Latent Binary Bayesian Neural Networks Using 'torch'

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Documentation for package ‘LBBNN’ version 0.1.3

• 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
train_lbbnn	Train an instance of 'lbbnn_net'.
validate_lbbnn	Validate a trained LBBNN model.