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Coxmos
View on CRAN: Click
here
Download and install Coxmos package within the R console
Install from CRAN:
install.packages("Coxmos")
Install from Github:
library("remotes")
install_github("cran/Coxmos")
Install by package version:
library("remotes")
install_version("Coxmos", "1.0.2")
Attach the package and use:
library("Coxmos")
Maintained by
Pedro Salguero García
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.Coxmos . https://github.com/BiostatOmics/Coxmos/issues . https://github.com/BiostatOmics/Coxmos . Coxmos results . Coxmos.pdf . Step-by-step guide to the MO-Coxmos pipeline . Step-by-step guide to the Coxmos pipeline . Coxmos_1.0.2.tar.gz . Coxmos_1.0.2.zip . Coxmos_1.0.2.zip . Coxmos_1.0.2.zip . Coxmos_1.0.2.tgz . Coxmos_1.0.2.tgz . Coxmos_1.0.2.tgz . Coxmos_1.0.2.tgz . Coxmos archive . https://CRAN.R-project.org/package=Coxmos .
First Published: 2024-03-12
Latest Update: 2024-03-12
Description:
This software package provides Cox survival analysis for high-dimensional and multiblock datasets.
It encompasses a suite of functions dedicated from the classical Cox regression to newest analysis,
including Cox proportional hazards model, Stepwise Cox regression, and Elastic-Net Cox regression,
Sparse Partial Least Squares Cox regression (sPLS-COX) incorporating three distinct strategies,
and two Multiblock-PLS Cox regression (MB-sPLS-COX) methods. This tool is designed to adeptly handle
high-dimensional data, and provides tools for cross-validation, plot generation, and additional resources
for interpreting results. While references are available within the corresponding functions,
key literature is mentioned below.
Terry M Therneau (2024) ,
Noah Simon et al. (2011) ,
Philippe Bastien et al. (2005) ,
Philippe Bastien (2008) ,
Philippe Bastien et al. (2014) ,
Kassu Mehari Beyene and Anouar El Ghouch (2020) ,
Florian Rohart et al. (2017) .
How to cite:
Pedro Salguero García (2024). Coxmos: Cox MultiBlock Survival. R package version 1.0.2, https://cran.r-project.org/web/packages/Coxmos. Accessed 12 Jan. 2025.
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