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.


Reference manual

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1.1.3 by William Fairweather, a year ago

Browse source code at https://github.com/cran/MVNtestchar

Authors: William Fairweather [aut, cre]

Documentation:   PDF Manual  

GPL (>= 2) license

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

Suggests markdown

See at CRAN