Nonlinear Time Series Analysis
Build the Takens' vectors
Clifford map
Obtain the contour lines of the space time plot.
Correlation sum, correlation dimension and generalized correlation dim...
Returns the correlation sums stored in the corrDim object
Detrended Fluctuation Analysis
Returns the rate of divergence of close trajectories needed for the ma...
Returns the time in which the divergence of close trajectories was com...
Get the embedding dimensions used for compute a chaotic invariant.
Estimate several chaotic invariants using linear regression
Estimate the embedding dimension
Generate surrogate data using the Fourier transform
neighbour search
fixed mass
Returns the fluctuation function obtained in a DFA and represented by ...
Gauss map
Obtain the contour lines of the space time plot.
Henon map
Ikeda map
Information dimension
Keenan's test
Logistic map
Obtain the the average log(radius) computed on the information dimensi...
Lorenz system
Maximum lyapunov exponent
McLeod-Li test
Average Mutual Information (AMI)
neighbour search
Get the order of the nonlinear chaotic invariant.
Nonlinearity test
Nonlinear noise reduction
Nonlinear time series prediction
Plot local scaling exponents
Poincare map
Get the radius of the neighborhoods used for the computations of a cha...
Recurrence Plot
Rossler system
Recurrence Quantification Analysis (RQA)
Sample Entropy (also known as Kolgomorov-Sinai Entropy)
Returns the sample entropy function used for the computatio...
Sinai map
Space Time plot
Surrogate data testing
Threshold nonlinearity test
Time Reversibility statistic
Time Reversibility statistic
Estimate an appropiate time lag for the Takens' vectors
Tsay's test
Returns the window sizes used for DFA in a dfa object.
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).
Useful links