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  Analyzing multivariate dynamics using Cross-Recurrence Quantification Analysis (CRQA), Diagonal-Cross-Recurrence Profiles (DCRP), and Multidimensional Recurrence Quantification Analysis (MdRQA) – A tutorial in R

Wallot, S., & Leonardi, G. (2018). Analyzing multivariate dynamics using Cross-Recurrence Quantification Analysis (CRQA), Diagonal-Cross-Recurrence Profiles (DCRP), and Multidimensional Recurrence Quantification Analysis (MdRQA) – A tutorial in R. Frontiers in Psychology, 9: 2232. doi:10.3389/fpsyg.2018.02232.

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Analyzing Multivariate Dynamics Using Cross-Recurrence.pdf (Publisher version), 8MB
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Analyzing Multivariate Dynamics Using Cross-Recurrence.pdf
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2018
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Copyright © 2018 Wallot and Leonardi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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 Creators:
Wallot, Sebastian1, Author           
Leonardi, Giuseppe2, Author
Affiliations:
1Department of Language and Literature, Max Planck Institute for Empirical Aesthetics, Max Planck Society, ou_2421695              
2Faculty of Psychology, University of Economics and Human Sciences, Warsaw, Poland, ou_persistent22              

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 Abstract: This paper provides a practical, hands-on introduction to cross-recurrence quantification analysis (CRQA), diagonal cross-recurrence profiles (DCRP), and multidimensional recurrence quantification analysis (MdRQA) in R. These methods have enjoyed increasing popularity in the cognitive and social sciences since a recognition that many behavioral and neurophysiological processes are intrinsically time dependent and reliant on environmental and social context has emerged. Recurrence-based methods are particularly suited for time-series that are non-stationary or have complicated dynamics, such as longer recordings of continuous physiological or movement data, but are also useful in the case of time-series of symbolic data, as in the case of text/verbal transcriptions or categorically coded behaviors. In the past, they have been used to assess changes in the dynamics of, or coupling between physiological and behavioral measures, for example in joint action research to determine the co-evolution of the behavior between individuals in dyads or groups, or for assessing the strength of coupling/correlation between two or more time-series. In this paper, we provide readers with a conceptual introduction, followed by a step-by-step explanation on how the analyses are performed in R with a summary of the current best practices of their application.

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Language(s): eng - English
 Dates: 2018-10-292018-02-192018-12-04
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.3389/fpsyg.2018.02232
 Degree: -

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Title: Frontiers in Psychology
  Abbreviation : Front Psychol
Source Genre: Journal
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Publ. Info: Pully, Switzerland : Frontiers Research Foundation
Pages: - Volume / Issue: 9 Sequence Number: 2232 Start / End Page: - Identifier: ISSN: 1664-1078
CoNE: https://pure.mpg.de/cone/journals/resource/1664-1078