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Computational tools for outlier detection and influence diagnostics in meta-analysis (Noma et al. (2025) <doi:10.1101/2025.09.18.25336125>). Bootstrap distributions of influence statistics are computed, and explicit thresholds for identifying outliers are provided. These methods can also be applied to the analysis of influential centers or regions in multicenter or multiregional clinical trials (Aoki and Noma (2021) <doi:10.1080/24709360.2021.1921944>, Nakamura and Noma (2021) <doi:10.5691/jjb.41.117>).
Version: | 2.1-1 |
Imports: | stats, metafor, MASS |
Published: | 2025-09-20 |
DOI: | 10.32614/CRAN.package.boutliers |
Author: | Hisashi Noma [aut, cre], Kazushi Maruo [aut], Masahiko Gosho [aut] |
Maintainer: | Hisashi Noma <noma at ism.ac.jp> |
License: | GPL-3 |
NeedsCompilation: | no |
Materials: | README, NEWS |
In views: | MetaAnalysis |
CRAN checks: | boutliers results |
Reference manual: | boutliers.html , boutliers.pdf |
Package source: | boutliers_2.1-1.tar.gz |
Windows binaries: | r-devel: boutliers_2.1-1.zip, r-release: boutliers_2.1-1.zip, r-oldrel: boutliers_2.1-1.zip |
macOS binaries: | r-release (arm64): boutliers_2.1-1.tgz, r-oldrel (arm64): boutliers_2.1-1.tgz, r-release (x86_64): boutliers_2.1-1.tgz, r-oldrel (x86_64): boutliers_2.1-1.tgz |
Old sources: | boutliers archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.