boot, plsRglm, lars, survival, pls, kernlab, mixOmics
This packages provides Partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models in high dimensional settings.

boot, plsRglm, lars, survival, pls, spls, kernlab, mixOmics
glcoxph, survivalROC, plsRglm, lars, survival, pls, spls,
kernlab, plsRcox, plsdof, mixOmics

survival, plsRglm, lars, pls, kernlab, mixOmics, risksetROC,
survcomp, survAUC, rms

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.

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.

Partial least squares Regression for generalized linear models
Frederic Bertrand <frederic.bertrand@math.unistra.fr>, Nicolas
Meyer <Nicolas.Meyer@nmeyer@unistra.fr>, Myriam Maumy-Bertrand
<myriam.maumy-bertrand@math.unistra.fr>.
This packages provides Partial least squares Regression
for generalized linear models and kfold crossvalidation of such
models using various criteria. It allows for missing data in
the eXplanatory variables. Bootstrap confidence intervals
constructions are also available.

Frederic Bertrand <frederic.bertrand@math.unistra.fr>, Nicolas Meyer <Nicolas.Meyer@nmeyer@unistra.fr>, Myriam Maumy-Bertrand <myriam.maumy-bertrand@math.unistra.fr>.
This package provides Partial least squares Regression for generalized linear models and kfold crossvalidation of such models using various criteria. It allows for missing data in the eXplanatory variables. Bootstrap confidence intervals constructions are also available.