BMIselect: Bayesian MI-LASSO for Variable Selection on Multiply-Imputed
Datasets
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, three-step projection predictive variable selection, and hyperparameter calibration. Methods are suitable for both continuous and binary covariates under missing-at-random assumptions. See Zou, J., Wang, S. and Chen, Q. (2022), Variable Selection for Multiply-imputed Data: A Bayesian Framework. ArXiv, 2211.00114. <doi:10.48550/arXiv.2211.00114> for more details. We also provide the frequentist's MI-LASSO function.
| Version: |
1.0.1 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
MCMCpack, mvnfast, GIGrvg, MASS, Rfast, foreach, doParallel, arm, mice, abind, stringr, stats, posterior |
| Suggests: |
testthat, knitr, rmarkdown |
| Published: |
2025-07-09 |
| Author: |
Jungang Zou [aut, cre],
Sijian Wang [aut],
Qixuan Chen [aut] |
| Maintainer: |
Jungang Zou <jungang.zou at gmail.com> |
| License: |
Apache License (≥ 2) |
| NeedsCompilation: |
no |
| CRAN checks: |
BMIselect results |
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