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crqa (version 2.0.6)

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

575

Version

2.0.6

License

GPL (>= 3)

Maintainer

Last Published

June 5th, 2024

Functions in crqa (2.0.6)

wincrqa

Windowed Recurrence Measures
mdFnn

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

Windowed Recurrence Profile
plot_rp

Plot a recurrence matrix
simts

Simulate dichotomous binary time-series
optimizeParam

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

Extract diagonal matrices
text

Categorical sequence of words
mdDelay

Find optimal delay from a multi-dimensional dataset.
piecewiseRQA

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

Diagonal recurrence profile
Figure_2

A unidimensional sinusoidal time series
Figure_6

Figure_6
handmovement

Continuous series of hand movements
lorenzattractor

Simulate the Lorenz Attractor
Figure_3

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

Eye-movement categorical time-series
crqa-package

Unidimensional and Multidimensional Methods for Recurrence Quantification Analysis
Figure_1

Eye-movement categorical time-series
crqa

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