The function map
EGAnet functions are organized by where they sit in an analysis, from raw correlations to a finished dimensionality estimate.
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 |
Exploratory Graph Analysis network psychometric framework functions
| CFA() | CFA Fit of EGA or hierEGA Structure |
| dimensionStability() | Dimension Stability Statistics from bootEGA |
| dynamic.network.compare() | Compares Dynamic Network Structures Using Permutation |
| invariance() | Measurement Invariance of EGA Structure |
| itemDiagnostics() | Diagnostics Analysis for Low Stability Items |
| itemStability() | Item Stability Statistics from bootEGA |
| LCT() | Loadings Comparison Test |
| net.loads() | Network Loadings |
| net.scores() | Network Scores |
| network.fit() | Traditional Fit Metrics for Networks |
| UVA() | Unique Variable Analysis |
Main Exploratory Graph Model functions
| simEGM() | Simulate data following a Exploratory Graph Model (EGM) |
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 |
| information() | Information Theory Metrics |
| jsd() | Jensen-Shannon Distance |
| tefi() | Total Entropy Fit Index using Von Neumman's entropy (Quantum Information Theory) for correlation matrices |
| tefi.compare() | Compare Total Entropy Fit Index (tefi) Between Two Structures |
| totalCor() | Total Correlation |
| totalCorMat() | Total Correlation Matrix |
| vn.entropy() | Entropy Fit Index using Von Neumman's entropy (Quantum Information Theory) for correlation matrices |
Plot-related functions
| color_palette_EGA() | EGA Color Palettes |
| compare.EGA.plots() | Visually Compare Two or More EGAnet plots |
| plot_clusters() | Plot Clustered Individual Networks |
| EGAnet-plot() | S3 Plot Methods for EGAnet |
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 |
| cosine() | Cosine similarity |
| EBICglasso.qgraph() | EBICglasso from qgraph 1.4.4 |
| Embed() | Time-delay Embedding |
| glla() | Generalized Local Linear Approximation |
| igraph2matrix() | Convert network to matrix |
| known.graph() | Re-fit Network |
| modularity() | Computes the (Signed) Modularity Statistic |
| network.compare() | Compares Network Structures Using Permutation |
| network.confusion() | Confusion Matrix Metrics for Edge Comparison and Recovery |
| network.estimation() | Apply a Network Estimation Method |
| network.predictability() | Predict New Data based on Network |
| polychoric.matrix() | Computes Polychoric Correlations |
| sF() | sF Similarity Metric |
| simDFM() | Simulate data following a Dynamic Factor Model |
| TMFG() | Triangulated Maximally Filtered Graph |
| wto() | Weighted Topological Overlap |
Data hosted in the {EGAnet} package
| 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 |