BayesMultiMode: Bayesian Mode Inference

A Bayesian approach for mode inference which works in two steps. First, a mixture distribution is fitted on the data using a sparse finite mixture (SFM) Markov chain Monte Carlo (MCMC) algorithm following Malsiner-Walli, Frühwirth-Schnatter and Grün (2016) <doi:10.1007/s11222-014-9500-2>). 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: 0.7.0
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: 2024-02-05
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>
License: GPL (≥ 3)
NeedsCompilation: no
Materials: README NEWS
CRAN checks: BayesMultiMode results


Reference manual: BayesMultiMode.pdf


Package source: BayesMultiMode_0.7.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): BayesMultiMode_0.7.0.tgz, r-oldrel (arm64): BayesMultiMode_0.7.0.tgz, r-release (x86_64): BayesMultiMode_0.7.0.tgz
Old sources: BayesMultiMode archive


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