Partial Least Squares for Cox Models with Big Matrices R package bigPLScox version 0.8.1 Classification/MSC: 62N01, 62N02, 62N03, 62N99 Provides Partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models for big data. Provides a Partial Least Squares (PLS) algorithm adapted to Cox proportional hazards models that works with 'bigmemory' matrices without loading the entire dataset in memory. Also implements a gradient-descent based solver for Cox proportional hazards models that works directly on 'bigmemory' matrices. Bertrand and Maumy (2023) <https://hal.science/hal-05352069>, and <https://hal.science/hal-05352061> highlighted fitting and cross-validating PLS-based Cox models to censored big data. Software Depends: R (>= 4.0.0) Imports: bigmemory, bigalgebra, bigSurvSGD, caret, doParallel, foreach, kernlab, methods, Rcpp, risksetROC, rms, sgPLS, survAUC, survcomp, survival LinkingTo: BH, Rcpp, RcppArmadillo, bigmemory Suggests: bench, knitr, plsRcox, mvtnorm, readr, rmarkdown, testthat (>= 3.0.0) Frederic Bertrand <frederic.bertrand@lecnam.net> Comprehensive R Archive Network (CRAN) Frederic Bertrand [cre, aut] (ORCID: <https://orcid.org/0000-0002-0837-8281>), Myriam Maumy-Bertrand [aut] (ORCID: <https://orcid.org/0000-0002-4615-1512>) GPL-3 2025-11-18 application/tgz https://CRAN.R-project.org/package=bigPLScox doi:10.32614/CRAN.package.bigPLScox