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A robust and powerful empirical Bayesian approach is developed for replicability analysis of two large-scale experimental studies. The method controls the false discovery rate by using the joint local false discovery rate based on the replicability null as the test statistic. An EM algorithm combined with a shape constraint nonparametric method is used to estimate unknown parameters and functions. [Li, Y. et al., (2023), <https://www.biorxiv.org/content/10.1101/2023.05.30.542607v1>].
| Version: | 1.0.3 |
| Depends: | Rcpp (≥ 1.0.9), qvalue |
| LinkingTo: | Rcpp, RcppArmadillo |
| Published: | 2023-08-15 |
| Author: | Yan Li [aut, cre, cph], Xiang Zhou [aut], Rui Chen [aut], Xianyang Zhang [aut], Hongyuan Cao [aut, ctb] |
| Maintainer: | Yan Li <yanli_ at jlu.edu.cn> |
| License: | GPL-3 |
| NeedsCompilation: | yes |
| CRAN checks: | STAREG results |
| Reference manual: | STAREG.pdf |
| Package source: | STAREG_1.0.3.tar.gz |
| Windows binaries: | r-devel: STAREG_1.0.3.zip, r-release: STAREG_1.0.3.zip, r-oldrel: STAREG_1.0.3.zip |
| macOS binaries: | r-release (arm64): STAREG_1.0.3.tgz, r-oldrel (arm64): STAREG_1.0.3.tgz, r-release (x86_64): STAREG_1.0.3.tgz, r-oldrel (x86_64): STAREG_1.0.3.tgz |
| Old sources: | STAREG archive |
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