This package implements the generalization of the Shapiro-Wilk test
        for multivariate normality proposed by Villasenor-Alva and
        Gonzalez-Estrada (2009).

Fits multivariate Ornstein-Uhlenbeck types of models to continues trait data from species related by a common evolutionary history. See K. Bartoszek, J, Pienaar, P. Mostad, S. Andersson, T. F. Hansen (2012) <doi:10.1016/j.jtbi.2012.08.005>. The suggested PCMBaseCpp package (which significantly speeds up the likelihood calculations) can be obtained from <https://github.com/venelin/PCMBaseCpp/>.

Estimates the multivariate skew-t and nested models, as described in the
    articles Liseo, B., Parisi, A. (2013). Bayesian inference for the multivariate skew-normal
    model: a population Monte Carlo approach. Comput. Statist. Data Anal.
    <doi:10.1016/j.csda.2013.02.007> and in Parisi, A., Liseo, B. (2017). Objective Bayesian
    analysis for the multivariate skew-t model. Statistical Methods & Applications
    <doi: 10.1007/s10260-017-0404-0>.

Multivariate Surrogate Synchrony ('mvSUSY') estimates the synchrony within datasets that contain more than two time series. 'mvSUSY' was developed from Surrogate Synchrony ('SUSY') with respect to implementing surrogate controls, and extends synchrony estimation to multivariate data. 'mvSUSY' works as described in Meier & Tschacher (2021).

Routines to perform estimation and inference under the multivariate
  t-distribution <doi:10.1007/s10182-022-00468-2>. Currently, the following methodologies 
  are implemented: multivariate mean and covariance estimation, hypothesis testing 
  about equicorrelation and homogeneity of variances, the Wilson-Hilferty transformation, 
  QQ-plots with envelopes and random variate generation.

Multivariate hypothesis tests and the confidence intervals. It can be used to test the hypothesizes about mean vector or vectors (one-sample, two independent samples, paired samples), covariance matrix (one or more matrices), and the correlation matrix. Moreover, it can be used for robust Hotelling T^2 test at one sample case in high dimensional data. For this package, we have benefited from the studies Rencher (2003), Nel and Merwe (1986) <DOI: 10.1080/03610928608829342>, Tatlidil (1996), Tsagris (2014), Villasenor Alva and Estrada (2009) <DOI: 10.1080/03610920802474465>.