WaveLab and reproducible research. WaveLab is a library of Matlab routines for wavelet analysis, wavelet-packet analysis, cosine-packet analysis and matching pursuit. The library is available free of charge over the Internet. Versions are provided for Macintosh, UNIX and Windows machines. WaveLab makes available, in one package, all the code to reproduce all the figures in our published wavelet articles. The interested reader can inspect the source code to see exactly what algorithms were used, how parameters were set in producing our figures, and can then modify the source to produce variations on our results. WaveLab has been developed, in part, because of exhortations by Jon Claerbout of Stanford that computational scientists should engage in “really reproducible” research.

References in zbMATH (referenced in 50 articles )

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  1. Antun, Vegard: Recovering wavelet coefficients from binary samples using fast transforms (2022)
  2. Antun, Vegard; Ryan, Øyvind: On the unification of schemes and software for wavelets on the interval (2021)
  3. Zeng Fung Liew; Shozen Dan; Naoki Saito: WaveletsExt.jl: Extending the boundaries of wavelets in Julia (2021) not zbMATH
  4. Amato, Umberto; Antoniadis, Anestis; De Feis, Italia: Flexible, boundary adapted, nonparametric methods for the estimation of univariate piecewise-smooth functions (2020)
  5. Selesnick, Ivan; Lanza, Alessandro; Morigi, Serena; Sgallari, Fiorella: Non-convex total variation regularization for convex denoising of signals (2020)
  6. Becker, Gabriel; Moore, Sara E.; Lawrence, Michael: trackr: a framework for enhancing discoverability and reproducibility of data visualizations and other artifacts in R (2019)
  7. Petersen, Philipp; Raslan, Mones: Approximation properties of hybrid shearlet-wavelet frames for Sobolev spaces (2019)
  8. Reisenhofer, Rafael; King, Emily J.: Edge, ridge, and blob detection with symmetric molecules (2019)
  9. Carmichael, Iain; Marron, J. S.: Data science vs. statistics: two cultures? (2018)
  10. Schimmack, Manuel; Mercorelli, Paolo: An on-line orthogonal wavelet denoising algorithm for high-resolution surface scans (2018)
  11. Yi, Hua; Xin, Shi-You; Yin, Jun-Feng: A class of algorithms for continuous wavelet transform based on the circulant matrix (2018)
  12. Gong, Tierui; Yang, Zhijia; Wang, Gengshan; Jiao, Ping: Supervised and unsupervised subband adaptive denoising frameworks with polynomial threshold function (2017)
  13. Navarro, Fabien; Saumard, Adrien: Slope heuristics and V-fold model selection in heteroscedastic regression using strongly localized bases (2017)
  14. Storath, Martin; Demaret, Laurent; Massopust, Peter: Signal analysis based on complex wavelet signs (2017)
  15. Hofner, Benjamin; Schmid, Matthias; Edler, Lutz: Reproducible research in statistics: a review and guidelines for the Biometrical Journal (2016)
  16. Mansour, Mohamed F.: Subspace design of compactly supported orthonormal wavelets (2014)
  17. Zhang, Hong; Chen, Lixing; Qu, Yong; Zhao, Guo; Guo, Zhenwei: Support vector regression based on grid-search method for short-term wind power forecasting (2014)
  18. Bigot, Jérémie; Gadat, Sébastien; Klein, Thierry; Marteau, Clément: Intensity estimation of non-homogeneous Poisson processes from shifted trajectories (2013)
  19. Fomel, S., Sava, P., Vlad, I., Liu, Y. and Bashkardin, V.,: Madagascar: open-source software project for multidimensional data analysis and reproducible computational experiments (2013) not zbMATH
  20. Mandelbaum, Avishai; Zeltyn, Sergey: Data-stories about (im)patient customers in tele-queues (2013)

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