Package: autotab
Title: Variational Autoencoders for Heterogeneous Tabular Data
Version: 1.0.1
Authors@R: 
    person("Sarah", "Milligan", email = "slm1999@bu.edu", role = c("aut", "cre"))
Description: Build and train a variational autoencoder (VAE) for mixed-type
    tabular data (continuous, binary, categorical).
    Models are implemented using 'TensorFlow' and 'Keras' via the 'reticulate' 
    interface, enabling reproducible VAE training for heterogeneous tabular 
    datasets.
License: MIT + file LICENSE
URL: https://github.com/SarahMilligan-hub/AutoTab
BugReports: https://github.com/SarahMilligan-hub/AutoTab/issues
Encoding: UTF-8
RoxygenNote: 7.3.3
Depends: R (>= 4.1)
Imports: keras, magrittr, R6, reticulate, tensorflow
Suggests: caret
SystemRequirements: Python (>= 3.8); TensorFlow (>= 2.10); Keras;
        TensorFlow Addons
LazyData: true
NeedsCompilation: no
Packaged: 2026-05-21 21:19:00 UTC; smill
Author: Sarah Milligan [aut, cre]
Maintainer: Sarah Milligan <slm1999@bu.edu>
Repository: CRAN
Date/Publication: 2026-05-21 22:50:12 UTC
Built: R 4.6.0; ; 2026-05-25 03:52:59 UTC; unix
