bioregion

This R package gathers a comprehensive set of algorithms to perform bioregionalization analyses.

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

:arrow_double_down: Installation

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

From the CRAN

install.packages("bioregion")

or from GitHub

# install.packages("pak")
# pak::pak("bioRgeo/bioregion")
install.packages("bioregion", repos = c('https://biorgeo.r-universe.dev', 'https://cloud.r-project.org'))

:scroll: Vignettes

We wrote several vignettes that will help you using the bioregion R package. Vignettes available are the following ones:

• 0. A note on terminology
• 1. Installation of the executable binary files
• 2. Matrix and network formats
• 3. Pairwise similarity/dissimilarity metrics
• 4.1 Hierarchical clustering
• 4.2 Non-hierarchical clustering
• 4.3 Network clustering
• 4.4 Microbenchmark
• 5.1 Visualization
• 5.2 Summary metrics
• 5.3 Compare bioregionalizations

:desktop_computer: Functions

An overview of all functions and data is given here.

:bug: Find a bug?

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

Dependencies

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

Citation

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