Package: rankrate 1.2.0
rankrate: Joint Statistical Models for Preference Learning with Rankings and Ratings
Statistical tools for the Mallows-Binomial model, the first joint statistical model for preference learning for rankings and ratings. This project was supported by the National Science Foundation under Grant No. 2019901.
Authors:
rankrate_1.2.0.tar.gz
rankrate_1.2.0.zip(r-4.5)rankrate_1.2.0.zip(r-4.4)rankrate_1.2.0.zip(r-4.3)
rankrate_1.2.0.tgz(r-4.4-any)rankrate_1.2.0.tgz(r-4.3-any)
rankrate_1.2.0.tar.gz(r-4.5-noble)rankrate_1.2.0.tar.gz(r-4.4-noble)
rankrate_1.2.0.tgz(r-4.4-emscripten)rankrate_1.2.0.tgz(r-4.3-emscripten)
rankrate.pdf |rankrate.html✨
rankrate/json (API)
NEWS
| # Install 'rankrate' in R: |
| install.packages('rankrate', repos = c('https://pearce790.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/pearce790/rankrate/issues
- AIBS - Real peer review data set from the American Institute of Biological Sciences
- ToyData1 - Toy data set of rankings and ratings demonstrating tie-breaking
- ToyData2 - Toy data set of rankings and ratings demonstrating decision-making with partial rankings
- ToyData3 - Toy data set of rankings and ratings when judges express internally inconsistent preferences
Last updated 7 months agofrom:511e20d398. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 10 2024 |
| R-4.5-win | OK | Nov 10 2024 |
| R-4.5-linux | OK | Nov 10 2024 |
| R-4.4-win | OK | Nov 10 2024 |
| R-4.4-mac | OK | Nov 10 2024 |
| R-4.3-win | OK | Nov 10 2024 |
| R-4.3-mac | OK | Nov 10 2024 |
Exports:ASTARci_mbdmalldmbfit_mbFVgetQGreedyGreedyLocalkendallpsirmallrmbto_rankings
rankrate: Joint Statistical Models for Preference Learning with Rankings and Ratings
Rendered fromoverview.Rmdusingknitr::rmarkdownon Nov 10 2024.Last update: 2024-05-06
Started: 2023-05-06
Tutorial: Toy Data Set
Rendered fromtutorial.Rmdusingknitr::rmarkdownon Nov 10 2024.Last update: 2023-05-07
Started: 2023-05-06
