SignifReg: Significant Variable Selection in Linear Regression

Provide a significant variable selection procedure with different directions (forward, backward, stepwise) based on diverse criteria (Mallows' Cp, AIC, BIC, adjusted r-square, 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: 1.0
Published: 2017-02-02
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_1.0.tar.gz
Windows binaries: r-devel: SignifReg_1.0.zip, r-release: SignifReg_1.0.zip, r-oldrel: SignifReg_1.0.zip
OS X El Capitan binaries: r-release: SignifReg_1.0.tgz
OS X Mavericks binaries: r-oldrel: SignifReg_1.0.tgz

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