ennemi : Non-linear correlation detection with mutual information

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Laarne , P , Zaidan , M A & Nieminen , T 2021 , ' ennemi : Non-linear correlation detection with mutual information ' , SoftwareX , vol. 14 , 100686 . https://doi.org/10.1016/j.softx.2021.100686

Title: ennemi : Non-linear correlation detection with mutual information
Author: Laarne, Petri; Zaidan, Martha A.; Nieminen, Tuomo
Contributor: University of Helsinki, Institute for Atmospheric and Earth System Research (INAR)
University of Helsinki, Helsinki Institute of Sustainability Science (HELSUS)
University of Helsinki, Global Atmosphere-Earth surface feedbacks
Date: 2021-06
Language: eng
Number of pages: 5
Belongs to series: SoftwareX
ISSN: 2352-7110
URI: http://hdl.handle.net/10138/332631
Abstract: We present ennemi, a Python package for correlation analysis based on mutual information (MI). MI is a measure of relationship between variables. Unlike Pearson correlation it is valid also for non-linear relationships, yet in the linear case the two are equivalent. The effect of other variables can be removed like with partial correlation, with the same equivalence. These features make MI a better correlation measure for exploratory analysis of many variable pairs. Our package provides methods for common correlation analysis tasks using MI. It is scalable, integrated with the Python data science ecosystem, and requires minimal configuration. (C) 2021 The Authors. Published by Elsevier B.V.
Subject: 114 Physical sciences
1171 Geosciences
1172 Environmental sciences

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