  We introduce the R package ContaminatedMixt, conceived to disseminate the use
of mixtures of multivariate contaminated normal distributions as a tool for
robust clustering and classification under the common assumption of
elliptically contoured groups. Thirteen variants of the model are also
implemented to introduce parsimony. The expectation-conditional maximization
algorithm is adopted to obtain maximum likelihood parameter estimates, and
likelihood-based model selection criteria are used to select the model and the
number of groups. Parallel computation can be used on multicore PCs and
computer clusters, when several models have to be fitted. Differently from the
more popular mixtures of multivariate normal and t distributions, this approach
also allows for automatic detection of mild outliers via the maximum a
posteriori probabilities procedure. To exemplify the use of the package,
applications to artificial and real data are presented.
