R: Variational Autoencoders for Heterogeneous Tabular Data

# Variational Autoencoders for Heterogeneous Tabular Data

## Documentation for package ‘autotab’ version 1.0

- DESCRIPTION file
- Package README

## Help Pages

decoder_model | Builds the decoder graph for an AutoTab VAE
Decoder_weights | Extract decoder-only weights from a trained Keras model
encoder_decoder_information | Specifying Encoder and Decoder Architectures for 'VAE_train()'
encoder_latent | Rebuild the encoder graph to export z_mean and z_log_var
Encoder_weights | Extract encoder-only weights from a trained Keras model
extracting_distribution | Build the 'feat_dist' data frame for AutoTab
feat_reorder | Reorder 'feat_dist' rows to match preprocessed data
get_feat_dist | Get the stored feature distribution
Latent_sample | Sample from the latent space
min_max_scale | Min–max scale continuous variables
mog_prior | Mixture-of-Gaussians (MoG) prior in AutoTab
reset_seeds | Reset all random seeds across R, TensorFlow, and Python
set_feat_dist | Set the feature distribution for AutoTab
VAE_train | Train an AutoTab VAE on mixed-type tabular data