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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