This package carries out 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.
Author  Bernard W. Silverman 
Date of publication  20121029 08:57:00 
Maintainer  Ludger Evers <ludger@stats.gla.ac.uk> 
License  GPL (>= 2) 
Version  1.3.2 
Package repository  View on CRAN 
Installation  Install the latest version of this package by entering the following in R:

All man pages Function index File listing
Man pages  

beta.cauchy: Function beta for the quasiCauchy prior  
beta.laplace: Function beta for the Laplace prior  
ebayesthresh: Empirical Bayes thresholding on a sequence  
ebayesthresh.wavelet: Empirical Bayes thresholding on the levels of a wavelet...  
isotone: Weighted least squares monotone regression  
postmean: Posterior mean estimator  
postmed: Posterior median estimator  
tfromw: Find threshold from mixing weight  
tfromx: Find threshold from data  
threshld: Threshold data with hard or soft thresholding  
vecbinsolv: Solve systems of nonlinear equations based on a monotonic...  
wandafromx: Find weight and scale factor from data if Laplace prior is...  
wfromt: Mixing weight from posterior median threshold  
wfromx: Find Empirical Bayes weight from data  
wmonfromx: Find monotone Empirical Bayes weights from data  
zetafromx: Estimation of a parameter in the prior weight sequence in the... 
Functions 

Files  

NAMESPACE
 
man
 
man/tfromw.Rd  
man/threshld.Rd  
man/isotone.Rd  
man/ebayesthresh.wavelet.Rd  
man/zetafromx.Rd  
man/beta.laplace.Rd  
man/beta.cauchy.Rd  
man/wmonfromx.Rd  
man/postmed.Rd  
man/wfromx.Rd  
man/postmean.Rd  
man/wfromt.Rd  
man/wandafromx.Rd  
man/tfromx.Rd  
man/ebayesthresh.Rd  
man/vecbinsolv.Rd  
DESCRIPTION
 
MD5
 
R
 
R/beta.cauchy.R  
R/postmean.laplace.R  
R/cauchy.medzero.R  
R/vecbinsolv.R  
R/beta.laplace.R  
R/threshld.R  
R/postmean.R  
R/zetafromx.R  
R/ebayesthresh.wavelet.R  
R/laplace.threshzero.R  
R/tfromw.R  
R/wfromx.R  
R/wfromt.R  
R/ebayesthresh.wavelet.dwt.R  
R/postmed.R  
R/ebayesthresh.R  
R/negloglik.laplace.R  
R/postmed.laplace.R  
R/wmonfromx.R  
R/postmean.cauchy.R  
R/wandafromx.R  
R/postmed.cauchy.R  
R/isotone.R  
R/cauchy.threshzero.R  
R/ebayesthresh.wavelet.splus.R  
R/tfromx.R  
R/ebayesthresh.wavelet.wd.R 
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