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        "author": "Frithjof Gressmann",
        "author_email": "ucjufgr@ucl.ac.uk",
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        "classifiers": [
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            "Programming Language :: Python",
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            "Topic :: Scientific/Engineering :: Mathematics",
            "Topic :: Software Development :: Libraries"
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        "description": "![skpro](/docs/_static/logo/logo.png)\n\n<p align=\"center\">\n  <a href=\"https://badge.fury.io/py/skpro\"><img src=\"https://badge.fury.io/py/skpro.svg\" alt=\"PyPI version\" height=\"18\"></a>\n  <a href=\"https://travis-ci.org/alan-turing-institute/skpro\"><img src=\"https://travis-ci.org/alan-turing-institute/skpro.svg?branch=master\" alt=\"Build Status\"></a>\n  <a href=\"https://opensource.org/licenses/BSD-3-Clause\"><img src=\"https://img.shields.io/badge/License-BSD%203--Clause-blue.svg\" alt=\"License\"></a>\n</p>\n\nA supervised domain-agnostic framework that allows for probabilistic modelling, namely the prediction of probability distributions for individual data points.\n\nThe package offers a variety of features and specifically allows for\n\n- the implementation of probabilistic prediction strategies in the supervised contexts\n- comparison of frequentist and Bayesian prediction methods\n- strategy optimization through hyperparamter tuning and ensemble methods (e.g. bagging)\n- workflow automation\n\nList of [developers and contributors](AUTHORS.rst)\n\n### Documentation\n\nThe full documentation is [available here](https://alan-turing-institute.github.io/skpro/).\n\n### Installation\n\nInstallation is easy using Python's package manager\n\n    $ pip install skpro\n\n### Contributing & Citation\n\nWe welcome contributions to the skpro project. Please read our [contribution guide](/CONTRIBUTING.md).\n\nIf you use skpro in a scientific publication, we would appreciate [citations](CITATION.rst).",
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