Rankcluster: Model-Based Clustering for Multivariate Partial Ranking Data

Implementation of a model-based clustering algorithm for ranking data. 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
Depends: R (≥ 2.10), methods
Imports: Rcpp
LinkingTo: Rcpp, RcppEigen
Published: 2016-07-29
Author: Quentin Grimonprez, Julien Jacques
Maintainer: Quentin Grimonprez <quentin.grimonprez at inria.fr>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Copyright: Inria 2012-2016
NeedsCompilation: yes
CRAN checks: Rankcluster results


Reference manual: Rankcluster.pdf
Vignettes: Using Rankcluster
Package source: Rankcluster_0.94.tar.gz
Windows binaries: r-devel: Rankcluster_0.94.zip, r-release: Rankcluster_0.94.zip, r-oldrel: Rankcluster_0.94.zip
OS X El Capitan binaries: r-release: Rankcluster_0.94.tgz
OS X Mavericks binaries: r-oldrel: Rankcluster_0.94.tgz
Old sources: Rankcluster archive


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