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# CITATION.cff file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "MultiModalR" in publications use:'
type: software
license: MIT
title: 'MultiModalR: Fast Bayesian Probability Estimation for Multimodal Categorical
  Data'
version: 1.0.0
abstract: 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:
- family-names: Dioszegi
  given-names: Gergo
  email: dijogergo@gmail.com
  orcid: https://orcid.org/0009-0003-3454-9093
repository: https://cran.r-universe.dev
repository-code: https://github.com/DijoG/MultiModalR
commit: 0b407d8808129a2b7c5ff69e9ac93c394021de52
url: https://github.com/DijoG/MultiModalR
date-released: '2026-06-18'
contact:
- family-names: Dioszegi
  given-names: Gergo
  email: dijogergo@gmail.com
  orcid: https://orcid.org/0009-0003-3454-9093