Bayesian Analysis of Finite Mixtures of Plackett-Luce Models for Partial Rankings/Orderings
Coercion into top-ordering datasets
BIC for the MLE of a mixture of Plackett-Luce models
Binary group membership matrix
Individual rankings/orderings from the frequency distribution
Gibbs sampling for a Bayesian mixture of Plackett-Luce models
MCMC class objects from the Gibbs sampling simulations of a Bayesian m...
Top-ordering datasets
Label switching adjustment of the Gibbs sampling simulations for Bayes...
Likelihood and log-likelihood evaluation for a mixture of Plackett-Luc...
Completion of partial rankings/orderings
Censoring of complete rankings/orderings
MAP estimation for a Bayesian mixture of Plackett-Luce models
MAP estimation for a Bayesian mixture of Plackett-Luce models with mul...
Paired comparison matrix for a partial ordering/ranking dataset
Bayesian Analysis of Finite Mixtures of Plackett-Luce Models for Parti...
Plot the Gibbs sampling simulations for a Bayesian mixture of Plackett...
Plot the MAP estimates for a Bayesian mixture of Plackett-Luce models
Posterior predictive check for Bayesian mixtures of Plackett-Luce mode...
Conditional posterior predictive check for Bayesian mixtures of Placke...
Print of the Gibbs sampling simulation of a Bayesian mixture of Placke...
Print of the MAP estimation algorithm for a Bayesian mixture of Placke...
Switch from orderings to rankings and vice versa
Descriptive summaries for a partial ordering/ranking dataset
Random sample from a mixture of Plackett-Luce models
Bayesian selection criteria for mixtures of Plackett-Luce models
Summary of the Gibbs sampling procedure for a Bayesian mixture of Plac...
Summary of the MAP estimation for a Bayesian mixture of Plackett-Luce ...
Frequency distribution from the individual rankings/orderings
Fit finite mixtures of Plackett-Luce models for partial top rankings/orderings within the Bayesian framework. It provides MAP point estimates via EM algorithm and posterior MCMC simulations via Gibbs Sampling. It also fits MLE as a special case of the noninformative Bayesian analysis with vague priors. In addition to inferential techniques, the package assists other fundamental phases of a model-based analysis for partial rankings/orderings, by including functions for data manipulation, simulation, descriptive summary, model selection and goodness-of-fit evaluation. Main references on the methods are Mollica and Tardella (2017) <doi.org/10.1007/s11336-016-9530-0> and Mollica and Tardella (2014) <doi/10.1002/sim.6224>.