Package: MultiModalR 1.0.0
Gergo Dioszegi
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).
Authors:
MultiModalR_1.0.0.tar.gz
MultiModalR_1.0.0.zip(r-4.7-x86_64)MultiModalR_1.0.0.zip(r-4.7-arm64)MultiModalR_1.0.0.zip(r-4.6-x86_64)MultiModalR_1.0.0.zip(r-4.6-arm64)
MultiModalR_1.0.0.tar.gz(r-4.7-arm64)MultiModalR_1.0.0.tar.gz(r-4.7-x86_64)MultiModalR_1.0.0.tar.gz(r-4.6-arm64)MultiModalR_1.0.0.tar.gz(r-4.6-x86_64)
MultiModalR_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
MultiModalR/json (API)
| # Install 'MultiModalR' in R: |
| install.packages('MultiModalR', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/dijog/multimodalr/issues
- multimodal_dummy - Multimodal Dummy Dataset
Last updated from:0b407d8808. Checks:10 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 125 | ||
| linux-devel-x86_64 | OK | 134 | ||
| source / vignettes | OK | 184 | ||
| linux-release-arm64 | OK | 135 | ||
| linux-release-x86_64 | OK | 134 | ||
| windows-devel-arm64 | OK | 258 | ||
| windows-devel-x86_64 | OK | 149 | ||
| windows-release-arm64 | OK | 251 | ||
| windows-release-x86_64 | OK | 104 | ||
| wasm-release | OK | 103 |
Exports:check_PACKScreate_MM_outputcreate_multimodal_dummyfuss_PARALLEL_mcmcget_MODES_enhancedgroup_MODES_enhancedMM_MHMM_MH_dirichletplot_VALIDATION
Dependencies:bitbit64clicliprcodetoolscpp11crayondigestdplyrfarverfurrrfuturegenericsggplot2globalsgluegtablehmsisobandlabelinglifecyclelistenvmagrittrparallellypillarpkgconfigprettyunitsprogresspurrrR6RColorBrewerRcppRcppArmadilloreadrrlangS7scalestibbletidyselecttruncnormtzdbutf8vctrsviridisLitevroomwithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Check and install required packages | check_PACKS |
| Create output data frame | create_MM_output |
| Create multimodal dummy dataset | create_multimodal_dummy |
| Parallel Bayesian mixture modeling using Markov Chain Monte Carlo (MCMC) | fuss_PARALLEL_mcmc |
| Density height-aware mode detection | get_MODES_enhanced |
| Density height-aware mode grouping | group_MODES_enhanced |
| Fast MCMC for mixture models (Metropolis-Hastings-within-partial-Gibbs) | MM_MH |
| Dirichlet MCMC (identical interface to MM_MH) | MM_MH_dirichlet |
| Multimodal Dummy Dataset | multimodal_dummy |
| Plot validation of subgroup assignments (handles both balanced and imbalanced data) | plot_VALIDATION |