Package: Rankcluster 0.98.0
Quentin Grimonprez
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
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Rankcluster_0.98.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
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
clusteringhacktoberfestrankcpp
Last updated from:ef9344d9c9. Checks:13 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 161 | ||
| linux-devel-x86_64 | OK | 175 | ||
| source / vignettes | OK | 257 | ||
| linux-release-arm64 | OK | 168 | ||
| linux-release-x86_64 | OK | 129 | ||
| macos-release-arm64 | OK | 101 | ||
| macos-release-x86_64 | OK | 227 | ||
| macos-oldrel-arm64 | OK | 137 | ||
| macos-oldrel-x86_64 | OK | 271 | ||
| windows-devel | OK | 184 | ||
| windows-release | OK | 147 | ||
| windows-oldrel | OK | 143 | ||
| wasm-release | OK | 146 |
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 |
