Package: BMIselect
Title: Bayesian MI-LASSO for Variable Selection on Multiply-Imputed
        Datasets
Version: 1.0.4
Authors@R: 
    c(person("Jungang", "Zou", email = "jungang.zou@gmail.com", role = c("aut", "cre")),
    person(given = "Sijian",
             family = "Wang",
             role = c("aut")
             ),
    person(given = "Qixuan",
             family = "Chen",
             role = c("aut")
             ))
Maintainer: Jungang Zou <jungang.zou@gmail.com>
Description: 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 MI-LASSO function.
License: Apache License (>= 2)
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.3
Depends: R (>= 3.5.0)
Imports: Rcpp, MASS, Rfast, foreach, doParallel, arm, mice, abind,
        stringr, stats, posterior
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat, knitr, rmarkdown
VignetteBuilder: knitr
Config/pak/sysreqs: cmake make libicu-dev libx11-dev zlib1g-dev
Repository: https://zjg540066169.r-universe.dev
Date/Publication: 2026-07-09 05:15:44 UTC
RemoteUrl: https://github.com/zjg540066169/bmiselect
RemoteRef: HEAD
RemoteSha: cf4c024a1d8acd18dc9ef06b55de9110dc5d9e16
NeedsCompilation: yes
Packaged: 2026-07-09 16:13:09 UTC; root
Author: Jungang Zou [aut, cre],
  Sijian Wang [aut],
  Qixuan Chen [aut]