Packaged: Fri Dec 15 18:54:30 2006; hornik

Packaged: Mon Nov 26 20:33:43 2007; hornik

Packaged: Mon Apr  7 13:36:09 2008; jarrod

Packaged: Thu Apr 10 17:54:45 2008; root

Packaged: Thu Jun 12 20:35:18 2008; root

Packaged: Mon Jun 16 11:01:44 2008; root

Packaged: Thu Sep  4 17:27:28 2008; jarrod

Packaged: Tue Dec  2 17:04:38 2008; jarrod

Depends: coda, genetics, gtools, kinship
Packaged: Fri Mar  6 17:29:49 2009; jarrod

Description: 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
Packaged: Sat Apr 4 21:16:12 2009; jarrod

Packaged: Tue Jul 21 14:03:43 2009; jarrod

Depends: coda, genetics, gtools, kinship2

Description: 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.

Description: 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.

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

Title: Matrix Skew-T Parameter Estimation
Description: Performs matrix skew-t parameter estimation, Gallaugher and McNicholas (2017)  <doi: 10.1002/sta4.143>.
Packaged: 2019-07-25 18:00:07 UTC; michaelgallaugher

Title: Clustering with Matrix Gaussian and Matrix Transformation
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")))
Description: 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.