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 heavytailed 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.
Package details 


Maintainer  
License  GPL (>= 2) 
Version  1.413 
URL  https://github.com/stephenslab/EbayesThresh 
Package repository  View on GitHub 
Installation 
Install the latest version of this package by entering the following in R:

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.