Function reference • EGAnet

EGA Functions

Main Exploratory Graph Analysis functions

bootEGA(): Bootstrap Exploratory Graph Analysis

dynEGA(): Dynamic Exploratory Graph Analysis

dynEGA.ind.pop(): Intra- and Inter-individual dynEGA

EGA(): Exploratory Graph Analysis

EGA.estimate(): Estimates EGA for Multidimensional Structures

EGA.fit(): EGA Optimal Model Fit using the Total Entropy Fit Index (tefi)

hierEGA(): Hierarchical EGA

riEGA(): Random-Intercept EGA

EGA Psychometric Functions

Exploratory Graph Analysis network psychometric framework functions

CFA(): CFA Fit of EGA or hierEGA Structure

dimensionStability(): Dimension Stability Statistics from bootEGA

invariance(): Measurement Invariance of EGA Structure

itemStability(): Item Stability Statistics from bootEGA

LCT(): Loadings Comparison Test

net.loads(): Network Loadings

net.scores(): Network Scores

UVA(): Unique Variable Analysis

Information Theory Functions

Information Theory-related functions for network psychometrics

boot.ergoInfo(): Bootstrap Test for the Ergodicity Information Index

entropyFit(): Entropy Fit Index

ergoInfo(): Ergodicity Information Index

genTEFI(): Generalized Total Entropy Fit Index using Von Neumman's entropy (Quantum Information Theory) for correlation matrices

infoCluster(): Information Theoretic Mixture Clustering for dynEGA

jsd(): Jensen-Shannon Distance

tefi(): Total Entropy Fit Index using Von Neumman's entropy (Quantum Information Theory) for correlation matrices

totalCor(): Total Correlation

totalCorMat(): Total Correlation Matrix

vn.entropy(): Entropy Fit Index using Von Neumman's entropy (Quantum Information Theory) for correlation matrices

Plots

Plot-related functions

color_palette_EGA(): EGA Color Palettes

compare.EGA.plots(): Visually Compare Two or More EGAnet plots

EGAnet-plot plot.EGAnet: S3 Plot Methods for EGAnet

Basic Functions

General purpose functions that allow for a more modular usage

auto.correlate(): Automatic correlations

community.compare(): Compares Community Detection Solutions Using Permutation

community.consensus(): Applies the Consensus Clustering Method (Louvain only)

community.detection(): Apply a Community Detection Algorithm

community.homogenize(): Homogenize Community Memberships

community.unidimensional(): Approaches to Detect Unidimensional Communities

convert2igraph(): Convert networks to igraph

convert2tidygraph(): Convert networks to tidygraph

EBICglasso.qgraph(): EBICglasso from qgraph 1.4.4

Embed(): Time-delay Embedding

glla(): Generalized Local Linear Approximation

igraph2matrix(): Convert igraph network to matrix

modularity(): Computes the (Signed) Modularity Statistic

network.estimation(): Apply a Network Estimation Method

network.generalizability(): Estimate the Generalizability of Network

network.predictability(): Predict New Data based on Network

polychoric.matrix(): Computes Polychoric Correlations

simDFM(): Simulate data following a Dynamic Factor Model

TMFG(): Triangulated Maximally Filtered Graph

wto(): Weighted Topological Overlap

Data and Package Information

Data hosted in the {EGAnet} package

EGAnet EGAnet-package: EGAnet-package

boot.wmt: bootEGA Results of wmt2Data

depression: Depression Data

dnn.weights: Loadings Comparison Test Deep Learning Neural Network Weights

ega.wmt: EGA Network of wmt2Data

intelligenceBattery: Intelligence Data

optimism: Optimism Data

prime.num: Prime Numbers through 100,000

sim.dynEGA: sim.dynEGA Data

wmt2: WMT-2 Data