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, ).

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:



deconvolveR 1.1

  • Major change to deconv. Now, ignoreZero = TRUE by default makes the thinning correction. Please note this change which is also discussed in the vignette.

deconvolveR 1.0

  • Initial release

Reference manual

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1.2-1 by Balasubramanian Narasimhan, a year ago


Report a bug at https://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