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:
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