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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.
Version: | 0.7.1 |
Depends: | R (≥ 3.5.0) |
Imports: | assertthat, bayesplot, dplyr, ggplot2, ggpubr, gtools, magrittr, MCMCglmm, mvtnorm, posterior, sn, stringr, tidyr, Rdpack |
Suggests: | testthat (≥ 3.0.0) |
Published: | 2024-03-21 |
DOI: | 10.32614/CRAN.package.BayesMultiMode |
Author: | Nalan Baştürk [aut], Jamie Cross [aut], Peter de Knijff [aut], Lennart Hoogerheide [aut], Paul Labonne [aut, cre], Herman van Dijk [aut] |
Maintainer: | Paul Labonne <paul.labonne at bi.no> |
BugReports: | https://github.com/paullabonne/BayesMultiMode/issues |
License: | GPL (≥ 3) |
URL: | https://github.com/paullabonne/BayesMultiMode |
NeedsCompilation: | no |
Materials: | README NEWS |
CRAN checks: | BayesMultiMode results |
Reference manual: | BayesMultiMode.pdf |
Package source: | BayesMultiMode_0.7.1.tar.gz |
Windows binaries: | r-devel: BayesMultiMode_0.7.1.zip, r-release: BayesMultiMode_0.7.1.zip, r-oldrel: BayesMultiMode_0.7.1.zip |
macOS binaries: | r-release (arm64): BayesMultiMode_0.7.1.tgz, r-oldrel (arm64): BayesMultiMode_0.7.1.tgz, r-release (x86_64): BayesMultiMode_0.7.1.tgz, r-oldrel (x86_64): BayesMultiMode_0.7.1.tgz |
Old sources: | BayesMultiMode archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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