deconvolveR: Empirical Bayes Estimation Strategies

Empirical Bayes methods for learning prior distributions from data. An unknown prior distribution (g) has yielded (unobservable) parameters, each of which produces a data point from a parametric exponential family (f). The goal is to estimate the unknown prior ("g-modeling") by deconvolution and Empirical Bayes methods.

Version: 1.0-3
Depends: R (≥ 3.0)
Imports: splines, stats
Suggests: cowplot, ggplot2, knitr, rmarkdown
Published: 2016-12-01
Author: Bradley Efron [aut], Balasubramanian Narasimhan [aut, cre]
Maintainer: Balasubramanian Narasimhan <naras at stat.Stanford.EDU>
BugReports: http://github.com/bnaras/deconvolveR/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: http://github.com/bnaras/deconvolveR
NeedsCompilation: no
Materials: README
CRAN checks: deconvolveR results

Downloads:

Reference manual: deconvolveR.pdf
Vignettes: Empirical Bayes Deconvolution
Package source: deconvolveR_1.0-3.tar.gz
Windows binaries: r-devel: deconvolveR_1.0-3.zip, r-release: deconvolveR_1.0-3.zip, r-oldrel: deconvolveR_1.0-3.zip
OS X Mavericks binaries: r-release: deconvolveR_1.0-3.tgz, r-oldrel: deconvolveR_1.0-3.tgz

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