rCISSVAE
Clustering-Informed Shared-Structure VAE for Imputation
Implements the Clustering-Informed Shared-Structure Variational Autoencoder ('CISS-VAE'), a deep learning framework for missing data imputation introduced in Khadem Charvadeh et al. (2025) <doi:10.1002/sim.70335>. The model accommodates all three types of missing data mechanisms: Missing Completely At Random (MCAR), Missing At Random (MAR), and Missing Not At Random (MNAR). While it is particularly well-suited to MNAR scenarios, where missingness patterns carry informative signals, 'CISS-VAE' also functions effectively under MAR assumptions.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.0.5 |
rolling linux/jammy R-4.5 | rCISSVAE_0.0.5.tar.gz |
3.0 MiB |
0.0.5 |
rolling linux/noble R-4.5 | rCISSVAE_0.0.5.tar.gz |
3.0 MiB |
0.0.5 |
rolling source/ R- | rCISSVAE_0.0.5.tar.gz |
3.0 MiB |
0.0.5 |
latest linux/jammy R-4.5 | rCISSVAE_0.0.5.tar.gz |
3.0 MiB |
0.0.5 |
latest linux/noble R-4.5 | rCISSVAE_0.0.5.tar.gz |
3.0 MiB |
0.0.5 |
latest source/ R- | rCISSVAE_0.0.5.tar.gz |
3.0 MiB |
0.0.5 |
2026-04-26 source/ R- | rCISSVAE_0.0.5.tar.gz |
3.0 MiB |
0.0.5 |
2026-04-23 source/ R- | rCISSVAE_0.0.5.tar.gz |
3.0 MiB |