Fits multivariate (Brownian Motion, Early Burst, ACDC, Ornstein-Uhlenbeck and Shifts) models of continuous traits evolution on trees and time series. 'mvMORPH' also proposes high-dimensional multivariate comparative tools (linear models using Generalized Least Squares and multivariate tests) based on penalized likelihood.  See
    Clavel et al. (2015) <DOI:10.1111/2041-210X.12420>, Clavel et al. (2019) <DOI:10.1093/sysbio/syy045>, and Clavel & Morlon (2020) <DOI:10.1093/sysbio/syaa010>.

A comprehensive suite for assessing multivariate normality using six statistical tests 
    (Mardia, Henze–Zirkler, Henze–Wagner, Royston, Doornik–Hansen, Energy). 
    Also includes univariate diagnostics, bivariate density visualization, robust outlier 
    detection, power transformations (e.g., Box–Cox, Yeo–Johnson), and imputation strategies 
    ("mean", "median", "mice") for handling missing data. Bootstrap resampling is supported 
    for selected tests to improve p-value accuracy in small samples.
    Diagnostic plots are available via both 'ggplot2' and interactive 'plotly' visualizations. See Korkmaz et al. (2014) <https://journal.r-project.org/archive/2014-2/korkmaz-goksuluk-zararsiz.pdf>.

Computes the Nelson-Aalen estimator of the cumulative transition hazard for arbitrary Markov multistate models <ISBN:978-0-387-68560-1>.

Tools of Bayesian analysis framework using the method
  suggested by Berger (1985) <doi:10.1007/978-1-4757-4286-2> for
  multivariate normal (MVN) distribution and multivariate normal
  mixture (MixMVN) distribution:
  a) calculating Bayesian posteriori of (Mix)MVN distribution;
  b) generating random vectors of (Mix)MVN distribution;
  c) Markov chain Monte Carlo (MCMC) for (Mix)MVN distribution.

Provides computationally efficient tools related to the
    multivariate normal and Student's t distributions. The main functionalities
    are: simulating multivariate random vectors, evaluating multivariate normal or
    Student's t densities and Mahalanobis distances. These tools are very efficient
    thanks to the use of C++ code and of the OpenMP API.