An easy tool to transform 2D longitudinal data into 3D arrays suitable for 
  Long short-term memory neural networks training. The array output can be
  used by the 'keras' package. Long short-term memory neural networks are described
  in: Hochreiter, S., & Schmidhuber, J. (1997) <doi:10.1162/neco.1997.9.8.1735>.

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.

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>.