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

Documentation:

Reference manual:	deconvolveR.pdf
Vignettes:	Empirical Bayes Deconvolution

Downloads:

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 (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

Linking:

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