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.

Documentation

Manual: deconvolveR.pdf
Vignette: Empirical Bayes Deconvolution

Maintainer: Balasubramanian Narasimhan <naras at stat.Stanford.EDU>

Author(s): Bradley Efron*, Balasubramanian Narasimhan*

Install package and any missing dependencies by running this line in your R console:

install.packages("deconvolveR")

Depends R (>= 3.0)
Imports splines, stats
Suggests cowplot, ggplot2, knitr, rmarkdown
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Package deconvolveR
Materials
URL http://github.com/bnaras/deconvolveR
Task Views
Version 1.0-3
Published 2016-12-01
License GPL (>= 2)
BugReports http://github.com/bnaras/deconvolveR/issues
SystemRequirements
NeedsCompilation no
Citation
CRAN checks deconvolveR check results
Package source deconvolveR_1.0-3.tar.gz