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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 "vennabers" in publications use:'
type: software
license: MIT
title: 'vennabers: Venn-Abers Classification and Regression Calibration'
version: 1.0.0
abstract: Calibrates the predictions of classification and regression models with
  Venn-Abers predictors. A Venn-Abers predictor turns the score of any model into
  a pair of probabilities that contains a calibrated value when the observations are
  independent and identically distributed (Vovk and Petej, 2014, <https://doi.org/10.48550/arXiv.1211.0025>);
  Inductive and Cross Venn-Abers predictors make this practical for large data sets
  (Vovk, Petej and Fedorova, 2015, <https://doi.org/10.48550/arXiv.1511.00213>). The
  package serves analysts and researchers who need calibrated probabilities or prediction
  bounds for binary classification, multiclass classification by one-vs-one coupling
  (Manokhin, 2017, <https://proceedings.mlr.press/v60/manokhin17a.html>) or one-vs-rest,
  and regression (Petej and Vovk, 2026, <https://doi.org/10.48550/arXiv.2605.06646>).
  Predictions of an existing model can be calibrated directly, or optional 'tidymodels'
  workflows fit the model, create the calibration splits and tune the complete procedure.
  Diagnostics include the Brier score, log loss, plots of fitted calibration maps
  and reliability diagrams with the CORP (consistent, optimally binned, reproducible
  and pool-adjacent-violators based) method of Dimitriadis, Gneiting and Jordan (2021)
  <https://doi.org/10.1073/pnas.2016191118>.
authors:
- family-names: Kruse
  given-names: René-M.
  email: rene.marcel.kruse@protonmail.com
  orcid: https://orcid.org/0000-0003-1655-9776
preferred-citation:
  type: manual
  title: 'vennabers: Venn-Abers Classification and Regression Calibration'
  authors:
  - family-names: Kruse
    given-names: René-M.
    orcid: https://orcid.org/0000-0003-1655-9776
    email: rene.marcel.kruse@protonmail.com
  year: '2026'
  notes: R package version 1.0.0
  url: https://github.com/RMKruse/vennabers
repository: https://cran.r-universe.dev
repository-code: https://github.com/RMKruse/vennabers
commit: 9fde43b6ae5cc97263bdd0ddd5fb4a391432b92f
url: https://rmkruse.github.io/vennabers/
date-released: '2026-11-07'
contact:
- family-names: Kruse
  given-names: René-M.
  email: rene.marcel.kruse@protonmail.com
  orcid: https://orcid.org/0000-0003-1655-9776
references:
- type: conference-paper
  title: Venn-Abers Predictors
  authors:
  - family-names: Vovk
    given-names: Vladimir
  - family-names: Petej
    given-names: Ivan
  collection-title: Proceedings of the Thirtieth Conference on Uncertainty in Artificial
    Intelligence (UAI 2014)
  collection-type: proceedings
  year: '2014'
  publisher:
    name: AUAI Press
  doi: 10.48550/arXiv.1211.0025
  start: '829'
  end: '838'
  conference:
    name: Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence
      (UAI 2014)
- type: conference-paper
  title: Large-Scale Probabilistic Predictors with and without Guarantees of Validity
  authors:
  - family-names: Vovk
    given-names: Vladimir
  - family-names: Petej
    given-names: Ivan
  - family-names: Fedorova
    given-names: Valentina
  collection-title: Advances in Neural Information Processing Systems 28
  collection-type: proceedings
  year: '2015'
  doi: 10.48550/arXiv.1511.00213
  start: '892'
  end: '900'
  conference:
    name: Advances in Neural Information Processing Systems 28
- type: conference-paper
  title: Multi-Class Probabilistic Classification Using Inductive and Cross Venn-Abers
    Predictors
  authors:
  - family-names: Manokhin
    given-names: Valery
  collection-title: Proceedings of the Sixth Workshop on Conformal and Probabilistic
    Prediction and Applications
  collection-type: proceedings
  volume: '60'
  year: '2017'
  publisher:
    name: PMLR
  url: https://proceedings.mlr.press/v60/manokhin17a.html
  start: '228'
  end: '240'
  conference:
    name: Proceedings of the Sixth Workshop on Conformal and Probabilistic Prediction
      and Applications
- type: article
  title: Stable Reliability Diagrams for Probabilistic Classifiers
  authors:
  - family-names: Dimitriadis
    given-names: Timo
  - family-names: Gneiting
    given-names: Tilmann
  - family-names: Jordan
    given-names: Alexander I.
  journal: Proceedings of the National Academy of Sciences
  volume: '118'
  issue: '8'
  year: '2021'
  doi: 10.1073/pnas.2016191118
  start: e2016191118