This R package implements fast, wavelet-based Empirical Bayes shrinkage methods for signal denoising. This includes smoothing Poisson-distributed data and Gaussian-distributed data, with possibly heteroskedastic error. The algorithms implement the methods described in Xing, Carbonetto & Stephens (2021).
If you find a bug, please post an issue.
Copyright (c) 2016-2021, Zhengrong Xing, Peter Carbonetto and Matthew Stephens.
All source code and software in this repository is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. See the LICENSE file for the full text of the license.
If you find that this R package useful for your work, please cite our paper:
Zhengrong Xing, Peter Carbonetto and Matthew Stephens (2021). Flexible signal denoising via flexible empirical Bayes shrinkage. Journal of Machine Learning Research 22(93), 1-28.
Follow these steps to quickly get started using smashr.
R
install.packages("devtools")
library(devtools)
install_github("stephenslab/smashr")
If you are interested in replicating results from the paper, we recommendg installing smashr 1.2-7:
R
install_github("stephenslab/smashr@v1.2-7")
This will build the smashr package without the vignettes. To build with the vignettes, do this instead:
R
install_github("stephenslab/smashr",build_vignettes = TRUE)
We caution that some of the simulation examples may take a long
time to run (20--30 minutes, or possibly longer). Also note that the
install_github
call should also install any missing packages that
are required for smashr to work.
R
library(smashr)
demo("smashr")
R
help(package = "smashr")
vignette("smashr")
This R package was developed by Zhengrong Xing and Matthew Stephens at the University of Chicago, with contributions from Peter Carbonetto.
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