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: 2.2.1
Depends: R (≥ 3.0.0)
Imports: RMTstat, stats, corpcor
Suggests: knitr
Published: 2017-10-11
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
Materials: README NEWS
CRAN checks: pcev results

Downloads:

Reference manual: pcev.pdf
Vignettes: Principal Component of Explained Variance
Package source: pcev_2.2.1.tar.gz
Windows binaries: r-devel: pcev_2.2.1.zip, r-release: pcev_2.2.1.zip, r-oldrel: pcev_2.2.1.zip
OS X El Capitan binaries: r-release: pcev_2.2.1.tgz
OS X Mavericks binaries: r-oldrel: pcev_2.2.1.tgz
Old sources: pcev archive

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