PCovR: Principal Covariates Regression

Analyzing regression data with many and/or highly collinear predictor variables, by simultaneously reducing the predictor variables to a limited number of components and regressing the criterion variables on these components (de Jong S. & Kiers H. A. L. (1992) <doi:10.1016/0169-7439(92)80100-I>). Several rotation and model selection options are provided.

Version:	2.7.1
Depends:	GPArotation, ThreeWay, MASS, stats, graphics, Matrix
Published:	2021-01-29
Author:	Marlies Vervloet [aut, cre], Henk Kiers [aut], Eva Ceulemans [ctb]
Maintainer:	Marlies Vervloet <marlies.vervloet at ppw.kuleuven.be>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	no
Citation:	PCovR citation info
CRAN checks:	PCovR results

Documentation:

Reference manual:

PCovR.pdf

Downloads:

Package source:	PCovR_2.7.1.tar.gz
Windows binaries:	r-devel: PCovR_2.7.1.zip, r-release: PCovR_2.7.1.zip, r-oldrel: PCovR_2.7.1.zip
macOS binaries:	r-release (arm64): PCovR_2.7.1.tgz, r-oldrel (arm64): PCovR_2.7.1.tgz, r-release (x86_64): PCovR_2.7.1.tgz, r-oldrel (x86_64): PCovR_2.7.1.tgz
Old sources:	PCovR archive

Reverse dependencies:

Reverse imports:

EFA.MRFA, vampyr

Linking:

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