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# CITATION file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "BayesMultiMode" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'BayesMultiMode: Bayesian Mode Inference'
version: 0.7.2
doi: 10.32614/CRAN.package.BayesMultiMode
abstract: A two-step Bayesian approach for mode inference following Cross, Hoogerheide,
  Labonne and van Dijk (2024) <https://doi.org/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:
- family-names: Baştürk
  given-names: Nalan
- family-names: Cross
  given-names: Jamie
- family-names: Knijff
  given-names: Peter
  name-particle: de
- family-names: Hoogerheide
  given-names: Lennart
- family-names: Labonne
  given-names: Paul
  email: labonnepaul@gmail.com
- family-names: Dijk
  given-names: Herman
  name-particle: van
repository: https://CRAN.R-project.org/package=BayesMultiMode
repository-code: https://github.com/paullabonne/BayesMultiMode
url: https://github.com/paullabonne/BayesMultiMode
date-released: '2024-10-25'
contact:
- family-names: Labonne
  given-names: Paul
  email: labonnepaul@gmail.com