Package: rCISSVAE
Title: Clustering-Informed Shared-Structure VAE for Imputation
Version: 0.0.4
Authors@R: 
    c(  person(given = "Yasin", family = "Khadem Charvadeh", email = "khademy@mskcc.org", role = c("aut")),
        person(given = "Kenneth", family = "Seier", role = c("aut")),
        person( given = c("Katherine", "S."), family = "Panageas", role = c("aut")),
        person(given = "Danielle", family = "Vaithilingam", email = "vaithid1@mskcc.org", role = c("aut", "cre")),
        person(given = "Mithat", family = "Gönen", role = c("aut")),
        person(given = "Yuan", family = "Chen", email = "cheny19@mskcc.org", role = c("aut"))
        )
Maintainer: Danielle Vaithilingam <vaithid1@mskcc.org>
Description: 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.
License: MIT + file LICENSE
Encoding: UTF-8
Depends: R (>= 4.2.0)
Imports: reticulate, purrr, gtsummary, rlang, ComplexHeatmap
Suggests: testthat (>= 3.0.0), dplyr, knitr, rmarkdown, tidyverse,
        kableExtra, MASS, fastDummies, palmerpenguins, glue, withr,
        ggplot2
URL: https://ciss-vae.github.io/rCISS-VAE/
BugReports: https://github.com/CISS-VAE/rCISS-VAE/issues
Config/testthat/edition: 3
VignetteBuilder: knitr
RoxygenNote: 7.3.3
LazyData: true
NeedsCompilation: no
Packaged: 2026-01-20 15:01:31 UTC; vaithid1
Author: Yasin Khadem Charvadeh [aut],
  Kenneth Seier [aut],
  Katherine S. Panageas [aut],
  Danielle Vaithilingam [aut, cre],
  Mithat Gönen [aut],
  Yuan Chen [aut]
Repository: CRAN
Date/Publication: 2026-01-23 21:20:07 UTC
Built: R 4.6.0; ; 2026-02-12 05:43:00 UTC; windows
