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) <https://CRAN.R-project.org/package=survival>,
             Noah Simon et al. (2011) <doi:10.18637/jss.v039.i05>,
             Philippe Bastien et al. (2005) <doi:10.1016/j.csda.2004.02.005>,
             Philippe Bastien (2008) <doi:10.1016/j.chemolab.2007.09.009>,
             Philippe Bastien et al. (2014) <doi:10.1093/bioinformatics/btu660>,
             Kassu Mehari Beyene and Anouar El Ghouch (2020) <doi:10.1002/sim.8671>,
             Florian Rohart et al. (2017) <doi:10.1371/journal.pcbi.1005752>.

Implements Firth's penalized maximum likelihood bias reduction method  for Cox regression
  which has been shown to provide a solution in case of monotone likelihood (nonconvergence of likelihood function), see 
  Heinze and Schemper (2001) and Heinze and Dunkler (2008).
  The program fits profile penalized likelihood confidence intervals which were proved to outperform
  Wald confidence intervals.

Fits a pseudo Cox proprotional hazards model when survival times are missing for control groups.

Estimate coefficients of Cox proportional hazards model using stochastic gradient descent algorithm for batch data.

Implements weighted estimation in Cox regression as proposed by
             Schemper, Wakounig and Heinze (Statistics in Medicine, 2009, 
             <doi:10.1002/sim.3623>) and as described in Dunkler, Ploner, Schemper and 
             Heinze (Journal of Statistical Software, 2018, <doi:10.18637/jss.v084.i02>). 
             Weighted Cox regression provides unbiased average hazard ratio 
             estimates also in case of non-proportional hazards. 
             Approximated generalized concordance probability an effect size measure for clear-cut
             decisions can be obtained.
             The package provides options to estimate time-dependent effects conveniently by
             including interactions of covariates with arbitrary functions of time, with or without
             making use of the weighting option.