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Rankcluster (version 0.98.0)

Model-Based Clustering for Multivariate Partial Ranking Data

Description

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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Version

Install

install.packages('Rankcluster')

Monthly Downloads

415

Version

0.98.0

License

GPL (>= 2)

Last Published

November 12th, 2022

Functions in Rankcluster (0.98.0)

distKendall

Kendall distance between two ranks
summary,Rankclust-method

Summary function.
kullback

Kullback-Leibler divergence
probability

Probability computation
Output-class

Constructor of Output class
distSpearman

Spearman distance between two ranks
eurovision

Multidimensional partial rank data: eurovision
convertRank

Change the representation of a rank
unfrequence

Convert data
show,Output-method

Show function.
simulISR

Simulate a sample of ISR(pi,mu)
[,Rankclust-method

Getter method for rankclust output
quiz

Multidimensional rank data: quiz
words

Rank data: words
rankclust

Model-based clustering for multivariate partial ranking
sports

Rank data: sports
distHamming

Hamming distance between two ranks
criteria

Criteria estimation
big4

Rank data: big4
Rankclust-class

Constructor of Rankclust class
distCayley

Cayley distance between two ranks
frequence

Convert data storage
APA

Rank data: APA
Rankcluster-package

Model-Based Clustering for Multivariate Partial Ranking Data
khi2

Khi2 test