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EbayesThresh  

Empirical Bayes Thresholding and Related Methods
View on CRAN: Click here


Download and install EbayesThresh package within the R console
Install from CRAN:
install.packages("EbayesThresh")

Install from Github:
library("remotes")
install_github("cran/EbayesThresh")

Install by package version:
library("remotes")
install_version("EbayesThresh", "1.4-12")



Attach the package and use:
library("EbayesThresh")
Maintained by
Peter Carbonetto
[Scholar Profile | Author Map]
First Published: 2005-03-26
Latest Update: 2017-08-08
Description:
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.
How to cite:
Peter Carbonetto (2005). EbayesThresh: Empirical Bayes Thresholding and Related Methods. R package version 1.4-12, https://cran.r-project.org/web/packages/EbayesThresh. Accessed 07 Apr. 2025.
Previous versions and publish date:
(2008-02-16 10:58), 1.3.0 (2005-03-26 22:42), 1.3.1 (2010-05-08 12:04), 1.3.2 (2012-10-29 08:57)
Other packages that cited EbayesThresh R package
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Complete documentation for EbayesThresh
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