  MSmix is a recently developed R package implementing maximum likelihood
estimation of finite mixtures of Mallows models with Spearman distance for full
and partial rankings. The package is designed to implement computationally
tractable estimation routines of the model parameters, with the ability to
handle arbitrary forms of partial rankings and sequences of a large number of
items. The frequentist estimation task is accomplished via EM algorithms,
integrating data augmentation strategies to recover the unobserved
heterogeneity and the missing ranks. The package also provides functionalities
for uncertainty quantification of the estimated parameters, via diverse
bootstrap methods and asymptotic confidence intervals. Generic methods for S3
class objects are constructed for more effectively managing the output of the
main routines. The usefulness of the package and its computational performance
compared with competing software is illustrated via applications to both
simulated and original real ranking datasets.
