# Latent Binary Bayesian Neural Networks Using 'torch'

## Documentation for package ‘LBBNN’ version 0.1.2

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

## Help Pages

coef.LBBNN_Net | Get model coefficients (local explanations) of an 'LBBNN_Net' object
Custom_activation | Generate a custom activation function.
FLOW | Class to generate a normalizing flow
Gallstone_Dataset | Gallstone Dataset
get_dataloaders | Wrapper around 'torch::dataloader'
get_local_explanations_gradient | Function to 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)
LBBNN_plot | Function to plot an input skip structure after removing weights in non-active paths.
mgp_dataset | Auto MPG daataset
Mice_Dataset | Mice Dataset
plot.LBBNN_Net | Plot 'LBBNN_Net' objects
plot_local_explanations_gradient | Plot the gradient based local explanations for one sample with input-skip LBBNNs.
predict.LBBNN_Net | Obtain predictions from the variational 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
train_LBBNN | Train an instance of 'LBBNN_Net'.
validate_LBBNN | Validate a trained LBBNN model.