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crqa (version 2.0.5)

Unidimensional and Multidimensional Methods for Recurrence Quantification Analysis

Description

Auto, Cross and Multi-dimensional recurrence quantification analysis. Different methods for computing recurrence, cross vs. multidimensional or profile iti.e., only looking at the diagonal recurrent points, as well as functions for optimization and plotting are proposed. in-depth measures of the whole cross-recurrence plot, Please refer to Coco and others (2021) , Coco and Dale (2014) and Wallot (2018) for further details about the method.

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Version

Install

install.packages('crqa')

Monthly Downloads

607

Version

2.0.5

License

GPL (>= 2)

Maintainer

Last Published

November 27th, 2023

Functions in crqa (2.0.5)

plotRP

Plot a recurrence matrix
spdiags

Extract diagonal matrices
simts

Simulate dichotomous binary time-series
wincrqa

Windowed Recurrence Measures
windowdrp

Windowed Recurrence Profile
text

Categorical sequence of words
mdDelay

Find optimal delay from a multi-dimensional dataset.
mdFnn

Find optimal embedding dimension of a multi-dimensional dataset.
crqa

Auto, cross and multidimensional recurrence measures of one, two or multiple time-series, time-delayed and embedded in higher dimensional space
drpfromts

Diagonal recurrence profile
Figure_1

Eye-movement categorical time-series
Figure_3

Simulated time series of the three dimensions from the Lorenz system
handmovement

Continuous series of hand movements
lorenzattractor

Simulate the Lorenz Attractor
piecewiseRQA

Compute recurrence plots for long time-series data series using a block (piece-wise) method.
optimizeParam

Estimate optimal delay, embedding dimension and radius for continuous time-series data
Figure_6

Figure_6
crqa-package

Unidimensional and Multidimensional Methods for Recurrence Quantification Analysis
Figure_2

A unidimensional sinusoidal time series
eyemovement

Eye-movement categorical time-series