LPRelevance: Relevance-Integrated Statistical Inference Engine

Provide 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 (2021, <doi:10.48550/arXiv.2004.09588>).

Version: 3.3
Depends: R (≥ 4.0.3), stats, BayesGOF, MASS
Imports: leaps, locfdr, Bolstad2, reshape2, ggplot2, polynom, glmnet, caret
Published: 2022-05-18
DOI: 10.32614/CRAN.package.LPRelevance
Author: Subhadeep Mukhopadhyay, Kaijun Wang
Maintainer: Kaijun Wang <kaijunwang.19 at gmail.com>
License: GPL-2
NeedsCompilation: no
CRAN checks: LPRelevance results


Reference manual: LPRelevance.pdf


Package source: LPRelevance_3.3.tar.gz
Windows binaries: r-devel: LPRelevance_3.3.zip, r-release: LPRelevance_3.3.zip, r-oldrel: LPRelevance_3.3.zip
macOS binaries: r-release (arm64): LPRelevance_3.3.tgz, r-oldrel (arm64): LPRelevance_3.3.tgz, r-release (x86_64): LPRelevance_3.3.tgz, r-oldrel (x86_64): LPRelevance_3.3.tgz
Old sources: LPRelevance archive


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