Provides tools to decompose differences in cohort health expectancy (HE)
    by age and cause using longitudinal data. The package implements a novel
    longitudinal attribution method based on a semiparametric additive hazards
    model with time-dependent covariates, specifically designed to address interval
    censoring and semi-competing risks via a copula framework. The resulting
    age-cause-specific contributions to disability prevalence and death probability
    can be used to quantify and decompose differences in cohort HE between groups.
    The package supports stepwise replacement decomposition algorithms and is
    applicable to cohort-based health disparity research across diverse populations.
    Related methods include
    Sun et al. (2023) <doi:10.1177/09622802221133552>.

A collection of parametric and nonparametric methods for the analysis of survival data. Parametric families implemented include Gompertz-Makeham, exponential and generalized Pareto models and extended models. The package includes an implementation of the nonparametric maximum likelihood estimator for arbitrary truncation and censoring pattern based on Turnbull (1976) <doi:10.1111/j.2517-6161.1976.tb01597.x>, along with graphical goodness-of-fit diagnostics. Parametric models for positive random variables and peaks over threshold models based on extreme value theory are described in Rootzén and Zholud (2017) <doi:10.1007/s10687-017-0305-5>; Belzile et al. (2021) <doi:10.1098/rsos.202097> and Belzile et al. (2022) <doi:10.1146/annurev-statistics-040120-025426>.

High dimensional longitudinal data analysis with Markov Chain Monte Carlo(MCMC). 
             Currently support mixed effect regression with or without missing observations by considering 
             covariance structures. It provides estimates by missing at random and missing not at random assumptions.
             In this R package, we present Bayesian approaches that statisticians and clinical 
             researchers can easily use. The functions' methodology is based on the book "Bayesian Approaches in Oncology Using R and OpenBUGS" by 
             Bhattacharjee A (2020) <doi:10.1201/9780429329449-14>.