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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>.

Version: 1.3-12
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
Published: 2025-12-15
DOI: 10.32614/CRAN.package.smashr
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 at uchicago.edu>
BugReports: https://github.com/stephenslab/smashr/issues
License: GPL (≥ 3)
Copyright: file COPYRIGHTS
smashr copyright details
URL: https://github.com/stephenslab/smashr
NeedsCompilation: yes
Citation: smashr citation info
Materials: README
CRAN checks: smashr results

Documentation:

Reference manual: smashr.html , smashr.pdf

Downloads:

Package source: smashr_1.3-12.tar.gz
Windows binaries: r-devel: smashr_1.3-12.zip, r-release: not available, r-oldrel: smashr_1.3-12.zip
macOS binaries: r-release (arm64): smashr_1.3-12.tgz, r-oldrel (arm64): smashr_1.3-12.tgz, r-release (x86_64): smashr_1.3-12.tgz, r-oldrel (x86_64): smashr_1.3-12.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=smashr to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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