LFDREmpiricalBayes: Estimating Local False Discovery Rates Using Empirical Bayes Methods

New empirical Bayes methods aiming at analyzing the association of single nucleotide polymorphisms (SNPs) to some particular disease are implemented in this package. The package uses local false discovery rate (LFDR) estimates of SNPs within a sample population defined as a "reference class" and discovers if SNPs are associated with the corresponding disease. Although SNPs are used throughout this document, other biological data such as protein data and other gene data can be used. Karimnezhad, Ali and Bickel, D. R. (2016) <http://hdl.handle.net/10393/34889>.

Version: 1.0
Depends: R (≥ 2.14.2)
Imports: matrixStats, stats, R6
Suggests: LFDR.MLE, testthat
Published: 2017-09-27
Author: Ali Karimnezhad, Johnary Kim, Anna Akpawu, Justin Chitpin and David R Bickel
Maintainer: Ali Karimnezhad <ali_karimnezhad at yahoo.com>
License: GPL-3
URL: https://davidbickel.com
NeedsCompilation: no
Materials: NEWS
CRAN checks: LFDREmpiricalBayes results


Reference manual: LFDREmpiricalBayes.pdf
Vignettes: LFDREmpiricalBayes
Package source: LFDREmpiricalBayes_1.0.tar.gz
Windows binaries: r-devel: LFDREmpiricalBayes_1.0.zip, r-release: LFDREmpiricalBayes_1.0.zip, r-oldrel: LFDREmpiricalBayes_1.0.zip
OS X El Capitan binaries: r-release: LFDREmpiricalBayes_1.0.tgz
OS X Mavericks binaries: r-oldrel: LFDREmpiricalBayes_1.0.tgz


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