TRUE))
+out = torch_qr(a)
+q = out[[1]]
+r = out[[2]]
+torch_mm(q, r)$round()
+torch_mm(q$t(), q)$round()
+}
 
    #> torch_tensor 
 #>  1  0  0
 #>  0  1  0
diff --git a/reference/torch_qscheme.html b/reference/torch_qscheme.html
index 557b34642..6e296af5c 100644
--- a/reference/torch_qscheme.html
+++ b/reference/torch_qscheme.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,13 +227,13 @@
     
    Creates the corresponding Scheme object
    
     
    
 
-    
    torch_per_channel_affine()
+    
    torch_per_channel_affine()
 
-torch_per_tensor_affine()
+torch_per_tensor_affine()
 
-torch_per_channel_symmetric()
+torch_per_channel_symmetric()
 
-torch_per_tensor_symmetric()
    
+torch_per_tensor_symmetric()
    
 
 
 
diff --git a/reference/torch_quantize_per_channel.html b/reference/torch_quantize_per_channel.html
index 590d80dae..7b6415fff 100644
--- a/reference/torch_quantize_per_channel.html
+++ b/reference/torch_quantize_per_channel.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Quantize_per_channel
    
     
 
-    
    torch_quantize_per_channel(self, scales, zero_points, axis, dtype)
    
+    
    torch_quantize_per_channel(self, scales, zero_points, axis, dtype)
    
 
     
    Arguments
    
     
    
@@ -230,13 +262,13 @@
 
    Converts a float tensor to per-channel quantized tensor with given scales and zero points.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-x = torch_tensor(matrix(c(-1.0, 0.0, 1.0, 2.0), ncol = 2, byrow = TRUE))
-torch_quantize_per_channel(x, torch_tensor(c(0.1, 0.01)), 
-                           torch_tensor(c(10L, 0L)), 0, torch_quint8())
-torch_quantize_per_channel(x, torch_tensor(c(0.1, 0.01)), 
-                           torch_tensor(c(10L, 0L)), 0, torch_quint8())$int_repr()
-}
+    
    if (torch_is_installed()) {
+x = torch_tensor(matrix(c(-1.0, 0.0, 1.0, 2.0), ncol = 2, byrow = TRUE))
+torch_quantize_per_channel(x, torch_tensor(c(0.1, 0.01)), 
+                           torch_tensor(c(10L, 0L)), 0, torch_quint8())
+torch_quantize_per_channel(x, torch_tensor(c(0.1, 0.01)), 
+                           torch_tensor(c(10L, 0L)), 0, torch_quint8())$int_repr()
+}
 
    #> torch_tensor 
 #>    0   10
 #>  100  200
diff --git a/reference/torch_quantize_per_tensor.html b/reference/torch_quantize_per_tensor.html
index 499139caa..87476be6e 100644
--- a/reference/torch_quantize_per_tensor.html
+++ b/reference/torch_quantize_per_tensor.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Quantize_per_tensor
    
     
    
 
-    
    torch_quantize_per_tensor(self, scale, zero_point, dtype)
    
+    
    torch_quantize_per_tensor(self, scale, zero_point, dtype)
    
 
     
    Arguments
    
     
    
@@ -226,10 +258,10 @@
 
    Converts a float tensor to quantized tensor with given scale and zero point.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-torch_quantize_per_tensor(torch_tensor(c(-1.0, 0.0, 1.0, 2.0)), 0.1, 10, torch_quint8())
-torch_quantize_per_tensor(torch_tensor(c(-1.0, 0.0, 1.0, 2.0)), 0.1, 10, torch_quint8())$int_repr()
-}
+    
    if (torch_is_installed()) {
+torch_quantize_per_tensor(torch_tensor(c(-1.0, 0.0, 1.0, 2.0)), 0.1, 10, torch_quint8())
+torch_quantize_per_tensor(torch_tensor(c(-1.0, 0.0, 1.0, 2.0)), 0.1, 10, torch_quint8())$int_repr()
+}
 
    #> torch_tensor 
 #>   0
 #>  10
diff --git a/reference/torch_rand.html b/reference/torch_rand.html
index c6fef07b6..d534fff22 100644
--- a/reference/torch_rand.html
+++ b/reference/torch_rand.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,14 +227,14 @@
     
    Rand
    
     
    
 
-    
    torch_rand(
-  ...,
-  names = NULL,
-  dtype = NULL,
-  layout = torch_strided(),
-  device = NULL,
-  requires_grad = FALSE
-)
    
+    
    torch_rand(
+  ...,
+  names = NULL,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
    
 
     
    Arguments
    
     
    
@@ -243,14 +275,14 @@ on the interval \([0, 1)\)
    
 
    The shape of the tensor is defined by the variable argument size.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_rand(4)
-torch_rand(c(2, 3))
-}
+torch_rand(4)
+torch_rand(c(2, 3))
+}
 
    #> torch_tensor 
-#>  0.4034  0.1802  0.2925
-#>  0.6916  0.6648  0.9430
+#>  0.7836  0.4775  0.1233
+#>  0.0427  0.2741  0.4000
 #> [ CPUFloatType{2,3} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,14 +227,14 @@
     
    Rand_like
    
     
 
-    
    torch_rand_like(
-  input,
-  dtype = NULL,
-  layout = torch_strided(),
-  device = NULL,
-  requires_grad = FALSE,
-  memory_format = torch_preserve_format()
-)
    
+    
    torch_rand_like(
+  input,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE,
+  memory_format = torch_preserve_format()
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/torch_randint.html b/reference/torch_randint.html
index 15ab1288a..c268a7fa3 100644
--- a/reference/torch_randint.html
+++ b/reference/torch_randint.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,17 +227,17 @@
     
    Randint
    
     
 
-    
    torch_randint(
-  low,
-  high,
-  size,
-  generator = NULL,
-  dtype = NULL,
-  layout = torch_strided(),
-  device = NULL,
-  requires_grad = FALSE,
-  memory_format = torch_preserve_format()
-)
    
+    
    torch_randint(
+  low,
+  high,
+  size,
+  generator = NULL,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE,
+  memory_format = torch_preserve_format()
+)
    
 
     
    Arguments
    
     
    
@@ -262,15 +294,15 @@ With the global dtype default (torch_float32), this function return
 a tensor with dtype torch_int64.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_randint(3, 5, list(3))
-torch_randint(0, 10, size = list(2, 2))
-torch_randint(3, 10, list(2, 2))
-}
+torch_randint(3, 5, list(3))
+torch_randint(0, 10, size = list(2, 2))
+torch_randint(3, 10, list(2, 2))
+}
 
    #> torch_tensor 
-#>  7  8
-#>  8  9
+#>  6  4
+#>  9  8
 #> [ CPUFloatType{2,2} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,15 +227,15 @@
     
    Randint_like
    
     
 
-    
    torch_randint_like(
-  input,
-  low,
-  high,
-  dtype = NULL,
-  layout = torch_strided(),
-  device = NULL,
-  requires_grad = FALSE
-)
    
+    
    torch_randint_like(
+  input,
+  low,
+  high,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/torch_randn.html b/reference/torch_randn.html
index 38ec31367..7d0f09e05 100644
--- a/reference/torch_randn.html
+++ b/reference/torch_randn.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,14 +227,14 @@
     
    Randn
    
     
 
-    
    torch_randn(
-  ...,
-  names = NULL,
-  dtype = NULL,
-  layout = torch_strided(),
-  device = NULL,
-  requires_grad = FALSE
-)
    
+    
    torch_randn(
+  ...,
+  names = NULL,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
    
 
     
    Arguments
    
     
    
@@ -247,14 +279,14 @@ $$
 The shape of the tensor is defined by the variable argument size.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_randn(c(4))
-torch_randn(c(2, 3))
-}
+torch_randn(c(4))
+torch_randn(c(2, 3))
+}
 
    #> torch_tensor 
-#>  1.7302  1.3721 -0.0691
-#>  0.4933 -0.7643 -0.5334
+#>  0.8924  0.1711  0.9711
+#> -0.5444  0.3223 -0.4884
 #> [ CPUFloatType{2,3} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,14 +227,14 @@
     
    Randn_like
    
     
 
-    
    torch_randn_like(
-  input,
-  dtype = NULL,
-  layout = torch_strided(),
-  device = NULL,
-  requires_grad = FALSE,
-  memory_format = torch_preserve_format()
-)
    
+    
    torch_randn_like(
+  input,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE,
+  memory_format = torch_preserve_format()
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/torch_randperm.html b/reference/torch_randperm.html
index 452ba130f..36fb08fab 100644
--- a/reference/torch_randperm.html
+++ b/reference/torch_randperm.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,13 +227,13 @@
     
    Randperm
    
     
 
-    
    torch_randperm(
-  n,
-  dtype = torch_int64(),
-  layout = torch_strided(),
-  device = NULL,
-  requires_grad = FALSE
-)
    
+    
    torch_randperm(
+  n,
+  dtype = torch_int64(),
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
    
 
     
    Arguments
    
     
    
@@ -236,15 +268,15 @@
 
    Returns a random permutation of integers from 0 to n - 1.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_randperm(4)
-}
+torch_randperm(4)
+}
 
    #> torch_tensor 
-#>  3
 #>  2
-#>  0
 #>  1
+#>  0
+#>  3
 #> [ CPULongType{4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,15 +227,15 @@
     
    Range
    
     
 
-    
    torch_range(
-  start,
-  end,
-  step = 1,
-  dtype = NULL,
-  layout = torch_strided(),
-  device = NULL,
-  requires_grad = FALSE
-)
    
+    
    torch_range(
+  start,
+  end,
+  step = 1,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
    
 
     
    Arguments
    
     
    
@@ -256,11 +288,11 @@ $$
    
 
    This function is deprecated in favor of torch_arange.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_range(1, 4)
-torch_range(1, 4, 0.5)
-}
+torch_range(1, 4)
+torch_range(1, 4, 0.5)
+}
 
    #> Warning: This function is deprecated in favor of torch_arange.
    #> Warning: This function is deprecated in favor of torch_arange.
    #> torch_tensor 
 #>  1.0000
 #>  1.5000
diff --git a/reference/torch_real.html b/reference/torch_real.html
index 77c9ca900..06ccb8bb8 100644
--- a/reference/torch_real.html
+++ b/reference/torch_real.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Real
    
     
    
 
-    
    torch_real(self)
    
+    
    torch_real(self)
    
 
     
    Arguments
    
     
    
@@ -224,11 +256,11 @@ returns it.
    
 $$
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-if (FALSE) {
-torch_real(torch_tensor(c(-1 + 1i, -2 + 2i, 3 - 3i)))
-}
-}
+    
    if (torch_is_installed()) {
+if (FALSE) {
+torch_real(torch_tensor(c(-1 + 1i, -2 + 2i, 3 - 3i)))
+}
+}
 
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Reciprocal
    
     
 
-    
    torch_reciprocal(self)
    
+    
    torch_reciprocal(self)
    
 
     
    Arguments
    
     
    
@@ -217,17 +249,17 @@
 $$
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(4))
-a
-torch_reciprocal(a)
-}
+a = torch_randn(c(4))
+a
+torch_reciprocal(a)
+}
 
    #> torch_tensor 
-#>  5.6343
-#> -0.4645
-#>  4.8170
-#>  2.1907
+#>  -0.4381
+#>  39.7676
+#>  -0.7620
+#>  -2.9071
 #> [ CPUFloatType{4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,11 +227,11 @@
     
    Creates the reduction objet
    
     
 
-    
    torch_reduction_sum()
+    
    torch_reduction_sum()
 
-torch_reduction_mean()
+torch_reduction_mean()
 
-torch_reduction_none()
    
+torch_reduction_none()
    
 
 
 
diff --git a/reference/torch_relu.html b/reference/torch_relu.html
index 9eedfcae4..d35ecd0c0 100644
--- a/reference/torch_relu.html
+++ b/reference/torch_relu.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Relu
    
     
 
-    
    torch_relu(self)
    
+    
    torch_relu(self)
    
 
     
    Arguments
    
     
    
diff --git a/reference/torch_relu_.html b/reference/torch_relu_.html
index 572dddd53..a46b0e7c9 100644
--- a/reference/torch_relu_.html
+++ b/reference/torch_relu_.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Relu_
    
     
 
-    
    torch_relu_(self)
    
+    
    torch_relu_(self)
    
 
     
    Arguments
    
     
    
diff --git a/reference/torch_remainder.html b/reference/torch_remainder.html
index 351ef4af7..8c9ac138e 100644
--- a/reference/torch_remainder.html
+++ b/reference/torch_remainder.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Remainder
    
     
 
-    
    torch_remainder(self, other)
    
+    
    torch_remainder(self, other)
    
 
     
    Arguments
    
     
    
@@ -222,11 +254,11 @@ numbers. The remainder has the same sign as the divisor.
    
 other must be broadcastable .
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_remainder(torch_tensor(c(-3., -2, -1, 1, 2, 3)), 2)
-torch_remainder(torch_tensor(c(1., 2, 3, 4, 5)), 1.5)
-}
+torch_remainder(torch_tensor(c(-3., -2, -1, 1, 2, 3)), 2)
+torch_remainder(torch_tensor(c(1., 2, 3, 4, 5)), 1.5)
+}
 
    #> torch_tensor 
 #>  1.0000
 #>  0.5000
diff --git a/reference/torch_renorm.html b/reference/torch_renorm.html
index 3f28bd7b5..7752bc9af 100644
--- a/reference/torch_renorm.html
+++ b/reference/torch_renorm.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Renorm
    
     
    
 
-    
    torch_renorm(self, p, dim, maxnorm)
    
+    
    torch_renorm(self, p, dim, maxnorm)
    
 
     
    Arguments
    
     
    
@@ -231,13 +263,13 @@
 than the value maxnorm
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-x = torch_ones(c(3, 3))
-x[2,]$fill_(2)
-x[3,]$fill_(3)
-x
-torch_renorm(x, 1, 1, 5)
-}
+    
    if (torch_is_installed()) {
+x = torch_ones(c(3, 3))
+x[2,]$fill_(2)
+x[3,]$fill_(3)
+x
+torch_renorm(x, 1, 1, 5)
+}
 
    #> torch_tensor 
 #>  1.0000  1.0000  1.0000
 #>  1.6667  1.6667  1.6667
diff --git a/reference/torch_repeat_interleave.html b/reference/torch_repeat_interleave.html
index 0e85bf392..d89d83de4 100644
--- a/reference/torch_repeat_interleave.html
+++ b/reference/torch_repeat_interleave.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Repeat_interleave
    
     
    
 
-    
    torch_repeat_interleave(self, repeats, dim = NULL)
    
+    
    torch_repeat_interleave(self, repeats, dim = NULL)
    
 
     
    Arguments
    
     
    
@@ -236,16 +268,16 @@
 1 appears n2 times, 2 appears n3 times, etc.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-if (FALSE) {
-x = torch_tensor(c(1, 2, 3))
-x$repeat_interleave(2)
-y = torch_tensor(matrix(c(1, 2, 3, 4), ncol = 2, byrow=TRUE))
-torch_repeat_interleave(y, 2)
-torch_repeat_interleave(y, 3, dim=1)
-torch_repeat_interleave(y, torch_tensor(c(1, 2)), dim=1)
-}
-}
+    
    if (torch_is_installed()) {
+if (FALSE) {
+x = torch_tensor(c(1, 2, 3))
+x$repeat_interleave(2)
+y = torch_tensor(matrix(c(1, 2, 3, 4), ncol = 2, byrow=TRUE))
+torch_repeat_interleave(y, 2)
+torch_repeat_interleave(y, 3, dim=1)
+torch_repeat_interleave(y, torch_tensor(c(1, 2)), dim=1)
+}
+}
 
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Reshape
    
     
 
-    
    torch_reshape(self, shape)
    
+    
    torch_reshape(self, shape)
    
 
     
    Arguments
    
     
    
@@ -225,13 +257,13 @@ depend on the copying vs. viewing behavior.
    
 dimensions and the number of elements in input.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_arange(0, 4)
-torch_reshape(a, list(2, 2))
-b = torch_tensor(matrix(c(0, 1, 2, 3), ncol = 2, byrow=TRUE))
-torch_reshape(b, list(-1))
-}
+a = torch_arange(0, 4)
+torch_reshape(a, list(2, 2))
+b = torch_tensor(matrix(c(0, 1, 2, 3), ncol = 2, byrow=TRUE))
+torch_reshape(b, list(-1))
+}
 
    #> torch_tensor 
 #>  0
 #>  1
diff --git a/reference/torch_result_type.html b/reference/torch_result_type.html
index 261709999..f70af9ba9 100644
--- a/reference/torch_result_type.html
+++ b/reference/torch_result_type.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Result_type
    
     
    
 
-    
    torch_result_type(tensor1, tensor2)
    
+    
    torch_result_type(tensor1, tensor2)
    
 
     
    Arguments
    
     
    
@@ -220,10 +252,10 @@ operation on the provided input tensors. See type promotion documentation
 for more information on the type promotion logic.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_result_type(tensor1 = torch_tensor(c(1, 2), dtype=torch_int()), tensor2 = 1)
-}
+torch_result_type(tensor1 = torch_tensor(c(1, 2), dtype=torch_int()), tensor2 = 1)
+}
 
    #> torch_Float
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Rfft
    
     
 
-    
    torch_rfft(self, signal_ndim, normalized = FALSE, onesided = TRUE)
    
+    
    torch_rfft(self, signal_ndim, normalized = FALSE, onesided = TRUE)
    
 
     
    Arguments
    
     
    
@@ -262,47 +294,47 @@ of input, but instead the last dimension will be halfed as of size
 torch_backends.mkl.is_available to check if MKL is installed.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-x = torch_randn(c(5, 5))
-torch_rfft(x, 2)
-torch_rfft(x, 2, onesided=FALSE)
-}
+x = torch_randn(c(5, 5))
+torch_rfft(x, 2)
+torch_rfft(x, 2, onesided=FALSE)
+}
 
    #> torch_tensor 
 #> (1,.,.) = 
-#>  -0.4625  0.0000
-#>  -5.1624  3.2586
-#>  -6.0212  1.9752
-#>  -6.0212 -1.9752
-#>  -5.1624 -3.2586
+#>  -2.6885  0.0000
+#>   2.2970 -3.1304
+#>  -3.3626  5.1310
+#>  -3.3626 -5.1310
+#>   2.2970  3.1304
 #> 
 #> (2,.,.) = 
-#>   1.6002 -2.9581
-#>   2.1200  0.1343
-#>   3.0293 -7.1277
-#>   3.5287  3.5903
-#>  -0.8434 -0.1112
+#>   3.5981 -1.6740
+#>   6.2369  6.0703
+#>  -3.2006  4.7044
+#>  -1.0957 -2.3358
+#>  -5.0663 -1.6792
 #> 
 #> (3,.,.) = 
-#>  -3.0107 -2.3239
-#>   0.9071  0.0358
-#>  -0.0607 -0.0974
-#>  -2.2262  2.3303
-#>   1.6252  1.9184
+#>  -1.1132  0.0063
+#>  -6.2288  2.1029
+#>   3.1367  4.8644
+#>   1.5096 -1.7360
+#>   3.2987  2.3868
 #> 
 #> (4,.,.) = 
-#>  -3.0107  2.3239
-#>   1.6252 -1.9184
-#>  -2.2262 -2.3303
-#>  -0.0607  0.0974
-#>   0.9071 -0.0358
+#>  -1.1132 -0.0063
+#>   3.2987 -2.3868
+#>   1.5096  1.7360
+#>   3.1367 -4.8644
+#>  -6.2288 -2.1029
 #> 
 #> (5,.,.) = 
-#>   1.6002  2.9581
-#>  -0.8434  0.1112
-#>   3.5287 -3.5903
-#>   3.0293  7.1277
-#>   2.1200 -0.1343
+#>   3.5981  1.6740
+#>  -5.0663  1.6792
+#>  -1.0957  2.3358
+#>  -3.2006 -4.7044
+#>   6.2369 -6.0703
 #> [ CPUFloatType{5,5,2} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Roll
    
     
 
-    
    torch_roll(self, shifts, dims = list())
    
+    
    torch_roll(self, shifts, dims = list())
    
 
     
    Arguments
    
     
    
@@ -225,14 +257,14 @@ specified, the tensor will be flattened before rolling and then restored
 to the original shape.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-
-x = torch_tensor(c(1, 2, 3, 4, 5, 6, 7, 8))$view(c(4, 2))
-x
-torch_roll(x, 1, 1)
-torch_roll(x, -1, 1)
-torch_roll(x, shifts=list(2, 1), dims=list(1, 2))
-}
+    
    if (torch_is_installed()) {
+
+x = torch_tensor(c(1, 2, 3, 4, 5, 6, 7, 8))$view(c(4, 2))
+x
+torch_roll(x, 1, 1)
+torch_roll(x, -1, 1)
+torch_roll(x, shifts=list(2, 1), dims=list(1, 2))
+}
 
    #> torch_tensor 
 #>  6  5
 #>  8  7
diff --git a/reference/torch_rot90.html b/reference/torch_rot90.html
index b71ff01b6..329fb66b5 100644
--- a/reference/torch_rot90.html
+++ b/reference/torch_rot90.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Rot90
    
     
    
 
-    
    torch_rot90(self, k = 1L, dims = c(0, 1))
    
+    
    torch_rot90(self, k = 1L, dims = c(0, 1))
    
 
     
    Arguments
    
     
    
@@ -223,15 +255,15 @@
 Rotation direction is from the first towards the second axis if k > 0, and from the second towards the first for k < 0.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-
-x = torch_arange(0, 4)$view(c(2, 2))
-x
-torch_rot90(x, 1, c(1, 2))
-x = torch_arange(0, 8)$view(c(2, 2, 2))
-x
-torch_rot90(x, 1, c(1, 2))
-}
+    
    if (torch_is_installed()) {
+
+x = torch_arange(0, 4)$view(c(2, 2))
+x
+torch_rot90(x, 1, c(1, 2))
+x = torch_arange(0, 8)$view(c(2, 2, 2))
+x
+torch_rot90(x, 1, c(1, 2))
+}
 
    #> torch_tensor 
 #> (1,.,.) = 
 #>   2  3
diff --git a/reference/torch_round.html b/reference/torch_round.html
index 24caf7992..b5e0db6dc 100644
--- a/reference/torch_round.html
+++ b/reference/torch_round.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Round
    
     
    
 
-    
    torch_round(self)
    
+    
    torch_round(self)
    
 
     
    Arguments
    
     
    
@@ -215,17 +247,17 @@
 to the closest integer.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(4))
-a
-torch_round(a)
-}
+a = torch_randn(c(4))
+a
+torch_round(a)
+}
 
    #> torch_tensor 
-#>  1
-#>  1
+#> -0
+#>  0
+#>  0
 #>  0
-#>  1
 #> [ CPUFloatType{4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,13 +227,13 @@
     
    Rrelu_
    
     
 
-    
    torch_rrelu_(
-  self,
-  lower = 0.125,
-  upper = 0.333333,
-  training = FALSE,
-  generator = NULL
-)
    
+    
    torch_rrelu_(
+  self,
+  lower = 0.125,
+  upper = 0.333333,
+  training = FALSE,
+  generator = NULL
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/torch_rsqrt.html b/reference/torch_rsqrt.html
index c172ff369..7388252cc 100644
--- a/reference/torch_rsqrt.html
+++ b/reference/torch_rsqrt.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Rsqrt
    
     
 
-    
    torch_rsqrt(self)
    
+    
    torch_rsqrt(self)
    
 
     
    Arguments
    
     
    
@@ -218,17 +250,17 @@ the elements of input.
    
 $$
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(4))
-a
-torch_rsqrt(a)
-}
+a = torch_randn(c(4))
+a
+torch_rsqrt(a)
+}
 
    #> torch_tensor 
 #>     nan
-#>  1.0875
-#>  1.7124
-#>  1.1438
+#>     nan
+#>  2.1289
+#>     nan
 #> [ CPUFloatType{4} ]
    
   
    
   
 
@@ -146,6 +158,9 @@ term storage." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ term storage." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,7 +229,7 @@ term storage." />
 term storage.
    
     
 
-    
    torch_save(obj, path, ...)
    
+    
    torch_save(obj, path, ...)
    
 
     
    Arguments
    
     
    
diff --git a/reference/torch_selu.html b/reference/torch_selu.html
index 5abb76651..ad94b56c1 100644
--- a/reference/torch_selu.html
+++ b/reference/torch_selu.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Selu
    
     
 
-    
    torch_selu(self)
    
+    
    torch_selu(self)
    
 
     
    Arguments
    
     
    
diff --git a/reference/torch_selu_.html b/reference/torch_selu_.html
index fd576b25e..f2531b73f 100644
--- a/reference/torch_selu_.html
+++ b/reference/torch_selu_.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Selu_
    
     
 
-    
    torch_selu_(self)
    
+    
    torch_selu_(self)
    
 
     
    Arguments
    
     
    
diff --git a/reference/torch_sigmoid.html b/reference/torch_sigmoid.html
index b375a2ed2..e5b9f3846 100644
--- a/reference/torch_sigmoid.html
+++ b/reference/torch_sigmoid.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Sigmoid
    
     
 
-    
    torch_sigmoid(self)
    
+    
    torch_sigmoid(self)
    
 
     
    Arguments
    
     
    
@@ -217,17 +249,17 @@
 $$
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(4))
-a
-torch_sigmoid(a)
-}
+a = torch_randn(c(4))
+a
+torch_sigmoid(a)
+}
 
    #> torch_tensor 
-#>  0.4169
-#>  0.4884
-#>  0.4421
-#>  0.6942
+#>  0.4851
+#>  0.6093
+#>  0.3732
+#>  0.2108
 #> [ CPUFloatType{4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Sign
    
     
 
-    
    torch_sign(self)
    
+    
    torch_sign(self)
    
 
     
    Arguments
    
     
    
@@ -217,12 +249,12 @@
 $$
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_tensor(c(0.7, -1.2, 0., 2.3))
-a
-torch_sign(a)
-}
+a = torch_tensor(c(0.7, -1.2, 0., 2.3))
+a
+torch_sign(a)
+}
 
    #> torch_tensor 
 #>  1
 #> -1
diff --git a/reference/torch_sin.html b/reference/torch_sin.html
index c90950e40..e3e6fdda4 100644
--- a/reference/torch_sin.html
+++ b/reference/torch_sin.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Sin
    
     
    
 
-    
    torch_sin(self)
    
+    
    torch_sin(self)
    
 
     
    Arguments
    
     
    
@@ -217,17 +249,17 @@
 $$
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(4))
-a
-torch_sin(a)
-}
+a = torch_randn(c(4))
+a
+torch_sin(a)
+}
 
    #> torch_tensor 
-#> -0.2805
-#>  0.5482
-#>  0.8525
-#> -0.6915
+#> -0.8386
+#> -0.5387
+#>  0.9056
+#> -0.8318
 #> [ CPUFloatType{4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Sinh
    
     
 
-    
    torch_sinh(self)
    
+    
    torch_sinh(self)
    
 
     
    Arguments
    
     
    
@@ -218,17 +250,17 @@
 $$
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(4))
-a
-torch_sinh(a)
-}
+a = torch_randn(c(4))
+a
+torch_sinh(a)
+}
 
    #> torch_tensor 
-#> -0.7905
-#>  1.1799
-#>  1.2017
-#> -2.5987
+#> -1.3742
+#> -1.2565
+#>  1.3291
+#> -0.5771
 #> [ CPUFloatType{4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Slogdet
    
     
 
-    
    torch_slogdet(self)
    
+    
    torch_slogdet(self)
    
 
     
    Arguments
    
     
    
@@ -226,14 +258,14 @@ See `~torch.svd` for details.
 
    Calculates the sign and log absolute value of the determinant(s) of a square matrix or batches of square matrices.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-
-A = torch_randn(c(3, 3))
-A
-torch_det(A)
-torch_logdet(A)
-torch_slogdet(A)
-}
+    
    if (torch_is_installed()) {
+
+A = torch_randn(c(3, 3))
+A
+torch_det(A)
+torch_logdet(A)
+torch_slogdet(A)
+}
 
    #> [[1]]
 #> torch_tensor 
 #> 1
@@ -241,7 +273,7 @@ See `~torch.svd` for details.
 #> 
 #> [[2]]
 #> torch_tensor 
-#> -0.447307
+#> 1.17265
 #> [ CPUFloatType{} ]
 #> 
    
   
    
diff --git a/reference/torch_solve.html b/reference/torch_solve.html
index 2f10717ce..3bad40994 100644
--- a/reference/torch_solve.html
+++ b/reference/torch_solve.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Solve
    
     
 
-    
    torch_solve(self, A)
    
+    
    torch_solve(self, A)
    
 
     
    Arguments
    
     
    
@@ -233,30 +265,30 @@ batches of 2D matrices. If the inputs are batches, then returns
 batched outputs solution, LU.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-
-A = torch_tensor(rbind(c(6.80, -2.11,  5.66,  5.97,  8.23),
-                      c(-6.05, -3.30,  5.36, -4.44,  1.08),
-                      c(-0.45,  2.58, -2.70,  0.27,  9.04),
-                      c(8.32,  2.71,  4.35,  -7.17,  2.14),
-                      c(-9.67, -5.14, -7.26,  6.08, -6.87)))$t()
-B = torch_tensor(rbind(c(4.02,  6.19, -8.22, -7.57, -3.03),
-                      c(-1.56,  4.00, -8.67,  1.75,  2.86),
-                      c(9.81, -4.09, -4.57, -8.61,  8.99)))$t()
-out = torch_solve(B, A)
-X = out[[1]]
-LU = out[[2]]
-torch_dist(B, torch_mm(A, X))
+    
    if (torch_is_installed()) {
+
+A = torch_tensor(rbind(c(6.80, -2.11,  5.66,  5.97,  8.23),
+                      c(-6.05, -3.30,  5.36, -4.44,  1.08),
+                      c(-0.45,  2.58, -2.70,  0.27,  9.04),
+                      c(8.32,  2.71,  4.35,  -7.17,  2.14),
+                      c(-9.67, -5.14, -7.26,  6.08, -6.87)))$t()
+B = torch_tensor(rbind(c(4.02,  6.19, -8.22, -7.57, -3.03),
+                      c(-1.56,  4.00, -8.67,  1.75,  2.86),
+                      c(9.81, -4.09, -4.57, -8.61,  8.99)))$t()
+out = torch_solve(B, A)
+X = out[[1]]
+LU = out[[2]]
+torch_dist(B, torch_mm(A, X))
 # Batched solver example
-A = torch_randn(c(2, 3, 1, 4, 4))
-B = torch_randn(c(2, 3, 1, 4, 6))
-out = torch_solve(B, A)
-X = out[[1]]
-LU = out[[2]]
-torch_dist(B, A$matmul(X))
-}
+A = torch_randn(c(2, 3, 1, 4, 4))
+B = torch_randn(c(2, 3, 1, 4, 6))
+out = torch_solve(B, A)
+X = out[[1]]
+LU = out[[2]]
+torch_dist(B, A$matmul(X))
+}
 
    #> torch_tensor 
-#> 1.29687e-05
+#> 6.1e-06
 #> [ CPUFloatType{} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Sort
    
     
 
-    
    torch_sort(self, dim = -1L, descending = FALSE)
    
+    
    torch_sort(self, dim = -1L, descending = FALSE)
    
 
     
    Arguments
    
     
    
@@ -229,26 +261,26 @@ sorted values and indices are the indices of the elements in the or
 input tensor.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-
-x = torch_randn(c(3, 4))
-out = torch_sort(x)
-out
-out = torch_sort(x, 1)
-out
-}
+    
    if (torch_is_installed()) {
+
+x = torch_randn(c(3, 4))
+out = torch_sort(x)
+out
+out = torch_sort(x, 1)
+out
+}
 
    #> [[1]]
 #> torch_tensor 
-#> -0.7961  0.1753 -0.2432 -1.1334
-#> -0.2084  0.5509  0.3876 -0.9865
-#>  0.1948  0.9346  0.5226 -0.1232
+#> -0.3537 -3.3591 -0.5009 -1.4206
+#>  0.2542 -0.4446 -0.4783 -0.1513
+#>  1.0143  1.4504  1.6122  1.3161
 #> [ CPUFloatType{3,4} ]
 #> 
 #> [[2]]
 #> torch_tensor 
-#>  0  1  0  2
-#>  2  2  1  0
-#>  1  0  2  1
+#>  1  2  2  0
+#>  2  0  0  1
+#>  0  1  1  2
 #> [ CPULongType{3,4} ]
 #> 
    
   
    
diff --git a/reference/torch_sparse_coo_tensor.html b/reference/torch_sparse_coo_tensor.html
index 99aa3f3df..6384205b2 100644
--- a/reference/torch_sparse_coo_tensor.html
+++ b/reference/torch_sparse_coo_tensor.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,14 +227,14 @@
     
    Sparse_coo_tensor
    
     
 
-    
    torch_sparse_coo_tensor(
-  indices,
-  values,
-  size = NULL,
-  dtype = NULL,
-  device = NULL,
-  requires_grad = FALSE
-)
    
+    
    torch_sparse_coo_tensor(
+  indices,
+  values,
+  size = NULL,
+  dtype = NULL,
+  device = NULL,
+  requires_grad = FALSE
+)
    
 
     
    Arguments
    
     
    
@@ -244,25 +276,25 @@ coordinates in the indices, and the value at that index is the sum of all duplic
 torch_sparse_.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-i = torch_tensor(matrix(c(1, 2, 2, 3, 1, 3), ncol = 3, byrow = TRUE), dtype=torch_int64())
-v = torch_tensor(c(3, 4, 5), dtype=torch_float32())
-torch_sparse_coo_tensor(i, v)
-torch_sparse_coo_tensor(i, v, c(2, 4))
+i = torch_tensor(matrix(c(1, 2, 2, 3, 1, 3), ncol = 3, byrow = TRUE), dtype=torch_int64())
+v = torch_tensor(c(3, 4, 5), dtype=torch_float32())
+torch_sparse_coo_tensor(i, v)
+torch_sparse_coo_tensor(i, v, c(2, 4))
 
 # create empty sparse tensors
-S = torch_sparse_coo_tensor(
-  torch_empty(c(1, 0), dtype = torch_int64()), 
-  torch_tensor(numeric(), dtype = torch_float32()), 
-  c(1)
-)
-S = torch_sparse_coo_tensor(
-  torch_empty(c(1, 0), dtype = torch_int64()), 
-  torch_empty(c(0, 2)), 
-  c(1, 2)
-)
-}
+S = torch_sparse_coo_tensor(
+  torch_empty(c(1, 0), dtype = torch_int64()), 
+  torch_tensor(numeric(), dtype = torch_float32()), 
+  c(1)
+)
+S = torch_sparse_coo_tensor(
+  torch_empty(c(1, 0), dtype = torch_int64()), 
+  torch_empty(c(0, 2)), 
+  c(1, 2)
+)
+}
 
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Split
    
     
 
-    
    torch_split(self, split_size, dim = 1L)
    
+    
    torch_split(self, split_size, dim = 1L)
    
 
     
    Arguments
    
     
    
diff --git a/reference/torch_sqrt.html b/reference/torch_sqrt.html
index b8b49a160..bbcd7945a 100644
--- a/reference/torch_sqrt.html
+++ b/reference/torch_sqrt.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Sqrt
    
     
 
-    
    torch_sqrt(self)
    
+    
    torch_sqrt(self)
    
 
     
    Arguments
    
     
    
@@ -217,17 +249,17 @@
 $$
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(4))
-a
-torch_sqrt(a)
-}
+a = torch_randn(c(4))
+a
+torch_sqrt(a)
+}
 
    #> torch_tensor 
 #>     nan
-#>  0.2255
-#>  0.5333
-#>  0.3449
+#>  0.7270
+#>  0.5041
+#>     nan
 #> [ CPUFloatType{4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Square
    
     
 
-    
    torch_square(self)
    
+    
    torch_square(self)
    
 
     
    Arguments
    
     
    
@@ -214,17 +246,17 @@
 
    Returns a new tensor with the square of the elements of input.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(4))
-a
-torch_square(a)
-}
+a = torch_randn(c(4))
+a
+torch_square(a)
+}
 
    #> torch_tensor 
-#>  1.5194
-#>  0.2682
-#>  1.6344
-#>  0.8489
+#>  1.6023
+#>  0.1200
+#>  1.5702
+#>  7.8365
 #> [ CPUFloatType{4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Squeeze
    
     
 
-    
    torch_squeeze(self, dim)
    
+    
    torch_squeeze(self, dim)
    
 
     
    Arguments
    
     
    
@@ -229,17 +261,17 @@ dimension. If input is of shape: \((A \times 1 \times B)\),
 will squeeze the tensor to the shape \((A \times B)\).
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-
-x = torch_zeros(c(2, 1, 2, 1, 2))
-x
-y = torch_squeeze(x)
-y
-y = torch_squeeze(x, 1)
-y
-y = torch_squeeze(x, 2)
-y
-}
+    
    if (torch_is_installed()) {
+
+x = torch_zeros(c(2, 1, 2, 1, 2))
+x
+y = torch_squeeze(x)
+y
+y = torch_squeeze(x, 1)
+y
+y = torch_squeeze(x, 2)
+y
+}
 
    #> torch_tensor 
 #> (1,1,.,.) = 
 #>   0  0
diff --git a/reference/torch_stack.html b/reference/torch_stack.html
index c29f7a7de..40db26fad 100644
--- a/reference/torch_stack.html
+++ b/reference/torch_stack.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Stack
    
     
    
 
-    
    torch_stack(tensors, dim = 1L)
    
+    
    torch_stack(tensors, dim = 1L)
    
 
     
    Arguments
    
     
    
diff --git a/reference/torch_std.html b/reference/torch_std.html
index 4bdd78bf1..cb66842d0 100644
--- a/reference/torch_std.html
+++ b/reference/torch_std.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Std
    
     
 
-    
    torch_std(self, dim, unbiased = TRUE, keepdim = FALSE)
    
+    
    torch_std(self, dim, unbiased = TRUE, keepdim = FALSE)
    
 
     
    Arguments
    
     
    
@@ -242,22 +274,22 @@ output tensor having 1 (or len(dim)) fewer dimension(s).
    
 via the biased estimator. Otherwise, Bessel's correction will be used.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(1, 3))
-a
-torch_std(a)
+a = torch_randn(c(1, 3))
+a
+torch_std(a)
 
 
-a = torch_randn(c(4, 4))
-a
-torch_std(a, dim=1)
-}
+a = torch_randn(c(4, 4))
+a
+torch_std(a, dim=1)
+}
 
    #> torch_tensor 
-#>  0.5958
-#>  0.5692
-#>  1.5368
-#>  0.7848
+#>  1.0153
+#>  0.6058
+#>  1.6137
+#>  0.7942
 #> [ CPUFloatType{4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Std_mean
    
     
 
-    
    torch_std_mean(self, dim, unbiased = TRUE, keepdim = FALSE)
    
+    
    torch_std_mean(self, dim, unbiased = TRUE, keepdim = FALSE)
    
 
     
    Arguments
    
     
    
@@ -242,31 +274,31 @@ output tensor having 1 (or len(dim)) fewer dimension(s).
    
 via the biased estimator. Otherwise, Bessel's correction will be used.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(1, 3))
-a
-torch_std_mean(a)
+a = torch_randn(c(1, 3))
+a
+torch_std_mean(a)
 
 
-a = torch_randn(c(4, 4))
-a
-torch_std_mean(a, 1)
-}
+a = torch_randn(c(4, 4))
+a
+torch_std_mean(a, 1)
+}
 
    #> [[1]]
 #> torch_tensor 
-#>  1.4613
-#>  0.7194
-#>  0.5630
-#>  1.2705
+#>  1.1390
+#>  1.6594
+#>  1.2557
+#>  0.9028
 #> [ CPUFloatType{4} ]
 #> 
 #> [[2]]
 #> torch_tensor 
-#>  0.2608
-#> -0.1669
-#> -0.7519
-#> -1.0647
+#>  0.6997
+#> -0.4549
+#>  0.0871
+#> -0.1986
 #> [ CPUFloatType{4} ]
 #> 
    
   
    
diff --git a/reference/torch_stft.html b/reference/torch_stft.html
index 1d6ef0f23..fca295cef 100644
--- a/reference/torch_stft.html
+++ b/reference/torch_stft.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,17 +227,17 @@
     
    Stft
    
     
 
-    
    torch_stft(
-  input,
-  n_fft,
-  hop_length = NULL,
-  win_length = NULL,
-  window = NULL,
-  center = TRUE,
-  pad_mode = "reflect",
-  normalized = FALSE,
-  onesided = TRUE
-)
    
+    
    torch_stft(
+  input,
+  n_fft,
+  hop_length = NULL,
+  win_length = NULL,
+  window = NULL,
+  center = TRUE,
+  pad_mode = "reflect",
+  normalized = FALSE,
+  onesided = TRUE
+)
    
 
     
    Arguments
    
     
    
diff --git a/reference/torch_sum.html b/reference/torch_sum.html
index 6498a6e4f..0e28c16ba 100644
--- a/reference/torch_sum.html
+++ b/reference/torch_sum.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Sum
    
     
 
-    
    torch_sum(self, dim, keepdim = FALSE, dtype = NULL)
    
+    
    torch_sum(self, dim, keepdim = FALSE, dtype = NULL)
    
 
     
    Arguments
    
     
    
@@ -238,19 +270,19 @@ Otherwise, dim is squeezed (see 
 output tensor having 1 (or len(dim)) fewer dimension(s).
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(1, 3))
-a
-torch_sum(a)
+a = torch_randn(c(1, 3))
+a
+torch_sum(a)
 
 
-a = torch_randn(c(4, 4))
-a
-torch_sum(a, 1)
-b = torch_arange(0, 4 * 5 * 6)$view(c(4, 5, 6))
-torch_sum(b, list(2, 1))
-}
+a = torch_randn(c(4, 4))
+a
+torch_sum(a, 1)
+b = torch_arange(0, 4 * 5 * 6)$view(c(4, 5, 6))
+torch_sum(b, list(2, 1))
+}
 
    #> torch_tensor 
 #>   435
 #>  1335
diff --git a/reference/torch_svd.html b/reference/torch_svd.html
index 1914c9694..29916f541 100644
--- a/reference/torch_svd.html
+++ b/reference/torch_svd.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Svd
    
     
    
 
-    
    torch_svd(self, some = TRUE, compute_uv = TRUE)
    
+    
    torch_svd(self, some = TRUE, compute_uv = TRUE)
    
 
     
    Arguments
    
     
    
@@ -249,24 +281,24 @@ i.e., if the last two dimensions of input are m and some will be ignored here.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-
-a = torch_randn(c(5, 3))
-a
-out = torch_svd(a)
-u = out[[1]]
-s = out[[2]]
-v = out[[3]]
-torch_dist(a, torch_mm(torch_mm(u, torch_diag(s)), v$t()))
-a_big = torch_randn(c(7, 5, 3))
-out = torch_svd(a_big)
-u = out[[1]]
-s = out[[2]]
-v = out[[3]]
-torch_dist(a_big, torch_matmul(torch_matmul(u, torch_diag_embed(s)), v$transpose(-2, -1)))
-}
+    
    if (torch_is_installed()) {
+
+a = torch_randn(c(5, 3))
+a
+out = torch_svd(a)
+u = out[[1]]
+s = out[[2]]
+v = out[[3]]
+torch_dist(a, torch_mm(torch_mm(u, torch_diag(s)), v$t()))
+a_big = torch_randn(c(7, 5, 3))
+out = torch_svd(a_big)
+u = out[[1]]
+s = out[[2]]
+v = out[[3]]
+torch_dist(a_big, torch_matmul(torch_matmul(u, torch_diag_embed(s)), v$transpose(-2, -1)))
+}
 
    #> torch_tensor 
-#> 2.94448e-06
+#> 2.16926e-06
 #> [ CPUFloatType{} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Symeig
    
     
 
-    
    torch_symeig(self, eigenvectors = FALSE, upper = TRUE)
    
+    
    torch_symeig(self, eigenvectors = FALSE, upper = TRUE)
    
 
     
    Arguments
    
     
    
@@ -242,23 +274,23 @@ only the upper triangular portion is used by default.
    
 
    If upper is FALSE, then lower triangular portion is used.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-
-a = torch_randn(c(5, 5))
-a = a + a$t()  # To make a symmetric
-a
-o = torch_symeig(a, eigenvectors=TRUE)
-e = o[[1]]
-v = o[[2]]
-e
-v
-a_big = torch_randn(c(5, 2, 2))
-a_big = a_big + a_big$transpose(-2, -1)  # To make a_big symmetric
-o = a_big$symeig(eigenvectors=TRUE)
-e = o[[1]]
-v = o[[2]]
-torch_allclose(torch_matmul(v, torch_matmul(e$diag_embed(), v$transpose(-2, -1))), a_big)
-}
+    
    if (torch_is_installed()) {
+
+a = torch_randn(c(5, 5))
+a = a + a$t()  # To make a symmetric
+a
+o = torch_symeig(a, eigenvectors=TRUE)
+e = o[[1]]
+v = o[[2]]
+e
+v
+a_big = torch_randn(c(5, 2, 2))
+a_big = a_big + a_big$transpose(-2, -1)  # To make a_big symmetric
+o = a_big$symeig(eigenvectors=TRUE)
+e = o[[1]]
+v = o[[2]]
+torch_allclose(torch_matmul(v, torch_matmul(e$diag_embed(), v$transpose(-2, -1))), a_big)
+}
 
    #> [1] TRUE
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    T
    
     
 
-    
    torch_t(self)
    
+    
    torch_t(self)
    
 
     
    Arguments
    
     
    
@@ -217,22 +249,22 @@ and 1.
    
 is equivalent to transpose(input, 0, 1).
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-
-x = torch_randn(c(2,3))
-x
-torch_t(x)
-x = torch_randn(c(3))
-x
-torch_t(x)
-x = torch_randn(c(2, 3))
-x
-torch_t(x)
-}
+    
    if (torch_is_installed()) {
+
+x = torch_randn(c(2,3))
+x
+torch_t(x)
+x = torch_randn(c(3))
+x
+torch_t(x)
+x = torch_randn(c(2, 3))
+x
+torch_t(x)
+}
 
    #> torch_tensor 
-#>  0.9294 -0.8336
-#>  1.1379  0.4580
-#> -2.2674  0.2512
+#> -1.9564  0.0201
+#> -0.0425  0.3108
+#>  0.4885 -0.3318
 #> [ CPUFloatType{3,2} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Take
    
     
 
-    
    torch_take(self, index)
    
+    
    torch_take(self, index)
    
 
     
    Arguments
    
     
    
@@ -220,11 +252,11 @@ The input tensor is treated as if it were viewed as a 1-D tensor. The result
 takes the same shape as the indices.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-src = torch_tensor(matrix(c(4,3,5,6,7,8), ncol = 3, byrow = TRUE))
-torch_take(src, torch_tensor(c(1, 2, 5), dtype = torch_int64()))
-}
+src = torch_tensor(matrix(c(4,3,5,6,7,8), ncol = 3, byrow = TRUE))
+torch_take(src, torch_tensor(c(1, 2, 5), dtype = torch_int64()))
+}
 
    #> torch_tensor 
 #>  4
 #>  3
diff --git a/reference/torch_tan.html b/reference/torch_tan.html
index 33954955b..986348332 100644
--- a/reference/torch_tan.html
+++ b/reference/torch_tan.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Tan
    
     
    
 
-    
    torch_tan(self)
    
+    
    torch_tan(self)
    
 
     
    Arguments
    
     
    
@@ -217,17 +249,17 @@
 $$
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(4))
-a
-torch_tan(a)
-}
+a = torch_randn(c(4))
+a
+torch_tan(a)
+}
 
    #> torch_tensor 
-#> -0.2036
-#> -0.6868
-#>  0.0959
-#>  1.0523
+#> -0.8057
+#>  1.1575
+#> -0.8553
+#> -1.2794
 #> [ CPUFloatType{4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Tanh
    
     
 
-    
    torch_tanh(self)
    
+    
    torch_tanh(self)
    
 
     
    Arguments
    
     
    
@@ -218,17 +250,17 @@ of input.
    
 $$
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(4))
-a
-torch_tanh(a)
-}
+a = torch_randn(c(4))
+a
+torch_tanh(a)
+}
 
    #> torch_tensor 
-#>  0.4501
-#> -0.2993
-#>  0.4767
-#>  0.4199
+#> -0.7525
+#> -0.8047
+#>  0.9685
+#> -0.1683
 #> [ CPUFloatType{4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,13 +227,13 @@
     
    Converts R objects to a torch tensor
    
     
 
-    
    torch_tensor(
-  data,
-  dtype = NULL,
-  device = NULL,
-  requires_grad = FALSE,
-  pin_memory = FALSE
-)
    
+    
    torch_tensor(
+  data,
+  dtype = NULL,
+  device = NULL,
+  requires_grad = FALSE,
+  pin_memory = FALSE
+)
    
 
     
    Arguments
    
     
    
@@ -230,11 +262,11 @@
 
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-torch_tensor(c(1,2,3,4))
-torch_tensor(c(1,2,3,4), dtype = torch_int())
+    
    if (torch_is_installed()) {
+torch_tensor(c(1,2,3,4))
+torch_tensor(c(1,2,3,4), dtype = torch_int())
 
-}
+}
 
    #> torch_tensor 
 #>  1
 #>  2
diff --git a/reference/torch_tensordot.html b/reference/torch_tensordot.html
index 567ab398d..587358df6 100644
--- a/reference/torch_tensordot.html
+++ b/reference/torch_tensordot.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -73,7 +85,7 @@ tensordot implements a generalized matrix product." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -146,6 +158,9 @@ tensordot implements a generalized matrix product." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ tensordot implements a generalized matrix product." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,7 +229,7 @@ tensordot implements a generalized matrix product." />
 tensordot implements a generalized matrix product.
    
     
    
 
-    
    torch_tensordot(a, b, dims = 2)
    
+    
    torch_tensordot(a, b, dims = 2)
    
 
     
    Arguments
    
     
    
@@ -218,17 +250,17 @@ tensordot implements a generalized matrix product." />
 
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-
-a = torch_arange(start = 0, end = 60.)$reshape(c(3, 4, 5))
-b = torch_arange(start = 0, end = 24.)$reshape(c(4, 3, 2))
-torch_tensordot(a, b, dims = list(c(2, 1), c(1, 2)))
-if (FALSE) {
-a = torch_randn(3, 4, 5, device='cuda')
-b = torch_randn(4, 5, 6, device='cuda')
-c = torch_tensordot(a, b, dims=2)$cpu()
-}
-}
+    
    if (torch_is_installed()) {
+
+a = torch_arange(start = 0, end = 60.)$reshape(c(3, 4, 5))
+b = torch_arange(start = 0, end = 24.)$reshape(c(4, 3, 2))
+torch_tensordot(a, b, dims = list(c(2, 1), c(1, 2)))
+if (FALSE) {
+a = torch_randn(3, 4, 5, device='cuda')
+b = torch_randn(4, 5, 6, device='cuda')
+c = torch_tensordot(a, b, dims=2)$cpu()
+}
+}
 
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Threshold_
    
     
 
-    
    torch_threshold_(self, threshold, value)
    
+    
    torch_threshold_(self, threshold, value)
    
 
     
    Arguments
    
     
    
diff --git a/reference/torch_topk.html b/reference/torch_topk.html
index 735d131fc..a0a9ce637 100644
--- a/reference/torch_topk.html
+++ b/reference/torch_topk.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Topk
    
     
 
-    
    torch_topk(self, k, dim = -1L, largest = TRUE, sorted = TRUE)
    
+    
    torch_topk(self, k, dim = -1L, largest = TRUE, sorted = TRUE)
    
 
     
    Arguments
    
     
    
@@ -237,12 +269,12 @@ of the elements in the original input tensor.
    
 k elements are themselves sorted
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-x = torch_arange(1., 6.)
-x
-torch_topk(x, 3)
-}
+x = torch_arange(1., 6.)
+x
+torch_topk(x, 3)
+}
 
    #> [[1]]
 #> torch_tensor 
 #>  5
diff --git a/reference/torch_trace.html b/reference/torch_trace.html
index fdb85b552..30f0f6551 100644
--- a/reference/torch_trace.html
+++ b/reference/torch_trace.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Trace
    
     
    
 
-    
    torch_trace(self)
    
+    
    torch_trace(self)
    
 
     
    Arguments
    
     
    
@@ -214,12 +246,12 @@
 
    Returns the sum of the elements of the diagonal of the input 2-D matrix.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-x = torch_arange(1., 10.)$view(c(3, 3))
-x
-torch_trace(x)
-}
+x = torch_arange(1., 10.)$view(c(3, 3))
+x
+torch_trace(x)
+}
 
    #> torch_tensor 
 #> 15
 #> [ CPUFloatType{} ]
    
diff --git a/reference/torch_transpose.html b/reference/torch_transpose.html
index 34b09b098..3494d3a4a 100644
--- a/reference/torch_transpose.html
+++ b/reference/torch_transpose.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Transpose
    
     
 
-    
    torch_transpose(self, dim0, dim1)
    
+    
    torch_transpose(self, dim0, dim1)
    
 
     
    Arguments
    
     
    
@@ -226,16 +258,16 @@ The given dimensions dim0 and dim1 are swapped.
    
 of the other.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-x = torch_randn(c(2, 3))
-x
-torch_transpose(x, 1, 2)
-}
+x = torch_randn(c(2, 3))
+x
+torch_transpose(x, 1, 2)
+}
 
    #> torch_tensor 
-#>  0.4633 -0.6867
-#> -1.8115  0.4476
-#> -1.5475  1.2365
+#> -1.6599 -0.1619
+#> -0.2997 -0.2021
+#>  1.1402  0.2918
 #> [ CPUFloatType{3,2} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Trapz
    
     
 
-    
    torch_trapz(y, dx = 1L, x, dim = -1L)
    
+    
    torch_trapz(y, dx = 1L, x, dim = -1L)
    
 
     
    Arguments
    
     
    
@@ -232,17 +264,17 @@
 
    As above, but the sample points are spaced uniformly at a distance of dx.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-y = torch_randn(list(2, 3))
-y
-x = torch_tensor(matrix(c(1, 3, 4, 1, 2, 3), ncol = 3, byrow=TRUE))
-torch_trapz(y, x = x)
+y = torch_randn(list(2, 3))
+y
+x = torch_tensor(matrix(c(1, 3, 4, 1, 2, 3), ncol = 3, byrow=TRUE))
+torch_trapz(y, x = x)
 
-}
+}
 
    #> torch_tensor 
-#> -1.7562
-#> -0.8343
+#> -3.0516
+#> -1.3811
 #> [ CPUFloatType{2} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,13 +227,13 @@
     
    Triangular_solve
    
     
 
-    
    torch_triangular_solve(
-  self,
-  A,
-  upper = TRUE,
-  transpose = FALSE,
-  unitriangular = FALSE
-)
    
+    
    torch_triangular_solve(
+  self,
+  A,
+  upper = TRUE,
+  transpose = FALSE,
+  unitriangular = FALSE
+)
    
 
     
    Arguments
    
     
    
@@ -242,24 +274,24 @@ batches of 2D matrices. If the inputs are batches, then returns
 batched outputs X
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-
-A = torch_randn(c(2, 2))$triu()
-A
-b = torch_randn(c(2, 3))
-b
-torch_triangular_solve(b, A)
-}
+    
    if (torch_is_installed()) {
+
+A = torch_randn(c(2, 2))$triu()
+A
+b = torch_randn(c(2, 3))
+b
+torch_triangular_solve(b, A)
+}
 
    #> [[1]]
 #> torch_tensor 
-#>  5.9921  7.3633  6.5760
-#>  0.6631  3.9283  2.7029
+#> -1.4151  1.2923  0.9190
+#> -0.3280  1.1489 -0.3906
 #> [ CPUFloatType{2,3} ]
 #> 
 #> [[2]]
 #> torch_tensor 
-#>  0.1910  0.0582
-#>  0.0000  0.2591
+#>  1.2681 -0.4428
+#>  0.0000 -1.2994
 #> [ CPUFloatType{2,2} ]
 #> 
    
   
    
diff --git a/reference/torch_tril.html b/reference/torch_tril.html
index 9fac64c1b..c0a377d18 100644
--- a/reference/torch_tril.html
+++ b/reference/torch_tril.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Tril
    
     
 
-    
    torch_tril(self, diagonal = 0L)
    
+    
    torch_tril(self, diagonal = 0L)
    
 
     
    Arguments
    
     
    
@@ -228,21 +260,21 @@ the main diagonal. The main diagonal are the set of indices
 \(d_{1}, d_{2}\) are the dimensions of the matrix.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-
-a = torch_randn(c(3, 3))
-a
-torch_tril(a)
-b = torch_randn(c(4, 6))
-b
-torch_tril(b, diagonal=1)
-torch_tril(b, diagonal=-1)
-}
+    
    if (torch_is_installed()) {
+
+a = torch_randn(c(3, 3))
+a
+torch_tril(a)
+b = torch_randn(c(4, 6))
+b
+torch_tril(b, diagonal=1)
+torch_tril(b, diagonal=-1)
+}
 
    #> torch_tensor 
 #>  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000
-#> -0.0705  0.0000  0.0000  0.0000  0.0000  0.0000
-#>  1.4173  0.3856  0.0000  0.0000  0.0000  0.0000
-#>  1.3653 -1.3079 -1.1473  0.0000  0.0000  0.0000
+#>  0.4164  0.0000  0.0000  0.0000  0.0000  0.0000
+#>  0.6462 -0.3278  0.0000  0.0000  0.0000  0.0000
+#>  0.0475  1.0216 -0.9426  0.0000  0.0000  0.0000
 #> [ CPUFloatType{4,6} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,14 +227,14 @@
     
    Tril_indices
    
     
 
-    
    torch_tril_indices(
-  row,
-  col,
-  offset = 0,
-  dtype = torch_long(),
-  device = "cpu",
-  layout = torch_strided()
-)
    
+    
    torch_tril_indices(
+  row,
+  col,
+  offset = 0,
+  dtype = torch_long(),
+  device = "cpu",
+  layout = torch_strided()
+)
    
 
     
    Arguments
    
     
    
@@ -260,16 +292,16 @@ the main diagonal. The main diagonal are the set of indices
 where \(d_{1}, d_{2}\) are the dimensions of the matrix.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-if (FALSE) {
-a = torch_tril_indices(3, 3)
-a
-a = torch_tril_indices(4, 3, -1)
-a
-a = torch_tril_indices(4, 3, 1)
-a
-}
-}
+    
    if (torch_is_installed()) {
+if (FALSE) {
+a = torch_tril_indices(3, 3)
+a
+a = torch_tril_indices(4, 3, -1)
+a
+a = torch_tril_indices(4, 3, 1)
+a
+}
+}
 
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Triu
    
     
 
-    
    torch_triu(self, diagonal = 0L)
    
+    
    torch_triu(self, diagonal = 0L)
    
 
     
    Arguments
    
     
    
@@ -228,23 +260,23 @@ the main diagonal. The main diagonal are the set of indices
 \(d_{1}, d_{2}\) are the dimensions of the matrix.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-
-a = torch_randn(c(3, 3))
-a
-torch_triu(a)
-torch_triu(a, diagonal=1)
-torch_triu(a, diagonal=-1)
-b = torch_randn(c(4, 6))
-b
-torch_triu(b, diagonal=1)
-torch_triu(b, diagonal=-1)
-}
+    
    if (torch_is_installed()) {
+
+a = torch_randn(c(3, 3))
+a
+torch_triu(a)
+torch_triu(a, diagonal=1)
+torch_triu(a, diagonal=-1)
+b = torch_randn(c(4, 6))
+b
+torch_triu(b, diagonal=1)
+torch_triu(b, diagonal=-1)
+}
 
    #> torch_tensor 
-#>  0.8511  0.3136 -0.8565 -0.3131  0.8333 -1.6256
-#>  0.6987 -0.8917  2.5117  1.6975 -0.5125  1.6937
-#>  0.0000  0.6684  1.6408  0.0282  0.3932 -1.6401
-#>  0.0000  0.0000 -0.9555  0.2990  0.3913 -0.5259
+#> -0.0346  0.4857 -0.6240 -0.0739 -1.5083  0.9504
+#> -0.6462  0.9285  0.0715 -0.7684 -0.3846 -1.2153
+#>  0.0000  0.7828  0.0337 -1.4075 -0.0488 -1.0085
+#>  0.0000  0.0000  1.3318  0.4527  0.3162 -0.7587
 #> [ CPUFloatType{4,6} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,14 +227,14 @@
     
    Triu_indices
    
     
 
-    
    torch_triu_indices(
-  row,
-  col,
-  offset = 0,
-  dtype = torch_long(),
-  device = "cpu",
-  layout = torch_strided()
-)
    
+    
    torch_triu_indices(
+  row,
+  col,
+  offset = 0,
+  dtype = torch_long(),
+  device = "cpu",
+  layout = torch_strided()
+)
    
 
     
    Arguments
    
     
    
@@ -260,16 +292,16 @@ the main diagonal. The main diagonal are the set of indices
 where \(d_{1}, d_{2}\) are the dimensions of the matrix.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-if (FALSE) {
-a = torch_triu_indices(3, 3)
-a
-a = torch_triu_indices(4, 3, -1)
-a
-a = torch_triu_indices(4, 3, 1)
-a
-}
-}
+    
    if (torch_is_installed()) {
+if (FALSE) {
+a = torch_triu_indices(3, 3)
+a
+a = torch_triu_indices(4, 3, -1)
+a
+a = torch_triu_indices(4, 3, 1)
+a
+}
+}
 
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    TRUE_divide
    
     
 
-    
    torch_true_divide(self, other)
    
+    
    torch_true_divide(self, other)
    
 
     
    Arguments
    
     
    
@@ -224,13 +256,13 @@ in which case they are cast to the default (floating) scalar type before the div
 $$
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-dividend = torch_tensor(c(5, 3), dtype=torch_int())
-divisor = torch_tensor(c(3, 2), dtype=torch_int())
-torch_true_divide(dividend, divisor)
-torch_true_divide(dividend, 2)
-}
+dividend = torch_tensor(c(5, 3), dtype=torch_int())
+divisor = torch_tensor(c(3, 2), dtype=torch_int())
+torch_true_divide(dividend, divisor)
+torch_true_divide(dividend, 2)
+}
 
    #> torch_tensor 
 #>  2.5000
 #>  1.5000
diff --git a/reference/torch_trunc.html b/reference/torch_trunc.html
index bf9f628f5..5be1898f6 100644
--- a/reference/torch_trunc.html
+++ b/reference/torch_trunc.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Trunc
    
     
    
 
-    
    torch_trunc(self)
    
+    
    torch_trunc(self)
    
 
     
    Arguments
    
     
    
@@ -215,17 +247,17 @@
 the elements of input.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(4))
-a
-torch_trunc(a)
-}
+a = torch_randn(c(4))
+a
+torch_trunc(a)
+}
 
    #> torch_tensor 
+#>  0
 #> -0
-#> -0
-#> -1
-#>  1
+#>  2
+#>  0
 #> [ CPUFloatType{4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Unbind
    
     
 
-    
    torch_unbind(self, dim = 1L)
    
+    
    torch_unbind(self, dim = 1L)
    
 
     
    Arguments
    
     
    
@@ -219,10 +251,10 @@
 
    Returns a tuple of all slices along a given dimension, already without it.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_unbind(torch_tensor(matrix(1:9, ncol = 3, byrow=TRUE)))
-}
+torch_unbind(torch_tensor(matrix(1:9, ncol = 3, byrow=TRUE)))
+}
 
    #> [[1]]
 #> torch_tensor 
 #>  1
diff --git a/reference/torch_unique_consecutive.html b/reference/torch_unique_consecutive.html
index ea5529bb9..19393e7bf 100644
--- a/reference/torch_unique_consecutive.html
+++ b/reference/torch_unique_consecutive.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,12 +227,12 @@
     
    Unique_consecutive
    
     
    
 
-    
    torch_unique_consecutive(
-  self,
-  return_inverse = FALSE,
-  return_counts = FALSE,
-  dim = NULL
-)
    
+    
    torch_unique_consecutive(
+  self,
+  return_inverse = FALSE,
+  return_counts = FALSE,
+  dim = NULL
+)
    
 
     
    Arguments
    
     
    
@@ -235,13 +267,13 @@
 
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-x = torch_tensor(c(1, 1, 2, 2, 3, 1, 1, 2))
-output = torch_unique_consecutive(x)
-output
-torch_unique_consecutive(x, return_inverse=TRUE)
-torch_unique_consecutive(x, return_counts=TRUE)
-}
+    
    if (torch_is_installed()) {
+x = torch_tensor(c(1, 1, 2, 2, 3, 1, 1, 2))
+output = torch_unique_consecutive(x)
+output
+torch_unique_consecutive(x, return_inverse=TRUE)
+torch_unique_consecutive(x, return_counts=TRUE)
+}
 
    #> [[1]]
 #> torch_tensor 
 #>  1
diff --git a/reference/torch_unsqueeze.html b/reference/torch_unsqueeze.html
index c051eef6a..f7a3207f5 100644
--- a/reference/torch_unsqueeze.html
+++ b/reference/torch_unsqueeze.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Unsqueeze
    
     
    
 
-    
    torch_unsqueeze(self, dim)
    
+    
    torch_unsqueeze(self, dim)
    
 
     
    Arguments
    
     
    
@@ -223,12 +255,12 @@ can be used. Negative dim will correspond to unsqueeze
 applied at dim = dim + input.dim() + 1.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-x = torch_tensor(c(1, 2, 3, 4))
-torch_unsqueeze(x, 1)
-torch_unsqueeze(x, 2)
-}
+x = torch_tensor(c(1, 2, 3, 4))
+torch_unsqueeze(x, 1)
+torch_unsqueeze(x, 2)
+}
 
    #> torch_tensor 
 #>  1
 #>  2
diff --git a/reference/torch_var.html b/reference/torch_var.html
index 56b75c8e4..6d50ca65c 100644
--- a/reference/torch_var.html
+++ b/reference/torch_var.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Var
    
     
    
 
-    
    torch_var(self, dim, unbiased = TRUE, keepdim = FALSE)
    
+    
    torch_var(self, dim, unbiased = TRUE, keepdim = FALSE)
    
 
     
    Arguments
    
     
    
@@ -241,22 +273,22 @@ output tensor having 1 (or len(dim)) fewer dimension(s).
    
 biased estimator. Otherwise, Bessel's correction will be used.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(1, 3))
-a
-torch_var(a)
+a = torch_randn(c(1, 3))
+a
+torch_var(a)
 
 
-a = torch_randn(c(4, 4))
-a
-torch_var(a, 1)
-}
+a = torch_randn(c(4, 4))
+a
+torch_var(a, 1)
+}
 
    #> torch_tensor 
-#>  0.3456
-#>  0.0521
-#>  1.0536
-#>  0.3194
+#>  0.2728
+#>  1.5850
+#>  0.9337
+#>  2.5722
 #> [ CPUFloatType{4} ]
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Var_mean
    
     
 
-    
    torch_var_mean(self, dim, unbiased = TRUE, keepdim = FALSE)
    
+    
    torch_var_mean(self, dim, unbiased = TRUE, keepdim = FALSE)
    
 
     
    Arguments
    
     
    
@@ -241,31 +273,31 @@ output tensor having 1 (or len(dim)) fewer dimension(s).
    
 biased estimator. Otherwise, Bessel's correction will be used.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-a = torch_randn(c(1, 3))
-a
-torch_var_mean(a)
+a = torch_randn(c(1, 3))
+a
+torch_var_mean(a)
 
 
-a = torch_randn(c(4, 4))
-a
-torch_var_mean(a, 1)
-}
+a = torch_randn(c(4, 4))
+a
+torch_var_mean(a, 1)
+}
 
    #> [[1]]
 #> torch_tensor 
-#>  0.2877
-#>  1.0334
-#>  0.7203
-#>  1.7788
+#>  1.9048
+#>  0.7948
+#>  0.6549
+#>  0.2514
 #> [ CPUFloatType{4} ]
 #> 
 #> [[2]]
 #> torch_tensor 
-#>  0.1609
-#> -0.4014
-#>  0.6819
-#>  0.3604
+#> -0.2047
+#> -0.4955
+#> -0.5893
+#>  0.0395
 #> [ CPUFloatType{4} ]
 #> 
    
   
    
diff --git a/reference/torch_where.html b/reference/torch_where.html
index 62993b0de..694ff715e 100644
--- a/reference/torch_where.html
+++ b/reference/torch_where.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Where
    
     
 
-    
    torch_where(condition, self, other)
    
+    
    torch_where(condition, self, other)
    
 
     
    Arguments
    
     
    
@@ -244,18 +276,18 @@ $$
    
 torch_nonzero(condition, as_tuple=TRUE).
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-if (FALSE) {
-x = torch_randn(c(3, 2))
-y = torch_ones(c(3, 2))
-x
-torch_where(x > 0, x, y)
-}
+if (FALSE) {
+x = torch_randn(c(3, 2))
+y = torch_ones(c(3, 2))
+x
+torch_where(x > 0, x, y)
+}
 
 
 
-}
+}
 
    
   
    
   
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,14 +227,14 @@
     
    Zeros
    
     
 
-    
    torch_zeros(
-  ...,
-  names = NULL,
-  dtype = NULL,
-  layout = torch_strided(),
-  device = NULL,
-  requires_grad = FALSE
-)
    
+    
    torch_zeros(
+  ...,
+  names = NULL,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
    
 
     
    Arguments
    
     
    
@@ -242,11 +274,11 @@
 by the variable argument size.
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-torch_zeros(c(2, 3))
-torch_zeros(c(5))
-}
+torch_zeros(c(2, 3))
+torch_zeros(c(5))
+}
 
    #> torch_tensor 
 #>  0
 #>  0
diff --git a/reference/torch_zeros_like.html b/reference/torch_zeros_like.html
index 01f033525..7d5693a59 100644
--- a/reference/torch_zeros_like.html
+++ b/reference/torch_zeros_like.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,14 +227,14 @@
     
    Zeros_like
    
     
    
 
-    
    torch_zeros_like(
-  input,
-  dtype = NULL,
-  layout = torch_strided(),
-  device = NULL,
-  requires_grad = FALSE,
-  memory_format = torch_preserve_format()
-)
    
+    
    torch_zeros_like(
+  input,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE,
+  memory_format = torch_preserve_format()
+)
    
 
     
    Arguments
    
     
    
@@ -250,11 +282,11 @@ the old torch_zeros_like(input, out=output) is equivalent to
 torch_zeros(input.size(), out=output).
    
 
     
    Examples
    
-    
    if (torch_is_installed()) {
+    
    if (torch_is_installed()) {
 
-input = torch_empty(c(2, 3))
-torch_zeros_like(input)
-}
+input = torch_empty(c(2, 3))
+torch_zeros_like(input)
+}
 
    #> torch_tensor 
 #>  0  0  0
 #>  0  0  0
diff --git a/reference/with_enable_grad.html b/reference/with_enable_grad.html
index 04143facb..e9e239867 100644
--- a/reference/with_enable_grad.html
+++ b/reference/with_enable_grad.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
 
 
 
+
+
+
+
 
   
 
@@ -73,7 +85,7 @@ Enables gradient calculation, if it has been disabled via with_no_grad." />
       
       
         torch
-        0.0.3
+        0.1.0
       
     
    
 
@@ -146,6 +158,9 @@ Enables gradient calculation, if it has been disabled via with_no_grad." />
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -159,7 +174,24 @@ Enables gradient calculation, if it has been disabled via with_no_grad." />
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -197,7 +229,7 @@ Enables gradient calculation, if it has been disabled via with_no_grad." />
 Enables gradient calculation, if it has been disabled via with_no_grad.
    
     
    
 
-    
    with_enable_grad(code)
    
+    
    with_enable_grad(code)
    
 
     
    Arguments
    
     
    
@@ -214,18 +246,18 @@ Enables gradient calculation, if it has been disabled via if (torch_is_installed()) {
-
-x <- torch_tensor(1, requires_grad=TRUE)
-with_no_grad({
-  with_enable_grad({
-    y = x * 2
-  })
-})
-y$backward()
-x$grad
-
-}
+    
    if (torch_is_installed()) {
+
+x <- torch_tensor(1, requires_grad=TRUE)
+with_no_grad({
+  with_enable_grad({
+    y = x * 2
+  })
+})
+y$backward()
+x$grad
+
+}
 
    #> torch_tensor 
 #>  2
 #> [ CPUFloatType{1} ]
    
diff --git a/reference/with_no_grad.html b/reference/with_no_grad.html
index 85d16d558..bc1defced 100644
--- a/reference/with_no_grad.html
+++ b/reference/with_no_grad.html
@@ -38,6 +38,8 @@
 
 
 
+  
+  
 
 
 
@@ -55,6 +57,16 @@
 
 
 
+
+
+
+
 
   
 
@@ -72,7 +84,7 @@
       
       
         torch
-        0.0.3
+        0.1.0
       
     
 
@@ -145,6 +157,9 @@
     
  • 
       Tensor class
     
    • 
+    
  • 
+      Serialization
+    
    • 
     
  • Datasets
    • 
     
  • 
       Loading Data
@@ -158,7 +173,24 @@
     
    • 
   
 
-
  • Examples
    • 
+
  • 
+  
+  
+
    • 
 
  • 
   Reference
 
    • 
@@ -195,7 +227,7 @@
     
    Temporarily modify gradient recording.
    
     
 
-    
    with_no_grad(code)
    
+    
    with_no_grad(code)
    
 
     
    Arguments
    
     
    
@@ -208,15 +240,15 @@
 
 
     
    Examples
    
-    
    if (torch_is_installed()) {
-x <- torch_tensor(runif(5), requires_grad = TRUE)
-with_no_grad({
-  x$sub_(torch_tensor(as.numeric(1:5)))
-})
-x
-x$grad
-
-}
+    
    if (torch_is_installed()) {
+x <- torch_tensor(runif(5), requires_grad = TRUE)
+with_no_grad({
+  x$sub_(torch_tensor(as.numeric(1:5)))
+})
+x
+x$grad
+
+}
 
    #> torch_tensor 
 #> [ Tensor (undefined) ]
    
   
    
-- 
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