LPRelevance: Relevance-Integrated Statistical Inference Engine

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 (2020, Technical Report).

Version: 3.0
Depends: R (≥ 3.5.0), stats, BayesGOF, MASS
Imports: leaps, locfdr, Bolstad2, reshape2, ggplot2, polynom, glmnet, caret
Published: 2020-03-29
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_3.0.tar.gz
Windows binaries: r-devel: LPRelevance_3.0.zip, r-devel-gcc8: LPRelevance_2.1.zip, r-release: LPRelevance_3.0.zip, r-oldrel: LPRelevance_3.0.zip
OS X binaries: r-release: LPRelevance_3.0.tgz, r-oldrel: LPRelevance_2.1.tgz
Old sources: LPRelevance archive


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