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 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 Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=BMIselect to link to this page.
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