Version 3.0.1

Changes with respect v3.0:

• Numpy version increased to 1.22 and PyYAML to 5.4 to appy security patches.
• Bugfix: when using a wrong observable in SMEFTglob.prediction, SMEFTglob.pull_obs or SMEFTglob.fastmeas, a KeyError is now raised.
• Released Docker containers, available using

docker pull jorgealda/smeft19

SMET19 v3.0

Version used for the final results of the PhD

• Updated to flavio and smelli v2.3: More observables, latest measurements, new for factors for RD*, etc.
• Results moved to https://github.com/Jorge-Alda/SMEFT19-notebooks.
• Fully documented in https://jorge-alda.github.io/SMEFT19.
• Many bugs fixed.
• Published to Zenodo: https://doi.org/10.5281/zenodo.5781795

SMEFT19 2.0

• Added a Machine Learning-based Montecarlo algorithm to generate points in parameter space around the best fit point following the chi^2 distribution of the fit. The Machine Learning uses a XGBoost model.

• Added SHAP values to interpret the results of the Machine Learning model.

SMEFT19 v1.0

• All results have been recalculated with the 2019 data from LHCb and Belle.

• Functions better organized in modules.

• Support for multithread calculations for some functions, like likelihood_plot.

• Documentation.

First working version

Everything should be working by now. All results produced with the minimal model.