MVNtestchar: Test for Multivariate Normal Distribution Based on a Characterization

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.

Version: 1.1.3
Depends: R (≥ 2.10)
Imports: graphics, grDevices, Hmisc, stats, utils, knitr, ggplot2
Suggests: markdown
Published: 2020-07-25
DOI: 10.32614/CRAN.package.MVNtestchar
Author: William Fairweather [aut, cre]
Maintainer: William Fairweather <wrf343 at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: NEWS
CRAN checks: MVNtestchar results


Reference manual: MVNtestchar.pdf
Vignettes: Theory_and_Implementation


Package source: MVNtestchar_1.1.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): MVNtestchar_1.1.3.tgz, r-oldrel (arm64): MVNtestchar_1.1.3.tgz, r-release (x86_64): MVNtestchar_1.1.3.tgz, r-oldrel (x86_64): MVNtestchar_1.1.3.tgz
Old sources: MVNtestchar archive


Please use the canonical form to link to this page.