  Ranking data represent a peculiar form of multivariate ordinal data taking
values in the set of permutations. Despite the numerous methodological
contributions to increase the flexibility of ranked data modeling, the
application of more sophisticated models is limited by the related
computational issues. The PLMIX package offers a comprehensive framework aimed
at endowing the R statistical environment with some recent methodological
advances in modeling and clustering partially ranked data. The usefulness of
the novel PLMIX package can be motivated from several perspectives: (i) it
contributes to fill the gap concerning Bayesian estimation of ranking models in
R, by focusing on the Plackett-Luce model and its extension within the finite
mixture approach as the generative sampling distribution; (ii) it addresses
computational complexity by combining the flexibility of R routines and the
speed of compiled C++ code, with possible parallel execution; (iii) it covers
the fundamental phases of ranking data analysis allowing for a more careful and
critical application of ranking models in real experiments; (iv) it provides
effective tools for clustering heterogeneous partially ranked data. The
functionality of the novel package is illustrated with several applications to
simulated and real datasets.
