plsRcox: Partial Least Squares Regression for Cox Models and Related Techniques

Provides Partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models in high dimensional settings <doi:10.1093/bioinformatics/btu660>, Bastien, P., Bertrand, F., Meyer N., Maumy-Bertrand, M. (2015), Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data, Bioinformatics, 31(3):397-404. Cross validation criteria were studied in <arXiv:1810.02962>, Bertrand, F., Bastien, Ph. and Maumy-Bertrand, M. (2018), Cross validating extensions of kernel, sparse or regular partial least squares regression models to censored data.

Version: 1.7.4
Depends: R (≥ 2.4.0)
Imports: survival, plsRglm, lars, pls, kernlab, mixOmics, risksetROC, survcomp, survAUC, rms
Suggests: survivalROC, plsdof
Published: 2019-02-03
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: 62N01, 62N02, 62N03, 62N99
Citation: plsRcox citation info
Materials: NEWS
CRAN checks: plsRcox results


Reference manual: plsRcox.pdf
Package source: plsRcox_1.7.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: not available, r-oldrel: plsRcox_1.7.4.tgz
Old sources: plsRcox archive

Reverse dependencies:

Reverse imports: biospear


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