EGAnet: Exploratory Graph Analysis – a Framework for Estimating the Number of Dimensions in Multivariate Data using Network Psychometrics

Implements the Exploratory Graph Analysis (EGA) framework for dimensionality and psychometric assessment. EGA estimates the number of dimensions in psychological data using network estimation methods and community detection algorithms. A bootstrap method is provided to assess the stability of dimensions and items. Fit is evaluated using the Entropy Fit family of indices. Unique Variable Analysis evaluates the extent to which items are locally dependent (or redundant). Network loadings provide similar information to factor loadings and can be used to compute network scores. A bootstrap and permutation approach are available to assess configural and metric invariance. Hierarchical structures can be detected using Hierarchical EGA. Time series and intensive longitudinal data can be analyzed using Dynamic EGA, supporting individual, group, and population level assessments.

Version: 2.0.3
Depends: R (≥ 3.5.0)
Imports: dendextend, future, future.apply, glasso, GGally, ggplot2, ggpubr, GPArotation, igraph (≥ 1.3.0), lavaan, Matrix, methods, network, progressr, qgraph, semPlot, sna, stats
Suggests: fitdistrplus, fungible, gridExtra, knitr, markdown, pbapply, progress, psych, pwr, RColorBrewer
Published: 2023-11-17
Author: Hudson Golino ORCID iD [aut, cre], Alexander Christensen ORCID iD [aut], Robert Moulder ORCID iD [ctb], Luis E. Garrido ORCID iD [ctb], Laura Jamison ORCID iD [ctb], Dingjing Shi ORCID iD [ctb]
Maintainer: Hudson Golino <hfg9s at>
License: GPL (≥ 3.0)
NeedsCompilation: yes
Citation: EGAnet citation info
Materials: NEWS
In views: Psychometrics
CRAN checks: EGAnet results


Reference manual: EGAnet.pdf


Package source: EGAnet_2.0.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): EGAnet_2.0.3.tgz, r-oldrel (arm64): EGAnet_2.0.3.tgz, r-release (x86_64): EGAnet_2.0.3.tgz, r-oldrel (x86_64): not available
Old sources: EGAnet archive

Reverse dependencies:

Reverse suggests: parameters


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