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VsusP: Variable Selection using Shrinkage Priors

Bayesian variable selection using shrinkage priors to identify significant variables in high-dimensional datasets. The package includes methods for determining the number of significant variables through innovative clustering techniques of posterior distributions, specifically utilizing the 2-Means and Sequential 2-Means (S2M) approaches. The package aims to simplify the variable selection process with minimal tuning required in statistical analysis.

Version: 1.0.0
Imports: bayesreg, stats
Suggests: covr, MASS, knitr, rmarkdown, tinytex, testthat (≥ 3.0.0)
Published: 2024-06-25
DOI: 10.32614/CRAN.package.VsusP
Author: Nilson Chapagain ORCID iD [aut, cre], Debdeep Pati [aut]
Maintainer: Nilson Chapagain <nilson.chapagain at gmail.com>
BugReports: https://github.com/nilson01/VsusP-variable-selection-using-shrinkage-priors/issues
License: GPL (≥ 3)
URL: https://github.com/nilson01/VsusP-variable-selection-using-shrinkage-priors
NeedsCompilation: no
Materials: README
CRAN checks: VsusP results

Documentation:

Reference manual: VsusP.pdf
Vignettes: Variable Selection using Shrinkage Priors (VsusP)

Downloads:

Package source: VsusP_1.0.0.tar.gz
Windows binaries: r-devel: VsusP_1.0.0.zip, r-release: VsusP_1.0.0.zip, r-oldrel: VsusP_1.0.0.zip
macOS binaries: r-release (arm64): VsusP_1.0.0.tgz, r-oldrel (arm64): VsusP_1.0.0.tgz, r-release (x86_64): VsusP_1.0.0.tgz, r-oldrel (x86_64): VsusP_1.0.0.tgz

Linking:

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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.