# Unidimensional and Multidimensional Methods for Recurrence Quantification Analysis with crqa Source: [Unidimensional and Multidimensional Methods for Recurrence Quantification Analysis with crqa]() Collection index: [llms.txt]() Data updated: 2025-10-25T04:31:48.969Z Authors: Moreno I. Coco, Dan M{\\o}nster, Giuseppe Leonardi, Rick Dale, Sebastian Wallot Published: 2020-05-28T14:15:39.000Z ## 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 to 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 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()