The R Journal: article published in 2014, volume 6:1

Rankcluster: An R Package for Clustering Multivariate Partial Rankings
Julien Jacques, Quentin Grimonprez and Christophe Biernacki , The R Journal (2014) 6:1, pages 101-110.

Abstract 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.

Received: 2013-10-04; online 2014-03-03
CRAN packages: Rankcluster, pmr, RMallow

This article is licensed under a Creative Commons Attribution 3.0 Unported license .

@article{RJ-2014-010,
  author = {Julien Jacques and Quentin Grimonprez and Christophe
          Biernacki},
  title = {{Rankcluster: An R Package for Clustering Multivariate
          Partial Rankings}},
  year = {2014},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2014-010},
  url = {https://doi.org/10.32614/RJ-2014-010},
  pages = {101--110},
  volume = {6},
  number = {1}
}