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: | 3.1 |
| Depends: | R (≥ 3.5.0), stats, BayesGOF, MASS |
| Imports: | leaps, locfdr, Bolstad2, reshape2, ggplot2, polynom, glmnet, caret |
| Published: | 2020-05-16 |
| 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_3.1.tar.gz |
| Windows binaries: | r-devel: LPRelevance_3.1.zip, r-release: LPRelevance_3.1.zip, r-oldrel: LPRelevance_3.1.zip |
| macOS binaries: | r-release: LPRelevance_3.1.tgz, r-oldrel: LPRelevance_3.1.tgz |
| Old sources: | LPRelevance archive |
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