gcor: Find the (Product-Moment) Correlation Between Two or More.
gclust.centralgraph: Get Central Graphs Associated with Graph Clusters.gclust.boxstats: Plot Statistics Associated with Graph Clusters.gapply: Apply Functions Over Vertex Neighborhoods.flowbet: Calculate Flow Betweenness Scores of Network Positions.event2dichot: Convert an Observed Event Matrix to a Dichotomous matrix.evcent: Find Eigenvector Centrality Scores of Network Positions.eval.edgeperturbation: Compute the Effects of Single-Edge Perturbations on.equiv.clust: Find Clusters of Positions Based on an Equivalence Relation.ego.extract: Extract Egocentric Networks from Complete Network Data.efficiency: Compute Graph Efficiency Scores.dyad.census: Compute a Holland and Leinhardt MAN Dyad Census.diag.remove: Remove the Diagonals of Adjacency Matrices in a Graph Stack.degree: Compute the Degree Centrality Scores of Network Positions.cutpoints: Identify the Cutpoints of a Graph or Digraph.cug.test: Univariate Conditional Uniform Graph Tests.cugtest: Perform Conditional Uniform Graph (CUG) Hypothesis Tests for.consensus: Estimate a Consensus Structure from Multiple Observations.connectedness: Compute Graph Connectedness Scores.components: Find the Number of (Maximal) Components Within a Given Graph.component.dist: Calculate the Component Size Distribution of a Graph.coleman: Coleman's High School Friendship Data.closeness: Compute the Closeness Centrality Scores of Network Positions.clique.census: Compute Cycle Census Information.centralization: Find the Centralization of a Given Network, for Some Measure.centralgraph: Find the Central Graph of a Labeled Graph Stack.brokerage: Perform a Gould-Fernandez Brokerage Analysis.bonpow: Find Bonacich Power Centrality Scores of Network Positions.blockmodel.expand: Generate a Graph (or Stack) from a Given Blockmodel Using.blockmodel: Generate Blockmodels Based on Partitions of Network Positions.bicomponent.dist: Calculate the Bicomponents of a Graph.betweenness: Compute the Betweenness Centrality Scores of Network.bbnam.bf: Estimate Bayes Factors for the bbnam.bbnam: Butts' (Hierarchical) Bayesian Network Accuracy Model.(Note that geodist does not accept sf-format objects the sf package itself should be used for that.) The mapbox cheap ruler algorithm is intended to provide approximate yet very fast distance calculations within small areas (tens to a few hundred kilometres across). Distance measures currently implemented are Haversine, Vincenty (spherical and elliptical)), the very fast mapbox cheap ruler (see their blog post), and the “reference” implementation of Karney (2013), as implemented in the package sf. #> 10 10Īll outputs are distances in metres, calculated with a variety of spherical and elliptical distance measures. The 'cheap' measure is inaccurate over such #> large distances, you'd likely be better using a different 'measure'.
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