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

v2.4.0 · Dec 6, 2025 · AGPL (>= 3.0)

Description

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.

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package dependencies

Package required but not available: 'semPlot'

See section 'The DESCRIPTION file' in the 'Writing R Extensions'
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ERROR 13 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE Mar 9, 2026
ERROR r-oldrel-windows-x86_64

package dependencies

Package required but not available: 'semPlot'

See section 'The DESCRIPTION file' in the 'Writing R Extensions'
manual.

Reverse Dependencies (5)

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Dependency Network

Dependencies Reverse dependencies clue dendextend future future.apply GGally ggplot2 ggpubr glasso glassoFast GPArotation igraph lavaan Matrix network progressr +3 more dependencies FAfA MixMashNet dySEM latentFactoR parameters EGAnet

Version History

new 2.4.0 Mar 10, 2026