R interface to Tisean algorithms

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Documentation for package ‘RTisean’ version 3.0.10

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RTisean-package The RTisean package
av_d2 Smoothing correlation sum data
boxcount Renyi entropy estimate
c1 Fixed mass estimation of information dimension
c2d Local slopes from correlation sums.
c2g Gaussian kernel correlation integral
c2t Maximum likelihood estimator from correlation sums
d2 Dimension and entropy estimation
endtoend End-to-end mismatch of a time series
ghkss Noise reduction
henon Henon Model
lazy Nonlinear noise reduction
lfo-ar Modeling data through a local linear ansatz
lfo.ar Modeling data through a local linear ansatz
lfo.run Modeling data through a local linear ansatz
lfo.test Local linear ansatz
ll_ar Modeling data through a local linear ansatz
logistic Logistic model
low121 Low pass filter
lyap_r Largest Lyapunov exponent
lzo.test Modeling data trough a zeroth order ansatz
notch Notch filter
nrlazy Nonlinear noise reduction
nstep Modeling data through a local linear ansatz
onestep Local linear ansatz
pc Embed using principal components
poincare Poincare section
polyback Backward elimination for a given polynomial
polynom Modeling data trough a polynomial ansatz
polynomp Modeling data trough a polynomial ansatz
polypar Polynomial parameter matrix
project Projective nonlinear noise reduction
rbf Modeling data using a radial basis function ansatz
RTisean The RTisean package
RT_delay Embed using delay coordinates
RT_pca PCA
RT_predict Simple nonlinear prediction
RT_svd PCA
sav_gol Savitzky-Golay filter
surrogates Making surrogate data
timerev Time reversal asymmetry statistic
wiener1 Wiener filter
wiener2 Wiener filter
xcor Cross correlations
xzero Zeroth order model
zeroth Modeling data trough a zeroth order ansatz