autotab: Variational Autoencoders for Heterogeneous Tabular Data
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.
| Version: |
1.1 |
| Depends: |
R (≥ 4.1) |
| Imports: |
keras, magrittr, R6, reticulate, tensorflow |
| Suggests: |
dplyr, caret, knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: |
2026-07-30 |
| DOI: |
10.32614/CRAN.package.autotab |
| Author: |
Sarah Milligan [aut, cre] |
| Maintainer: |
Sarah Milligan <slm1999 at bu.edu> |
| BugReports: |
https://github.com/SarahMilligan-hub/AutoTab/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/SarahMilligan-hub/AutoTab |
| NeedsCompilation: |
no |
| SystemRequirements: |
Python (>= 3.11); TensorFlow (>= 2.11, < 2.14);
Keras; TensorFlow Addons |
| Materials: |
README |
| CRAN checks: |
autotab results |
Documentation:
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