R: Latent Binary Bayesian Neural Networks Using 'torch'

# Latent Binary Bayesian Neural Networks Using 'torch'

## Documentation for package ‘LBBNN’ version 0.1.5

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

coef.lbbnn_net | Get model coefficients (local explanations) of 'lbbnn_net' object
custom_activation | Generate a custom activation function.
gallstone_dataset | Gallstone Dataset
get_dataloaders | Wrapper around 'torch::dataloader'
get_local_explanations_gradient | Get gradient based local explanations for input-skip LBBNNs.
lbbnn_conv2d | Class to generate an LBBNN convolutional layer.
lbbnn_linear | Class to generate an LBBNN feed forward layer
lbbnn_net | Feed-forward Latent Binary Bayesian Neural Network (LBBNN)
normalizing_flow | Class to generate a normalizing flow
plot.lbbnn_net | Plot 'lbbnn_net' objects
plot_active_paths | Function to plot an input skip structure after removing weights in non-active paths.
plot_local_explanations_gradient | Plot the gradient based local explanations for one sample.
predict.lbbnn_net | Obtain predictions from the posterior of an 'LBBNN model'
print.lbbnn_net | Print summary of an 'lbbnn_net' object
quants | Function to obtain empirical 95% confidence interval, including the mean
raisin_dataset | Raisins Dataset
residuals.lbbnn_net | Residuals from LBBNN fit
rnvp_layer | Single RNVP transform layer.
summary.lbbnn_net | Summary of LBBNN fit
torch_available | Check if torch/libtorch is available for examples
train_lbbnn | Train an instance of 'lbbnn_net'.
validate_lbbnn | Validate a trained LBBNN model.