Coxmos: Cox MultiBlock Survival

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) &lt;<a href="https://CRAN.R-project.org/package=survival" target="_top">https://CRAN.R-project.org/package=survival</a>&gt;, Noah Simon et al. (2011) &lt;<a href="https://doi.org/10.18637%2Fjss.v039.i05" target="_top">doi:10.18637/jss.v039.i05</a>&gt;, Philippe Bastien et al. (2005) &lt;<a href="https://doi.org/10.1016%2Fj.csda.2004.02.005" target="_top">doi:10.1016/j.csda.2004.02.005</a>&gt;, Philippe Bastien (2008) &lt;<a href="https://doi.org/10.1016%2Fj.chemolab.2007.09.009" target="_top">doi:10.1016/j.chemolab.2007.09.009</a>&gt;, Philippe Bastien et al. (2014) &lt;<a href="https://doi.org/10.1093%2Fbioinformatics%2Fbtu660" target="_top">doi:10.1093/bioinformatics/btu660</a>&gt;, Kassu Mehari Beyene and Anouar El Ghouch (2020) &lt;<a href="https://doi.org/10.1002%2Fsim.8671" target="_top">doi:10.1002/sim.8671</a>&gt;, Florian Rohart et al. (2017) &lt;<a href="https://doi.org/10.1371%2Fjournal.pcbi.1005752" target="_top">doi:10.1371/journal.pcbi.1005752</a>&gt;.