c(person("Bernard W.","Silverman",role="aut"),
person("Ludger","Evers",role="aut",
email = "ludger@stats.gla.ac.uk"),
person("Kan","Xu",role="aut"),
person("Peter","Carbonetto",role=c("aut","cre"),
email = "peter.carbonetto@gmail.com"),
person("Matthew","Stephens",role="aut"))
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.
Bernard W. Silverman [aut],
Ludger Evers [aut],
Kan Xu [aut],
Peter Carbonetto [aut, cre],
Matthew Stephens [aut]

car, systemfit, sem, lmtest, sandwich, gdata

car, systemfit, sem, lmtest, sandwich, gdata, RCurl, XML, tis

car, systemfit, sem, lmtest, sandwich, gdata, RCurl, XML,
tis, knitr

Yves Croissant <yves.croissant@let.ish-lyon.cnrs.fr> and Spencer Graves
Data sets for econometrics, including political science.

car, systemfit, sem, lmtest, sandwich, gdata, RCurl, XML,
tis, Ecdat, fda
Functions to update datasets in Ecdat and to create,
manipulate, plot and analyze those and similar datasets.

BMA, car, DescTools, Ecdat, maps, grid, gridBase, pryr
fda, gdata, RCurl, XML, tis, jpeg, MASS, TeachingDemos

fda, gdata, RCurl, XML, tis, jpeg, MASS, TeachingDemos,
stringi

fda, gdata, RCurl, XML, tis, jpeg, MASS, TeachingDemos,
stringi, methods

BMA, car, DescTools, Ecdat, maps, grid, gridBase, pryr, knitr
Functions to update data sets in Ecdat and to create,
manipulate, plot and analyze those and similar data sets.