Multivariate Normal Distribution Characterization Test

Provides a test of multivariate normality of a sample which 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 hyperrectangle if and only if the original data is multivariate normal. The package performs a goodness of fit test of this hypothesis. Four sample datasets are included: a bivariate and a trivariate normal set and a bivariate and trivariate Bernoulli set. In addition to the test, functions in the package give rotatable visualizations of the support region of positive definite matrices for bivariate samples.


Reference manual

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1.0.2 by William Fairweather, a month ago

Browse source code at

Authors: William Fairweather [aut, cre]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports graphics, grDevices, Hmisc, stats, utils, ggplot2

Suggests testthat, knitr, rmarkdown

See at CRAN