Implements estimation for the Cox (1972, 1975)
    <doi:10.1111/j.2517-6161.1972.tb00899.x> <doi:10.1093/biomet/62.2.269>
    proportional hazards model when the failure indicator (cause of failure) is
    missing not at random (MNAR), following the two adjusted imputation-based
    estimating equations of Liu and Liu (2026) <doi:10.1007/s11222-026-10857-1>.
    Also provided for comparison are the full-data partial-likelihood estimator of
    Andersen and Gill (1982) <doi:10.1214/aos/1176345976>, the complete-case
    estimator, and the missing-at-random imputation estimator of Liu and Wang
    (2010, Statistica Sinica, 20, 1125-1142). The probability models for the
    failure indicator and for the missingness mechanism are estimated jointly by
    maximum likelihood following Sun, Xie, and Liang (2013)
    <doi:10.1007/s11425-012-4492-x>, and a Nadaraya-Watson kernel-smoothed
    estimator of the missingness propensity is constructed following Qiu, Chen,
    and Zhou (2015) <doi:10.1016/j.spl.2014.12.006>. Both an asymptotic
    (sandwich-type) variance estimator and a nonparametric bootstrap variance
    estimator are provided. When failure indicators are fully observed the
    estimators reduce algebraically to the classical Cox partial-likelihood
    estimator.

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>.