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            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Physics"
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        "description": "PhenoAI is a machine learning interface for applications in High Energy Physics Phenomenology. It allows the user to use trained machine learning algorithms from the PhenoAI algorithm library (see link below) via a consistent interface. Trained algorithms can be stored in a folder with PhenoAI structure using the maker module of the package and shared, making it possible to communicate full-dimensional results so that one does not have to flee to models with lower dimensionality or to project out informative dimensions of the full problem.\n\n# Algorithm library\n\nThe current version of the package allows the user to use algorithms trained by scikit-learn and keras. These algorithms have to be created by the user, or have to be loaded from an external source like the algorithm library: [http://hef.ru.nl/~bstienen/phenoai](http://hef.ru.nl/~bstienen/phenoai) (work in progress).\n\n# Documentation\n\nDocumentation, a quick start guide and a range of examples can be found on the official PhenoAI website: [http://hef.ru.nl/~bstienen/phenoai](http://hef.ru.nl/~bstienen/phenoai).\n\n# Citation\n\n[coming soon]\n\n# To Do\n\n- Add support for ROOT trained algorithms",
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        "keywords": "high energy physics machine learning phenomenology limits exclusion likleihood likelihoods",
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