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        "author": "Nicole Carlson",
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        "description": "# PyMC3 Models\n\nCustom PyMC3 models built on top of the scikit-learn API. Check out the [docs](http://pymc3-models.readthedocs.io/).\n\n## Features\n\n- Reusable PyMC3 models including LinearRegression and HierarchicalLogisticRegression\n- A base class, BayesianModel, for building your own PyMC3 models\n\n## Installation\nThe latest release of PyMC3 Models can be installed from PyPI using `pip`:\n\n``` bash\npip install pymc3_models\n```\n\nThe current development branch of PyMC3 Models can be installed from GitHub, also using `pip`:\n\n``` bash\npip install git+https://github.com/parsing-science/pymc3_models.git\n```\n\nTo run the package locally (in a virtual environment):\n\n```\ngit clone https://github.com/parsing-science/pymc3_models.git\ncd pymc3_models\nvirtualenv venv\nsource venv/bin/activate\npip install -r requirements.txt\n```\n\n## Usage\nSince PyMC3 Models is built on top of scikit-learn, you can use the same methods as with a scikit-learn model.\n\n``` python\nfrom pymc3_models import LinearRegression\n\nLR = LinearRegression()\nLR.fit(X, Y)\nLR.predict(X)\nLR.score(X, Y)\n```\n\n## Contribute\nFor more info, see [CONTRIBUTING](https://github.com/parsing-science/pymc3_models/blob/master/CONTRIBUTING.rst).\n\n### Contributor Code of Conduct\n\nPlease note that this project is released with a [Contributor Code of Conduct](http://contributor-covenant.org/). By participating in this project you agree to abide by its terms. See [CODE_OF_CONDUCT](https://github.com/parsing-science/pymc3_models/blob/master/CODE_OF_CONDUCT.rst).\n\n## Acknowledgments\nThis library is built on top of [PyMC3](http://docs.pymc.io/) and [scikit-learn](http://scikit-learn.org).\n\n## License\n[Apache License, Version 2.0](https://github.com/parsing-science/pymc3_models/blob/master/LICENSE)",
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