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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 |
Reference manual: | BMIselect.pdf |
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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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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