Comparison of Bioregionalisation Methods.

bioregion

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

Bioregionalisation 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("devtools")
devtools::install_github("bioRgeo/bioregion")

:scroll: Vignettes

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

• 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 Compare partitions

Alternatively, if you prefer to view the vignettes in R, you can install the package with build_vignettes = TRUE. But be aware that some vignettes can be slow to generate.

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

vignette("bioregion")

: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!

References and dependencies

bioregion depends on ape, bipartite, cluster, data.table, dbscan, dynamicTreeCut, earth, fastcluster, ggplot2, grDevices, igraph, mathjaxr, Matrix, Rcpp, Rdpack, rlang, rmarkdown, segmented,sf, stats, tidyr and utils.