Bayesian MI-LASSO for Variable Selection on Multiply-Imputed Datasets
Overview
Provides a suite of Bayesian MI-LASSO for variable selection methods for multiply-imputed datasets. The package includes four Bayesian MI-LASSO models using shrinkage (Multi-Laplace, Horseshoe, ARD) and Spike-and-Slab (Spike-and-Laplace) priors, along with tools for model fitting via MCMC, four-step projection predictive variable selection, and hyperparameter calibration. Methods are suitable for both continuous and binary covariates under missing-at-random or missing-completely-at-random assumptions. See Zou, J., Wang, S. and Chen, Q. (2025), Bayesian MI-LASSO for Variable Selection on Multiply-Imputed Data. ArXiv, 2211.00114. doi:10.48550/arXiv.2211.00114 for more details. We also provide the frequentist MI-LASSO function.
Install
Health
- OK13 OK2026-10-07
- ERROR12 OK, 1 ERROR2026-07-10
- OK13 OK2026-06-09
- ERROR12 OK, 1 ERROR2026-06-08
- OK13 OK2026-05-02
Show 3 earlier changesHide 3 earlier changes
- ERROR11 OK, 1 ERROR2026-04-25
- OK14 OK2026-03-30
- ERROR13 OK, 1 ERROR2026-03-10
Documentation
- Help pages
- 13 (8 internal)includes a package overview page
- Examples that run
- 100%5 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 97%
- Return-value docs
- 100%
- Help pages citing references
- 0%
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Code & Tests
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- 1.0.10Latest
- 1.0.92026-08-21 · diff ↗ ·
removed 6 exports
ARD_mcmc, horseshoe_mcmc, multi_laplace_mcmc, projection_mean, projection_posterior, spike_laplace_partially_mcmc - 1.0.42026-07-09 · diff ↗
- RR 4.6.0 released · 2026-04-24
- 1.0.32025-08-25 · diff ↗
- 1.0.22025-08-20 · diff ↗
- 1.0.12025-07-09
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2025-07-09
- Total releases
- 6 / 1 yrs
- License
- Apache License (>= 2)
- Minimum R
- ≥ 3.5.0
- Download size
- 70 KB
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