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.
- Version1.2-1
- R versionunknown
- LicenseGPL-3
- Needs compilation?No
- Last release11/08/2023
Documentation
Team
Hisashi Noma
MaintainerShow author detailsShonosuke Sugasawa
Show author detailsRolesAuthorToshi A. Furukawa
Show author detailsRolesAuthor
Insights
Last 30 days
This package has been downloaded 122 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 2 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 1,952 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Jul 23, 2024 with 32 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
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Dependencies
- Imports1 package