partR2: Partitioning R2 in GLMMs

Partitioning the R2 of GLMMs into variation explained by each predictor and combination of predictors using semi-partial (part) R2 and inclusive R2. Methods are based on the R2 for GLMMs described in Nakagawa & Schielzeth (2013) <doi:10.1111/j.2041-210x.2012.00261.x> and Nakagawa, Johnson & Schielzeth (2017) <doi:10.1098/rsif.2017.0213>.

Version: 0.9.1
Depends: R (≥ 3.5.0)
Imports: methods, stats, lme4 (≥ 1.1-21), pbapply (≥ 1.4-2), dplyr (≥ 0.8.3), purrr (≥ 0.3.3), rlang (≥ 0.4.2), tibble (≥ 2.1.3), magrittr (≥ 1.5), ggplot2 (≥ 3.3.0), tidyr (≥ 1.1)
Suggests: testthat, future, furrr, knitr, rmarkdown, patchwork, covr
Published: 2021-01-18
Author: Martin A. Stoffel [aut, cre], Shinichi Nakagawa [aut], Holger Schielzeth [aut]
Maintainer: Martin A. Stoffel <martin.adam.stoffel at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Citation: partR2 citation info
Materials: README NEWS
CRAN checks: partR2 results


Reference manual: partR2.pdf
Vignettes: Using partR2
Package source: partR2_0.9.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: partR2_0.9.1.tgz, r-oldrel: partR2_0.9.1.tgz


Please use the canonical form to link to this page.