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

Conducts hierarchical partitioning to calculate individual contributions of each predictor (fixed effects) towards marginal R2 for generalized linear mixed-effect model (including lm, glm and glmm) based on output of r.squaredGLMM() in 'MuMIn', applying the algorithm of Lai J.,Zou Y., Zhang S.,Zhang X.,Mao L.(2022)glmm.hp: an R package for computing individual effect of predictors in generalized linear mixed models.Journal of Plant Ecology,15(6)1302-1307<doi:10.1093/jpe/rtac096>.

Version: 0.1-3
Depends: R (≥ 3.4.0), MuMIn, ggplot2, vegan
Imports: lme4
Published: 2024-05-14
DOI: 10.32614/CRAN.package.glmm.hp
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
Citation: glmm.hp citation info
CRAN checks: glmm.hp results


Reference manual: glmm.hp.pdf


Package source: glmm.hp_0.1-3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): glmm.hp_0.1-3.tgz, r-oldrel (arm64): glmm.hp_0.1-3.tgz, r-release (x86_64): glmm.hp_0.1-3.tgz, r-oldrel (x86_64): glmm.hp_0.1-3.tgz
Old sources: glmm.hp archive


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