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        "author": "David J. Skelton",
        "author_email": "d.j.skelton1@gmail.com",
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        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7"
        ],
        "description": "<p align=\"center\">\n    <a href=\"https://pypi.python.org/pypi/mcpt/\" alt=\"version\">\n        <img src=\"https://img.shields.io/pypi/v/mcpt.svg\" /></a>\n    <a href=\"https://pypi.python.org/pypi/mcpt/\" alt=\"status\">\n        <img src=\"https://img.shields.io/pypi/status/mcpt.svg\" /></a>\n    <a href=\"https://pypi.python.org/pypi/mcpt/\" alt=\"status\">\n        <img src=\"https://img.shields.io/pypi/pyversions/mcpt.svg\" /></a>\n    <br>\n    <a href=\"https://doi.org/10.5281/zenodo.3387528\" alt=\"status\">\n        <img src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.3387528.svg\" /></a>\n     <a href=\"https://pypi.python.org/pypi/mcpt/\" alt=\"status\">\n        <img src=\"https://img.shields.io/pypi/l/mcpt.svg\" /></a> \n     <a href=\"https://pypi.python.org/pypi/mcpt/\" alt=\"status\">\n        <img src=\"https://img.shields.io/pypi/dm/mcpt.svg\" /></a> \n</p>\n\n\n# mcpt: Monte Carlo permutation tests for Python\n`mcpt` is a Python 3 library for calculating p-values through Monte Carlo permutation tests, providing an intuitive, simple, and highly customisable interface to determining statistical significance.\n\nTo get started, we recommend you read through Installation, Quickstart, and Functions sections of our [read the docs documentation](https://mcpt.readthedocs.io/en/latest/). Also check out the [FAQ](https://mcpt.readthedocs.io/en/latest/documentation/faq.html), which we update regularly. If you have concerns about the software, or feel that there is something that should be more explicit, then we\u2019d love to hear from you \u2013 [please open an issue on Github](https://github.com/Ravenlocke/mcpt/issues) and we\u2019ll get back in touch ASAP.\n\nIf you use `mcpt` in your research, please support us by citing the initial release:\n\n> David J. Skelton. (2019, September 5). mcpt: Monte Carlo permutation tests for Python (Version 0). Zenodo. http://doi.org/10.5281/zenodo.3387528\n\n\n\n## TLDR;\n### Installation\nThe simplest way to install this package is directly from PyPI using pip\n\n<pre>\npip install mcpt\n</pre>\n\n### Usage\n`mcpt` contains two main functions: `mcpt.permutation_test` and `mcpt.correlation_permutation_test`. \n\n\nBelow is an example of the `mcpt.permutation_test` - for more info, please see the documentation [here](https://mcpt.readthedocs.io/en/latest/documentation/quickstart.html#permutation-test)\n<pre>\n>> import mcpt\n>> x = [10, 9, 11]\n>> y = [12, 11, 13]\n>> f = \"mean\"\n>> n = 100_000\n>> side = \"lower\"\n\n>> result = mcpt.permutation_test(x, y, f, side, n=n)\n>> print(result)\nResult(lower=0.09815650454064283, upper=0.10305649415095638, confidence=0.99)\n</pre>\n\nBelow is an example of `mcpt.correlation_permutation_test` - for more information, please see the documentation [here](https://mcpt.readthedocs.io/en/latest/documentation/quickstart.html#correlation-permutation-test)\n\n<pre>\n>> import mcpt\n>> x = [-2.31, 1.06, 0.76, 1.38, -0.26, 1.29, -1.31, 0.41, -0.67, -0.58]\n>> y = [-1.08, 1.03, 0.90, 0.24, -0.24, 0.76, -0.57, -0.05, -1.28, 1.04]\n>> side = \"both\"\n>> f = \"pearsonr\"\n\n>> result = mcpt.correlation_permutation_test(x, y, f=f, side=side)\n>> print(result)\nResult(lower=0.021282451892029475, upper=0.029347445354757373, confidence=0.99)\n</pre>\n\n\n",
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