Package: StatRank 0.0.6

StatRank: Statistical Rank Aggregation: Inference, Evaluation, and Visualization

A set of methods to implement Generalized Method of Moments and Maximal Likelihood methods for Random Utility Models. These methods are meant to provide inference on rank comparison data. These methods accept full, partial, and pairwise rankings, and provides methods to break down full or partial rankings into their pairwise components. Please see Generalized Method-of-Moments for Rank Aggregation from NIPS 2013 for a description of some of our methods.

Authors:Hossein Azari Soufiani, William Chen

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StatRank.pdf |StatRank.html✨
StatRank/json (API)

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

Peer review:

Datasets:

On CRAN:

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

3.24 score 2 packages 58 scripts 240 downloads 46 exports 32 dependencies

Last updated 9 years agofrom:0a24708e6f. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 20 2024
R-4.5-winOKNov 20 2024
R-4.5-linuxOKNov 20 2024
R-4.4-winOKNov 20 2024
R-4.4-macOKNov 20 2024
R-4.3-winOKNov 20 2024
R-4.3-macOKNov 20 2024

Exports:Breakingconvert.vector.to.listEstimation.GRUM.MLEEstimation.Normal.GMMEstimation.PL.GMMEstimation.PL.MLEEstimation.RUM.MLEEstimation.RUM.MultiType.MLEEstimation.RUM.NonparametricEstimation.Zemel.MLEEvaluation.AveragePrecisionEvaluation.KendallTauEvaluation.KLEvaluation.LocationofWinnerEvaluation.MSEEvaluation.NDCGEvaluation.Precision.at.kEvaluation.TVDExpo.MultiType.Pairwise.ProbGenerate.NPRUM.DataGenerate.RUM.DataGenerate.RUM.ParametersGenerate.Zemel.ParametersGenerate.Zemel.Ranks.PairsgenerateCgenerateC.modelgenerateC.model.NonparametricKLLikelihood.NonparametricLikelihood.PLLikelihood.RUMLikelihood.RUM.MultitypeLikelihood.ZemelMSENormal.MultiType.Pairwise.ProbNormal.Pairwise.ProbPL.Pairwise.Probscores.to.orderscrambleturn_matrix_into_tableTVDVisualization.EmpiricalVisualization.MultiTypeVisualization.Pairwise.ProbabilitiesVisualization.RUMplotsZemel.Pairwise.Prob

Dependencies:clicolorspaceevdfansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigplyrR6RColorBrewerRcpprlangscalestibbletruncdistutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Breaks full or partial orderings into pairwise comparisonsBreaking
Helper function for the graphing interfaceconvert.vector.to.list
A1 Election DataData.Election1
A6 Election DataData.Election6
A9 Election DataData.Election9
Nascar DataData.Nascar
Trimmed Nascar DataData.NascarTrimmed
Tiny test datasetData.Test
Performs parameter estimation for a Generalized Random Utility Model with user and alternative characteristicsEstimation.GRUM.MLE
GMM Method for Estimating Random Utility Model wih Normal dsitributionsEstimation.Normal.GMM
GMM Method for estimating Plackett-Luce model parametersEstimation.PL.GMM
Performs parameter estimation for the Plackett-Luce model using an Minorize Maximize algorithmEstimation.PL.MLE
Performs parameter estimation for a Random Utility Model with different noise distributionsEstimation.RUM.MLE
Performs parameter estimation for a Multitype Random Utility ModelEstimation.RUM.MultiType.MLE
Nonparametric RUM EstimatorEstimation.RUM.Nonparametric
Estimates Zemel Parameters via Gradient DescentEstimation.Zemel.MLE
Calculates the Average PrecisionEvaluation.AveragePrecision
Calculates the Kendall Tau correlation between two ranksEvaluation.KendallTau
Calculates KL divergence between empirical pairwise preferences and modeled pairwise preferencesEvaluation.KL
Calculates the location of the True winner in the estimated rankingEvaluation.LocationofWinner
Calculates MSE between empirical pairwise preferences and modeled pairwise preferencesEvaluation.MSE
Calculates the Normalized Discounted Cumluative GainEvaluation.NDCG
Calculates the Average Precision at kEvaluation.Precision.at.k
Calculates TVD between empirical pairwise preferences and modeled pairwise preferencesEvaluation.TVD
Pairwise Probability for PL Multitype ModelExpo.MultiType.Pairwise.Prob
Generate data from an NPRUM modelGenerate.NPRUM.Data
Generate observation of ranks given parametersGenerate.RUM.Data
Parameter Generation for a RUM modelGenerate.RUM.Parameters
Generates possible scores for a Zemel modelGenerate.Zemel.Parameters
Generates pairwise ranks from a Zemel model given a set of scoresGenerate.Zemel.Ranks.Pairs
Generate a matrix of pairwise winsgenerateC
Turns inference object into modeled C matrix.generateC.model
Generate pairwise matrix for an NPRUM modelgenerateC.model.Nonparametric
Calculates KL Divergence between non-diagonal entries of two matricesKL
Calculate Likelihood for the nonparametric modelLikelihood.Nonparametric
A faster Likelihood for Plackett-Luce ModelLikelihood.PL
Likelihood for general Random Utility ModelsLikelihood.RUM
Likelihood for Multitype Random Utility ModelsLikelihood.RUM.Multitype
Gives Zemel pairwise Log-likelihood with data and scoresLikelihood.Zemel
Calculates MSE between non-diagonal entries of two matrices if the diagonal elements are 0sMSE
Pairwise Probability for Normal Multitype ModelNormal.MultiType.Pairwise.Prob
Pairwise Probability for Normal ModelNormal.Pairwise.Prob
Pairwise Probability for PL ModelPL.Pairwise.Prob
Converts scores to a rankingscores.to.order
Scramble a vectorscramble
Converts a matrix into a tableturn_matrix_into_table
Calculates TVD between two matricesTVD
RPD VisualizationVisualization.Empirical
Multitype Random Utility visualizerVisualization.MultiType
Creates pairwise matrices to compare inference results with the empirical pairwise probabilitiesVisualization.Pairwise.Probabilities
RUMplot visualizationVisualization.RUMplots
Pairwise Probability for ZemelZemel.Pairwise.Prob