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: 2026-06-30 21:31:06 UTC; root
Author: Gergo Dioszegi [aut, cre] (ORCID:
        <https://orcid.org/0009-0003-3454-9093>)
Maintainer: Gergo Dioszegi <dijogergo@gmail.com>
Repository: https://cran.r-universe.dev
Date/Publication: 2026-06-30 21:09:56 UTC
RemoteUrl: https://github.com/cran/MultiModalR
RemoteRef: HEAD
RemoteSha: 0b407d8808129a2b7c5ff69e9ac93c394021de52