Rankcluster (version 0.94.1)
Model-Based Clustering for Multivariate Partial Ranking Data
Description
Implementation of a model-based clustering algorithm for
ranking data (C. Biernacki, J. Jacques (2013) ).
Multivariate rankings as well as partial rankings are taken
into account. This algorithm is based on an extension of the Insertion
Sorting Rank (ISR) model for ranking data, which is a meaningful and
effective model parametrized by a position parameter (the modal ranking,
quoted by mu) and a dispersion parameter (quoted by pi). The heterogeneity
of the rank population is modelled by a mixture of ISR, whereas conditional
independence assumption is considered for multivariate rankings.