156
edits
No edit summary |
No edit summary |
||
Line 53: | Line 53: | ||
|lazy || Simple nonlinear noise reduction || There is not || FORTRAN | |lazy || Simple nonlinear noise reduction || There is not || FORTRAN | ||
|- | |- | ||
|lfo-ar || Locally first order model vs. global AR model (old ll-ar) || | |lfo-ar || Locally first order model vs. global AR model (old ll-ar) || Does not exist || C | ||
|- | |- | ||
|lfo-run || Iterate a locally first order model (old nstep) || | |lfo-run || Iterate a locally first order model (old nstep) || Does not exist || C | ||
|- | |- | ||
|lfo-test || Test a locally first order model (old onestep) || | |lfo-test || Test a locally first order model (old onestep) || Does not exist || C | ||
|- | |- | ||
|lorenz || Create a Lorenz time series || || | |lorenz || Create a Lorenz time series || || | ||
Line 63: | Line 63: | ||
|low121 || Time domain low pass filter || || | |low121 || Time domain low pass filter || || | ||
|- | |- | ||
|lyap_k || Maximal Lyapunov exponent with the Kantz algorithm || | |lyap_k || Maximal Lyapunov exponent with the Kantz algorithm || Does not exist || C | ||
|- | |- | ||
|lyap_r || Maximal Lyapunov exponent with the Rosenstein algorithm || | |lyap_r || Maximal Lyapunov exponent with the Rosenstein algorithm || Does not exist || C | ||
|- | |- | ||
|lyap_spec || Full spectrum of Lyapunov exponents || | |lyap_spec || Full spectrum of Lyapunov exponents || Does not exist || C | ||
|- | |- | ||
|lzo-gm || Locally zeroth order model vs. global mean || | |lzo-gm || Locally zeroth order model vs. global mean || Does not exist || C | ||
|- | |- | ||
|lzo-run || Iterate a locally zeroth order model || | |lzo-run || Iterate a locally zeroth order model || Does not exist || C | ||
|- | |- | ||
|lzo-test || Test a locally zeroth order model (old zeroth) || | |lzo-test || Test a locally zeroth order model (old zeroth) || Doest not exist || C | ||
|- | |- | ||
|makenoise || Produce noise || || | |makenoise || Produce noise || || | ||
Line 89: | Line 89: | ||
|poincare || Create Poincaré sections || || | |poincare || Create Poincaré sections || || | ||
|- | |- | ||
|polyback || Fit a polynomial model (backward elimination) || | |polyback || Fit a polynomial model (backward elimination) || polyfit, detrend, wpolyfit || I do not know if they work the same way, but it does seem so, written in C | ||
|- | |- | ||
|polynom || Fit a polynomial model || | |polynom || Fit a polynomial model || same as above || same as above | ||
|- | |- | ||
|polynomp || Fit a polynomial model (reads terms to fit from file) || || | |polynomp || Fit a polynomial model (reads terms to fit from file) || same as above || same as above | ||
|- | |- | ||
|polypar || Creates parameter file for polynomp || | |polypar || Creates parameter file for polynomp || same as above || same as above | ||
|- | |- | ||
|predict || Forecast discriminating statistics for surrogates || Does not exist || FORTRAN | |predict || Forecast discriminating statistics for surrogates || Does not exist || FORTRAN | ||
Line 105: | Line 105: | ||
|randomize_spikespec_exp_event || Surrogate data preserving event time power spectrum || || | |randomize_spikespec_exp_event || Surrogate data preserving event time power spectrum || || | ||
|- | |- | ||
|rbf || Radial basis functions fit || | |rbf || Radial basis functions fit || Does not exist || C | ||
|- | |- | ||
|recurr || Creates a recurrence plot || || | |recurr || Creates a recurrence plot || || | ||
Line 141: | Line 141: | ||
|xrecur || Cross-recurrence Plot || || | |xrecur || Cross-recurrence Plot || || | ||
|- | |- | ||
|xzero || Locally zeroth order cross-prediction | |xzero || Locally zeroth order cross-prediction || Does not exist || C | ||
|} | |} |
edits