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: | 0.1.1 |
| Depends: | R (≥ 4.1) |
| Imports: | keras, magrittr, R6, reticulate, tensorflow |
| Suggests: | caret |
| Published: | 2025-11-24 |
| DOI: | 10.32614/CRAN.package.autotab (may not be active yet) |
| 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 (>= 3.8); TensorFlow (>= 2.10); Keras; TensorFlow Addons |
| Materials: | README |
| CRAN checks: | autotab results |
| Reference manual: | autotab.html , autotab.pdf |
| Package source: | autotab_0.1.1.tar.gz |
| Windows binaries: | r-devel: not available, r-release: not available, r-oldrel: not available |
| macOS binaries: | r-release (arm64): autotab_0.1.1.tgz, r-oldrel (arm64): autotab_0.1.1.tgz, r-release (x86_64): autotab_0.1.1.tgz, r-oldrel (x86_64): autotab_0.1.1.tgz |
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