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rankdist (version 1.1.4)

Distance Based Ranking Models

Description

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.

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Version

Install

install.packages('rankdist')

Monthly Downloads

223

Version

1.1.4

License

GPL (>= 2)

Maintainer

Zhaozhi Qian

Last Published

July 27th, 2019

Functions in rankdist (1.1.4)

RankControl-class

RankControl Class
RankControlFootrule-class

RankControlFootrule Class
RankControlCayley-class

RankControlCayley Class
RankControlPhiComponent-class

RankControlPhiComponent Class
RankControlSpearman-class

RankControlSpearman Class
RanktoHash

Create Hash Value for Ranking
RankInit-class

RankInit Class
RankData-class

RankData Class
RankDistanceModel

Fit A Mixture of Distance-based Models
RankControlKendall-class

RankControlKendall Class
RankControlHamming-class

RankControlHamming Class
rankdist-package

A package for fitting distance based ranking models
RankControlWtau-class

RankControlWtau Class
apa_obj

American Psychological Association (APA) election data
RankControlWeightedKendall-class

RankControlWeightedKendall Class
apa_partial_obj

American Psychological Association (APA) election data (partial rankings included)
GenerateExampleTopQ

Generate simple examples of top-q rankings
ModelSummary

Print a brief summary of the fitted model
OrderingToRanking

Transformation between Rankings and Orderings
GenerateExample

Generate simple examples
DistanceMatrix

Calculate Kendall distance matrix between rankings
HashtoRank

Obtain Ranking from Hash Value
DistancePair

Calculate Kendall distance between a pair of rankings
DistanceBlock

Calculate Kendall distance between one ranking and a matrix of rankings
MomentsEst

Find Initial Values of phi