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
DOI: NA
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:

Reference manual: STAREG.pdf

Downloads:

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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