BayesMultiMode: Testing and Detecting Multimodality using Bayesian Methods

The testing approach works in two stages. 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 specified; it is estimated simultaneously with the mixture weights and components through the SFM approach. Second, the resulting MCMC output is used to calculate the number of modes and their locations following Basturk, Hoogerheide and van Dijk (2021) <doi:10.2139/ssrn.3783351>. Posterior probabilities are retrieved for both of these quantities providing a powerful tool for mode inference. Currently the package supports a flexible mixture of shifted Poisson distributions (see Basturk, Hoogerheide and van Dijk (2021) <doi:10.2139/ssrn.3783351>).

Version: 0.1.1
Depends: R (≥ 3.5.0)
Imports: ggpubr, dplyr, tidyr, ggplot2, stringr, ggh4x, magrittr, gtools, Rdpack
Published: 2022-10-12
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
CRAN checks: BayesMultiMode results


Reference manual: BayesMultiMode.pdf


Package source: BayesMultiMode_0.1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): BayesMultiMode_0.1.1.tgz, r-oldrel (arm64): BayesMultiMode_0.1.1.tgz, r-release (x86_64): BayesMultiMode_0.1.1.tgz, r-oldrel (x86_64): BayesMultiMode_0.1.1.tgz


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