  The rebmix package provides R functions for random univariate and
multivariate finite mixture model generation, estimation, clustering and
classification. The paper is focused on multivariate normal mixture models with
unrestricted variance-covariance matrices. The objective is to show how to
generate datasets for a known number of components, numbers of observations and
component parameters, how to estimate the number of components, component
weights and component parameters and how to predict cluster and class
membership based upon a model trained by the REBMIX algorithm. The accompanying
plotting, bootstrapping and other features of the package are dealt with, too.
For demonstration purpose a multivariate normal dataset with unrestricted
variance-covariance matrices is studied.
