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glmpermu: Permutation-Based Inference for Generalized Linear Models

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

Documentation:

Reference manual: glmpermu.pdf

Downloads:

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

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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.