Fast Bayesian Probability Estimation for Multimodal Categorical Data R package MultiModalR version 1.0.0 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). Software 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 Gergo Dioszegi <dijogergo@gmail.com> Comprehensive R Archive Network (CRAN) Gergo Dioszegi [aut, cre] (ORCID: <https://orcid.org/0009-0003-3454-9093>) MIT + file LICENSE (https://CRAN.R-project.org/package=MultiModalR/LICENSE) 2026-06-30 application/tgz https://CRAN.R-project.org/package=MultiModalR doi:10.32614/CRAN.package.MultiModalR