Package: smashr 1.3-12

Peter Carbonetto

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>.

Authors: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]

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manual.pdf |manual.html✨
card.svg |card.png
smashr/json (API)

# Install 'smashr' in R:
install.packages('smashr', repos = c('https://stephenslab.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/stephenslab/smashr/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • treas - Three-month treasury bill data.

On CRAN:

Conda:

cpp

6.96 score 8 stars 143 scripts 149 downloads 8 exports 16 dependencies

Last updated from:e06efa41a3. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK151
linux-devel-x86_64OK168
source / vignettesOK1685
linux-release-arm64OK156
linux-release-x86_64OK164
macos-release-arm64OK179
macos-release-x86_64OK241
macos-oldrel-arm64OK118
macos-oldrel-x86_64OK246
windows-develOK182
windows-releaseOK142
windows-oldrelOK187
wasm-releaseOK109

Exports:glm.approxreflectreverse.pwavesd_estimate_gasser_etalsmashsmash.gaussmash.poissti.thresh

Dependencies:ashrbitopscaToolsdata.tableetrunctinvgammairlbalatticeMASSMatrixmixsqpRcppRcppArmadilloSQUAREMtruncnormwavethresh

ASH-based wavelet shrinkage for heteroskedastic models

Rendered fromsmashr.Rmdusingknitr::rmarkdownon May 15 2026.

Last update: 2024-07-08
Started: 2018-05-08