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qgraph (version 1.9.8)

Graph Plotting Methods, Psychometric Data Visualization and Graphical Model Estimation

Description

Fork of qgraph - Weighted network visualization and analysis, as well as Gaussian graphical model computation. See Epskamp et al. (2012) .

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Version

Install

install.packages('qgraph')

Monthly Downloads

20,515

Version

1.9.8

License

GPL-2

Maintainer

Last Published

November 3rd, 2023

Functions in qgraph (1.9.8)

pathways

Highlight shortest pathways in a network
summary.qgraph

Summary method for "qgraph"
qgraph.loadings

qgraph.loadings
smallworldIndex

Small-world index of unweighted graph
qgraph.layout.fruchtermanreingold

qgraph.layout.fruchtermanreingold
smallworldness

Compute the small-worldness index.
qgraph.animate

Animate a growing network
wi2net

Converts precision matrix to partial correlation matrix
qgraphMixed

Plots a mixed graph with both directed and undirected edges.
qgraph

qgraph
plot.qgraph

Plot method for "qgraph"
print.qgraph

Print edgelist
VARglm

Computes a vector autoregressive lag-1 model using GLM
big5groups

Big 5 groups list
averageLayout

Computes an average layout over several graphs
centrality_auto

Automatic centrality statistics of graphs
centrality and clustering plots

Centrality and Clustering plots and tables
FDRnetwork

Model selection using local False Discovery Rate
EBICglasso

Compute Gaussian graphical model using graphical lasso based on extended BIC criterium.
as.igraph.qgraph

Converts qgraph object to igraph object.
big5

Big 5 dataset
ggmFit

Obtain fit measures of a Gaussian graphical model
flow

Draws network as a flow diagram showing how one node is connected to all other nodes
cor_auto

Automatically compute an apppropriate correlation matrix
mutualInformation

Computes the mutual information between nodes
centrality

Centrality statistics of graphs
mat2vec

Weights matrix to vector
getWmat

Obtain a weights matrix
ggmModSelect

Unregularized GGM model search
clustcoef_auto

Local clustering coefficients.
makeBW

A qgraph plot can be understood in black and white