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ThisR package gathers a comprehensive set of algorithms to performbioregionalization analyses.

Bioregionalization methods can be based on hierarchical clusteringalgorithms, non-hierarchical clustering algorithms or networkalgorithms.

The package can be installed with the following command line in Rsession:

From the CRAN

install.packages("bioregion")

or from GitHub

# install.packages("devtools")devtools::install_github("bioRgeo/bioregion")

We wrote several vignettes that will help you using thebioregion Rpackage. Vignettes available are the following ones:

• 1. Installation of the executable binaryfiles
• 2. Matrix and networkformats
• 3. Pairwise similarity/dissimilaritymetrics
• 4.1 Hierarchicalclustering
• 4.2 Non-hierarchicalclustering
• 4.3 Networkclustering
• 4.4Microbenchmark
• 5.1Visualization
• 5.2 Summarymetrics
• 5.3 Comparebioregionalizations

Alternatively, if you prefer to view the vignettes in R, you can installthe package withbuild_vignettes = TRUE. But be aware that somevignettes can be slow to generate.

remotes::install_github("bioRgeo/bioregion",dependencies=TRUE,upgrade="ask",build_vignettes=TRUE)vignette("bioregion")

An overview of all functions and data is givenhere.

Thank you for finding it. Head over to the GitHub Issues tab and let usknow about it. Alternatively, you can also send us an e-mail. We willtry to get to it as soon as we can!

bioregion depends onape,apcluster,bipartite,cluster,data.table,dbscan,dynamicTreeCut,fastcluster,fastkmedoids,ggplot2,grDevices,httr,igraph,mathjaxr,Matrix,phangorn,rcartocolor,Rdpack,rlang,rmarkdown,segmented,sf,stats,tidyr andutils.

Denelle P, Leroy B & Lenormand M (2025)Bioregionalization analyseswith the bioregion Rpackage.Methods in Ecology and Evolution 16, 496-506.