Finds the Maximum Likelihood (ML) Estimate of the mean vector
        and variance-covariance matrix for multivariate normal data
        with missing values.

A simple informative powerful test (mvnTest()) for multivariate normality proposed by 
    Zhou and Shao (2014) <doi:10.1080/02664763.2013.839637>, which combines kurtosis 
    with Shapiro-Wilk test that is easy for biomedical researchers to understand and 
    easy to implement in all dimensions. This package also contains some other multivariate 
    normality tests including Fattorini's FA test (faTest()), Mardia's skewness and kurtosis 
    test (mardia()), Henze-Zirkler's test (mhz()), Bowman and Shenton's test (msk()), 
    Royston’s H test (msw()), and Villasenor-Alva and Gonzalez-Estrada's test (msw()). Empirical 
    power calculation functions for these tests are also provided. In addition, this package 
    includes some functions to generate several types of multivariate distributions mentioned in 
    Zhou and Shao (2014).

Generalization of Shapiro-Wilk test for multivariate variables.

Given a vector of multivariate normal data, a matrix of
    covariates and the data covariance matrix, generate new multivariate normal
    samples that have the same covariance matrix based on permutations of
    the transformed data residuals.

Routines for assessing multivariate normality. Implements three Wald's type chi-squared tests; non-parametric Anderson-Darling and Cramer-von Mises tests; Doornik-Hansen test,  Royston test and Henze-Zirkler test.

Provides a test of multivariate normality of an unknown sample 
    that does not require estimation of the nuisance parameters, the mean and covariance 
    matrix.  Rather, a sequence of transformations removes these nuisance parameters and
    results in a set of sample matrices that are positive definite.  These matrices are 
    uniformly distributed on the space of positive definite matrices in the unit 
    hyper-rectangle if and only if the original data is multivariate normal (Fairweather,
    1973, Doctoral dissertation, University of Washington). The package performs a 
    goodness of fit test of this hypothesis. In addition to the test, functions in the 
    package give visualizations of the support region of positive definite matrices for 
    bivariate samples.