VariableSelection: Select Variables for Linear Models

Provides variable selection for linear models and generalized linear models using Bayesian information criterion (BIC) and model posterior probability (MPP). Given a set of candidate predictors, it evaluates candidate models and returns model-level summaries (BIC and MPP) and predictor-level posterior inclusion probabilities (PIP). For more details see Xu, S., Ferreira, M. A., & Tegge, A. N. (2025) <doi:10.48550/arXiv.2510.02628>.

Version: 1.0.0
Depends: R (≥ 3.5.0)
Imports: stats (≥ 4.2.2), GA (≥ 3.2.3), memoise (≥ 2.0.1)
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0)
Published: 2026-02-17
DOI: 10.32614/CRAN.package.VariableSelection (may not be active yet)
Author: Shuangshuang Xu [aut, cre]
Maintainer: Shuangshuang Xu <xshuangshuang at vt.edu>
License: GPL-3
NeedsCompilation: no
Materials: README
CRAN checks: VariableSelection results

Documentation:

Reference manual: VariableSelection.html , VariableSelection.pdf
Vignettes: Variable selection for linear models and generalized linear models with BIC-based posterior probability (source, R code)

Downloads:

Package source: VariableSelection_1.0.0.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries: r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available

Linking:

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