Package: BayesMultiMode 0.7.1

Paul Labonne

BayesMultiMode: Bayesian Mode Inference

A two-step Bayesian approach for mode inference following Cross, Hoogerheide, Labonne and van Dijk (2024) <doi:10.1016/j.econlet.2024.111579>). First, a mixture distribution is fitted on the data using a sparse finite mixture (SFM) Markov chain Monte Carlo (MCMC) algorithm. The number of mixture components does not have to be known; the size of the mixture is estimated endogenously through the SFM approach. Second, the modes of the estimated mixture at each MCMC draw are retrieved using algorithms specifically tailored for mode detection. These estimates are then used to construct posterior probabilities for the number of modes, their locations and uncertainties, providing a powerful tool for mode inference.

Authors:Nalan Baştürk [aut], Jamie Cross [aut], Peter de Knijff [aut], Lennart Hoogerheide [aut], Paul Labonne [aut, cre], Herman van Dijk [aut]

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Bug tracker:https://github.com/paullabonne/bayesmultimode/issues

On CRAN:

6 exports 1 stars 1.53 score 89 dependencies 6 scripts 249 downloads

Last updated 16 days agofrom:0639bc7315. Checks:OK: 7. Indexed: yes.

Exports:bayes_fitbayes_mixturebayes_modebayes_tracemix_modemixture

Dependencies:abindapeassertthatbackportsbayesplotbootbroomcarcarDatacheckmateclicodacolorspacecorpcorcorrplotcowplotcpp11cubatureDerivdigestdistributionaldoBydplyrfansifarvergenericsggplot2ggpubrggrepelggridgesggsciggsignifgluegridExtragtablegtoolsisobandlabelinglatticelifecyclelme4magrittrMASSMatrixMatrixModelsmatrixStatsMCMCglmmmgcvmicrobenchmarkminqamnormtmodelrmunsellmvtnormnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigplyrpolynomposteriorpurrrquantregR6rbibutilsRColorBrewerRcppRcppEigenRdpackreshape2rlangrstatixscalessnSparseMstringistringrsurvivaltensorAtibbletidyrtidyselectutf8vctrsviridisLitewithr