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nonlinearTseries (version 0.3.2)

Nonlinear Time Series Analysis

Description

Functions for nonlinear time series analysis. This package permits the computation of the most-used nonlinear statistics/algorithms including generalized correlation dimension, information dimension, largest Lyapunov exponent, sample entropy and Recurrence Quantification Analysis (RQA), among others. Basic routines for surrogate data testing are also included. Part of this work was based on the book "Nonlinear time series analysis" by Holger Kantz and Thomas Schreiber (ISBN: 9780521529020).

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install.packages('nonlinearTseries')

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1,041

Version

0.3.2

License

GPL-3

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Repository

https://github.com/constantino-garcia/nonlinearTseries

Maintainer

Constantino A. Garcia

Last Published

March 23rd, 2026

Functions in nonlinearTseries (0.3.2)

Get the order of the nonlinear chaotic invariant.

mutualInformation

Average Mutual Information (AMI)

nonLinearNoiseReduction

Nonlinear noise reduction

estimateEmbeddingDim

Estimate the embedding dimension

neighbourSearch

neighbour search

Recurrence Plot

Get the radius of the neighborhoods used for the computations of a chaotic invariant.

sampleEntropyFunction

Returns the sample entropy function \(h_q(m,r)\) used for the computations of the sample entropy.

Sample Entropy (also known as Kolgomorov-Sinai Entropy)

Time Reversibility statistic

Recurrence Quantification Analysis (RQA)

Estimate an appropiate time lag for the Takens' vectors

nonLinearPrediction

Nonlinear time series prediction

Surrogate data testing

Returns the window sizes used for DFA in a dfa object.

nonlinearityTest

Nonlinearity test

Maximum lyapunov exponent

Time Reversibility statistic

Threshold nonlinearity test

Obtain the the average log(radius) computed on the information dimension algorithm.

Space Time plot

plotLocalScalingExp

Plot local scaling exponents

Returns the time in which the divergence of close trajectories was computed in order to estimate the maximum Lyapunov exponent.

Obtain the contour lines of the space time plot.

Returns the correlation sums stored in the corrDim object

Returns the rate of divergence of close trajectories needed for the maximum Lyapunov exponent estimation.

Get the embedding dimensions used for compute a chaotic invariant.

Detrended Fluctuation Analysis

Build the Takens' vectors

Generate surrogate data using the Fourier transform

Correlation sum, correlation dimension and generalized correlation dimension (order q >1).

findAllNeighbours

neighbour search

getContourLines

Obtain the contour lines of the space time plot.

Estimate several chaotic invariants using linear regression

Information dimension

fluctuationFunction

Returns the fluctuation function obtained in a DFA and represented by a dfa object.