network.descriptives {EGAnet}  R Documentation 
Computes descriptive statistics for network models
network.descriptives(network)
network 
Numeric vector including:
Mean_weight 
The average of the edge weights in the network 
SD_weight 
The standard deviation of the edge weights in the network 
Min_weight 
The minimum of the edge weights in the network 
Max_weight 
The minimum of the edge weights in the network 
Density 
The density of the network 
ASPL 
The average shortest path length (ASPL) of the network (computed as unweighted) 
CC 
The clustering coefficent (CC) of the network (computed as unweighted) 
swn.rand 
Smallworldness measure based on random networks:

swn.HG 
Smallworldness measure based on Humphries & Gurney (2008):

swn.TJHBL 
Smallworldness measure based on Telesford, Joyce, Hayasaka, Burdette, & Laurienti (2011):

scalefree_Rsq 
The Rsquared fit of whether the degree distribution follows the powerlaw (many small degrees, few large degrees) 
Hudson Golino <hfg9s at virginia.edu> and Alexander P. Christensen <alexpaulchristensen@gmail.com>
# swn.HG
Humphries, M. D., & Gurney, K. (2008).
Network 'smallworldness': A quantitative method for determining canonical network equivalence.
PLoS one, 3, e0002051
# swn.TJHBL
Telesford, Q. K., Joyce, K. E., Hayasaka, S., Burdette, J. H., & Laurienti, P. J. (2011).
The ubiquity of smallworld networks.
Brain Connectivity, 1(5), 367375
# scalefree_Rsq
Langfelder, P., & Horvath, S. (2008).
WGCNA: an R package for weighted correlation network analysis.
BMC Bioinformatics, 9, 559
# Load data
wmt < wmt2[,7:24]
# EGA example
## plot.type = "qqraph" used for CRAN checks
## plot.type = "GGally" is the default
ega.wmt < EGA(data = wmt, plot.type = "qgraph")
# Compute descriptives
network.descriptives(ega.wmt)