Model-Based Clustering for Multivariate Partial Ranking Data R package Rankcluster version 0.98.0 Implementation of a model-based clustering algorithm for ranking data (C. Biernacki, J. Jacques (2013) <doi:10.1016/j.csda.2012.08.008>). 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. Software Depends: R (>= 2.10) Imports: Rcpp, methods LinkingTo: Rcpp, RcppEigen Suggests: knitr, rmarkdown, testthat Quentin Grimonprez <quentingrim@yahoo.fr> Comprehensive R Archive Network (CRAN) Quentin Grimonprez [aut, cre], Julien Jacques [aut], Christophe Biernacki [aut] GPL (>= 2) 2022-11-12 application/tgz https://CRAN.R-project.org/package=Rankcluster doi:10.32614/CRAN.package.Rankcluster