Package: vennabers 1.0.0

vennabers: Venn-Abers Classification and Regression Calibration

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, <doi:10.48550/arXiv.1211.0025>); Inductive and Cross Venn-Abers predictors make this practical for large data sets (Vovk, Petej and Fedorova, 2015, <doi: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, <doi: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) <doi:10.1073/pnas.2016191118>.

Authors:René-M. Kruse [aut, cre, cph]