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        "author": "Ryan Giordano",
        "author_email": "rgiordan@gmail.com",
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            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: Apache Software License",
            "Natural Language :: English",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Mathematics"
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        "description": "# \"Parameter origami\": `paragami`.\n\n[![image](https://travis-ci.org/rgiordan/paragami.svg?branch=master)](https://travis-ci.org/rgiordan/paragami)\n[![image](https://codecov.io/gh/rgiordan/paragami/branch/master/graph/badge.svg)](https://codecov.io/gh/rgiordan/paragami)\n\n## Description.\n\nParameter folding and flattening, parameter origami: `paragami`\\*!\n\nThis is a library (very much still in development) intended to make\nsensitivity analysis easier for optimization problems. The core\nfunctionality consists of tools for \"folding\" and \"flattening\"\ncollections of parameters -- i.e., for converting data structures of\nconstrained parameters to and from vectors of unconstrained parameters.\n\nFor background and motivation, see the following papers:\n\nCovariances, Robustness, and Variational Bayes\nRyan Giordano, Tamara Broderick, Michael I. Jordan\n<https://arxiv.org/abs/1709.02536>\n\nA Swiss Army Infinitesimal Jackknife\nRyan Giordano, Will Stephenson, Runjing Liu, Michael I. Jordan, Tamara\nBroderick\n<https://arxiv.org/abs/1806.00550>\n\nEvaluating Sensitivity to the Stick Breaking Prior in Bayesian\nNonparametrics\nRunjing Liu, Ryan Giordano, Michael I. Jordan, Tamara Broderick\n<https://arxiv.org/abs/1810.06587>\n\n## Using the package.\n\nWe welcome new users\\! However, please be aware that the package is\nstill in development. We encourage users to contact the author (github\nuser `rgiordan`) for advice, bugs, or if you're using the package for\nsomething important.\n\n### Installation.\n\nTo install the latest tagged version, install with `pip`:\n\n`python3 -m pip install paragami`.\n\nThe `paragami` package is under rapid development, so you may want to\nclone the respository and use the master branch instead.\n\n**Note**: In order to use the functions in\n`sparse_preconditioners_lib`, you must additionally manually install\n[``scikit-sparse``](https://github.com/scikit-sparse/scikit-sparse/),\nwhich requires the C++ libraries in ``libsuitesparse-dev``.\nMost users will not require this functionality so `scikit-sparse` is not installed by default with `paragami` for simplicity.  See the\n[``scikit-sparse`` requirements](https://scikit-sparse.readthedocs.io/en/latest/overview.html#requirements)\nfor more details on installation.\n\n### Documentation and Examples.\n\nFor examples and API documentation, see\n[readthedocs](https://paragami.readthedocs.io/).\n\nAlternatively, check out the repo and run `make html` in `docs/`.\n\n\\*  Thanks to St\u00e9fan van der Walt for the suggesting the package name.\n\n\n",
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