Rankcluster: Model-Based Clustering for Multivariate Partial Ranking Data

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.

Version: 0.94.5
Depends: R (≥ 2.10)
Imports: Rcpp, methods
LinkingTo: Rcpp, RcppEigen
Suggests: knitr, rmarkdown
Published: 2021-01-27
Author: Quentin Grimonprez [aut, cre], Julien Jacques [aut], Christophe Biernacki [aut]
Maintainer: Quentin Grimonprez <quentingrim at yahoo.fr>
BugReports: https://github.com/modal-inria/Rankcluster/issues/
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Copyright: Inria - Université de Lille
NeedsCompilation: yes
Citation: Rankcluster citation info
CRAN checks: Rankcluster results


Reference manual: Rankcluster.pdf
Vignettes: Data Format
Using Rankcluster
Package source: Rankcluster_0.94.5.tar.gz
Windows binaries: r-devel: Rankcluster_0.94.5.zip, r-devel-UCRT: Rankcluster_0.94.5.zip, r-release: Rankcluster_0.94.5.zip, r-oldrel: Rankcluster_0.94.5.zip
macOS binaries: r-release (arm64): Rankcluster_0.94.5.tgz, r-release (x86_64): Rankcluster_0.94.5.tgz, r-oldrel: Rankcluster_0.94.5.tgz
Old sources: Rankcluster archive


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