Rankcluster

R package Rankcluster. 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.

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References in zbMATH (referenced in 7 articles )

Showing results 1 to 7 of 7.
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  1. Mollica, Cristina; Tardella, Luca: Bayesian analysis of ranking data with the extended Plackett-Luce model (2021)
  2. Mollica, Cristina; Tardella, Luca: PLMIX: an R package for modelling and clustering partially ranked data (2020)
  3. Turner, Heather L.; van Etten, Jacob; Firth, David; Kosmidis, Ioannis: Modelling rankings in (\mathsfR): the \textbfPlackettLucepackage (2020)
  4. Zhaozhi Qian; Philip Yu: Weighted Distance-Based Models for Ranking Data Using the R Package rankdist (2019) not zbMATH
  5. Vitelli, Valeria; Sørensen, Øystein; Crispino, Marta; Frigessi, Arnoldo; Arjas, Elja: Probabilistic preference learning with the Mallows rank model (2018)
  6. Biernacki, Christophe; Jacques, Julien: Model-based clustering of multivariate ordinal data relying on a stochastic binary search algorithm (2016)
  7. Cristina Mollica, Luca Tardella: PLMIX: An R package for modeling and clustering partially ranked data (2016) arXiv