Package: rankdist 1.1.4

Zhaozhi Qian

rankdist: Distance Based Ranking Models

Implements distance based probability models for ranking data. The supported distance metrics include Kendall distance, Spearman distance, Footrule distance, Hamming distance, Weighted-tau distance and Weighted Kendall distance. Phi-component model and mixture models are also supported.

Authors:Zhaozhi Qian

rankdist_1.1.4.tar.gz
rankdist_1.1.4.tar.gz(r-4.5-noble)rankdist_1.1.4.tar.gz(r-4.4-noble)
rankdist_1.1.4.tgz(r-4.4-emscripten)rankdist_1.1.4.tgz(r-4.3-emscripten)
rankdist.pdf |rankdist.html✨
rankdist/json (API)

# Install 'rankdist' in R:
install.packages('rankdist', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • apa_obj - American Psychological Association (APA) election data
  • apa_partial_obj - American Psychological Association

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

11 exports 0.23 score 7 dependencies 1 dependents 42 scripts 315 downloads

Last updated 5 years agofrom:5deb852108. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 07 2024
R-4.5-linux-x86_64OKSep 07 2024

Exports:DistanceBlockDistanceMatrixDistancePairGenerateExampleGenerateExampleTopQHashtoRankModelSummaryMomentsEstOrderingToRankingRankDistanceModelRanktoHash

Dependencies:hashnloptrnumDerivoptimxpermutepracmaRcpp

Readme and manuals

Help Manual

Help pageTopics
A package for fitting distance based ranking modelsrankdist-package rankdist
American Psychological Association (APA) election dataapa_obj
American Psychological Association (APA) election data (partial rankings included)apa_partial_obj
Calculate Kendall distance between one ranking and a matrix of rankingsDistanceBlock
Calculate Kendall distance matrix between rankingsDistanceMatrix
Calculate Kendall distance between a pair of rankingsDistancePair
Generate simple examplesGenerateExample
Generate simple examples of top-q rankingsGenerateExampleTopQ
Obtain Ranking from Hash ValueHashtoRank
Print a brief summary of the fitted modelModelSummary
Find Initial Values of phiMomentsEst
Transformation between Rankings and OrderingsOrderingToRanking
RankControl ClassRankControl RankControl-class
RankControlCayley ClassRankControlCayley RankControlCayley-class
RankControlFootrule ClassRankControlFootrule RankControlFootrule-class
RankControlHamming ClassRankControlHamming RankControlHamming-class
RankControlKendall ClassRankControlKendall RankControlKendall-class
RankControlPhiComponent ClassRankControlPhiComponent RankControlPhiComponent-class
RankControlSpearman ClassRankControlSpearman RankControlSpearman-class
RankControlWeightedKendall ClassRankControlWeightedKendall RankControlWeightedKendall-class
RankControlWtau ClassRankControlWtau RankControlWtau-class
RankData ClassRankData RankData-class
Fit A Mixture of Distance-based ModelsRankDistanceModel
RankInit ClassRankInit RankInit-class
Create Hash Value for RankingRanktoHash