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. 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.

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

Wrap functions of mvpart function providing for instance a more
descriptive tree, the discriminant species at each node (table
1 of Dea'th (2002)), the cascade MRT analysis and an adjusted
R^2 for an MRT model.
Marie-Helene Ouellette <marieheleneo@gmail.com>, with contributions from Pierre Legendre <pierre.legendre@umontreal.ca>.
Hmisc, mvpart, labdsv, vegan, fBasics, ade4
The main function of this package, MRT, provides to the following information : the proper cluster membership allocation in the tree, the discriminant species for each cluster, the tree nodes at the proper level of R2 (1-relative error), the total species variance partitionned by species, by the tree, and by the splits of the tree, plus a different graphic representation of the final model in the form of a tree. Moreover, the CascadeMRT function allows a cascade MRT analysis and the CasMRTR2 function allows to illustrate the (unadjusted) variation explained by each wave/drops. Last but not least, the R2aGDF function permits the calculation of an adjusted R^2 for an MRT model.