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qgraph (version 1.10.1)
Graph Plotting Methods, Psychometric Data Visualization and Graphical Model Estimation
Description
Weighted network visualization and analysis, as well as Gaussian graphical model computation. See Epskamp et al. (2012)
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Install
install.packages('qgraph')
Monthly Downloads
23,334
Version
1.10.1
License
GPL-2
Maintainer
Sacha Epskamp
Last Published
July 18th, 2026
Functions in qgraph (1.10.1)
Search all functions
clustcoef_auto
Local clustering coefficients.
qgraph.loadings
qgraph.loadings
pathways
Highlight shortest pathways in a network
qgraph.layout.fruchtermanreingold
qgraph.layout.fruchtermanreingold
plot.qgraph
Plot method for "qgraph"
qgraphMixed
Plots a mixed graph with both directed and undirected edges.
qgraphHTML
Interactive HTML output for qgraph graphs
qgraph.animate
Animate a growing network
smallworldIndex
Small-world index of unweighted graph
print.qgraph
Print edgelist
qgraph
qgraph
summary.qgraph
Summary method for "qgraph"
smallworldness
Compute the small-worldness index.
wi2net
Converts precision matrix to partial correlation matrix
averageLayout
Computes an average layout over several graphs
EBICglasso
Compute Gaussian graphical model using graphical lasso based on extended BIC criterion.
VARglm
Computes a vector autoregressive lag-1 model using GLM
centrality
Centrality statistics of graphs
as.igraph.qgraph
Converts qgraph object to igraph object.
FDRnetwork
Model selection using local False Discovery Rate
centrality and clustering plots
Centrality and Clustering plots and tables
big5
Big 5 dataset
big5groups
Big 5 groups list
mat2vec
Weights matrix to vector
mutualInformation
Computes the mutual information between nodes
ggmModSelect
Unregularized GGM model search
flow
Draws network as a flow diagram showing how one node is connected to all other nodes
ggmFit
Obtain fit measures of a Gaussian graphical model
getWmat
Obtain a weights matrix
cor_auto
Automatically compute an appropriate correlation matrix
bridgeCentrality
Bridge centrality statistics
makeBW
A qgraph plot can be understood in black and white
centrality_auto
Automatic centrality statistics of graphs