plsRglm: Partial least squares Regression for generalized linear models

This package provides (weighted) Partial least squares Regression for generalized linear models and repeated k-fold cross-validation of such models using various criteria. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.

Version: 1.1.0
Depends: R (≥ 2.4.0)
Imports: mvtnorm, boot, bipartite
Suggests: MASS, plsdof, R.rsp
Enhances: pls
Published: 2014-07-19
Author: Frederic Bertrand, Nicolas Meyer, Myriam Maumy-Bertrand.
Maintainer: Frederic Bertrand <frederic.bertrand at math.unistra.fr>
License: GPL-3
URL: http://www-irma.u-strasbg.fr/~fbertran/
NeedsCompilation: no
Classification/MSC: 62J12, 62J99
Citation: plsRglm citation info
Materials: NEWS
CRAN checks: plsRglm results

Downloads:

Reference manual: plsRglm.pdf
Vignettes: plsRglm: Manual
plsRglm: Algorithmic insights and applications
Package source: plsRglm_1.1.0.tar.gz
Windows binaries: r-devel: plsRglm_1.1.0.zip, r-release: plsRglm_1.1.0.zip, r-oldrel: plsRglm_1.1.0.zip
OS X Snow Leopard binaries: r-release: plsRglm_1.1.0.tgz, r-oldrel: plsRglm_1.1.0.tgz
OS X Mavericks binaries: r-release: plsRglm_1.1.0.tgz
Old sources: plsRglm archive

Reverse dependencies:

Reverse imports: plsRbeta, plsRcox