Basic Wavelet Routines for One-, Two-, and Three-Dimensional Signal Processing
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Updated
Jun 3, 2024 - R
Basic Wavelet Routines for One-, Two-, and Three-Dimensional Signal Processing
Study of time-frequency representations in the presence of heteroscedastic dependent noise
Development version of the TrendLSW R package
Estimation of Hurst parameter of a fractional Gaussian noise on the basis of the modified Whittle maximum likelihood estimator in presence of outliers or an additive noise
Discrete Wavelet Transform using Fractional Spline Wavelets in R
R package to implement 2D wavelet decompositions for irregularly spaced and irregularly shaped data
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