Package: smashr
Encoding: UTF-8
Type: Package
Maintainer: Peter Carbonetto <pcarbo@uchicago.edu>
Authors@R: c(person("Zhengrong","Xing",role="aut"),
	     person("Matthew","Stephens",role="aut"),
	     person("Kaiqian","Zhang",role="ctb"),
             person("Daniel","Nachun",role="ctb"),
             person("Guy","Nason", role="cph"),
             person("Stuart","Barber",role="cph"), 
             person("Tim","Downie",role="cph"),
             person("Piotr","Frylewicz",role="cph"),
             person("Arne","Kovac",role="cph"),
             person("Todd","Ogden",role="cph"),
             person("Bernard","Silverman",role="cph"),
             person("Peter","Carbonetto",role=c("aut","cre"),
	            email="pcarbo@uchicago.edu"))
Title: Smoothing by Adaptive Shrinkage
Version: 1.3-12
Date: 2025-12-09
Description: 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>.
License: GPL (>= 3)
Copyright: file COPYRIGHTS
Depends: R (>= 3.1.1),
Imports: utils, stats, data.table, caTools, wavethresh, ashr, Rcpp (>=
        1.1.0)
Suggests: knitr, rmarkdown, MASS, EbayesThresh, testthat
LinkingTo: Rcpp
NeedsCompilation: yes
LazyData: true
URL: https://github.com/stephenslab/smashr
BugReports: https://github.com/stephenslab/smashr/issues
RoxygenNote: 7.3.1
Packaged: 2025-12-09 16:54:02 UTC; pcarbo
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]
Repository: CRAN
Date/Publication: 2025-12-15 20:10:02 UTC
Built: R 4.6.0; x86_64-apple-darwin20; 2026-04-28 11:12:44 UTC; unix
Archs: smashr.so.dSYM
