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 organization: Institute for Atmospheric and Earth System Research (INAR)
Helsinki Institute of Sustainability Science (HELSUS)
Global Atmosphere-Earth surface feedbacks
Ecosystem processes (INAR Forest Sciences)
Date: 2021-06
Language: eng
Number of pages: 5
Belongs to series: SoftwareX
ISSN: 2352-7110
DOI: https://doi.org/10.1016/j.softx.2021.100686
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
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion

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