Package: PLMIX 2.2.0

Cristina Mollica

PLMIX: Bayesian Analysis of Finite Mixture of Plackett-Luce Models

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:10.1007/s11336-016-9530-0> and Mollica and Tardella (2014) <doi:10.1002/sim.6224>.

Authors:Cristina Mollica [aut, cre], Luca Tardella [aut]

PLMIX_2.2.0.tar.gz
PLMIX_2.2.0.zip(r-4.7)PLMIX_2.2.0.zip(r-4.6)PLMIX_2.2.0.zip(r-4.5)
PLMIX_2.2.0.tgz(r-4.6-x86_64)PLMIX_2.2.0.tgz(r-4.6-arm64)PLMIX_2.2.0.tgz(r-4.5-x86_64)PLMIX_2.2.0.tgz(r-4.5-arm64)
PLMIX_2.2.0.tar.gz(r-4.7-arm64)PLMIX_2.2.0.tar.gz(r-4.7-x86_64)PLMIX_2.2.0.tar.gz(r-4.6-arm64)PLMIX_2.2.0.tar.gz(r-4.6-x86_64)
PLMIX_2.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
PLMIX/json (API)
NEWS

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

Bug tracker:https://github.com/cranhaven/cranhaven.r-universe.dev/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

archivedpackagesr-universecpp

2.89 score 5 stars 31 scripts 485 downloads 30 exports 108 dependencies

Last updated from:1ab05077b5 (on package/PLMIX). Checks:13 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK255
linux-devel-x86_64OK183
source / vignettesOK335
linux-release-arm64OK233
linux-release-x86_64OK274
macos-release-arm64OK128
macos-release-x86_64OK522
macos-oldrel-arm64OK149
macos-oldrel-x86_64OK460
windows-develOK267
windows-releaseOK246
windows-oldrelOK247
wasm-releaseOK202

Exports:as.top_orderingbicPLMIXbinary_group_indfreq_to_unitgibbsPLMIXgsPLMIX_to_mcmcis.top_orderinglabel_switchPLMIXlikPLMIXloglikPLMIXmake_completemake_partialmapPLMIXmapPLMIX_multistartpaired_comparisonsplot.gsPLMIXplot.mpPLMIXppcheckPLMIXppcheckPLMIX_condprint.gsPLMIXprint.mpPLMIXprint.summary.gsPLMIXprint.summary.mpPLMIXrank_ord_switchrank_summariesrPLMIXselectPLMIXsummary.gsPLMIXsummary.mpPLMIXunit_to_freq

Dependencies:abindbackportsbase64encbslibcachemcheckmateclarabelclicodacodetoolscombinatcpp11crayonCVXRdigestdplyrevaluatefarverfastmapfontawesomeforcatsforeachFormulafsgenericsGGallyggmcmcggplot2ggstatsgluegmpgridExtragtablehighrhighshmshtmltoolshtmlwidgetsigraphinumisobanditeratorsjquerylibjsonliteknitrlabel.switchinglabelinglatticelibcoinlifecyclelpSolvemagrittrMASSMatrixMatrixModelsmatrixStatsmcmcMCMCpackmemoisemimemvtnormosqppartykitpatchworkpillarpkgconfigPlackettLuceplyrprettyunitsprogresspsychotoolspsychotreepurrrquantregqvcalcR6radarchartrappdirsrcddRColorBrewerRcppRcppEigenreshape2rlangrmarkdownrpartRSpectraS7sandwichsassscalesscsslamSparseMstringistringrsurvivaltibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunyamlzoo

Readme and manuals

Help Manual

Help pageTopics
Coercion into top-ordering datasetsas.top_ordering
BIC for the MLE of a mixture of Plackett-Luce modelsbicPLMIX
Binary group membership matrixbinary_group_ind
American Psychological Association Data (partial orderings)d_apa
Car Configurator Data (partial orderings)d_carconf
Dublin West Data (partial orderings)d_dublinwest
Gaming Platforms Data (complete orderings)d_gaming
German Sample Data (complete orderings)d_german
NASCAR Data (partial orderings)d_nascar
Occupation Data (complete orderings)d_occup
Rice Voting Data (partial orderings)d_rice
Utility to fill in single missing entries of top-(K-1) sequences in partial ordering/ranking datasetsfill_single_entries
Individual rankings/orderings from the frequency distributionfreq_to_unit
Gibbs sampling for a Bayesian mixture of Plackett-Luce modelsgibbsPLMIX
MCMC class objects from the Gibbs sampling simulations of a Bayesian mixture of Plackett-Luce modelsgsPLMIX_to_mcmc
Top-ordering datasetsis.top_ordering
Label switching adjustment of the Gibbs sampling simulations for Bayesian mixtures of Plackett-Luce modelslabel_switchPLMIX
Label switching adjustment for mixtures of Plackett-Luce modelslabel_switchPLMIX_single
Likelihood and log-likelihood evaluation for a mixture of Plackett-Luce modelsLikelihood likelihood likPLMIX Loglikelihood loglikelihood loglikPLMIX
Completion of partial rankings/orderingsmake_complete
Censoring of complete rankings/orderingsmake_partial
MAP estimation for a Bayesian mixture of Plackett-Luce modelsmapPLMIX
MAP estimation for a Bayesian mixture of Plackett-Luce models with multiple starting valuesmapPLMIX_multistart
Utility to switch from a partial ranking to a partial ordering (missing positions denoted with zero)myorder
Paired comparison matrix for a partial ordering/ranking datasetpaired_comparisons
Bayesian Analysis of Finite Mixtures of Plackett-Luce Models for Partial Rankings/OrderingsPLMIX-package PLMIX
Plot the Gibbs sampling simulations for a Bayesian mixture of Plackett-Luce modelsplot.gsPLMIX
Plot the MAP estimates for a Bayesian mixture of Plackett-Luce modelsplot.mpPLMIX
Posterior predictive check for Bayesian mixtures of Plackett-Luce modelsppcheckPLMIX
Conditional posterior predictive check for Bayesian mixtures of Plackett-Luce modelsppcheckPLMIX_cond
Conditional predictive posterior p-valuesppcheckPLMIX_cond_single
Posterior predictive check for a mixture of Plackett-Luce modelsppcheckPLMIX_single
Print of the Gibbs sampling simulation of a Bayesian mixture of Plackett-Luce modelsprint.gsPLMIX
Print of the MAP estimation algorithm for a Bayesian mixture of Plackett-Luce modelsprint.mpPLMIX
Print of the summary of Gibbs sampling simulation of a Bayesian mixture of Plackett-Luce models.print.summary.gsPLMIX
Print of the summary of MAP estimation for a Bayesian mixture of Plackett-Luce modelsprint.summary.mpPLMIX
Appropriate simulation of starting values for tandom initialization of Gibbs Sampling. It start from the mle corresponding to no-group structure and then it randomly selects rescaled random support points (with sum 1) of G mixture components such that the marginal support coincides with the mle support for G=1 Random generation of starting values of the component-specific support parameters for Gibbs samplingrandom_start
Switch from orderings to rankings and vice versarank_ord_switch
Descriptive summaries for a partial ordering/ranking datasetrank_summaries
Random sample from a mixture of Plackett-Luce modelsrPLMIX
Bayesian selection criteria for mixtures of Plackett-Luce modelsselectPLMIX
Bayesian selection criteria for mixtures of Plackett-Luce modelsselectPLMIX_single
Summary of the Gibbs sampling procedure for a Bayesian mixture of Plackett-Luce modelssummary.gsPLMIX
Summary of the MAP estimation for a Bayesian mixture of Plackett-Luce modelssummary.mpPLMIX
Frequency distribution from the individual rankings/orderingsunit_to_freq