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.
| Version: |
1.1.3 |
| Depends: |
R (≥ 2.10) |
| Imports: |
graphics, grDevices, Hmisc, stats, utils, knitr, ggplot2 |
| Suggests: |
markdown |
| Published: |
2020-07-25 |
| 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 |
Documentation:
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