{ "info": { "author": "Jason Aebischer , Jacky Kumar , Peter Stangl , David M. Straub ", "author_email": "", "bugtrack_url": null, "classifiers": [], "description": "![Build Status](https://travis-ci.org/smelli/smelli.svg?branch=master) [![Coverage Status](https://coveralls.io/repos/github/smelli/smelli/badge.svg)](https://coveralls.io/github/smelli/smelli)\n\n# smelli \u2013 a global likelihood for precision constraints\n\n`smelli` is a Python package providing a global likelihood function in the\nspace of dimension-six Wilson coefficients in the Standard Model Effective\nField Theory (SMEFT). The likelihood includes contributions from\nquark and lepton flavour physics, electroweak precision tests, and other\nprecision observables.\n\nThe package is based on [flavio](https://github.com/flav-io/flavio) for the\ncalculation of observables and statistical treatment and\n[wilson](https://github.com/wilson-eft/wilson) for the running, translation,\nand matching of Wilson coefficients.\n\n## Installation\n\nThe package requires Python version 3.5 or above. It can be installed with\n\n```bash\npython3 -m pip install smelli --user\n```\n\n\n## Documentation\n\nA brief user manual can be found in the paper cited below.\n\n## Citation\n\nIf you use `smelli` in a scientific publication, please cite\n\n> J. Aebischer, J. Kumar, P. Stangl, and D. M. Straub\n>\n> \"A Global Likelihood for Precision Constraints and Flavour Anomalies\"\n>\n> [arXiv:1810.07698 [hep-ph]](https://arxiv.org/abs/1810.07698)\n\nPlease also cite the publications on [flavio](https://arxiv.org/abs/1810.08132) and [wilson](https://arxiv.org/abs/1804.05033), which are the pillars `smelli` is built on.\n\n## Bugs and feature requests\n\nPlease submit bugs and feature requests using\n[Github's issue system](https://github.com/smelli/smelli/issues).\n\n## Contributing\n\nThe aim of the package is to provide a likelihood in the\nspace of dimension-6 SMEFT Wilson coefficients using all\nrelevant available experimental measurements. If you want\nto contribute additional observables, the easiest way is\nto implement the observable in [flavio](https://github.com/flav-io/flavio). Observables\nimplemented there can be added to the likelihood simply\nby adding a corresponding entry in one of the\n[observable YAML files](https://github.com/smelli/smelli/tree/master/smelli/data/yaml).\n\nAlternatively, also observables computed in any other standalone Python package can be incorporated in principle as long as it adheres to the [WCxf standard](https://wcxf.github.io).\nIf you want to follow this route, please open an [issue](https://github.com/smelli/smelli/issues) to start the discussion on how to integrate it.\n\n## Contributors\n\nIn alphabetical order:\n\n- Jason Aebischer\n- Jacky Kumar\n- Peter Stangl\n- David M. 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