BMIselect: Bayesian MI-LASSO for Variable Selection on Multiply-ImputedDatasets
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's MI-LASSO function.
| Version: | 1.0.3 |
| Depends: | R (≥ 3.5.0) |
| Imports: | MCMCpack,mvnfast,GIGrvg,MASS,Rfast,foreach,doParallel,arm,mice,abind,stringr, stats,posterior |
| Suggests: | testthat,knitr,rmarkdown |
| Published: | 2025-08-25 |
| DOI: | 10.32614/CRAN.package.BMIselect |
| 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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