Package: EGAnet 2.5.0

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.

Authors:Hudson Golino [aut, cre], Alexander Christensen [aut], Robert Moulder [ctb], Luis E. Garrido [ctb], Laura Jamison [ctb], Dingjing Shi [ctb], John Fox [cph, ctb], Yves Rosseel [cph, ctb], Alan Genz [cph, ctb], David Blackman [ctb], Sebastiano Vigna [ctb], John Burkardt [cph, ctb], Bill Venables [cph, ctb], Brian Ripley [cph, ctb], Pascal Deboeck [cph, ctb], Steve Boker [cph, ctb]