plsRglm: Partial Least Squares Regression for Generalized Linear Models

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: 1.2.5
Depends: R (≥ 2.10)
Imports: mvtnorm, boot, bipartite, car, MASS
Suggests: plsdof, R.rsp, chemometrics, plsdepot
Enhances: pls
Published: 2019-02-02
Author: Frederic Bertrand ORCID iD [cre, aut], Myriam Maumy-Bertrand ORCID iD [aut]
Maintainer: Frederic Bertrand <frederic.bertrand at math.unistra.fr>
BugReports: https://github.com/fbertran/plsRglm/issues
License: GPL-3
URL: http://www-irma.u-strasbg.fr/~fbertran/, https://github.com/fbertran/plsRglm
NeedsCompilation: no
Classification/MSC: 62J12, 62J99
Citation: plsRglm citation info
Materials: NEWS
In views: MissingData
CRAN checks: plsRglm results

Downloads:

Reference manual: plsRglm.pdf
Vignettes: plsRglm: Manual
plsRglm: Algorithmic insights and applications
Package source: plsRglm_1.2.5.tar.gz
Windows binaries: r-prerelease: plsRglm_1.2.5.zip, r-release: plsRglm_1.2.5.zip, r-oldrel: plsRglm_1.2.5.zip
macOS binaries: r-prerelease: plsRglm_1.2.5.tgz, r-release: plsRglm_1.2.5.tgz, r-oldrel: plsRglm_1.2.5.tgz
Old sources: plsRglm archive

Reverse dependencies:

Reverse imports: plsRbeta, plsRcox

Linking:

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