Metadata-Version: 2.4
Name: pauc
Version: 0.1.1
Summary: Compute ROC AUC and confidence intervals using DeLong’s method
Home-page: https://github.com/srijitseal/pauc
Author: Srijit Seal
Author-email: srijit@understanding.bio
License: MIT
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: pandas
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# pAUC

[![PyPI](https://img.shields.io/pypi/v/pauc.svg)](https://pypi.org/project/pauc/)
[![Python Tests](https://github.com/srijitseal/pauc/actions/workflows/ci.yml/badge.svg)](https://github.com/srijitseal/pauc/actions/workflows/ci.yml)

A simple Python package to calculate ROC AUC confidence intervals using DeLong’s method.

## Installation

pip install pauc

## Usage

from pauc import roc_auc_ci_score

auc, (lb, ub) = roc_auc_ci_score(y_true, y_pred)
print(f'AUC: {auc}, 95% CI: ({lb}, {ub})')
