Constructs tree for continuous longitudinal data and survival data using baseline covariates as partitioning variables according to the 'LongCART' and 'SurvCART' algorithm, respectively. Later also included functions to calculate conditional power and predictive power of success based on interim results and probability of success for a prospective trial.

Clustering or classification of longitudinal data based on a mixture of multivariate t or Gaussian distributions with a Cholesky-decomposed covariance structure. Details in McNicholas and Murphy (2010) <doi:10.1002/cjs.10047> and McNicholas and Subedi (2012) <doi:10.1016/j.jspi.2011.11.026>.

This tool takes longitudinal dataset as input and analyzes if there is significant 
             change of the features over time (a proxy for treatments), while detects and controls 
             for 'covariates' simultaneously. 'LongDat' is able to take in several data types as input, 
             including count, proportion, binary, ordinal and continuous data. The output table contains 
              p values, effect sizes and 'covariates' of each feature, making the downstream analysis easy.

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>.