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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0 |
rolling linux/jammy R-4.5 | autotab_1.0.tar.gz |
198.3 KiB |
1.0 |
rolling linux/noble R-4.5 | autotab_1.0.tar.gz |
198.1 KiB |
1.0 |
rolling source/ R- | autotab_1.0.tar.gz |
33.3 KiB |
1.0 |
latest linux/jammy R-4.5 | autotab_1.0.tar.gz |
198.3 KiB |
1.0 |
latest linux/noble R-4.5 | autotab_1.0.tar.gz |
198.1 KiB |
1.0 |
latest source/ R- | autotab_1.0.tar.gz |
33.3 KiB |
1.0 |
2026-04-26 source/ R- | autotab_1.0.tar.gz |
33.3 KiB |
1.0 |
2026-04-23 source/ R- | autotab_1.0.tar.gz |
33.3 KiB |
1.0 |
2026-04-09 windows/windows R-4.5 | autotab_1.0.zip |
201.1 KiB |