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.
- Version1.4-12
- R versionunknown
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- Last release08/08/2017
Documentation
Team
Peter Carbonetto
Matthew Stephens
Show author detailsRolesAuthorBernard W. Silverman
Show author detailsRolesAuthorLudger Evers
Show author detailsRolesAuthorKan Xu
Show author detailsRolesAuthor
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Last 30 days
This package has been downloaded 78,159 times in the last 30 days. The kind of number that gets mentioned in a keynote speech. Well done! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 2,611 times.
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Last 365 days
This package has been downloaded 650,556 times in the last 365 days. Half a million downloads! This work is now a household name in certain academic circles. The day with the most downloads was Jan 03, 2025 with 2,685 downloads.
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- Imports1 package
- Suggests5 packages
- Reverse Depends5 packages
- Reverse Imports2 packages