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 [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:

Please use the canonical form https://CRAN.R-project.org/package=VsusP to link to this page.