The primary aim of MasterBayes is to use MCMC techniques
to integrate over uncertainty in pedigree configurations
estimated from molecular markers and phenotypic data.  Emphasis
is put on the marginal distribution of parameters that relate
the phenotypic data to the pedigree. All simulation is done in
compiled C++ for efficency.

The primary aim of MasterBayes is to use MCMC techniques
to integrate over uncertainty in pedigree configurations
estimated from molecular markers and phenotypic data.  Emphasis
is put on the marginal distribution of parameters that relate
the phenotypic data to the pedigree. All simulation is done in
compiled C++ for efficiency.

The primary aim of MasterBayes is to use MCMC techniques to integrate over uncertainty in pedigree configurations estimated from molecular markers and phenotypic data.  Emphasis is put on the marginal distribution of parameters that relate the phenotypic data to the pedigree. All simulation is done in compiled C++ for efficiency.

The primary aim of 'MasterBayes' is to use MCMC techniques to integrate over uncertainty in pedigree configurations estimated from molecular markers and phenotypic data.  Emphasis is put on the marginal distribution of parameters that relate the phenotypic data to the pedigree. All simulation is done in compiled 'C++' for efficiency.

Matrix Clustering with Gaussian and Manly Mixture Models
Matrix clustering with finite mixture models.

Performs matrix skew-t parameter estimation, Gallaugher and McNicholas (2017)  <doi: 10.1002/sta4.143>.

Clustering with Matrix Gaussian and Matrix Transformation
Mixture Models
c(person("Xuwen", "Zhu", role = c("aut", "cre"),
email = "xuwen.zhu@louisville.edu"),
person("Volodymyr", "Melnykov", role = "aut"),
person("Shuchismita", "Sarkar", role = "ctb"),
person("Michael", "Hutt", role = c("ctb", "cph")),
person("Stephen", "Moshier", role = c("ctb", "cph")),
person("Rouben", "Rostamian", role = c("ctb", "cph")),
person("Carl Edward", "Rasmussen", role = c("ctb", "cph")),
person("Dianne", "Cook", role = c("ctb", "cph")))
Provides matrix Gaussian mixture models, matrix transformation mixture models and their model-based clustering results. The parsimonious models of the mean matrices and variance covariance matrices are implemented with a total of 196 variations.
Xuwen Zhu [aut, cre],
Volodymyr Melnykov [aut],
Shuchismita Sarkar [ctb],
Michael Hutt [ctb, cph],
Stephen Moshier [ctb, cph],
Rouben Rostamian [ctb, cph],
Carl Edward Rasmussen [ctb, cph],
Dianne Cook [ctb, cph]