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:	0.98.0
Depends:	R (≥ 2.10)
Imports:	Rcpp, methods
LinkingTo:	Rcpp, RcppEigen
Suggests:	knitr, rmarkdown, testthat
Published:	2022-11-12
DOI:	10.32614/CRAN.package.Rankcluster
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
Materials:	NEWS
CRAN checks:	Rankcluster results

Documentation:

Reference manual:	Rankcluster.pdf
Vignettes:	Data Format Using Rankcluster

Downloads:

Package source:	Rankcluster_0.98.0.tar.gz
Windows binaries:	r-devel: Rankcluster_0.98.0.zip, r-release: Rankcluster_0.98.0.zip, r-oldrel: Rankcluster_0.98.0.zip
macOS binaries:	r-release (arm64): Rankcluster_0.98.0.tgz, r-oldrel (arm64): Rankcluster_0.98.0.tgz, r-release (x86_64): Rankcluster_0.98.0.tgz, r-oldrel (x86_64): Rankcluster_0.98.0.tgz
Old sources:	Rankcluster archive

Reverse dependencies:

Reverse imports:

MSmix

Linking:

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