Running examples in 'innsight-Ex.R' failed
The error most likely occurred in:

> ### Name: ConnectionWeights
> ### Title: Connection Weights Method
> ### Aliases: ConnectionWeights
> 
> ### ** Examples
> 
> ## Don't show: 
> if (torch::torch_is_installed()) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
+ ## End(Don't show)
+ #----------------------- Example 1: Torch ----------------------------------
+ library(torch)
+ 
+ # Create nn_sequential model
+ model <- nn_sequential(
+   nn_linear(5, 12),
+   nn_relu(),
+   nn_linear(12, 1),
+   nn_sigmoid()
+ )
+ 
+ # Create Converter with input names
+ converter <- Converter$new(model,
+   input_dim = c(5),
+   input_names = list(c("Car", "Cat", "Dog", "Plane", "Horse"))
+ )
+ 
+ # Apply method Connection Weights
+ cw <- ConnectionWeights$new(converter)
+ 
+ # Print the result as a data.frame
+ cw$get_result("data.frame")
+ 
+ # Plot the result
+ plot(cw)
+ 
+ #----------------------- Example 2: Neuralnet ------------------------------
+ library(neuralnet)
+ data(iris)
+ 
+ # Train a Neural Network
+ nn <- neuralnet((Species == "setosa") ~ Petal.Length + Petal.Width,
+   iris,
+   linear.output = FALSE,
+   hidden = c(3, 2), act.fct = "tanh", rep = 1
+ )
+ 
+ # Convert the trained model
+ converter <- Converter$new(nn)
+ 
+ # Apply the Connection Weights method
+ cw <- ConnectionWeights$new(converter)
+ 
+ # Get the result as a torch tensor
+ cw$get_result(type = "torch.tensor")
+ 
+ # Plot the result
+ plot(cw)
+ 
+ #----------------------- Example 3: Keras ----------------------------------
+ library(keras)
+ 
+ if (is_keras_available()) {
+   # Define a model
+   model <- keras_model_sequential()
+   model %>%
+     layer_conv_1d(
+       input_shape = c(64, 3), kernel_size = 16, filters = 8,
+       activation = "softplus"
+     ) %>%
+     layer_conv_1d(kernel_size = 16, filters = 4, activation = "tanh") %>%
+     layer_conv_1d(kernel_size = 16, filters = 2, activation = "relu") %>%
+     layer_flatten() %>%
+     layer_dense(units = 64, activation = "relu") %>%
+     layer_dense(units = 2, activation = "softmax")
+ 
+   # Convert the model
+   converter <- Converter$new(model)
+ 
+   # Apply the Connection Weights method
+   cw <- ConnectionWeights$new(converter)
+ 
+   # Get the result as data.frame
+   cw$get_result(type = "data.frame")
+ 
+   # Plot the result for all classes
+   plot(cw, output_idx = 1:2)
+ }
+ 
+ # ------------------------- Advanced: Plotly -------------------------------
+ # If you want to create an interactive plot of your results with custom
+ # changes, you can take use of the method plotly::ggplotly
+ library(ggplot2)
+ library(plotly)
+ library(neuralnet)
+ data(iris)
+ 
+ nn <- neuralnet(Species ~ .,
+   iris,
+   linear.output = FALSE,
+   hidden = c(10, 8), act.fct = "tanh", rep = 1, threshold = 0.5
+ )
+ # create an converter for this model
+ converter <- Converter$new(nn)
+ 
+ # create new instance of 'LRP'
+ cw <- ConnectionWeights$new(converter)
+ 
+ library(plotly)
+ 
+ # Get the ggplot and add your changes
+ p <- plot(cw, output_idx = 1) +
+   theme_bw() +
+   scale_fill_gradient2(low = "green", mid = "black", high = "blue")
+ 
+ # Now apply the method plotly::ggplotly with argument tooltip = "text"
+ plotly::ggplotly(p, tooltip = "text")
+ ## Don't show: 
+ }) # examplesIf
> library(torch)
> model <- nn_sequential(nn_linear(5, 12), nn_relu(), nn_linear(12, 1), 
+     nn_sigmoid())
Error in cpp_torch_strided() : 
  Lantern is not loaded. Please use `install_torch()` to install additional dependencies.
Calls: <Anonymous> ... torch_strided -> <Anonymous> -> initialize -> cpp_torch_strided
Execution halted