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.


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1.1.3 by William Fairweather, 3 months 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