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. Details and examples are in the paper by Narasimhan and Efron (2020, <doi:10.18637/jss.v094.i11>).

Version: 1.2-1
Depends: R (≥ 3.0)
Imports: splines, stats
Suggests: cowplot, ggplot2, knitr, rmarkdown
Published: 2020-08-30
DOI: 10.32614/CRAN.package.deconvolveR
Author: Bradley Efron [aut], Balasubramanian Narasimhan [aut, cre]
Maintainer: Balasubramanian Narasimhan <naras at stat.Stanford.EDU>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Citation: deconvolveR citation info
Materials: README NEWS
CRAN checks: deconvolveR results


Reference manual: deconvolveR.pdf
Vignettes: Empirical Bayes Deconvolution


Package source: deconvolveR_1.2-1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): deconvolveR_1.2-1.tgz, r-oldrel (arm64): deconvolveR_1.2-1.tgz, r-release (x86_64): deconvolveR_1.2-1.tgz, r-oldrel (x86_64): deconvolveR_1.2-1.tgz
Old sources: deconvolveR archive

Reverse dependencies:

Reverse imports: ebnm


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