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 <doi:10.48550/arXiv.1810.01005>. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.

Version: 1.5.1
Depends: R (≥ 2.10)
Imports: mvtnorm, boot, bipartite, car, MASS
Suggests: plsdof, R.rsp, chemometrics, plsdepot
Enhances: pls
Published: 2023-03-14
DOI: 10.32614/CRAN.package.plsRglm
Author: Frederic Bertrand ORCID iD [cre, aut], Myriam Maumy-Bertrand ORCID iD [aut]
Maintainer: Frederic Bertrand <frederic.bertrand at>
License: GPL-3
NeedsCompilation: no
Classification/MSC: 62J12, 62J99
Citation: plsRglm citation info
Materials: NEWS
In views: MissingData
CRAN checks: plsRglm results


Reference manual: plsRglm.pdf
Vignettes: plsRglm: Manual
plsRglm: Algorithmic insights and applications


Package source: plsRglm_1.5.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): plsRglm_1.5.1.tgz, r-oldrel (arm64): plsRglm_1.5.1.tgz, r-release (x86_64): plsRglm_1.5.1.tgz, r-oldrel (x86_64): plsRglm_1.5.1.tgz
Old sources: plsRglm archive

Reverse dependencies:

Reverse imports: bootPLS, plsRbeta, plsRcox


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