glmm.hp: Hierarchical Partitioning of Marginal R2 for Generalized Mixed-Effect Models

Conducts hierarchical partitioning to calculate individual contributions of each fixed effects towards marginal R2 for generalized mixed-effect model based on output of r.squaredGLMM() in 'MuMIn', applying the algorithm of Lai J.,Zou Y., Zhang J.,Peres-Neto P.(2022) Generalizing hierarchical and variation partitioning in multiple regression and canonical analyses using the rdacca.hp R package.Methods in Ecology and Evolution,13:782-788<doi:10.1111/2041-210X.13800>.

Version: 0.0-4
Depends: R (≥ 3.4.0), MuMIn, ggplot2
Imports: lme4
Published: 2022-05-01
Author: Jiangshan Lai ORCID iD [aut, cre], Kim Nimon [aut]
Maintainer: Jiangshan Lai <lai at>
License: GPL-2 | GPL-3 [expanded from: GPL]
NeedsCompilation: no
CRAN checks: glmm.hp results


Reference manual: glmm.hp.pdf


Package source: glmm.hp_0.0-4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): glmm.hp_0.0-4.tgz, r-oldrel (arm64): glmm.hp_0.0-4.tgz, r-release (x86_64): glmm.hp_0.0-4.tgz, r-oldrel (x86_64): glmm.hp_0.0-4.tgz
Old sources: glmm.hp archive


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