Package index • bioregion

Utils

install_binaries(): Download, unzip, check permission and test the bioregion's binary files

mat_to_net(): Create a data.frame from a contingency table

net_to_mat(): Create a contingency table from a data.frame

subset_node(): Extract a subset of nodes from a bioregion.clusters object

Pairwise similarity and distance metrics

similarity(): Compute similarity metrics between sites based on species composition

similarity_to_dissimilarity(): Convert similarity metrics to dissimilarity metrics

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

dissimilarity_to_similarity(): Convert dissimilarity metrics to similarity metrics

betapart_to_bioregion(): Convert betapart Dissimilarity to bioregion Dissimilarity

Clustering

Hierarchical clustering

hclu_hierarclust(): Hierarchical clustering based on dissimilarity or beta-diversity

cut_tree(): Cut a hierarchical tree

hclu_diana(): Divisive hierarchical clustering based on dissimilarity or beta-diversity

hclu_optics(): OPTICS hierarchical clustering algorithm

Non-hierarchical clustering

nhclu_clara(): Non hierarchical clustering: CLARA

nhclu_clarans(): Non hierarchical clustering: CLARANS

nhclu_dbscan(): dbscan clustering

nhclu_kmeans(): Non hierarchical clustering: k-means analysis

nhclu_pam(): Non hierarchical clustering: partitioning around medoids

nhclu_affprop(): Non hierarchical clustering: Affinity Propagation

Network clustering

netclu_beckett(): Community structure detection in weighted bipartite network via modularity optimization

netclu_infomap(): Infomap community finding

netclu_greedy(): Community structure detection via greedy optimization of modularity

netclu_labelprop(): Finding communities based on propagating labels

netclu_leiden(): Finding communities using the Leiden algorithm

netclu_leadingeigen(): Finding communities based on leading eigen vector of the community matrix

netclu_louvain(): Louvain community finding

netclu_oslom(): OSLOM community finding

netclu_walktrap(): Community structure detection via short random walks

Clustering analysis

compare_partitions(): Compare cluster memberships among multiple partitions

find_optimal_n(): Search for an optimal number of clusters in a list of partitions

partition_metrics(): Calculate metrics for one or several partitions

contribution(): Calculate contribution metrics of sites and species

Visualisation

map_clusters(): Create a map of bioregions

Data

fishdf: Spatial distribution of fish in Europe (data.frame)

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

fishsf: Spatial distribution of fish in Europe

vegedf: Spatial distribution of Mediterranean vegetation (data.frame)

vegemat: Spatial distribution of Mediterranean vegetation (co-occurrence matrix)

vegesf: Spatial distribution of Mediterranean vegetation (spatial grid)