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 |
| Reference manual: | MVNtestchar.pdf |
| Vignettes: |
Theory_and_Implementation |
| Package source: | MVNtestchar_1.1.3.tar.gz |
| Windows binaries: | r-devel: MVNtestchar_1.1.3.zip, r-release: MVNtestchar_1.1.3.zip, r-oldrel: MVNtestchar_1.1.3.zip |
| macOS binaries: | r-release: MVNtestchar_1.1.3.tgz, r-oldrel: MVNtestchar_1.1.3.tgz |
| Old sources: | MVNtestchar archive |
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