  This paper describes an algorithm for fitting finite mixtures of unrestricted
Multivariate Skew t (FM-uMST) distributions. The package EMMIX-uskew implements
a closed-form expectation-maximization (EM) algorithm for computing the maximum
likelihood (ML) estimates of the parameters for the (unrestricted) FM-MST model
in R. EMMIX-uskew also supports visualization of fitted contours in two and
three dimensions, and random sample generation from a specified FM-uMST
distribution.
  Finite mixtures of skew t-distributions have proven to be useful in modelling
heterogeneous data with asymmetric and heavy tail behaviour, for example,
datasets from flow cytometry. In recent years, various versions of mixtures
with multivariate skew t (MST) distributions have been proposed. However, these
models adopted some restricted characterizations of the component MST
distributions so that the E-step of the EM algorithm can be evaluated in closed
form. This paper focuses on mixtures with unrestricted MST components, and
describes an iterative algorithm for the computation of the ML estimates of its
model parameters.
  The usefulness of the proposed algorithm is demonstrated in three
applications to real data sets. The first example illustrates the use of the
main function fmmst in the package by fitting a MST distribution to a bivariate
unimodal flow cytometric sample. The second example fits a mixture of MST
distributions to the Australian Institute of Sport (AIS) data, and demonstrate
that EMMIX-uskew can provide better clustering results than mixtures with
restricted MST components. In the third example, EMMIX-uskew is applied to
classify cells in a trivariate flow cytometric dataset. Comparisons with other
available methods suggests that the EMMIX-uskew result achieved a lower
misclassification rate with respect to the labels given by benchmark gating
analysis.
