Package: Rankcluster 0.98.0
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.
Authors:
Rankcluster_0.98.0.tar.gz
Rankcluster_0.98.0.zip(r-4.5)Rankcluster_0.98.0.zip(r-4.4)Rankcluster_0.98.0.zip(r-4.3)
Rankcluster_0.98.0.tgz(r-4.4-x86_64)Rankcluster_0.98.0.tgz(r-4.4-arm64)Rankcluster_0.98.0.tgz(r-4.3-x86_64)Rankcluster_0.98.0.tgz(r-4.3-arm64)
Rankcluster_0.98.0.tar.gz(r-4.5-noble)Rankcluster_0.98.0.tar.gz(r-4.4-noble)
Rankcluster_0.98.0.tgz(r-4.4-emscripten)Rankcluster_0.98.0.tgz(r-4.3-emscripten)
Rankcluster.pdf |Rankcluster.html✨
Rankcluster/json (API)
NEWS
# Install 'Rankcluster' in R: |
install.packages('Rankcluster', repos = c('https://modal-inria.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/modal-inria/rankcluster/issues
Last updated 2 years agofrom:ef9344d9c9. Checks:OK: 9. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 13 2024 |
R-4.5-win-x86_64 | OK | Nov 13 2024 |
R-4.5-linux-x86_64 | OK | Nov 13 2024 |
R-4.4-win-x86_64 | OK | Nov 13 2024 |
R-4.4-mac-x86_64 | OK | Nov 13 2024 |
R-4.4-mac-aarch64 | OK | Nov 13 2024 |
R-4.3-win-x86_64 | OK | Nov 13 2024 |
R-4.3-mac-x86_64 | OK | Nov 13 2024 |
R-4.3-mac-aarch64 | OK | Nov 13 2024 |
Exports:convertRankcriteriadistCayleydistHammingdistKendalldistSpearmanfrequencekhi2kullbackprobabilityrankclustsimulISRsummaryunfrequence
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Model-Based Clustering for Multivariate Partial Ranking Data | Rankcluster-package |
Getter method for rankclust output | [,Rankclust-method |
Rank data: APA | APA |
Rank data: big4 | big4 |
Change the representation of a rank | convertRank |
Criteria estimation | criteria |
Cayley distance between two ranks | distCayley |
Hamming distance between two ranks | distHamming |
Kendall distance between two ranks | distKendall |
Spearman distance between two ranks | distSpearman |
Multidimensional partial rank data: eurovision | eurovision |
Convert data storage | frequence |
Khi2 test | khi2 |
Kullback-Leibler divergence | kullback |
Constructor of Output class | Output-class |
Probability computation | probability |
Multidimensional rank data: quiz | quiz |
Model-based clustering for multivariate partial ranking | rankclust |
Constructor of Rankclust class | Rankclust-class |
Show function. | show,Output-method show,Rankclust-method |
Simulate a sample of ISR(pi,mu) | simulISR |
Rank data: sports | sports |
Summary function. | summary,Rankclust-method |
Convert data | unfrequence |
Rank data: words | words |