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.
python -m pip install likefit- 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
These demos contain Jupyter notebooks that show how to use the LikeFit library.
If you want to contribute, please fork the repository and use a feature branch. Pull requests are warmly welcome.
- Repository: https://github.com/ravignad/likefit/
- Demos: https://github.com/ravignad/likefit_demos.git
The code in this project is licensed under MIT license.