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
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:

Reference manual:

BMIselect.pdf

Downloads:

Package source:	BMIselect_1.0.1.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): BMIselect_1.0.1.tgz, r-oldrel (arm64): BMIselect_1.0.1.tgz, r-release (x86_64): BMIselect_1.0.1.tgz, r-oldrel (x86_64): BMIselect_1.0.1.tgz

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