Bayesian MI-LASSO for Variable Selection on Multiply-Imputed Datasets R package BMIselect version 1.0.3 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. Software Depends: R (>= 3.5.0) Imports: MCMCpack, mvnfast, GIGrvg, MASS, Rfast, foreach, doParallel, arm, mice, abind, stringr, stats, posterior Suggests: testthat, knitr, rmarkdown Jungang Zou <jungang.zou@gmail.com> Comprehensive R Archive Network (CRAN) Jungang Zou [aut, cre], Sijian Wang [aut], Qixuan Chen [aut] Apache License (>= 2) 2025-08-25 application/tgz https://CRAN.R-project.org/package=BMIselect doi:10.32614/CRAN.package.BMIselect