# --------------------------------------------
# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
# --------------------------------------------
 
cff-version: 1.2.0
message: 'To cite package "EbayesThresh" in publications use:'
type: software
license: GPL-2.0-or-later
title: 'EbayesThresh: Empirical Bayes Thresholding and Related Methods'
version: 1.4-13
doi: 10.32614/CRAN.package.EbayesThresh
abstract: Empirical Bayes thresholding using the methods developed by I. M. Johnstone
  and B. W. Silverman. The basic problem is to estimate a mean vector given a vector
  of observations of the mean vector plus white noise, taking advantage of possible
  sparsity in the mean vector. Within a Bayesian formulation, the elements of the
  mean vector are modelled as having, independently, a distribution that is a mixture
  of an atom of probability at zero and a suitable heavy-tailed distribution. The
  mixing parameter can be estimated by a marginal maximum likelihood approach. This
  leads to an adaptive thresholding approach on the original data. Extensions of the
  basic method, in particular to wavelet thresholding, are also implemented within
  the package.
authors:
- family-names: Silverman
  given-names: Bernard W.
- family-names: Evers
  given-names: Ludger
  email: ludger@stats.gla.ac.uk
- family-names: Xu
  given-names: Kan
- family-names: Carbonetto
  given-names: Peter
  email: peter.carbonetto@gmail.com
- family-names: Stephens
  given-names: Matthew
repository: https://stephenslab.r-universe.dev
repository-code: https://github.com/stephenslab/EbayesThresh
commit: 924b907ab3d1e9c4b80d45b9da9bd8eb7062cf9f
url: https://github.com/stephenslab/EbayesThresh
date-released: '2017-12-12'
contact:
- family-names: Carbonetto
  given-names: Peter
  email: peter.carbonetto@gmail.com