Title: Contrasts in mixed effects for EMMIX model with random effects.
Description: For forming contrasts in the mixed effects for mixtures of linear mixed models fitted to the gene profiles.
Author: Angus Ng, Geoff McLachlan, Kui Wang.

Title: Contrasts in Mixed Effects for EMMIX Model with Random Effects 2

Title: Fitting Mixtures of CFUST Distributions
Maintainer: Sharon X. Lee <s.lee11@uq.edu.au>
Description: Functions to fit finite mixture of multivariate canonical fundamental skew t (FM-CFUST) distributions, random sample generation, 2D and 3D contour plots.
Imports: rgl, mnormt, graphics, MASS, grDevices, stats

Description: Functions to fit finite mixture of multivariate canonical fundamental skew t (FM-CFUST) distributions, random sample generation, 2D and 3D contour plots. See Lee and McLachlan (2017) <doi:10.18637/jss.v083.i03> for more information and examples.

Title: A Mixture Model-Based Approach to the Clustering of Microarray
Description: Provides unsupervised selection and clustering of microarray data
using mixture models. Following the methods described in McLachlan, Bean and
Peel (2002) <doi:10.1093/bioinformatics/18.3.413> a subset of genes are selected
based one the likelihood ratio statistic for the test of one versus two
components when fitting mixtures of t-distributions to the expression data
for each gene. The dimensionality of this gene subset is further reduced through
the use of mixtures of factor analyzers, allowing the tissue samples to be
clustered by fitting mixtures of normal distributions.
Imports: Rcpp (>= 0.12.5), stats, mclust, reshape, ggplot2, scales,

Packaged: 2019-01-04 10:11:41 UTC; andrewjones

Packaged: 2020-02-09 05:04:08 UTC; andrewjones

Packaged: 2020-03-22 06:05:44 UTC; andrewjones

Title: Mixture Models with Component-Wise Factor Analyzers
Author: Suren Rathnayake, Geoff McLachlan, David Peel, Jangsun Baek
Description: We provide functions to fit finite mixtures of multivariate normal or t-distributions to
data with various factor analytic structures adopted for the covariance/scale matrices. The
factor analytic structures available include mixtures of factor analyzers and mixtures of common
factor analyzers. The latter approach is so termed because the matrix of factor loadings is
common to components before the component-specific rotation of the component factors to
make them white noise. Note that the component-factor loadings are not common after
this rotation. Maximum likelihood estimators of model parameters are obtained via the
Expectation-Maximization algorithm. See descriptions of the algorithms used in
McLachlan GJ, Bean RW, Ben-Tovim Jones L (2007) <doi:10.1016/j.csda.2006.09.015>
Baek J, McLachlan GJ, Flack LK (2010) <doi:10.1109/TPAMI.2009.149>
Baek J, McLachlan GJ (2011) <doi:10.1093/bioinformatics/btr112>