rCISSVAE
0.0.5Clustering-Informed Shared-Structure VAE for Imputation
Overview
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.
Install
Health
- NOTE r-devel-linux-x86_64-debian-gcc
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-06-0913 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-06-0812 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-05-0213 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-03-1013 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
Documentation
- Examples that run
- 65%
- Documented parameters
- 95%
- Return-value docs
- 100%
- References docs
- 0%
Downloads
Repository
Repository practices
1 development-tooling and community-health practice detected across 1 family in the upstream repository
Checks run against github.com/ciss-vae/rciss-vae on 2026-08-02.
Dependencies
Nothing depends on this yet.
Code & Tests
People & History
2 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 0.0.5Latest
- 0.0.42026-01-23
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2026-01-23
- Total releases
- 2 / 1 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 4.2.0
- Bundled data
- 1.2 MB / 4 files
- Download size
- 3.1 MB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet