High-Performance Open-Source Archive
A two-step Bayesian approach for mode inference (Baştürk et al., 2026) <doi:10.18637/jss.v116.i03>. 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: | 1.0.0 |
| Depends: | R (≥ 3.5.0) |
| Imports: | assertthat, bayesplot, dplyr, ggplot2 (≥ 3.4.0), ggpubr, gtools, magrittr, MCMCglmm, mvtnorm, posterior, sn, stringr, tidyr, Rdpack |
| Suggests: | testthat (≥ 3.0.0) |
| Published: | 2026-06-01 |
| 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 <labonnepaul at gmail.com> |
| BugReports: | https://github.com/paullabonne/BayesMultiMode/issues |
| License: | GPL (≥ 3) |
| URL: | https://github.com/paullabonne/BayesMultiMode |
| NeedsCompilation: | no |
| Citation: | BayesMultiMode citation info |
| Materials: | README, NEWS |
| CRAN checks: | BayesMultiMode results |
| Reference manual: | BayesMultiMode.html , BayesMultiMode.pdf |
| Package source: | BayesMultiMode_1.0.0.tar.gz |
| Windows binaries: | r-devel: BayesMultiMode_1.0.0.zip, r-release: BayesMultiMode_1.0.0.zip, r-oldrel: BayesMultiMode_1.0.0.zip |
| macOS binaries: | r-release (arm64): BayesMultiMode_1.0.0.tgz, r-oldrel (arm64): BayesMultiMode_1.0.0.tgz, r-release (x86_64): BayesMultiMode_1.0.0.tgz, r-oldrel (x86_64): BayesMultiMode_1.0.0.tgz |
| Old sources: | BayesMultiMode archive |
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