AICcPermanova: Model Selection of PERMANOVA Models Using AICc

Provides tools for model selection and model averaging of PerMANOVA models using Akaike Information Criterion corrected for small sample sizes (AICc) and Information Theoretic criteria principles. The package is built around the PERMANOVA analysis from the 'vegan' package and provides a streamlined workflow for generating and comparing models, obtaining model weights, and summarizing results using model averaging approaches. The methods implemented in this package are based on the practical information- theoretic approach described by Burnham, K. P. and Anderson, D. R. (2002) (<doi:10.1007/b97636>).

Version: 0.0.2
Imports: broom, car, data.table, doParallel, dplyr, foreach, furrr, future, parallel, stats, stringr, tidyr, vegan
Suggests: covr, testthat (≥ 3.0.0)
Published: 2023-04-11
DOI: 10.32614/CRAN.package.AICcPermanova
Author: Derek Corcoran [aut, cre]
Maintainer: Derek Corcoran <derek.corcoran.barrios at>
License: MIT + file LICENSE
NeedsCompilation: no
CRAN checks: AICcPermanova results


Reference manual: AICcPermanova.pdf


Package source: AICcPermanova_0.0.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): AICcPermanova_0.0.2.tgz, r-oldrel (arm64): AICcPermanova_0.0.2.tgz, r-release (x86_64): AICcPermanova_0.0.2.tgz, r-oldrel (x86_64): AICcPermanova_0.0.2.tgz
Old sources: AICcPermanova archive


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