Smoothing by Adaptive Shrinkage R package smashr version 1.3-12 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>. Software Depends: R (>= 3.1.1), Imports: utils, stats, data.table, caTools, wavethresh, ashr, Rcpp (>= 1.1.0) LinkingTo: Rcpp Suggests: knitr, rmarkdown, MASS, EbayesThresh, testthat Peter Carbonetto <pcarbo@uchicago.edu> Comprehensive R Archive Network (CRAN) 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] GPL (>= 3) 2025-12-15 application/tgz https://CRAN.R-project.org/package=smashr doi:10.32614/CRAN.package.smashr