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smashr: Smoothing by Adaptive Shrinkage
Fast, wavelet-based Empirical Bayes shrinkage methods for signal denoising, including smoothing Poisson-distributed data and Gaussian-distributed data with possibly heteroskedastic error. The algorithms implement the methods described Z. Xing, P. Carbonetto & M. Stephens (2021) <https://jmlr.org/papers/v22/19-042.html>.
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Package details |
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| Author | Zhengrong Xing [aut], Matthew Stephens [aut], Kaiqian Zhang [ctb], Daniel Nachun [ctb], Guy Nason [cph], Stuart Barber [cph], Tim Downie [cph], Piotr Frylewicz [cph], Arne Kovac [cph], Todd Ogden [cph], Bernard Silverman [cph], Peter Carbonetto [aut, cre] |
| Maintainer | Peter Carbonetto <pcarbo@uchicago.edu> |
| License | GPL (>= 3) |
| Version | 1.3-12 |
| URL | https://github.com/stephenslab/smashr |
| Package repository | View on CRAN |
| Installation |
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