MixMatrix: Classification with Matrix Variate Normal and t Distributions

Provides sampling and density functions for matrix variate normal, t, and inverted t distributions; ML estimation for matrix variate normal and t distributions using the EM algorithm, including some restrictions on the parameters; and classification by linear and quadratic discriminant analysis for matrix variate normal and t distributions described in Thompson et al. (2019) <arXiv:1907.09565>. Performs clustering with matrix variate normal and t mixture models.

Version: 0.2.4
Depends: R (≥ 3.5.0)
Imports: stats, CholWishart, Rcpp
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, testthat, covr, ggplot2, dplyr, magrittr, spelling
Published: 2019-11-14
Author: Geoffrey Thompson ORCID iD [aut, cre], B. D. Ripley author of original lda and qda functions [ctb, cph], W. N. Venables author of original lda and qda functions [ctb, cph]
Maintainer: Geoffrey Thompson <gzthompson at gmail.com>
BugReports: http://github.com/gzt/MixMatrix/issues
License: GPL-3
URL: http://github.com/gzt/MixMatrix/, https://gzt.github.io/MixMatrix/
NeedsCompilation: yes
Language: en-us
Materials: README NEWS
CRAN checks: MixMatrix results


Reference manual: MixMatrix.pdf
Vignettes: Discriminant Analysis
Matrix Normal Distributions
Mixture Models
Package source: MixMatrix_0.2.4.tar.gz
Windows binaries: r-devel: MixMatrix_0.2.4.zip, r-release: MixMatrix_0.2.4.zip, r-oldrel: MixMatrix_0.2.4.zip
macOS binaries: r-release: MixMatrix_0.2.4.tgz, r-oldrel: MixMatrix_0.2.4.tgz
Old sources: MixMatrix archive


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