Datasets and Functionality from
  'Jan Beran' (1994). Statistics for Long-Memory Processes; Chapman & Hall.
  Estimation of Hurst (and more) parameters for fractional Gaussian noise,
  'fARIMA' and 'FEXP' models.

Long Memory Time Series is a collection of functions for estimation, simulation and testing of long memory processes, spurious long memory processes and fractionally cointegrated systems.

An adaption of the consensus clustering approach from
    'ConsensusClusterPlus' for longitudinal data. The longitudinal data is
    clustered with flexible mixture models from 'flexmix', while the consensus
    matrices are hierarchically clustered as in 'ConsensusClusterPlus'. By using
    the flexibility from 'flexmix' and 'FactoMineR', one can use mixed data
    types for the clustering.

Compute power and sample size for linear models of longitudinal
    data. Supported models include mixed-effects models and models fit by
    generalized least squares and generalized estimating equations. The package
    is described in Iddi and Donohue (2022) <DOI:10.32614/RJ-2022-022>. Relevant
    formulas are derived by Liu and Liang (1997) <DOI:10.2307/2533554>, 
    Diggle et al (2002) <ISBN:9780199676750>, and Lu, Luo, and Chen (2008)
    <DOI:10.2202/1557-4679.1098>.

Performs variety of viral quasispecies diversity analyses [see Gregori et al. (2016) <doi:10.1016/j.virol.2016.03.017>] based on long-read sequence alignment. Main functions include 1) sequencing error minimization and read sampling, 2) Single nucleotide variant (SNV) profiles comparison, and 3) viral quasispecies profiles comparison and visualization.

Time-dependent Receiver Operating Characteristic curves, Area Under the Curve, and Net Reclassification Indexes for repeated measures. It is based on methods in Barbati and Farcomeni (2017) <doi:10.1007/s10260-017-0410-2>.

Performs recursive partitioning of linear and nonlinear mixed effects models,
    specifically for longitudinal data. The package is an
    extension of the original 'longRPart' package by 
    Stewart and Abdolell (2013) <https://cran.r-project.org/package=longRPart>.