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. 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: 1.1.0
Depends: R (≥ 2.10)
Imports: graphics, grDevices, Hmisc, stats, utils
Published: 2020-07-03
Author: William Fairweather [aut, cre]
Maintainer: William Fairweather <wrf343 at flowervalleyconsulting.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: NEWS
CRAN checks: MVNtestchar results

Downloads:

Reference manual: MVNtestchar.pdf
Package source: MVNtestchar_1.1.0.tar.gz
Windows binaries: r-devel: MVNtestchar_1.1.0.zip, r-release: MVNtestchar_1.1.0.zip, r-oldrel: MVNtestchar_1.1.0.zip
macOS binaries: r-release: MVNtestchar_1.0.2.tgz, r-oldrel: MVNtestchar_1.1.0.tgz
Old sources: MVNtestchar archive

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