STAREG: An Empirical Bayes Approach for Replicability Analysis Across
Two Studies
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 |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=STAREG
to link to this page.