mvnormalTest: Powerful Tests for Multivariate Normality

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).

Version: 1.0.0
Depends: R (≥ 3.5.0)
Imports: stats, nortest, moments, copula
Suggests: knitr
Published: 2020-04-28
Author: Yian Zhang [aut, cre], Ming Zhou [aut], Yongzhao Shao [aut]
Maintainer: Yian Zhang <yz2777 at nyu.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Language: en-US
CRAN checks: mvnormalTest results

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Reference manual: mvnormalTest.pdf
Package source: mvnormalTest_1.0.0.tar.gz
Windows binaries: r-devel: mvnormalTest_1.0.0.zip, r-release: mvnormalTest_1.0.0.zip, r-oldrel: mvnormalTest_1.0.0.zip
macOS binaries: r-release (arm64): mvnormalTest_1.0.0.tgz, r-release (x86_64): mvnormalTest_1.0.0.tgz, r-oldrel: mvnormalTest_1.0.0.tgz

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