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.

Install the latest version of this package by entering the following in R:
install.packages("deconvolveR")
AuthorBradley Efron [aut], Balasubramanian Narasimhan [aut, cre]
Date of publication2016-12-01 19:44:32
MaintainerBalasubramanian Narasimhan <naras@stat.Stanford.EDU>
LicenseGPL (>= 2)
Version1.0-3
http://github.com/bnaras/deconvolveR

View on CRAN

Files

inst
inst/doc
inst/doc/deconvolution.Rmd
inst/doc/deconvolution.R
inst/doc/deconvolution.html
NAMESPACE
data
data/surg.rda
data/bardWordCount.rda
data/disjointTheta.rda
R
R/deconvolveR.R R/deconv.R
vignettes
vignettes/deconvolution.Rmd
README.md
MD5
build
build/vignette.rds
DESCRIPTION
man
man/bardWordCount.Rd man/disjointTheta.Rd man/surg.Rd man/deconvolveR.Rd man/deconv.Rd

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