Learn R Programming

bioregion (version 1.3.0)

Comparison of Bioregionalization Methods

Description

The main purpose of this package is to propose a transparent methodological framework to compare bioregionalization methods based on hierarchical and non-hierarchical clustering algorithms (Kreft & Jetz (2010) ) and network algorithms (Lenormand et al. (2019) and Leroy et al. (2019) ).

Copy Link

Version

Install

install.packages('bioregion')

Monthly Downloads

488

Version

1.3.0

License

GPL-3

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/bioRgeo/bioregion

Homepage

https://bioRgeo.github.io/bioregion/

Maintainer

Maxime Lenormand

Last Published

January 23rd, 2026

Functions in bioregion (1.3.0)

install_binaries

Download, unzip, check permissions, and test the bioregion's binary files

OPTICS hierarchical clustering algorithm

Create a map of bioregions

Spatial distribution of fish in Europe (data.frame)

Divisive hierarchical clustering based on dissimilarity or beta-diversity

Spatial distribution of fish in Europe

Export a network to GDF format for Gephi visualization

Search for an optimal number of clusters in a list of bioregionalizations

Spatial distribution of fish in Europe (co-occurrence matrix)

Finding communities using the Leiden algorithm

netclu_leadingeigen

Finding communities based on the leading eigenvector of the community matrix

Community structure detection in weighted bipartite networks via modularity optimization

netclu_labelprop

Finding communities based on propagating labels

Community structure detection via greedy optimization of modularity

Infomap community finding

Create a data.frame from a contingency table

OSLOM community finding

Create a contingency table from a data.frame

Louvain community finding

Compute similarity metrics between sites based on species composition

netclu_walktrap

Community structure detection via short random walks

Non-hierarchical clustering: Affinity Propagation

similarity_to_dissimilarity

Convert similarity metrics to dissimilarity metrics

Non-hierarchical clustering: CLARA

Non-hierarchical clustering: K-means analysis

Non-hierarchical clustering: DBSCAN

Non-hierarchical clustering: Partitioning Around Medoids

site_species_metrics

Calculate metrics for sites and species relative to bioregions and chorotypes

Non-hierarchical clustering: CLARANS

Spatial distribution of Mediterranean vegetation (co-occurrence matrix)

site_species_subset

Extract a subset of sites or species from a bioregion.clusters object

Spatial distribution of Mediterranean vegetation (spatial grid)

Spatial distribution of Mediterranean vegetation (data.frame)

dissimilarity_to_similarity

Convert dissimilarity metrics to similarity metrics

bioregion_colors

Add color palettes to bioregion cluster objects

Combine and enrich bioregion (dis)similarity object(s)

compare_bioregionalizations

Compare cluster memberships among multiple bioregionalizations

bioregionalization_metrics

Calculate metrics for one or several bioregionalizations

as_bioregion_pairwise

Convert a matrix or list of matrices to a bioregion (dis)similarity object

Compute dissimilarity metrics (beta-diversity) between sites based on species composition

Cut a hierarchical tree

betapart_to_bioregion

Convert betapart dissimilarity to bioregion dissimilarity (DEPRECATED)

bioregion_metrics

Calculate contribution metrics for bioregions

hclu_hierarclust

Hierarchical clustering based on dissimilarity or beta-diversity