R package waveslim: Basic wavelet routines for one-, two- and three-dimensional signal processing. Basic wavelet routines for time series (1D), image (2D) and array (3D) analysis. The code provided here is based on wavelet methodology developed in Percival and Walden (2000); Gencay, Selcuk and Whitcher (2001); the dual-tree complex wavelet transform (DTCWT) from Kingsbury (1999, 2001) as implemented by Selesnick; and Hilbert wavelet pairs (Selesnick 2001, 2002). All figures in chapters 4-7 of GSW (2001) are reproducible using this package and R code available at the book website(s) below.
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References in zbMATH (referenced in 9 articles )
Showing results 1 to 9 of 9.
- Zhao, Xin; Barber, Stuart; Taylor, Charles C.; Milan, Zoka: Interval forecasts based on regression trees for streaming data (2021)
- Jörg Polzehl, Kostas Papafitsoros, Karsten Tabelow: Patch-Wise Adaptive Weights Smoothing in R (2020) not zbMATH
- Sophie Achard and Irène Gannaz: Wavelet-Based and Fourier-Based Multivariate Whittle Estimation: multiwave (2019) not zbMATH
- Zhao, Xin; Barber, Stuart; Taylor, Charles C.; Milan, Zoka: Classification tree methods for panel data using wavelet-transformed time series (2018)
- McGinnity, K.; Varbanov, R.; Chicken, E.: Cross-validated wavelet block thresholding for non-Gaussian errors (2017)
- Carl, Gudrun; Kühn, Ingolf: Analyzing spatial ecological data using linear regression and wavelet analysis (2008)
- Jörg Polzehl; Karsten Tabelow: Adaptive Smoothing of Digital Images: The R Package adimpro (2007) not zbMATH
- Marc Raimondo; Michael Stewart: The WaveD Transform in R: Performs Fast Translation-Invariant Wavelet Deconvolution (2007) not zbMATH
- Iain Johnstone; Bernard Silverman: EbayesThresh: R Programs for Empirical Bayes Thresholding (2005) not zbMATH