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
Author: Bradley Efron [aut], Balasubramanian Narasimhan [aut, cre]
Maintainer: Balasubramanian Narasimhan <naras at stat.Stanford.EDU>
BugReports: https://github.com/bnaras/deconvolveR/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://bnaras.github.io/deconvolveR/
NeedsCompilation: no
Citation: deconvolveR citation info
Materials: README NEWS
CRAN checks: deconvolveR results

Downloads:

Reference manual: deconvolveR.pdf
Vignettes: Empirical Bayes Deconvolution
Package source: deconvolveR_1.2-1.tar.gz
Windows binaries: r-devel: deconvolveR_1.2-1.zip, r-release: deconvolveR_1.2-1.zip, r-oldrel: deconvolveR_1.2-1.zip
macOS binaries: r-release: deconvolveR_1.2-1.tgz, r-oldrel: deconvolveR_1.2-1.tgz
Old sources: deconvolveR archive

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