Package: MultiModalR
Title: Fast Bayesian Probability Estimation for Multimodal Categorical
        Data
Version: 1.0.0
Date: 2026-06-18
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 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).
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
Packaged: 2026-06-25 12:33:16 UTC; Dijo
Author: Gergo Dioszegi [aut, cre] (ORCID:
    <https://orcid.org/0009-0003-3454-9093>)
Maintainer: Gergo Dioszegi <dijogergo@gmail.com>
Repository: CRAN
Date/Publication: 2026-06-30 20:10:08 UTC
Built: R 4.7.0; x86_64-w64-mingw32; 2026-06-30 23:51:55 UTC; windows
Archs: x64
