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BayesMultiMode  

Bayesian Mode Inference
View on CRAN: Click here


Download and install BayesMultiMode package within the R console
Install from CRAN:
install.packages("BayesMultiMode")

Install from Github:
library("remotes")
install_github("cran/BayesMultiMode")

Install by package version:
library("remotes")
install_version("BayesMultiMode", "0.7.4")



Attach the package and use:
library("BayesMultiMode")
Maintained by
Paul Labonne
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-10-12
Latest Update: 2024-10-31
Description:
A two-step Bayesian approach for mode inference following Cross, Hoogerheide, Labonne and van Dijk (2024) ). 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.
How to cite:
Paul Labonne (2022). BayesMultiMode: Bayesian Mode Inference. R package version 0.7.4, https://cran.r-project.org/web/packages/BayesMultiMode. Accessed 22 Sep. 2026.
Previous versions and publish date:
0.1.1 (2022-10-12 10:33), 0.5.1 (2023-04-06 13:20), 0.6.0 (2023-08-08 12:50), 0.7.0 (2024-02-05 23:00), 0.7.1 (2024-03-21 15:40), 0.7.2 (2024-10-25 13:20), 0.7.3 (2024-10-31 16:30), 0.7.4 (2025-09-28 01:10), 0.7.5 (2026-02-27 16:32), (2026-07-09 07:58)
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Complete documentation for BayesMultiMode
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