rqa
(Package: nonlinearTseries) :
Recurrence Quantification Analysis (RQA)
The Recurrence Quantification Analysis (RQA) is an advanced technique for the nonlinear analysis that allows to quantify the number and duration of the recurrences in the phase space.
● Data Source:
CranContrib
● Keywords:
● Alias: rqa
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Generates a 3-dimensional time series using the Lorenz equations.
● Data Source:
CranContrib
● Keywords:
● Alias: lorenz
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corrDim
(Package: nonlinearTseries) :
Correlation sum, correlation dimension and generalized correlation dimension
Functions for estimating the correlation sum and the correlation dimension of a dynamical system from 1-dimensional time series using Takens' vectors.
● Data Source:
CranContrib
● Keywords:
● Alias: corrDim, corrMatrix.corrDim, embeddingDims.corrDim, estimate.corrDim, nlOrder.corrDim, plot.corrDim, plotLocalScalingExp.corrDim, print.corrDim, radius.corrDim
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contourLines
(Package: nonlinearTseries) :
Obtain the contour lines of the space time plot.
Obtain the contour lines of the space time plot.
● Data Source:
CranContrib
● Keywords:
● Alias: contourLines
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sampleEntropy
(Package: nonlinearTseries) :
Sample Entropy (also known as Kolgomorov-Sinai Entropy)
The Sample Entropy measures the complexity of a time series. Large values of the Sample Entropy indicate high complexity whereas that smaller values characterize more regular signals.
● Data Source:
CranContrib
● Keywords:
● Alias: embeddingDims.sampleEntropy, estimate.sampleEntropy, nlOrder.sampleEntropy, plot.sampleEntropy, radius.sampleEntropy, sampleEntropy, sampleEntropyFunction.sampleEntropy
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The space time separation is a broadly-used method of detecting non-stationarity and temporal correlations in the time series being analyzed. The space time separation plot is also used to select a proper Theiler window by selecting a temporal separation enough to saturate the contour lines.
● Data Source:
CranContrib
● Keywords:
● Alias: contourLines.spaceTimePlot, plot.spaceTimePlot, spaceTimePlot
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Functions for estimating the Average Mutual Information (AMI) of a time series.
● Data Source:
CranContrib
● Keywords:
● Alias: [.mutualInf, [[.mutualInf, as.numeric.mutualInf, mutualInformation, plot.mutualInf
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This function determines the minimum embedding dimension from a scalar time series using the algorithm proposed by L. Cao (see references).
● Data Source:
CranContrib
● Keywords:
● Alias: estimateEmbeddingDim
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estimate
(Package: nonlinearTseries) :
Estimate several chaotic invariants using linear regression
Several chaotic invariants are estimated by using linear regression. This function provides a common interface for the estimate of all these parameters (see corrDim, dfa and maxLyapunov for examples).
● Data Source:
CranContrib
● Keywords:
● Alias: estimate
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Generates a time series using the logistic map.
● Data Source:
CranContrib
● Keywords:
● Alias: logisticMap
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providing 
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