The implementation of bias-corrected sandwich variance estimators for the analysis of cluster randomized trials with time-to-event outcomes using the marginal Cox model, proposed by Wang et al. (under review).

Functions for generating, simulating, and visualizing expected durations and marginal changes in duration from the Cox proportional hazards model as described in Kropko and Harden (2017) <doi:10.1017/S000712341700045X> and Harden and Kropko (2018) <doi:10.1017/psrm.2018.19>.

Perform the functional modeling methods of Huang and Wang (2018) <doi:10.1111/biom.12741> to accommodate dependent error in covariates of the proportional hazards model. The adopted measurement error model has minimal assumptions on the dependence structure, and an instrumental variable is supposed to be available.

Perform variable selection for Cox regression model with interval-censored data. Can deal with both low-dimensional and high-dimensional data. Case-cohort design can be incorporated. Two sets of covariates scenario can also be considered. The references are listed in the URL below.

Fit Cox proportional hazards models containing both 
 fixed and random effects.  The random effects can have a general form, of which
 familial interactions (a "kinship" matrix) is a particular special case. 
 Note that the simplest case of a mixed effects Cox model, i.e. a single random 
 per-group intercept, is also called a "frailty" model.  The approach is based
 on Ripatti and Palmgren, Biometrics 2002.

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>.