The Mutual Information Index (M) introduced to social science literature by
    Theil and Finizza (1971) <doi:10.1080/0022250X.1971.9989795> is a multigroup
    segregation measure that is highly decomposable and that according to Frankel
    and Volij (2011) <doi:10.1016/j.jet.2010.10.008> and Mora and Ruiz-Castillo
    (2011) <doi:10.1111/j.1467-9531.2011.01237.x> satisfies the Strong Unit
    Decomposability and Strong Group Decomposability properties. This package allows
    computing and decomposing the total index value into its "between" and
    "within" terms. These last terms can also be decomposed into their
    contributions, either by group or unit characteristics. The factors that produce
    each "within" term can also be displayed at the user's request. The results can
    be computed considering a variable or sets of variables that define separate
    clusters.

An ensemble classifier for multiclass classification. This is a novel classifier that natively works as an ensemble. It projects data on a large number of matrices, and uses very simple classifiers on each of these projections. The results are then combined, ideally via Dempster-Shafer Calculus.

Predictive multivariate modelling for metabolomics. 
    Types: Classification and regression. 
    Methods: Partial Least Squares, Random Forest ans Elastic Net 
    Data structures: Paired and unpaired Validation: repeated double cross-validation (Westerhuis et al. (2008)<doi:10.1007/s11306-007-0099-6>, Filzmoser et al. (2009)<doi:10.1002/cem.1225>) 
    Variable selection: Performed internally, through tuning in the inner cross-validation loop.

Functions, data sets, analyses and examples from the book 
  `An Introduction to Applied Multivariate Analysis with R' 
  (Brian S. Everitt and Torsten Hothorn, Springer, 2011).

A set of tools for displaying, modeling and analysing
        multivariate abundance data in community ecology. See
        'mvabund-package.Rd' for details of overall package organization.
        The package is implemented with the Gnu Scientific Library
        (<http://www.gnu.org/software/gsl/>) and 'Rcpp'
        (<http://dirk.eddelbuettel.com/code/rcpp.html>) 'R' / 'C++' classes.