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.

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

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Files

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

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