The Rankcluster package is the first R package proposing both modeling and clustering tools for ranking data, potentially multivariate and partial. Ranking data are modeled by the Insertion Sorting Rank (ISR) model, which is a meaningful model parametrized by a central ranking and a dispersion parameter. A conditional independence assumption allows multivariate rankings to be taken into account, and clustering is performed by means of mixtures of multivariate ISR models. The parameters of the cluster (central rankings and dispersion parameters) help the practitioners to interpret the clustering. Moreover, the Rankcluster package provides an estimate of the missing ranking positions when rankings are partial. After an overview of the mixture of multivariate ISR models, the Rankcluster package is described and its use is illustrated through the analysis of two real datasets.
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For attribution, please cite this work as
Jacques, et al., "The R Journal: Rankcluster: An R Package for Clustering Multivariate Partial Rankings", The R Journal, 2014
BibTeX citation
@article{RJ-2014-010,
author = {Jacques, Julien and Grimonprez, Quentin and Biernacki, Christophe},
title = {The R Journal: Rankcluster: An R Package for Clustering Multivariate Partial Rankings},
journal = {The R Journal},
year = {2014},
note = {https://doi.org/10.32614/RJ-2014-010},
doi = {10.32614/RJ-2014-010},
volume = {6},
issue = {1},
issn = {2073-4859},
pages = {101-110}
}