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). Performs clustering with matrix variate normal and t mixture models.

See also

Useful links:

• https://github.com/gzt/MixMatrix/

• https://gzt.github.io/MixMatrix/

• Report bugs at https://github.com/gzt/MixMatrix/issues

Author

Maintainer: Geoffrey Thompson gzthompson@gmail.com (ORCID)

Other contributors:

• B. D. Ripley ripley@stats.ox.ac.uk (author of original lda and qda functions) [contributor, copyright holder]

• W. N. Venables (author of original lda and qda functions) [contributor, copyright holder]