pcev: Principal Component of Explained Variance

Principal component of explained variance (PCEV) is a statistical tool for the analysis of a multivariate response vector. It is a dimension- reduction technique, similar to Principal component analysis (PCA), that seeks to maximize the proportion of variance (in the response vector) being explained by a set of covariates.

Version: 2.2.2
Depends: R (≥ 3.0.0)
Imports: RMTstat, stats, corpcor
Suggests: knitr
Published: 2018-02-03
DOI: 10.32614/CRAN.package.pcev
Author: Maxime Turgeon [aut, cre], Aurelie Labbe [aut], Karim Oualkacha [aut], Stepan Grinek [aut]
Maintainer: Maxime Turgeon <maxime.turgeon at mail.mcgill.ca>
BugReports: http://github.com/GreenwoodLab/pcev/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: http://github.com/GreenwoodLab/pcev
NeedsCompilation: no
Citation: pcev citation info
Materials: README NEWS
CRAN checks: pcev results


Reference manual: pcev.pdf
Vignettes: Principal Component of Explained Variance


Package source: pcev_2.2.2.tar.gz
Windows binaries: r-devel: pcev_2.2.2.zip, r-release: pcev_2.2.2.zip, r-oldrel: pcev_2.2.2.zip
macOS binaries: r-release (arm64): pcev_2.2.2.tgz, r-oldrel (arm64): pcev_2.2.2.tgz, r-release (x86_64): pcev_2.2.2.tgz, r-oldrel (x86_64): pcev_2.2.2.tgz
Old sources: pcev archive


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