End-member modelling analysis of grain-size data is an approach 
    to unmix a data set's underlying distributions and their contribution to 
    the data set. EMMAgeo provides deterministic and robust protocols for 
    that purpose.

Obtain estimated marginal means (EMMs) for many linear, generalized 
  linear, and mixed models. Compute contrasts or linear functions of EMMs,
  trends, and comparisons of slopes. Plots and other displays.
  Least-squares means are discussed, and the term "estimated marginal means"
  is suggested, in Searle, Speed, and Milliken (1980) Population marginal means 
  in the linear model: An alternative to least squares means, The American 
  Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>.

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.

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, Peel D (2000) <doi:10.1002/0471721182.ch8>
    McLachlan GJ, Peel D (2000) <ISBN:1-55860-707-2> 
    McLachlan GJ, Peel D, Bean RW (2003) <doi:10.1016/S0167-9473(02)00183-4> 
    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> 
    McLachlan GJ, Baek J, Rathnayake SI (2011) <doi:10.1002/9781119995678.ch9>.