Empirical Bayes Estimation Strategies R package deconvolveR version 1.2-1 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, <doi:10.18637/jss.v094.i11>). Software Depends: R (>= 3.0) Imports: splines, stats Suggests: cowplot, ggplot2, knitr, rmarkdown Balasubramanian Narasimhan <naras@stat.Stanford.EDU> Comprehensive R Archive Network (CRAN) Bradley Efron [aut], Balasubramanian Narasimhan [aut, cre] GPL (>= 2) 2020-08-30 application/tgz https://CRAN.R-project.org/package=deconvolveR doi:10.32614/CRAN.package.deconvolveR