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# CITATION 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 "PDEnaiveBayes" in publications use:'
type: software
license: GPL-3.0-only
title: 'PDEnaiveBayes: Plausible Naive Bayes Classifier Using PDE'
version: 0.2.9
doi: 10.3390/make8010013
identifiers:
- type: doi
  value: 10.32614/CRAN.package.PDEnaiveBayes
abstract: 'A nonparametric, multicore-capable plausible naive Bayes classifier based
  on the Pareto density estimation (PDE) featuring a plausible approach to a pitfall
  in the Bayesian theorem covering low evidence cases. Stier, Q., Hoffmann, J., and
  Thrun, M.C.: "Classifying with the Fine Structure of Distributions: Leveraging Distributional
  Information for Robust and Plausible Naive Bayes" (2026), Machine Learning and Knowledge
  Extraction (MAKE), <DOI:10.3390/make8010013>.'
authors:
- family-names: Thrun
  given-names: Michael
  email: m.thrun@gmx.net
  orcid: https://orcid.org/0000-0001-9542-5543
- family-names: Stier
  given-names: Quirin
  email: Quirin_Stier@gmx.de
  orcid: https://orcid.org/0000-0002-7896-4737
preferred-citation:
  type: article
  title: 'Classifying with the Fine Structure of Distributions: Leveraging Distributional
    Information for Robust and Plausible Naïve Bayes'
  authors:
  - family-names: Stier
    given-names: Quirin
    email: Quirin_Stier@gmx.de
    orcid: https://orcid.org/0000-0002-7896-4737
  - family-names: Hoffmann
    given-names: Jörg
  - family-names: Thrun
    given-names: Michael C.
  publisher:
    name: MDPI
  journal: Machine Learning and Knowledge Extraction (MAKE)
  volume: '8'
  year: '2026'
  issn: 2504-4990
  doi: 10.3390/make8010013
  url: https://doi.org/10.3390/make8010013
  start: '13'
repository: https://mthrun.r-universe.dev
repository-code: https://github.com/Mthrun/PDEbayes
commit: 2d34f6b83a3a3700ee1eed9ebe7741986199727e
url: https://github.com/Mthrun/PDEbayes
date-released: '2026-01-09'
contact:
- family-names: Thrun
  given-names: Michael
  email: m.thrun@gmx.net
  orcid: https://orcid.org/0000-0001-9542-5543