Work

rCISSVAE: Clustering-Informed Shared-Structure VAE for Imputation

Year: 2026

Type: dataset

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) <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.

DOI: 10.32614/cran.package.rcissvae

Authors: Yasin Khadem Charvadeh, Kenneth Seier, Katherine S. Panageas, Danielle Vaithilingam, Mithat Gönen, Yuan Chen

Language: English

Topic: Statistical Methods and Bayesian Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: gold

OpenAlex ID: W7125595702

Cites: 1

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Related to: 10