Package: robustmeta 1.2-1

robustmeta: Robust Inference for Meta-Analysis with Influential Outlying Studies

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>.

Authors:Hisashi Noma [aut, cre], Shonosuke Sugasawa [aut], Toshi A. Furukawa [aut]

robustmeta_1.2-1.tar.gz
robustmeta_1.2-1.zip(r-4.7-any)robustmeta_1.2-1.zip(r-4.6-any)robustmeta_1.2-1.zip(r-4.5-any)
robustmeta_1.2-1.tgz(r-4.6-any)robustmeta_1.2-1.tgz(r-4.5-any)
robustmeta_1.2-1.tar.gz(r-4.7-any)robustmeta_1.2-1.tar.gz(r-4.6-any)
robustmeta_1.2-1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
robustmeta/json (API)

# Install 'robustmeta' in R:
install.packages('robustmeta', repos = c('https://nomahi.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • clbp - Rubinstein et al. (2019)'s chronic low back pain data
  • varenicline - Thomas et al. (2015)'s varenicline data

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 289 downloads 1 exports 9 dependencies

Last updated from:7993550ac4. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK126
source / vignettesOK230
linux-releaseOK129
macos-releaseOK88
macos-oldrelOK114
windows-develOK82
windows-releaseOK94
windows-oldrelOK66
wasm-releaseOK103

Exports:rmeta

Dependencies:digestlatticemathjaxrMatrixmetadatmetafornlmenumDerivpbapply