Package: RMallow 1.1

RMallow: Fit Multi-Modal Mallows' Models to Ranking Data

An EM algorithm to fit Mallows' Models to full or partial rankings, with or without ties. Based on Adkins and Flinger (1998) <doi:10.1080/03610929808832223>.

Authors:Erik Gregory [aut, cre]

RMallow_1.1.tar.gz
RMallow_1.1.zip(r-4.5)RMallow_1.1.zip(r-4.4)RMallow_1.1.zip(r-4.3)
RMallow_1.1.tgz(r-4.4-any)RMallow_1.1.tgz(r-4.3-any)
RMallow_1.1.tar.gz(r-4.5-noble)RMallow_1.1.tar.gz(r-4.4-noble)
RMallow_1.1.tgz(r-4.4-emscripten)RMallow_1.1.tgz(r-4.3-emscripten)
RMallow.pdf |RMallow.html✨
RMallow/json (API)

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

Peer review:

Datasets:
  • datas - Sample data set.
  • elect - 1980 APA Presidential Candidate ranking data.
  • three.mode - Fitted version of the toy datas data set, with three modal sequences.
  • two.mode - Two-mode Mallows' model fit to toy data set "datas"
  • two.seq - Bi-modal Mallow's model fit to the APA data set.

On CRAN:

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

20 exports 1.31 score 1 dependencies 3 dependents 1 mentions 10 scripts 173 downloads

Last updated 5 years agofrom:6252507fc1. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 28 2024
R-4.5-winOKAug 28 2024
R-4.5-linuxOKAug 28 2024
R-4.4-winOKAug 28 2024
R-4.4-macOKAug 28 2024
R-4.3-winOKAug 28 2024
R-4.3-macOKAug 28 2024

Exports:AllKendallAllSeqDistsBestFitC_lamConstructSeqsDistanceDistributionEStepFormatOuthelloKendallInfoLambdaLikelihoodMallowsNextTableRgenSeqDistributionSimplifySequencesUpdateLambdaUpdatePUpdateR

Dependencies:combinat

Readme and manuals

Help Manual

Help pageTopics
Fit Multi-modal Mallows' models to ranking data.RMallow-package RMallow
All Kendall's distances between two sets of rankings.AllKendall
Calculate all distances between a set of sequences and a fixed sequence.AllSeqDists
Fit Mallows model N times and select most likely model. The EM algorithm to fit Multi-Modal Mallows' models is prone to getting stuck in local maxima, so we run it several times and selec the best one.BestFit
Calculate the normalizing coefficient for Mallow's model in a sequence space.C_lam
Constructs sequences from Kendall Information matricies.ConstructSeqs
Sample data set.datas
Calculate the Kendall distance distribution in N! space.DistanceDistribution
1980 APA Presidential Candidate ranking data.elect
The Expectation step of the EM algorithm.EStep
Formats the data in the "Solve" function for output.FormatOut
Hello, World!hello
All information used to calculate Kendall's distance.KendallInfo
Objective function to determine lambda.Lambda
Likelihood of the data and parameters.Likelihood
Fits a Multi-Modal Mallows' model to ranking data.Mallows
Calculates the table of Kendall distances in (N+1)! space, given those in N! space.NextTable
Initialize sequence modes for the clustering process.Rgen
Calculates distances in N! space.SeqDistribution
Change the form of ordered sequences.SimplifySequences
Fitted version of the toy datas data set, with three modal sequences.three.mode
Two-mode Mallows' model fit to toy data set "datas"two.mode
Bi-modal Mallow's model fit to the APA data set.two.seq
Update the Lambda parameters of clusters.UpdateLambda
Update Proportion in each cluster.UpdateP
Update modal sequences in each cluster.UpdateR