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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.

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VersionRepositoryFileSize
0.98.0 rolling linux/jammy R-4.5 Rankcluster_0.98.0.tar.gz 576.5 KiB
0.98.0 rolling linux/noble R-4.5 Rankcluster_0.98.0.tar.gz 577.0 KiB
0.98.0 rolling source/ R- Rankcluster_0.98.0.tar.gz 426.6 KiB
0.98.0 latest linux/jammy R-4.5 Rankcluster_0.98.0.tar.gz 576.5 KiB
0.98.0 latest linux/noble R-4.5 Rankcluster_0.98.0.tar.gz 577.0 KiB
0.98.0 latest source/ R- Rankcluster_0.98.0.tar.gz 426.6 KiB
0.98.0 2026-04-26 source/ R- Rankcluster_0.98.0.tar.gz 426.6 KiB
0.98.0 2026-04-23 source/ R- Rankcluster_0.98.0.tar.gz 426.6 KiB
0.98.0 2026-04-09 windows/windows R-4.5 Rankcluster_0.98.0.zip 897.1 KiB
0.98.0 2025-04-20 source/ R- Rankcluster_0.98.0.tar.gz 426.6 KiB

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