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    "info": {
        "author": "Sanhe Hu",
        "author_email": "husanhe@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: MacOS",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: Unix",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5"
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        "description": ".. image:: https://travis-ci.org/MacHu-GWU/ctmatching-project.svg?branch=master\r\n\r\n.. image:: https://img.shields.io/pypi/v/ctmatching.svg\r\n\r\n.. image:: https://img.shields.io/pypi/l/ctmatching.svg\r\n\r\n.. image:: https://img.shields.io/pypi/pyversions/ctmatching.svg\r\n\r\n\r\nWelcome to ctmatching Documentation\r\n===============================================================================\r\nThe `Treatment and control group <https://en.wikipedia.org/wiki/Treatment_and_control_groups>`_ experiment is widely used in comparative experiments. In order to verify that whether a method is significantly working for certain problem, usually we select group of object having same problem, and having similar circumstance. Then we divide them into control and treatment, and perform the method to treatment group.\r\n\r\nBUT! Sometimes there's not enough people available that willing to accept the treatment. Usually **treatment group is greatly less than control group**. Then we have to select control group with similar circumstance from public. ``stratified propensity score matching algorithm`` (PSM) is a method for selecting control group. ``ctmatching`` is a python implementation of it.\r\n\r\n\r\n\u4e2d\u6587\u4ecb\u7ecd (Chinese Introduction)\r\n-------------------------------------------------------------------------------\r\n**Propensity score matching (PSM) \u503e\u5411\u8bc4\u5206\u5339\u914d**\r\n\r\nPSM\u4e3b\u8981\u7528\u4e8e\u751f\u7269\u7edf\u8ba1\u4e2d\u3002\u5728\u751f\u7269\u7edf\u8ba1\u4e2d, \u5f88\u591a\u5b9e\u9a8c\u90fd\u8981\u8bbe\u7f6e ``\u5bf9\u7167\u7ec4/\u63a7\u5236\u7ec4\uff08control group\uff09`` \u548c ``\u5b9e\u9a8c\u7ec4\uff08treatment group\uff09``\u3002\u5728\u4e00\u4e2a\u7cbe\u5fc3\u8bbe\u8ba1\u7684\u5b9e\u9a8c\u4e2d, \u5bf9\u7167\u7ec4\u548c\u5b9e\u9a8c\u7ec4\u901a\u5e38\u5728\u9664\u4e86\u5b9e\u9a8c\u6240\u5bfc\u81f4\u7684\u7ed3\u679c\u90e8\u5206, \u90fd\u8981\u4fdd\u8bc1\u5176\u4ed6\u60c5\u51b5\u5c3d\u91cf\u4e00\u81f4, \u8fd9\u6837\u5b9e\u9a8c\u7ed3\u679c\u624d\u6709\u8bf4\u670d\u529b\u3002\r\n\r\n\u4f8b\u5982\u6211\u4eec\u7814\u7a76X\u836f\u54c1\u5bf9A\u75be\u75c5\u7684\u6cbb\u7597\u6548\u679c, \u5982\u679c\u6211\u4eec\u9009\u62e9100\u4e2a\u75c5\u4eba, \u4ed6\u4eec\u7684\u8eab\u4f53\u72b6\u51b5\u90fd\u6bd4\u8f83\u76f8\u4f3c, \u7ed9\u5176\u4e2d50\u4e2a\u4eba\u5403\u836f, \u53e6\u591650\u4e2a\u4eba\u4e0d\u5403\u836f\u3002\u5982\u679c\u8fc7\u4e86\u4e00\u4e2a\u6708\u5bf9\u7167\u7ec4\u4e2d\u7684\u75c5\u4eba\u670930%\u8eab\u4f53\u6076\u5316, 10%\u7684\u72b6\u6001\u4e0d\u53d8, 10%\u7684\u7a0d\u8bb8\u597d\u8f6c\u3002\u800c\u5b9e\u9a8c\u7ec4\u4e2d\u7684\u75c5\u4eba\u670930%\u90fd\u6709\u597d\u8f6c, \u90a3\u4e48\u6211\u4eec\u5927\u4f53\u4e0a\u53ef\u4ee5\u8bf4\u660e\u8fd9\u4e2a\u836f\u662f\u6709\u6548\u7684\u3002\r\n\r\n\u4f46\u662f, \u5728\u5b9e\u9645\u7684\u60c5\u51b5\u4e0b\u6211\u4eec\u901a\u5e38\u505a\u4e0d\u5230\u8fd9\u4e00\u70b9\u3002\u56e0\u4e3a\u6211\u4eec\u5f88\u96be\u627e\u5230\u5927\u91cf\u7684\u72b6\u6001\u76f8\u4f3c\u7684\u5b9e\u9a8c\u5bf9\u8c61, \u5f80\u5f80\u6211\u4eec\u53ea\u80fd\u5bf9\u90e8\u5206\u5bf9\u8c61\u505a\u5b9e\u9a8c, \u800c\u53bb\u5728\u5176\u4ed6\u5927\u91cf\u6ca1\u6709\u505a\u5b9e\u9a8c\u7684\u5bf9\u8c61\u4e2d, \u6839\u636e\u6570\u7406\u7edf\u8ba1\u9009\u62e9\u5bf9\u7167\u7ec4\u3002\r\n\r\n\u540c\u6837\u662fX\u836f\u54c1\u548cA\u75be\u75c5\u7684\u4f8b\u5b50: \u6211\u4eec\u5728\u6d4b\u8bd5A\u836f\u5bf9B\u75c5\u7684\u6cbb\u7597\u6548\u679c, \u4e8e\u662f\u6211\u4eec\u5bf950\u540d\u75c5\u75c5\u4eba\u505a\u5b9e\u9a8c, \u7ed9\u4ed6\u4eec\u5403\u4e86\u836f\u3002\u63a5\u7740\u5c31\u9700\u8981\u5230\u793e\u4f1a\u4e0a\u627e\u4e0e\u8fd950\u540d\u75c5\u4eba\u60c5\u51b5\u7c7b\u4f3c, \u4f46\u6ca1\u6709\u5403\u836f\u7684\u75c5\u4eba\u3002\u4e3a\u4e86\u7814\u7a76X\u836f\u7684\u6709\u6548\u6027, \u5bf9\u4e8e\u6bcf\u4e00\u4e2a\u75c5\u4eba\u6211\u4eec\u9700\u8981\u4ee5\u67d0\u79cd\u65b9\u5f0f\u5728\u5927\u91cf\u4eba\u7fa4\u4e2d, \u627e\u5230\u4e0e\u4ed6\u4eec\u5728\u5176\u4ed6\u6761\u4ef6\u76f8\u4f3c\u7684\u5bf9\u7167\u7ec4\u3002\u800c\u8fd9\u79cd\u9009\u53d6\u5bf9\u7167\u7ec4\u7684\u65b9\u6cd5, \u5c31\u53ebPSM\u3002\r\n\r\n\r\n**Quick Links**\r\n-------------------------------------------------------------------------------\r\n- `GitHub Homepage <https://github.com/MacHu-GWU/ctmatching-project>`_\r\n- `Online Documentation <http://pythonhosted.org/ctmatching>`_\r\n- `PyPI download <https://pypi.python.org/pypi/ctmatching>`_\r\n- `Install <install_>`_\r\n- `Issue submit and feature request <https://github.com/MacHu-GWU/ctmatching-project/issues>`_\r\n- `API reference and source code <http://pythonhosted.org/ctmatching/py-modindex.html>`_\r\n\r\n\r\n.. _install:\r\n\r\nInstall\r\n-------------------------------------------------------------------------------\r\n``ctmatching`` requires ``numpy >= 1.8.1``, ``scipy >= 0.14.1``, ``pandas >= 0.14.1``, ``scikit-learn >= 0.15.2``.\r\n\r\n``ctmatching`` is released on PyPI, so all you need is:\r\n\r\n.. code-block:: console\r\n\r\n\t$ pip install ctmatching\r\n\r\nTo upgrade to latest version:\r\n\r\n.. code-block:: console\r\n\r\n\t$ pip install --upgrade ctmatching",
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