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: 0.94.1
Depends: R (≥ 2.10), methods
Imports: Rcpp
LinkingTo: Rcpp, RcppEigen
Published: 2019-08-27
Author: Quentin Grimonprez [aut, cre], Julien Jacques [aut], Christophe Biernacki [aut]
Maintainer: Quentin Grimonprez <quentin.grimonprez at inria.fr>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Copyright: Inria - Université de Lille
NeedsCompilation: yes
CRAN checks: Rankcluster results


Reference manual: Rankcluster.pdf
Vignettes: Using Rankcluster
Package source: Rankcluster_0.94.1.tar.gz
Windows binaries: r-devel: Rankcluster_0.94.1.zip, r-devel-gcc8: Rankcluster_0.94.1.zip, r-release: Rankcluster_0.94.1.zip, r-oldrel: Rankcluster_0.94.1.zip
OS X binaries: r-release: Rankcluster_0.94.1.tgz, r-oldrel: Rankcluster_0.94.1.tgz
Old sources: Rankcluster archive


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