Unidimensional and Multidimensional Methods for Recurrence Quantification Analysis with crqa

Authors

Moreno I. Coco, University of East London
Dan Mønster, Aarhus University
Giuseppe Leonardi, University of Economics and Human Sciences in Warsaw
Rick Dale, University of California, Los Angeles
Sebastian Wallot, Max Planck Institute for Empirical Aesthetics

Date of this Version

6-2021

Document Type

Article

Citation

The R Journal (June 2021) 13(1); Editor: Dianne Cook

Comments

Copyright 2021, The R Foundation. Open access material. License: CC BY 4.0 International

Abstract

Recurrence quantification analysis is a widely used method for characterizing patterns in time series. This article presents a comprehensive survey for conducting a wide range of recurrence-based analyses to quantify the dynamical structure of single and multivariate time series and capture coupling properties underlying leader-follower relationships. The basics of recurrence quantification analysis (RQA) and all its variants are formally introduced step-by-step from the simplest auto-recurrence to the most advanced multivariate case. Importantly, we show how such RQA methods can be deployed under a single computational framework in R using a substantially renewed version of our crqa 2.0 package. This package includes implementations of several recent advances in recurrence-based analysis, among them applications to multivariate data and improved entropy calculations for categorical data. We show concrete applications of our package to example data, together with a detailed description of its functions and some guidelines on their usage.