Kevin Gross <gross@stat.wisc.edu>, with help from Douglas Bates
Finds the maximum likelihood estimate of the mean vector
and variance-covariance matrix for multivariate normal data with
missing values.

ML estimation for multivariate normal data with missing values

Powerful Tests for Multivariate Normality
c(
person("Yian", "Zhang", email = "yz2777@nyu.edu", role = c("aut","cre")),
person("Ming", "Zhou", email = "zhouming1027@gmail.com", role = "aut"),
person("Yongzhao", "Shao", email = "yongzhao.shao@nyulangone.org", role = "aut"))
Yian Zhang [aut, cre],
Ming Zhou [aut],
Yongzhao Shao [aut]
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).

Normality test for multivariate variables
Generalization of shapiro-wilk test for multivariate variables.

Generalization of shapiro-wilk test for multivariate
variables.

Generate New Multivariate Normal Samples from Permutations
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.

person("Mark", "Abney", email = "abney@uchicago.edu",
role = c("cre", "aut"))

Rainer Hirk [aut, cre],
Kurt Hornik [aut],
Laura Vana [aut],
Alan Genz [ctb] (Fortran Code)
A flexible framework for fitting multivariate
ordinal regression models with composite likelihood methods.