The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

deconvolveR: 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, <doi:10.18637/jss.v094.i11>).

Version: 1.2-1
Depends: R (≥ 3.0)
Imports: splines, stats
Suggests: cowplot, ggplot2, knitr, rmarkdown
Published: 2020-08-30
DOI: 10.32614/CRAN.package.deconvolveR
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
Citation: deconvolveR citation info
Materials: README NEWS
CRAN checks: deconvolveR results

Documentation:

Reference manual: deconvolveR.pdf
Vignettes: Empirical Bayes Deconvolution

Downloads:

Package source: deconvolveR_1.2-1.tar.gz
Windows binaries: r-devel: deconvolveR_1.2-1.zip, r-release: deconvolveR_1.2-1.zip, r-oldrel: deconvolveR_1.2-1.zip
macOS binaries: r-release (arm64): deconvolveR_1.2-1.tgz, r-oldrel (arm64): deconvolveR_1.2-1.tgz, r-release (x86_64): deconvolveR_1.2-1.tgz, r-oldrel (x86_64): deconvolveR_1.2-1.tgz
Old sources: deconvolveR archive

Reverse dependencies:

Reverse imports: ebnm

Linking:

Please use the canonical form https://CRAN.R-project.org/package=deconvolveR to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.