SignifReg: Consistent Significance Controlled Variable Selection in Linear Regression

Provides significance controlled variable selection algorithms with different directions (forward, backward, stepwise) based on diverse criteria (AIC, BIC, adjusted r-square, PRESS, or p-value). The algorithm selects a final model with only significant variables based on a correction choice of False Discovery Rate, Bonferroni, or no correction.

Version: 3.0
Published: 2020-04-17
Author: Jongwook Kim, Adriano Zanin Zambom
Maintainer: Jongwook Kim <jongwook226 at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: SignifReg results

Downloads:

Reference manual: SignifReg.pdf
Package source: SignifReg_3.0.tar.gz
Windows binaries: r-devel: SignifReg_3.0.zip, r-release: SignifReg_3.0.zip, r-oldrel: SignifReg_3.0.zip
macOS binaries: r-release (arm64): SignifReg_3.0.tgz, r-release (x86_64): SignifReg_3.0.tgz, r-oldrel: SignifReg_3.0.tgz
Old sources: SignifReg archive

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