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cff-version: 1.2.0
message: 'To cite package "rCISSVAE" in publications use:'
type: software
license: MIT
title: 'rCISSVAE: Clustering-Informed Shared-Structure VAE for Imputation'
version: 1.0.1
doi: 10.32614/CRAN.package.rCISSVAE
abstract: '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) <https://doi.org/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.'
authors:
- family-names: Khadem Charvadeh
  given-names: Yasin
  email: khademy@mskcc.org
- family-names: Seier
  given-names: Kenneth
- family-names: Panageas
  given-names: Katherine S.
- family-names: Vaithilingam
  given-names: Danielle
  email: vaithid1@mskcc.org
- family-names: Gönen
  given-names: Mithat
- family-names: Chen
  given-names: Yuan
  email: cheny19@mskcc.org
repository: https://ciss-vae.r-universe.dev
repository-code: https://github.com/CISS-VAE/rCISS-VAE
commit: b13cacd85446e69e417d5ab674b11bd161675845
url: https://ciss-vae.github.io/rCISS-VAE/
date-released: '2026-05-14'
contact:
- family-names: Vaithilingam
  given-names: Danielle
  email: vaithid1@mskcc.org