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CalibrationTests.jl\n\nHypothesis tests of calibration.\n\n[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://devmotion.github.io/CalibrationTests.jl/stable)\n[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://devmotion.github.io/CalibrationTests.jl/dev)\n[![Build Status](https://github.com/devmotion/CalibrationTests.jl/workflows/CI/badge.svg?branch=main)](https://github.com/devmotion/CalibrationTests.jl/actions?query=workflow%3ACI+branch%3Amain)\n[![DOI](https://zenodo.org/badge/215970266.svg)](https://zenodo.org/badge/latestdoi/215970266)\n[![Codecov](https://codecov.io/gh/devmotion/CalibrationTests.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/devmotion/CalibrationTests.jl)\n[![Coveralls](https://coveralls.io/repos/github/devmotion/CalibrationTests.jl/badge.svg?branch=main)](https://coveralls.io/github/devmotion/CalibrationTests.jl?branch=main)\n[![Code Style: Blue](https://img.shields.io/badge/code%20style-blue-4495d1.svg)](https://github.com/invenia/BlueStyle)\n[![Aqua QA](https://raw.githubusercontent.com/JuliaTesting/Aqua.jl/master/badge.svg)](https://github.com/JuliaTesting/Aqua.jl)\n\n**There are also [Python](https://github.com/devmotion/pycalibration) and [R](https://github.com/devmotion/rcalibration) interfaces for this package**\n\n## Overview\n\nThis package implements different hypothesis tests for calibration of\nprobabilistic models in the Julia language.\n\n## Related packages\n\nThe statistical tests in this package are based on the calibration error estimators\nin the package [CalibrationErrors.jl](https://github.com/devmotion/CalibrationErrors.jl).\n\n[pycalibration](https://github.com/devmotion/pycalibration) is a Python interface for CalibrationErrors.jl and CalibrationTests.jl.\n\n[rcalibration](https://github.com/devmotion/rcalibration) is an R interface for CalibrationErrors.jl and CalibrationTests.jl.\n\n## Citing\n\nIf you use CalibrationsTests.jl as part of your research, teaching, or other activities, please consider citing the following publications:\n\nWidmann, D., Lindsten, F., \u0026 Zachariah, D. (2019). [Calibration tests in multi-class\nclassification: A unifying framework](https://proceedings.neurips.cc/paper/2019/hash/1c336b8080f82bcc2cd2499b4c57261d-Abstract.html). In\n*Advances in Neural Information Processing Systems 32 (NeurIPS 2019)* (pp. 12257–12267).\n\nWidmann, D., Lindsten, F., \u0026 Zachariah, D. (2021).\n[Calibration tests beyond classification](https://openreview.net/forum?id=-bxf89v3Nx).\nTo be presented at *ICLR 2021*.\n\n## Acknowledgements\n\nThis work was financially supported by the Swedish Research Council via the projects *Learning of Large-Scale Probabilistic Dynamical Models* (contract number: 2016-04278), *Counterfactual Prediction Methods for Heterogeneous Populations* (contract number: 2018-05040), and *Handling Uncertainty in Machine Learning Systems* (contract number: 2020-04122), by the Swedish Foundation for Strategic Research via the project *Probabilistic Modeling and Inference for Machine Learning* (contract number: ICA16-0015), by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation, and by ELLIIT.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdevmotion%2Fcalibrationtests.jl","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdevmotion%2Fcalibrationtests.jl","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdevmotion%2Fcalibrationtests.jl/lists"}