Metadata-Version: 2.1
Name: likefit
Version: 0.2.2
Summary: Fit data with least squares and other likelihood methods
Author-email: Diego Ravignani <diego.ravignani@gmail.com>
License: MIT License
        
        Copyright (c) 2024 Diego Ravignani Guerrero
        
        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.
        
Project-URL: Homepage, https://github.com/ravignad/likefit
Keywords: fit,likelihood,scipy
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: scipy>=1.9.3
Requires-Dist: matplotlib>=3.7.4
Provides-Extra: dev
Requires-Dist: pip-tools; extra == "dev"
Requires-Dist: pandas; extra == "dev"
Requires-Dist: pytest; extra == "dev"

# LikeFit

LikeFit is a library to fit data for science and engineering. 
It provides a user-friendly and comprehensive interface for linear and non-linear least squares, and likelihood fits.


## Install

```sh
python -m pip install likefit
```

## Features
  * Linear and nonlinear least squares fit with a consistent interface
  * Fit of histograms with a Poisson likelihood
  * Calculation of estimators, errors, and correlations
  * Evaluation of goodness-of-fit with chi-squared test
  * Plot of error bands, confidence regions, and likelihood functions

## How to use
These [demos](https://github.com/ravignad/likefit_demos.git) contain Jupyter notebooks that show how to use the LikeFit library.

## Contributing
If you want to contribute, please fork the repository and use a feature
branch. Pull requests are warmly welcome.

## Links
- Repository: https://github.com/ravignad/likefit/
- Demos: https://github.com/ravignad/likefit_demos.git

## Licensing
The code in this project is licensed under MIT license.

