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:	0.1-6
Depends:	R (≥ 3.4.0), MuMIn, ggplot2, vegan
Imports:	lme4
Published:	2024-10-26
DOI:	10.32614/CRAN.package.glmm.hp
Author:	Jiangshan Lai [aut, cre], Kim Nimon [aut]
Maintainer:	Jiangshan Lai <lai at njfu.edu.cn>
License:	GPL-2 | GPL-3 [expanded from: GPL]
URL:	https://github.com/laijiangshan/glmm.hp
NeedsCompilation:	no
Citation:	glmm.hp citation info
CRAN checks:	glmm.hp results

Documentation:

Reference manual:

glmm.hp.pdf

Downloads:

Package source:	glmm.hp_0.1-6.tar.gz
Windows binaries:	r-devel: glmm.hp_0.1-6.zip, r-release: glmm.hp_0.1-6.zip, r-oldrel: glmm.hp_0.1-6.zip
macOS binaries:	r-release (arm64): glmm.hp_0.1-6.tgz, r-oldrel (arm64): glmm.hp_0.1-6.tgz, r-release (x86_64): glmm.hp_0.1-6.tgz, r-oldrel (x86_64): glmm.hp_0.1-6.tgz
Old sources:	glmm.hp archive

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