A framework of methods to perform customized inference at individual level by taking contextual covariates into account. Three main functions are provided in this package: (i) LASER(): it generates specially-designed artificial relevant samples for a given case; (ii) g2l.proc(): computes customized fdr(z|x); and (iii) rEB.proc(): performs empirical Bayes inference based on LASERs. The details can be found in Mukhopadhyay, S., and Wang, K (2019, Technical Report).
Version: | 2.1 |
Depends: | R (≥ 3.5.0), stats, BayesGOF |
Imports: | leaps, locfdr, Bolstad2, reshape2, ggplot2, polynom |
Published: | 2019-10-07 |
Author: | Subhadeep Mukhopadhyay, Kaijun Wang |
Maintainer: | Kaijun Wang <kwang2 at fredhutch.org> |
License: | GPL-2 |
NeedsCompilation: | no |
CRAN checks: | LPRelevance results |
Reference manual: | LPRelevance.pdf |
Package source: | LPRelevance_2.1.tar.gz |
Windows binaries: | r-devel: LPRelevance_2.1.zip, r-release: LPRelevance_2.1.zip, r-oldrel: LPRelevance_2.1.zip |
OS X binaries: | r-release: LPRelevance_2.1.tgz, r-oldrel: LPRelevance_2.1.tgz |
Old sources: | LPRelevance archive |
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