EGAnet-package {EGAnet} | R Documentation |

Implements the Exploratory Graph Analysis (`EGA`

; Golino & Epskamp, 2017; Golino, Shi et al., 2020) framework for dimensionality and psychometric assessment.
EGA is part of a new area called *network psychometrics* that uses undirected network models for the assessment
of psychometric properties. EGA estimates the number of dimensions (or factors) using graphical lasso `EBICglasso`

or
Triangulated Maximally Filtered Graph (`TMFG`

) and a weighted network community detection algorithm (Christensen, Garrido, Golino, under review A). A bootstrap method for
verifying the stability of the dimensions and items in those dimensions is available (`bootEGA`

; Christensen & Golino, 2021a). The fit of the structure suggested by EGA
can be verified using Entropy Fit Indices (`entropyFit`

, `tefi`

; Golino, Moulder et al., 2020). A novel approach called Unique Variable Analysis (`UVA`

) can be used to
identify and reduce redundant variables in multivariate data (Christensen, Garrido, & Golino, under review B). Network loadings (`net.loads`

),
which are roughly equivalent to factor loadings when the data generating model is a factor model, are available (Christensen & Golino, 2021b, 2021c).
Network scores (`net.scores`

) can also be computed using the network loadings. Finally, dynamic EGA (`dynEGA`

) will estimate dimensions from time series data for individual, group, and sample levels (Golino, Christensen et al., 2021).

Hudson Golino <hfg9s@virginia.edu> and Alexander P. Christensen <alexpaulchristensen@gmail.com>

Christensen, A. P., Garrido, L. E., & Golino, H. (under review A).
Comparing community detection algorithms in psychological data: A Monte Carlo simulation.
*PsyArXiv*.

# Related functions: `EGA`

Christensen, A. P., Garrido, L. E., & Golino, H. (under review B).
Unique Variable Analysis: A novel approach to detect redundant variables in multivariate data.
*PsyArXiv*.

# Related functions: `UVA`

Christensen, A. P., & Golino, H. (2021a).
Estimating the stability of the number of factors via Bootstrap Exploratory Graph Analysis: A tutorial.
*Psych*, *3*(3), 479-500.

# Related functions: `bootEGA`

, `dimensionStability`

,
# and `itemStability`

Christensen, A. P., & Golino, H. (2021b).
Factor or network model? Predictions from neural networks.
*Journal of Behavioral Data Science*, *1*(1), 85-126.

# Related functions: `LCT`

Christensen, A. P., & Golino, H. (2021c).
On the equivalency of factor and network loadings.
*Behavior Research Methods*, *53*, 1563-1580.

# Related functions: `LCT`

and `net.loads`

Christensen, A. P., Golino, H., & Silvia, P. J. (2020).
A psychometric network perspective on the validity and validation of personality trait questionnaires.
*European Journal of Personality*, *34*, 1095-1108.

# Related functions: `bootEGA`

, `dimensionStability`

,
# `EGA`

, `itemStability`

, and `UVA`

Golino, H., Christensen, A. P., Moulder, R., Kim, S., & Boker, S. M. (2021).
Modeling latent topics in social media using Dynamic Exploratory Graph Analysis: The case of the right-wing and left-wing trolls in the 2016 US elections.
*Psychometrika*.

# Related functions: `dynEGA`

and `simDFM`

Golino, H., & Demetriou, A. (2017).
Estimating the dimensionality of intelligence like data using Exploratory Graph Analysis.
*Intelligence*, *62*, 54-70.

# Related functions: `EGA`

Golino, H., & Epskamp, S. (2017).
Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research.
*PLoS ONE*, *12*, e0174035.

# Related functions: `EGA`

Golino, H., Moulder, R., Shi, D., Christensen, A. P., Garrido, L. E., Nieto, M. D., Nesselroade, J., Sadana, R., Thiyagarajan, J. A., & Boker, S. M. (2020).
Entropy fit indices: New fit measures for assessing the structure and dimensionality of multiple latent variables.
*Multivariate Behavioral Research*.

# Related functions: `entropyFit`

, `tefi`

, and `vn.entropy`

Golino, H., Shi, D., Christensen, A. P., Garrido, L. E., Nieto, M. D., Sadana, R., Thiyagarajan, J. A., & Martinez-Molina, A. (2020).
Investigating the performance of exploratory graph analysis and traditional techniques to identify the number of latent factors:
A simulation and tutorial.
*Psychological Methods*, *25*, 292-320.

# Related functions: `EGA`

Golino, H., Thiyagarajan, J. A., Sadana, M., Teles, M., Christensen, A. P., & Boker, S. M. (under review).
Investigating the broad domains of intrinsic capacity, functional ability, and environment:
An exploratory graph analysis approach for improving analytical methodologies for measuring healthy aging.
*PsyArXiv*.

# Related functions: `EGA.fit`

and `tefi`

Jamison, L., Christensen, A. P., & Golino, H. (under review).
Optimizing Walktrap's community detection in networks using the Total Entropy Fit Index.
*PsyArXiv*.

# Related functions: `EGA.fit`

and `tefi`

[Package *EGAnet* version 1.1.0 Index]