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.


An unknown prior density $g(\theta)$ has yielded (unobservable) $\Theta_1, \Theta_2,\ldots,\Theta_N$, and each $\Theta_i$ produces an observation $X_i$ from an exponential family. deconvolveR is an R package for estimating prior distribution $g(\theta)$ from the data using Empirical Bayes deconvolution.

The current package is still under construction but will soon appear on CRAN along with a manuscript. Meanwhile, you can reproduce many examples by installing the package in R thus:

devtools::install_github("bnaras/deconvolveR")
library(deconvolveR)
vignette("deconvolution")

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Reference manual

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install.packages("deconvolveR")

1.0-3 by Balasubramanian Narasimhan, 3 months ago


http://github.com/bnaras/deconvolveR


Report a bug at http://github.com/bnaras/deconvolveR/issues


Browse source code at https://github.com/cran/deconvolveR


Authors: Bradley Efron [aut], Balasubramanian Narasimhan [aut, cre]


Documentation:   PDF Manual  


GPL (>= 2) license


Imports splines, stats

Suggests cowplot, ggplot2, knitr, rmarkdown


See at CRAN