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BayesMultiMode
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", "0.7.3")
Attach the package and use:
library("BayesMultiMode")
Maintained by
Paul Labonne
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.BayesMultiMode . https://github.com/paullabonne/BayesMultiMode/issues . https://github.com/paullabonne/BayesMultiMode . BayesMultiMode results . BayesMultiMode.pdf . BayesMultiMode_0.7.3.tar.gz . BayesMultiMode_0.7.3.zip . BayesMultiMode_0.7.3.zip . BayesMultiMode_0.7.3.zip . BayesMultiMode_0.7.3.tgz . BayesMultiMode_0.7.3.tgz . BayesMultiMode_0.7.3.tgz . BayesMultiMode_0.7.3.tgz . BayesMultiMode archive . https://CRAN.R-project.org/package=BayesMultiMode .
First Published: 2022-10-12
Latest Update: 2023-08-08
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 0.7.3, https://cran.r-project.org/web/packages/BayesMultiMode. Accessed 06 May. 2025.
Previous versions and publish date:
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Other R packages that BayesMultiMode depends,
imports, suggests or enhances
Complete documentation for BayesMultiMode
Functions, R codes and Examples using
the BayesMultiMode R package
Some associated functions: MEM . bayes_estimation . bayes_mode . bayes_trace . ct47 . cyclone . d4z4 . discrete_MF . dspmix . fixed_point . galaxy . gibbs_SFM_normal . gibbs_SFM_poisson . gibbs_SFM_skew_n . gibbs_SFM_sp . new_BayesMixture . plot.BayesMixture . plot.BayesMode . plots_mcmc . post_sfm_mcmc . sfm_mcmc_spmix . summary.BayesMode .
Some associated R codes: MEM.R . bayes_estimation.R . bayes_mode.R . bayes_trace.R . count_modes.R . data.R . discrete_MF.R . dspmix.R . fixed_point.R . ggplot_theme.R . gibbs_SFM_normal.R . gibbs_SFM_poisson.R . gibbs_SFM_skew_normal.R . gibbs_SFM_sp.R . mcmc.R . mixture_functions.R . new_BayesMixture.R . plot_methods.R . plots_mcmc.R . post_sfm_mcmc.R . sfm_mcmc_spmix.R . sfm_step.R . summary_methods.R . Full BayesMultiMode package functions and examples
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