Fit data with least squares and other likelihood methods
Project description
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
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 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.
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