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rCISSVAE  

Clustering-Informed Shared-Structure VAE for Imputation
View on CRAN: Click here


Download and install rCISSVAE package within the R console
Install from CRAN:
install.packages("rCISSVAE")

Install from Github:
library("remotes")
install_github("cran/rCISSVAE")

Install by package version:
library("remotes")
install_version("rCISSVAE", "0.0.4")



Attach the package and use:
library("rCISSVAE")
Maintained by
Danielle Vaithilingam
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-01-23
Latest Update: 2026-01-23
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.
How to cite:
Danielle Vaithilingam (2026). rCISSVAE: Clustering-Informed Shared-Structure VAE for Imputation. R package version 0.0.4, https://cran.r-project.org/web/packages/rCISSVAE. Accessed 12 Sep. 2026.
Previous versions and publish date:
(2026-07-09 06:47), 0.0.4 (2026-01-23 22:20)
Other packages that cited rCISSVAE R package
View rCISSVAE citation profile
Other R packages that rCISSVAE depends, imports, suggests or enhances
Complete documentation for rCISSVAE
Functions, R codes and Examples using the rCISSVAE R package
Full rCISSVAE package functions and examples
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