An implementation of modified maximum contrast methods (Sato et al. (2009) <doi:10.1038/tpj.2008.17>;
  Nagashima et al. (2011) <doi:10.2202/1544-6115.1560>) and the maximum contrast method (Yoshimura et al. (1997)
  <doi:10.1177/009286159703100213>): Functions mmcm.mvt() and mcm.mvt() give P-value by using randomized quasi-Monte
  Carlo method with pmvt() function of package 'mvtnorm', and mmcm.resamp() gives P-value by using a permutation method.

Implements differential methylation region (DMR) detection using a
    multistage Markov chain Monte Carlo (MCMC) algorithm based on the
    alpha-skew generalized normal (ASGN) distribution. Version 0.2.0 removes
    the Anderson-Darling test stage, improves computational efficiency of the
    core ASGN and multistage MCMC routines, and adds convenience functions for
    summarizing and visualizing detected DMRs. The methodology is based on
    Yang (2025) <https://www.proquest.com/docview/3218878972>.

Convert mouse genome positions between the build 39 physical map and the genetic map of Cox et al. (2009) <doi:10.1534/genetics.109.105486>.

Matching longitudinal methodology models with complex sampling design. It fits fixed
    and random effects models and covariance structured models so far. It also
    provides tools to perform statistical tests considering these specifications as described in : Pacheco, P. H. (2021). "Modeling complex longitudinal data in R: development of a statistical package." <https://repositorio.ufjf.br/jspui/bitstream/ufjf/13437/1/pedrohenriquedemesquitapacheco.pdf>.

The aim of the package is two-fold: (i) To implement 
    the MMD method for attribution of individuals to sources using 
    the Hamming distance between multilocus genotypes. (ii) To select 
    informative genetic markers based on information theory concepts 
    (entropy, mutual information and redundancy). The package implements the 
    functions introduced by Perez-Reche, F. J., Rotariu, O., Lopes, B. S., 
    Forbes, K. J. and Strachan, N. J. C. Mining whole genome sequence data to 
    efficiently attribute individuals to source populations. 
    Scientific Reports 10, 12124 (2020) <doi:10.1038/s41598-020-68740-6>.
    See more details and examples in the README file.