EbayesThresh: Empirical Bayes Thresholding and Related Methods
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.
||testthat, knitr, rmarkdown, dplyr, ggplot2
||Bernard W. Silverman [aut],
Ludger Evers [aut],
Kan Xu [aut],
Peter Carbonetto [aut, cre],
Matthew Stephens [aut]
||Peter Carbonetto <peter.carbonetto at gmail.com>
||GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Please use the canonical form
to link to this page.