Metadata-Version: 2.4
Name: contingency-tools
Version: 0.2.2
Summary: Fast, vectorized metrology with binary contingency counts.
Keywords: binary classification,classifier,machine learning,metrics,performance,experiments,accuracy,recall,precision,contingency table,marginal counts,error,type I,type II,vectorized,batched,approximation
Author: Rachael T. Sexton
Author-email: Rachael T. Sexton <rachael.sexton@nist.gov>
License-Expression: NIST-Software
License-File: LICENSE.md
Classifier: Natural Language :: English
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Software Development
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Programming Language :: Python :: Implementation :: CPython
Requires-Dist: jaxtyping>=0.3.3
Requires-Dist: numpy>=2.3.5
Requires-Dist: scipy>=1.16.3
Requires-Dist: matplotlib>=3.10.6 ; extra == 'plot'
Requires-Python: >=3.12, <3.14
Project-URL: documentation, https://pages.nist.gov/contingency
Project-URL: homepage, https://datapub.nist.gov/od/id/mds2-4079
Project-URL: source, https://github.com/usnistgov/Contingency
Provides-Extra: plot
Description-Content-Type: text/markdown

# Contingency

> Fast, vectorized metrology with binary contingency counts.

Rapidly calculate binary classifier metrics like MCC, F-Scores, and Average Precision Scores from scalar and binary predictions.

For an overview of features, usage, and performance, see the [documentation site](https://pages.nist.gov/contingency).
The canonical entry for this publication on the NIST data repository is available at [doi:10.18434/mds2-4079](https://doi.org/10.18434/mds2-4079).

## Installation 

`contingency` can be installed from PyPI: 

```
pip install contingency-tools
```

If you would like access to the most recent/unstable version on GitHub (before a release): 

```
pip install git+https://github.com/usnistgov/Contingency.git
```

For access to the NIST PDR entry for `contingency`, see [`data.nist.gov`](https://data.nist.gov/od/id/mds2-4079).


## Contact the PI

[Rachael Sexton](https://www.nist.gov/people/rachael-t-sexton)

- [`rachael.sexton@nist.gov`](mailto:rachael.sexton@nist.gov)
- NIST Engineering Laboratory
- Systems Integration Division
- Information Modeling & Testing Group
