PLMIX: an R package for modelling and clustering partially ranked data
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Publication:5107752
DOI10.1080/00949655.2020.1711909OpenAlexW3000592230MaRDI QIDQ5107752
Luca Tardella, Cristina Mollica
Publication date: 28 April 2020
Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1612.08141
Related Items (3)
Remarkable properties for diagnostics and inference of ranking data modelling ⋮ Modelling rankings in R: the \textbf{PlackettLuce} package ⋮ PLMIX
Uses Software
Cites Work
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- Statistical methods for ranking data
- Mixtures of distance-based models for ranking data
- Probability models on rankings
- Bayesian Plackett-Luce mixture models for partially ranked data
- MM algorithms for generalized Bradley-Terry models.
- Bayesian analysis of order-statistics models for ranking data
- Distance-based tree models for ranking data
- Model-based clustering for multivariate partial ranking data
- Bayesian analysis of ranking data with the extended Plackett-Luce model
- Exploring Voting Blocs Within the Irish Electorate
- NON-NULL RANKING MODELS. I
- Factor Analysis for Ranked Data with Application to a Job Selection Attitude Survey
- Log linear representation for paired and multiple comparisons models
- Bayesian Measures of Model Complexity and Fit
- Bayesian predictive information criterion for the evaluation of hierarchical Bayesian and empirical Bayes models
- Multistage Ranking Models
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