MultiModalR: Fast Bayesian Probability Estimation for Multimodal Categorical Data

Fast Bayesian probability estimation for multimodal categorical data using speed-optimized Markov chain Monte Carlo (MCMC) implementation (Metropolis-Hastings-within-partial-Gibbs). The package provides efficient algorithms for detecting subpopulations, estimating mixture components, and assigning observations to subgroups with probability estimates. The methods are described in Dioszegi, G. et al. (2026) "Automatic Bayesian Mixture Modeling for Multimodal Categorical Data via Integrated Mode Detection and Metropolis-Hastings-within-Gibbs Sampling" (submitted to Journal of Statistical Software).

Version:	1.0.0
Depends:	R (≥ 3.5.0)
Imports:	Rcpp (≥ 1.0.10), dplyr, purrr, readr, ggplot2, furrr, future, truncnorm, rlang
LinkingTo:	Rcpp, RcppArmadillo
Suggests:	testthat (≥ 3.0.0), knitr, rmarkdown, multimode, tictoc, tidyr
Published:	2026-06-30
DOI:	10.32614/CRAN.package.MultiModalR (may not be active yet)
Author:	Gergo Dioszegi [aut, cre]
Maintainer:	Gergo Dioszegi <dijogergo at gmail.com>
BugReports:	https://github.com/DijoG/MultiModalR/issues
License:	MIT + file LICENSE
URL:	https://github.com/DijoG/MultiModalR
NeedsCompilation:	yes
SystemRequirements:	C++17
Materials:	README
CRAN checks:	MultiModalR results

Documentation:

Reference manual:

MultiModalR.html , MultiModalR.pdf

Downloads:

Package source:	MultiModalR_1.0.0.tar.gz
Windows binaries:	r-devel: MultiModalR_1.0.0.zip, r-release: not available, r-oldrel: MultiModalR_1.0.0.zip
macOS binaries:	r-release (arm64): MultiModalR_1.0.0.tgz, r-oldrel (arm64): MultiModalR_1.0.0.tgz, r-release (x86_64): MultiModalR_1.0.0.tgz, r-oldrel (x86_64): MultiModalR_1.0.0.tgz

Linking:

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