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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "smashr" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'smashr: Smoothing by Adaptive Shrinkage'
version: 1.3-12
identifiers:
- type: doi
  value: 10.32614/CRAN.package.smashr
abstract: 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:
- family-names: Xing
  given-names: Zhengrong
- family-names: Stephens
  given-names: Matthew
- family-names: Carbonetto
  given-names: Peter
  email: pcarbo@uchicago.edu
preferred-citation:
  type: article
  title: Flexible signal denoising via flexible empirical Bayes shrinkage
  authors:
  - family-names: Xing
    given-names: Zhengrong
  - family-names: Carbonetto
    given-names: Peter
    email: pcarbo@uchicago.edu
  - family-names: Stephens
    given-names: Matthew
  journal: Journal of Machine Learning Research
  volume: '22'
  issue: '93'
  year: '2021'
  url: https://jmlr.org/papers/v22/19-042.html
  start: '1'
  end: '28'
repository: https://stephenslab.r-universe.dev
repository-code: https://github.com/stephenslab/smashr
commit: e06efa41a3a523833fc0ee743a34cb02da0f26a7
url: https://github.com/stephenslab/smashr
date-released: '2025-12-09'
contact:
- family-names: Carbonetto
  given-names: Peter
  email: pcarbo@uchicago.edu