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rankdist (version 1.0.0)

Distance Based Ranking Models

Description

Implements distance based probability models for ranking data. The supported distance metrics include Kendall distance and Weighted Kendall distance. Mixture models are also supported.

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Version

Install

install.packages('rankdist')

Monthly Downloads

240

Version

1.0.0

License

GPL (>= 2)

Maintainer

Zhaozhi Qian

Last Published

July 13th, 2015

Functions in rankdist (1.0.0)

HashtoRank

Obtain Ranking from Hash Value

GenerateExample

Generate simple examples

GenerateExampleTopQ

Generate simple examples of top-q

ModelSummary

Print a brief summary of the fitted model Print a brief summary of the fitted model. This includes information about goodness of fit as well as parameter estimation.

DistanceBlock

Calculate Kendall distance #' Calculate Kendall distance matrix between one ranking and a matrix of rankings

DistancePair

Calculate Kendall distance between a pair of rankings #' Calculate Kendall distance matrix between a pair of rankings

RankData-class

RankData Class

apa_partial_obj

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

DistanceMatrix

Calculate Kendall distance matrix between rankings

MomentsEst

Find Initial Values of param

OrderingToRanking

Transformation between Rankings and Orderings

RankControlWeightedKendall-class

RankControlWeightedKendall Class

RankInit-class

RankInit Class

rankdist-package

A package for fitting distance based ranking models

RankDistanceModel

Fit A Mixture of Distance-based Models

RankControl-class

RankControl Class

RankControlPhiComponent-class

RankControlPhiComponent Class

apa_obj

American Psychological Association (APA) election data

RanktoHash

Create Hash Value for Rank

RankControlKendall-class

RankControlKendall Class