mcpt: Monte Carlo permutation tests for Python

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.

To get started, we recommend you read through Installation, Quickstart, and Functions sections of our read the docs documentation. Also check out the FAQ, which we update regularly. If you have concerns about the software, or feel that there is something that should be more explicit, then we’d love to hear from you – please open an issue on Github and we’ll get back in touch ASAP.

If you use mcpt in your research, please support us by citing the initial release:

David J. Skelton. (2019, September 5). mcpt: Monte Carlo permutation tests for Python (Version 0). Zenodo. http://doi.org/10.5281/zenodo.3387528

TLDR;

Installation

The simplest way to install this package is directly from PyPI using pip

pip install mcpt

Usage

mcpt contains two main functions: mcpt.permutation_test and mcpt.correlation_permutation_test.

Below is an example of the mcpt.permutation_test - for more info, please see the documentation here

>> import mcpt
>> x = [10, 9, 11]
>> y = [12, 11, 13]
>> f = "mean"
>> n = 100_000
>> side = "lower"

>> result = mcpt.permutation_test(x, y, f, side, n=n)
>> print(result)
Result(lower=0.09815650454064283, upper=0.10305649415095638, confidence=0.99)

Below is an example of mcpt.correlation_permutation_test - for more information, please see the documentation here

>> import mcpt
>> x = [-2.31, 1.06, 0.76, 1.38, -0.26, 1.29, -1.31, 0.41, -0.67, -0.58]
>> y = [-1.08, 1.03, 0.90, 0.24, -0.24, 0.76, -0.57, -0.05, -1.28, 1.04]
>> side = "both"
>> f = "pearsonr"

>> result = mcpt.correlation_permutation_test(x, y, f=f, side=side)
>> print(result)
Result(lower=0.021282451892029475, upper=0.029347445354757373, confidence=0.99)