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 hevay-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.