Model-Based Clustering for Multivariate Partial Ranking Data

Implementation of a model-based clustering algorithm for ranking data (C. Biernacki, J. Jacques (2013) ). 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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0.94.5 by Quentin Grimonprez, a year ago

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Authors: Quentin Grimonprez [aut, cre] , Julien Jacques [aut] , Christophe Biernacki [aut]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports Rcpp, methods

Suggests knitr, rmarkdown

Linking to Rcpp, RcppEigen

See at CRAN