MixMatrix: Classification with Matrix Variate Normal and t Distributions
In gzt/MixMatrix: Classification with Matrix Variate Normal and t Distributions
Description
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.
gzt/MixMatrix documentation built on Nov. 18, 2021, 11:02 a.m.
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Description
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.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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