Package: miselect
Title: Variable Selection for Multiply Imputed Data
Version: 0.9.2
Authors@R: c(
    person("Michael", "Kleinsasser", email = "biostat-cran-manager@umich.edu", role = c("cre")),
    person("Alexander", "Rix", email = "alexrix@umich.edu", role = c("aut")),
    person("Jiacong", "Du", email = "jiacong@umich.edu", role = "aut")
    )
Description: Penalized regression methods, such as lasso and elastic
        net, are used in many biomedical applications when simultaneous
        regression coefficient estimation and variable selection is
        desired. However, missing data complicates the implementation
        of these methods, particularly when missingness is handled
        using multiple imputation. Applying a variable selection
        algorithm on each imputed dataset will likely lead to different
        sets of selected predictors, making it difficult to ascertain a
        final active set without resorting to ad hoc combination rules.
        'miselect' presents Stacked Adaptive Elastic Net (saenet) and
        Grouped Adaptive LASSO (galasso) for continuous and binary
        outcomes, developed by Du et al (2022)
        <doi:10.1080/10618600.2022.2035739>. They, by construction,
        force selection of the same variables across multiply imputed
        data. 'miselect' also provides cross validated variants of
        these methods.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.2.3
Depends: R (>= 3.5.0)
Suggests: mice, knitr, rmarkdown, testthat
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-07-04 12:53:15 UTC; root
Author: Michael Kleinsasser [cre], Alexander Rix [aut], Jiacong Du
        [aut]
Maintainer: Michael Kleinsasser <biostat-cran-manager@umich.edu>
Repository: https://cran.r-universe.dev
Date/Publication: 2024-03-06 02:30:53 UTC
RemoteUrl: https://github.com/cran/miselect
RemoteRef: HEAD
RemoteSha: 70a62a58acc223364520997fed01bcb82a7080de