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.

Getting started

Package details

AuthorFrederic Bertrand [cre, aut] (<https://orcid.org/0000-0002-0837-8281>), Myriam Maumy-Bertrand [aut] (<https://orcid.org/0000-0002-4615-1512>)
MaintainerFrederic Bertrand <[email protected]>
LicenseGPL-3
Version1.7.4
URL http://www-irma.u-strasbg.fr/~fbertran/ https://github.com/fbertran/plsRcox
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("plsRcox")

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plsRcox documentation built on May 2, 2019, 3:43 a.m.