Package: autotab
Title: Variational Autoencoders for Heterogeneous Tabular Data
Version: 1.1
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: dplyr, caret, knitr, rmarkdown, testthat (>= 3.0.0)
SystemRequirements: Python (>= 3.11); TensorFlow (>= 2.11, < 2.14);
        Keras; TensorFlow Addons
LazyData: true
VignetteBuilder: knitr
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2026-07-30 21:13:02 UTC; smill
Author: Sarah Milligan [aut, cre]
Maintainer: Sarah Milligan <slm1999@bu.edu>
Repository: CRAN
Date/Publication: 2026-07-30 21:30:02 UTC
