ebnm: Solve the Empirical Bayes Normal Means Problem

Provides simple, fast, and stable functions to fit the normal means model using empirical Bayes. For available models and details, see function ebnm(). A detailed introduction to the package is provided by Willwerscheid and Stephens (2021) <doi:10.48550/arXiv.2110.00152>.

Version: 1.1-2
Depends: R (≥ 3.3.0)
Imports: stats, ashr, mixsqp, truncnorm, trust, horseshoe, deconvolveR, magrittr, rlang, dplyr, ggplot2
Suggests: testthat, numDeriv, REBayes, knitr, rmarkdown, cowplot
Published: 2023-10-12
Author: Jason Willwerscheid [aut], Matthew Stephens [aut], Peter Carbonetto [aut, cre], Andrew Goldstein [ctb], Yusha Liu [ctb]
Maintainer: Peter Carbonetto <peter.carbonetto at gmail.com>
BugReports: https://github.com/stephenslab/ebnm/issues
License: GPL (≥ 3)
URL: https://github.com/stephenslab/ebnm
NeedsCompilation: no
Citation: ebnm citation info
Materials: README
CRAN checks: ebnm results

Documentation:

Reference manual: ebnm.pdf
Vignettes: Getting started with the ebnm package

Downloads:

Package source: ebnm_1.1-2.tar.gz
Windows binaries: r-devel: ebnm_1.1-2.zip, r-release: ebnm_1.1-2.zip, r-oldrel: ebnm_1.1-2.zip
macOS binaries: r-release (arm64): ebnm_1.1-2.tgz, r-oldrel (arm64): ebnm_1.1-2.tgz, r-release (x86_64): ebnm_1.1-2.tgz
Old sources: ebnm archive

Reverse dependencies:

Reverse depends: flashier
Reverse suggests: mashr

Linking:

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