Metadata-Version: 2.4
Name: bezierv
Version: 0.1.2
Summary: This package serves as a computational framework for Bézier distributions.
Author-email: Esteban Leiva <e.leivam@uniandes.edu.co>, "Andrés L. Medaglia" <amedagli@uniandes.edu.co>
Maintainer-email: Esteban Leiva <e.leivam@uniandes.edu.co>
License: MIT License
        
        Copyright (c) 2025 Esteban Leiva and Andrés L. Medaglia
        
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Project-URL: Homepage, https://github.com/you/your-package
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Keywords: bezier,distribution,optimization
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Requires-Python: <3.14,>=3.10
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License-File: LICENSE
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Dynamic: license-file

<p align="center">
  <!-- If you used a different path, update the src accordingly -->
  <img src="docs/assets/logo.png" alt="bezierv logo" width="260"/>
</p>

<h1 align="center">bezierv</h1>
<p align="center">
  <em>Fit smooth Bézier random variables to empirical data</em>
</p>

<p align="center">
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   <a href="https://estebanleiva.github.io/bezierv/"><img alt="Docs" src="https://img.shields.io/badge/docs-online-brightgreen?style=flat-square"></a>
</p>

---

## Why Bézier random variables?  
Classical parametric distributions can be too rigid.

Bézier curves offer a sweet spot: **smooth** and **shape-controlled**.  
With **bezierv** you can:

* Fit Bézier CDFs/PDFs to sample data of any shape.
* Evaluate moments and quantiles.
* Compose variables via convolution.

---

## Installation

```bash
pip install bezierv
```
