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Robust inference methods for fixed-effect and random-effects models of meta-analysis are implementable. The robust methods are developed using the density power divergence that is a robust estimating criterion developed in machine learning theory, and can effectively circumvent biases and misleading results caused by influential outliers. The density power divergence is originally introduced by Basu et al. (1998) <doi:10.1093/biomet/85.3.549>, and the meta-analysis methods are developed by Noma et al. (2022) <forthcoming>.
| Version: | 1.2-1 |
| Depends: | R (≥ 3.5.0) |
| Imports: | stats, metafor |
| Published: | 2023-11-08 |
| Author: | Hisashi Noma [aut, cre], Shonosuke Sugasawa [aut], Toshi A. Furukawa [aut] |
| Maintainer: | Hisashi Noma <noma at ism.ac.jp> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Materials: | NEWS |
| In views: | MetaAnalysis |
| CRAN checks: | robustmeta results |
| Reference manual: | robustmeta.pdf |
| Package source: | robustmeta_1.2-1.tar.gz |
| Windows binaries: | r-devel: robustmeta_1.2-1.zip, r-release: robustmeta_1.2-1.zip, r-oldrel: robustmeta_1.2-1.zip |
| macOS binaries: | r-release (arm64): robustmeta_1.2-1.tgz, r-oldrel (arm64): robustmeta_1.2-1.tgz, r-release (x86_64): robustmeta_1.2-1.tgz, r-oldrel (x86_64): robustmeta_1.2-1.tgz |
| Old sources: | robustmeta archive |
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