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.
| Version: | 1.1 |
| Depends: | R (≥ 3.0) |
| Imports: | splines, stats |
| Suggests: | cowplot, ggplot2, knitr, rmarkdown |
| Published: | 2019-02-08 |
| Author: | Bradley Efron [aut], Balasubramanian Narasimhan [aut, cre] |
| Maintainer: | Balasubramanian Narasimhan <naras at stat.Stanford.EDU> |
| BugReports: | https://github.com/bnaras/deconvolveR/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://bnaras.github.io/deconvolveR |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | deconvolveR results |
| Reference manual: | deconvolveR.pdf |
| Vignettes: |
Empirical Bayes Deconvolution |
| Package source: | deconvolveR_1.1.tar.gz |
| Windows binaries: | r-devel: deconvolveR_1.1.zip, r-release: deconvolveR_1.1.zip, r-oldrel: deconvolveR_1.1.zip |
| macOS binaries: | r-release: deconvolveR_1.1.tgz, r-oldrel: deconvolveR_1.1.tgz |
| Old sources: | deconvolveR archive |
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