Package: bioregion 1.1.1-1

Maxime Lenormand

bioregion: Comparison of Bioregionalisation Methods

The main purpose of this package is to propose a transparent methodological framework to compare bioregionalisation methods based on hierarchical and non-hierarchical clustering algorithms (Kreft & Jetz (2010) <doi:10.1111/j.1365-2699.2010.02375.x>) and network algorithms (Lenormand et al. (2019) <doi:10.1002/ece3.4718> and Leroy et al. (2019) <doi:10.1111/jbi.13674>).

Authors:Maxime Lenormand [aut, cre], Boris Leroy [aut], Pierre Denelle [aut]

bioregion_1.1.1-1.tar.gz
bioregion_1.1.1-1.tar.gz(r-4.5-noble)bioregion_1.1.1-1.tar.gz(r-4.4-noble)
bioregion_1.1.1-1.tgz(r-4.4-emscripten)bioregion_1.1.1-1.tgz(r-4.3-emscripten)
bioregion.pdf |bioregion.html✨
bioregion/json (API)
NEWS

# Install 'bioregion' in R:

install.packages('bioregion', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/biorgeo/bioregion/issues

Uses libs:

c++– GNU Standard C++ Library v3

Datasets:

fishdf - Spatial distribution of fish in Europe
fishmat - Spatial distribution of fish in Europe
fishsf - Spatial distribution of fish in Europe
vegedf - Spatial distribution of Mediterranean vegetation
vegemat - Spatial distribution of Mediterranean vegetation
vegesf - Spatial distribution of Mediterranean vegetation

3.18 score 10 scripts 391 downloads 30 exports 91 dependencies

Last updated 9 days agofrom:8d3fcd4039. Checks:OK: 2. Indexed: no.

Target	Result	Date
Doc / Vignettes	OK	Nov 13 2024
R-4.5-linux-x86_64	OK	Nov 13 2024

Exports:compare_partitions cut_tree dissimilarity dissimilarity_to_similarity find_optimal_n hclu_diana hclu_hierarclust hclu_optics install_binaries map_clusters mat_to_net net_to_mat netclu_beckett netclu_greedy netclu_infomap netclu_labelprop netclu_leadingeigen netclu_leiden netclu_louvain netclu_oslom netclu_walktrap nhclu_clara nhclu_clarans nhclu_dbscan nhclu_kmeans nhclu_pam partition_metrics similarity similarity_to_dissimilarity subset_node

Dependencies:ape base64enc bipartite bslib cachem class classInt cli cluster coda colorspace cpp11 data.table DBI dbscan digest dotCall64 dplyr dynamicTreeCut e1071 evaluate fansi farver fastcluster fastkmedoids fastmap fields fontawesome fs generics ggplot2 glue gtable highr htmltools igraph isoband jquerylib jsonlite KernSmooth knitr labeling lattice lifecycle magrittr maps MASS mathjaxr Matrix memoise mgcv mime munsell network nlme permute pillar pkgconfig proxy purrr R6 rappdirs rbibutils RColorBrewer Rcpp Rdpack rlang rmarkdown s2 sass scales segmented sf sna spam statnet.common stringi stringr tibble tidyr tidyselect tinytex units utf8 vctrs vegan viridisLite withr wk xfun yaml

Tutorial for bioregion

Maxime Lenormand, Boris Leroy and Pierre Denelle

Rendered frombioregion.Rmdusingknitr::rmarkdownon Nov 13 2024.

Last update: 2024-04-20
Started: 2023-04-14

Citation

Readme and manuals

Help Manual

Help page	Topics
Compare cluster memberships among multiple partitions	compare_partitions
Cut a hierarchical tree	cut_tree
Compute dissimilarity metrics (beta-diversity) between sites based on species composition	dissimilarity
Convert dissimilarity metrics to similarity metrics	dissimilarity_to_similarity
Search for an optimal number of clusters in a list of partitions	find_optimal_n
Spatial distribution of fish in Europe (data.frame)	fishdf
Spatial distribution of fish in Europe (co-occurrence matrix)	fishmat
Spatial distribution of fish in Europe	fishsf
Divisive hierarchical clustering based on dissimilarity or beta-diversity	hclu_diana
Hierarchical clustering based on dissimilarity or beta-diversity	hclu_hierarclust
OPTICS hierarchical clustering algorithm	hclu_optics
Download, unzip, check permission and test the bioregion's binary files	install_binaries
Create a map of bioregions	map_clusters
Create a data.frame from a contingency table	mat_to_net
Create a contingency table from a data.frame	net_to_mat
Community structure detection in weighted bipartite network via modularity optimization	netclu_beckett
Community structure detection via greedy optimization of modularity	netclu_greedy
Infomap community finding	netclu_infomap
Finding communities based on propagating labels	netclu_labelprop
Finding communities based on leading eigen vector of the community matrix	netclu_leadingeigen
Finding communities using the Leiden algorithm	netclu_leiden
Louvain community finding	netclu_louvain
OSLOM community finding	netclu_oslom
Community structure detection via short random walks	netclu_walktrap
Non hierarchical clustering: CLARA	nhclu_clara
Non hierarchical clustering: CLARANS	nhclu_clarans
dbscan clustering	nhclu_dbscan
Non hierarchical clustering: k-means analysis	nhclu_kmeans
Non hierarchical clustering: partitioning around medoids	nhclu_pam
Calculate metrics for one or several partitions	partition_metrics
Compute similarity metrics between sites based on species composition	similarity
Convert similarity metrics to dissimilarity metrics	similarity_to_dissimilarity
Extract a subset of node from a bioregion.clusters object	subset_node
Spatial distribution of Mediterranean vegetation (data.frame)	vegedf
Spatial distribution of Mediterranean vegetation (co-occurrence matrix)	vegemat
Spatial distribution of Mediterranean vegetation (spatial grid)	vegesf