The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
In practical applications, the assumptions underlying generalized linear models frequently face violations, including incorrect specifications of the outcome variable's distribution or omitted predictors. These deviations can render the results of standard generalized linear models unreliable. As the sample size increases, what might initially appear as minor issues can escalate to critical concerns. To address these challenges, we adopt a permutation-based inference method tailored for generalized linear models. This approach offers robust estimations that effectively counteract the mentioned problems, and its effectiveness remains consistent regardless of the sample size.
| Version: | 0.0.1 |
| Published: | 2024-03-12 |
| DOI: | 10.32614/CRAN.package.glmpermu |
| Author: | Xuekui Zhang [aut, cre], Li Xing [aut], Jing Zhang [aut], Soojeong Kim [aut] |
| Maintainer: | Xuekui Zhang <xuekui at uvic.ca> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| CRAN checks: | glmpermu results |
| Reference manual: | glmpermu.pdf |
| Package source: | glmpermu_0.0.1.tar.gz |
| Windows binaries: | r-devel: glmpermu_0.0.1.zip, r-release: glmpermu_0.0.1.zip, r-oldrel: glmpermu_0.0.1.zip |
| macOS binaries: | r-release (arm64): glmpermu_0.0.1.tgz, r-oldrel (arm64): glmpermu_0.0.1.tgz, r-release (x86_64): glmpermu_0.0.1.tgz, r-oldrel (x86_64): glmpermu_0.0.1.tgz |
Please use the canonical form https://CRAN.R-project.org/package=glmpermu to link to this page.
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.
Health stats visible at Monitor.