| Name | bezierv JSON |
| Version |
1.0.0
JSON |
| download |
| home_page | None |
| Summary | This package serves as a computational framework for Bézier distributions. |
| upload_time | 2025-08-23 17:54:45 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <3.14,>=3.10 |
| license | MIT License
Copyright (c) 2025 Esteban Leiva and Andrés L. Medaglia
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
| keywords |
bezier
distribution
optimization
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
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|
<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">
<!-- Add real badges once you publish to PyPI / set up CI -->
<img alt="PyPI" src="https://img.shields.io/pypi/v/bezierv?style=flat-square">
<img alt="CI" src="https://img.shields.io/github/actions/workflow/status/EstebanLeiva/bezierv/ci.yml?style=flat-square">
<img alt="License" src="https://img.shields.io/badge/License-MIT-informational?style=flat-square">
<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
```
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