A permutation-based hypothesis test for statistical comparison of two networks based on the invariance measures of the R package 'NetworkComparisonTest' by van Borkulo et al. (2022), <doi:10.1037/met0000476>: network structure invariance, global strength invariance, edge invariance, and various centrality measures. Edgelists from dependent or independent samples are used as input. These edgelists are generated from concept maps and summed into two comparable group networks. The networks can be directed or undirected.

Creates 'D3' 'JavaScript' network, tree, dendrogram, and Sankey
    graphs from 'R'.

Network is a prevalent form of data structure in many fields. As an object of analysis, many distance or metric measures have been proposed to define the concept of similarity between two networks. We provide a number of distance measures for networks. See Jurman et al (2011) <doi:10.3233/978-1-60750-692-8-227> for an overview on spectral class of inter-graph distance measures.

Simple interface routines to facilitate the handling of network objects with complex intertemporal data. This is a part of the "statnet" suite of packages for network analysis.

A collection of dynamic network data sets from various sources and multiple authors represented as 'networkDynamic'-formatted objects.

Simulates the extinction of species in ecological networks and it analyzes
             its cascading effects, described in Dunne et al. (2002) <doi:10.1073/pnas.192407699>.

A network Maze generator that creates different types of network mazes.

This is an R implementation of the netinf algorithm (Gomez Rodriguez, Leskovec, and Krause, 2010)<doi:10.1145/1835804.1835933>. Given a set of events that spread between a set of nodes the algorithm infers the most likely stable diffusion network that is underlying the diffusion process.

An implementation of some of the core 'network' package functionality based on a 
    simplified data structure that is faster in many research applications. This package is designed 
    for back-end use in the 'statnet' family of packages, including 'EpiModel'. Support is provided for
    binary and weighted, directed and undirected, bipartite and unipartite networks; no current 
    support for multigraphs, hypergraphs, or loops.