S+WAVELETS: An Object-Oriented Toolkit for Wavelet Analysis. S+WAVELETS is an object-oriented toolkit for wavelet analysis of signals, time series, images, and other data. It is a module of the S-Plus language for data analysis, statistics, and scientific computing. The module is oriented towards engineers, mathemeticians, statisticians, and scientists in a broad range of disciplines. S+WAVELETS is being used for applications as diverse as data visualization and analysis, nonparametric statistical estimation, signal and image compression, signal processing, and prototyping of new fast algorithms. The toolkit offers a rich collection of transforms, ranging from the classical discrete wavelet transform to timefrequency decompositions such as wavelet packets and cosine packets. A variety of algorithms and tools for selecting transforms are available, including the Coifman and Wickerhauser ”best basis” algorithm and the Mallat and Zhang ”matching pursuit” algorithm. With over 500 functions, S+WAVELETS provides a complete computing environment for wavelet analysis, allowing the user to manipulate, visualize, synthesize, and analyze wavelet objects. The object-oriented design of S+WAVELETS makes these functions easy to use and provides an organizing framework for wavelet analysis.

References in zbMATH (referenced in 36 articles )

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  1. Qiu, Duo; Bai, Minru; Ng, Michael K.; Zhang, Xiongjun: Nonlocal robust tensor recovery with nonconvex regularization (2021)
  2. Aravkin, Aleksandr; Davis, Damek: Trimmed statistical estimation via variance reduction (2020)
  3. Bertsimas, Dimitris; Pauphilet, Jean; van Parys, Bart: Sparse regression: scalable algorithms and empirical performance (2020)
  4. Mazumder, Rahul; Weng, Haolei: Computing the degrees of freedom of rank-regularized estimators and cousins (2020)
  5. Xie, Dongxiu; Woerdeman, Hugo J.; Xu, An-Bao: Parametrized quasi-soft thresholding operator for compressed sensing and matrix completion (2020)
  6. Chzhen, Evgenii; Hebiri, Mohamed; Salmon, Joseph: On Lasso refitting strategies (2019)
  7. Shen, Lixin; Suter, Bruce W.; Tripp, Erin E.: Structured sparsity promoting functions (2019)
  8. Shi, Yue Yong; Jiao, Yu Ling; Cao, Yong Xiu; Liu, Yan Yan: An alternating direction method of multipliers for MCP-penalized regression with high-dimensional data (2018)
  9. Zhang, Xun; Li, Juelong; Xing, Jianchun; Wang, Ping; Yang, Qiliang; He, Can: A particle swarm optimization technique-based parametric wavelet thresholding function for signal denoising (2017)
  10. Antoniadis, Anestis; Gijbels, Irène; Lambert-Lacroix, Sophie; Poggi, Jean-Michel: Joint estimation and variable selection for mean and dispersion in proper dispersion models (2016)
  11. Woodworth, Joseph; Chartrand, Rick: Compressed sensing recovery via nonconvex shrinkage penalties (2016)
  12. Breheny, Patrick; Huang, Jian: Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors (2015)
  13. Jain, Paras; Tyagi, Vipin: LAPB: locally adaptive patch-based wavelet domain edge-preserving image denoising (2015)
  14. Dalmo, Rune; Bratlie, Jostein: Discrete wavelet compression of ERBS (2014)
  15. Benedetto, John J.; Czaja, Wojciech; Ehler, Martin: Wavelet packets for time-frequency analysis of multispectral imagery (2013)
  16. Da Silva, Ricardo Dutra; Minetto, Rodrigo; Schwartz, William Robson; Pedrini, Helio: Adaptive edge-preserving image denoising using wavelet transforms (2013)
  17. Strawderman, Robert L.; Wells, Martin T.; Schifano, Elizabeth D.: Hierarchical Bayes, maximum a posteriori estimators, and minimax concave penalized likelihood estimation (2013)
  18. Heinen, Dennis; Plonka, Gerlind: Wavelet shrinkage on paths for denoising of scattered data (2012)
  19. Lee, Kichun; Vidakovic, Brani: Semi-supervised wavelet shrinkage (2012)
  20. She, Yiyuan: An iterative algorithm for fitting nonconvex penalized generalized linear models with grouped predictors (2012)

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