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Rankcluster  

Model-Based Clustering for Multivariate Partial Ranking Data
View on CRAN: Click here


Download and install Rankcluster package within the R console
Install from CRAN:
install.packages("Rankcluster")

Install from Github:
library("remotes")
install_github("cran/Rankcluster")

Install by package version:
library("remotes")
install_version("Rankcluster", "0.98.0")



Attach the package and use:
library("Rankcluster")
Maintained by
Quentin Grimonprez
[Scholar Profile | Author Map]
First Published: 2013-08-28
Latest Update: 2022-11-12
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.
How to cite:
Quentin Grimonprez (2013). Rankcluster: Model-Based Clustering for Multivariate Partial Ranking Data. R package version 0.98.0, https://cran.r-project.org/web/packages/Rankcluster. Accessed 16 Apr. 2025.
Previous versions and publish date:
0.89 (2013-08-28 19:16), 0.90.1 (2013-08-30 19:45), 0.90.2 (2013-09-05 09:55), 0.90.3 (2013-09-12 09:19), 0.91.5 (2013-12-05 09:26), 0.91.6 (2013-12-19 12:24), 0.91 (2013-11-03 18:23), 0.92.9 (2014-07-25 16:31), 0.92 (2014-02-12 11:41), 0.93.1 (2016-01-12 22:46), 0.94.1 (2019-08-28 01:40), 0.94.2 (2020-02-20 11:30), 0.94.4 (2020-10-06 17:20), 0.94.5 (2021-01-27 09:40), 0.94 (2016-07-29 00:55)
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