Package: rCISSVAE Title: Clustering-Informed Shared-Structure VAE for Imputation Version: 1.0.1 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 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) . 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, stats 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 Roxygen: list(markdown = TRUE) RoxygenNote: 7.3.3 LazyData: true Config/pak/sysreqs: cmake make libicu-dev libpng-dev libuv1-dev libxml2-dev libssl-dev perl python3 libnode-dev Repository: https://ciss-vae.r-universe.dev Date/Publication: 2026-05-14 13:12:39 UTC RemoteUrl: https://github.com/ciss-vae/rciss-vae RemoteRef: HEAD RemoteSha: b13cacd85446e69e417d5ab674b11bd161675845 NeedsCompilation: no Packaged: 2026-07-13 06:44:11 UTC; root Author: Yasin Khadem Charvadeh [aut], Kenneth Seier [aut], Katherine S. Panageas [aut], Danielle Vaithilingam [aut, cre], Mithat Gönen [aut], Yuan Chen [aut]