Package: MultiModalR Title: Bayesian Mixture Modeling for Stratified and Hierarchical Multimodal Data Version: 1.1.0 Date: 2026-08-29 Authors@R: person(given = "Gergo", family = "Dioszegi", role = c("aut", "cre"), email = "dijogergo@gmail.com", comment = c(ORCID = "0009-0003-3454-9093")) Description: Fast Bayesian probability estimation for multimodal continuous data using speed-optimized Markov chain Monte Carlo (MCMC) implementation. The package provides two complementary approaches: (1) stratified mixture modeling with independent fits per category (fuss_PARALLEL_mcmc), and (2) a unified hierarchical mixture model with category-specific parameters and shared number of components (fuss_COVARIATE_mcmc). Both approaches detect subpopulations, estimate mixture components, and assign 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). License: MIT + file LICENSE URL: https://github.com/DijoG/MultiModalR BugReports: https://github.com/DijoG/MultiModalR/issues Depends: R (>= 3.5.0) Imports: Rcpp (>= 1.0.10), dplyr, purrr, readr, ggplot2, furrr, future, truncnorm, rlang Suggests: testthat (>= 3.0.0), knitr, rmarkdown, multimode, tictoc, tidyr LinkingTo: Rcpp, RcppArmadillo SystemRequirements: C++17 Encoding: UTF-8 RoxygenNote: 7.3.2 NeedsCompilation: yes LazyData: true Config/pak/sysreqs: libx11-dev Repository: https://dijog.r-universe.dev Date/Publication: 2026-09-05 16:56:06 UTC RemoteUrl: https://github.com/dijog/multimodalr RemoteRef: HEAD RemoteSha: 434985d5d3640cf41dcd1cd87e9eee30555c439a Packaged: 2026-10-05 12:34:07 UTC; root Author: Gergo Dioszegi [aut, cre] (ORCID: ) Maintainer: Gergo Dioszegi