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bigPLScox  

Partial Least Squares for Cox Models with Big Matrices
View on CRAN: Click here


Download and install bigPLScox package within the R console
Install from CRAN:
install.packages("bigPLScox")

Install from Github:
library("remotes")
install_github("cran/bigPLScox")

Install by package version:
library("remotes")
install_version("bigPLScox", "0.8.1")



Attach the package and use:
library("bigPLScox")
Maintained by
Frederic Bertrand
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-11-11
Latest Update: 2025-11-11
Description:
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.
How to cite:
Frederic Bertrand (2025). bigPLScox: Partial Least Squares for Cox Models with Big Matrices. R package version 0.8.1, https://cran.r-project.org/web/packages/bigPLScox. Accessed 16 Jul. 2026.
Previous versions and publish date:
(2026-07-09 07:21), 0.6.0 (2025-11-11 22:20)
Other packages that cited bigPLScox R package
View bigPLScox citation profile
Other R packages that bigPLScox depends, imports, suggests or enhances
Complete documentation for bigPLScox
Functions, R codes and Examples using the bigPLScox R package
Full bigPLScox package functions and examples
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