Implements methods for analyzing competing risks data in
    multi-center survival studies using frailty models. The approach relies on
    a mixed proportional hazards model for the sub-distribution, allowing for
    cluster-specific random effects. The package provides tools for model
    estimation with or without frailty using Maximum Likelihood (ML) and
    Restricted Maximum Likelihood (REML). It supports flexible modeling of
    between-center heterogeneity and is particularly suited for multi-center
    clinical trials or registries. Core features include data simulation,
    likelihood computation, cluster-dependent censoring options, and testing of
    frailty effects. For methodological details, see Katsahian et al. (2006) <doi:10.1002/sim.2684>.

Contains functions for fitting shared frailty models with a semi-parametric
    baseline hazard with the Expectation-Maximization algorithm. Supported data formats 
    include clustered failures with left truncation and recurrent events in gap-time
    or Andersen-Gill format. Several frailty distributions, such as the the gamma, positive stable
    and the Power Variance Family are supported.

Implements the h-likelihood estimation procedures for general frailty models including competing-risk models and joint models.

The penalized and non-penalized Minorize-Maximization (MM) method for frailty models to fit the clustered data, multi-event data and recurrent data. Least absolute shrinkage and selection operator (LASSO), minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalized functions are implemented. All the methods are computationally efficient. These general methods are proposed based on the following papers,
    Huang, Xu and Zhou (2022) <doi:10.3390/math10040538>,
    Huang, Xu and Zhou (2023) <doi:10.1177/09622802221133554>.

This contains functions that can be used to estimate a smoothed and 
              a non-smoothed (empirical) time-dependent receiver operating characteristic
              curve and the corresponding area under the receiver operating characteristic
              curve for correlated right-censored time-to-event data. See
              Beyene and Chen (2024) <doi:10.1177/09622802231220496>.