Function reference • MixMatrix

Package Introduction
`MixMatrix`	Classification with Matrix Variate Normal and t Distributions
Distributions Sampling and density functions for matrix variate distributions
`rmatrixnorm()` `dmatrixnorm()`	Matrix variate Normal distribution functions
`rmatrixt()` `dmatrixt()`	Distribution functions for the matrix variate t distribution.
`rmatrixinvt()` `dmatrixinvt()`	Distribution functions for matrix variate inverted t distributions
Maximum Likelihood Estimation Maximum likelihood estimation for the normal and t distribution
`MLmatrixnorm()`	Maximum likelihood estimation for matrix normal distributions
`MLmatrixt()`	Maximum likelihood estimation for matrix variate t distributions
`matrixmixture()`	Fit a matrix variate mixture model
`init_matrixmixture()`	Initializing settings for Matrix Mixture Models
Discriminant Analysis Linear and quadratic discriminant analysis
`matrixlda()`	LDA for matrix variate distributions
`matrixqda()`	Quadratic Discriminant Analysis for Matrix Variate Observations
`predict(<matrixlda>)`	Classify Matrix Variate Observations by Linear Discrimination
`predict(<matrixqda>)`	Classify Matrix Variate Observations by Quadratic Discrimination
Accessory functions
`ARgenerate()`	Generate a unit AR(1) covariance matrix
`CSgenerate()`	Generate a compound symmetric correlation matrix

Reference