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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 ("gmodeling") by deconvolution and Empirical Bayes methods. Details and examples are in the paper by Narasimhan and Efron (2020, <doi:10.18637/jss.v094.i11>).
Package details 


Author  Bradley Efron [aut], Balasubramanian Narasimhan [aut, cre] 
Maintainer  Balasubramanian Narasimhan <naras@stat.Stanford.EDU> 
License  GPL (>= 2) 
Version  1.21 
URL  https://bnaras.github.io/deconvolveR/ 
Package repository  View on CRAN 
Installation 
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