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:	0.0-5
Depends:	R (≥ 3.4.0), MuMIn, ggplot2
Imports:	lme4
Published:	2022-07-18
Author:	Jiangshan Lai [aut, cre], Kim Nimon [aut]
Maintainer:	Jiangshan Lai <lai at ibcas.ac.cn>
License:	GPL-2 | GPL-3 [expanded from: GPL]
URL:	https://github.com/laijiangshan/glmm.hp
NeedsCompilation:	no
CRAN checks:	glmm.hp results

Documentation:

Reference manual:

glmm.hp.pdf

Downloads:

Package source:	glmm.hp_0.0-5.tar.gz
Windows binaries:	r-devel: glmm.hp_0.0-5.zip, r-release: glmm.hp_0.0-5.zip, r-oldrel: glmm.hp_0.0-5.zip
macOS binaries:	r-release (arm64): glmm.hp_0.0-5.tgz, r-oldrel (arm64): glmm.hp_0.0-5.tgz, r-release (x86_64): glmm.hp_0.0-5.tgz, r-oldrel (x86_64): glmm.hp_0.0-5.tgz
Old sources:	glmm.hp archive

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