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.
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