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compare_to_baseline()
|
Compare motif occurence in empirical network to occurence in a baseline model |
|
count_motifs()
|
Count multi-level motifs |
|
critical_dyads()
|
List critical dyads |
|
directed_dummy_net
|
Two-level directed network dummy example |
|
dummy_net
|
Three-level network dummy example |
|
edge_contribution()
|
List edge contribution |
|
exemplify_motif()
|
Returns an example for a motif found in a given network |
|
explore_motifs()
|
Explore the motif zoo interactively in a shiny app |
|
identify_gaps()
|
List gaps |
|
induced_level_subgraph()
|
Returns subgraph induced by one level of the network |
|
is.directed()
|
Checks whether the given network is directed |
|
large_directed_dummy_net
|
Large two-level directed network dummy example |
|
list_motifs()
|
Lists motifs of a given class or all motifs with a given signature |
|
ml_net
|
Two-level network example (wetlands management) |
|
motif_summary()
|
Summary for motif counts and Erdős-Rényi distribution |
|
motifs_distribution()
|
Compute statistical properties (expectation and variance) of the distribution
of motifs in a baseline model |
|
plot_critical_dyads()
|
Plot critical dyads in network visualisation |
|
plot_gaps()
|
Plot gaps in network visualisation |
|
plot_gaps_or_critical_dyads()
|
Helper function for plotting gaps and critical edges |
|
plot_mnet()
|
Visualize a multi-level network (using ggraph) |
|
show_motif()
|
Plots an example for a motif with given motif identifier string taken from
the given graph. |
|
simulate_baseline()
|
Simulate a baseline baseline model |
|
supported_classes()
|
Lists all supported motif classes for a given signature |
|
supported_signatures()
|
Lists all supported signatures |
|
tidygraph_dummy_net
|
Two-level tidygraph network example |
|
to_py_graph()
|
Translate multi-level statnet or igraph network object to Python networkx
object |
|
update_motifr()
|
Checks for updates for motifr's Python core, the sma package |