Worked analyses
Each of {EGAnet}'s core functions, worked through on real data with real output — on their own, and chained together into the full analysis path.
| Exploratory Graph Analysis | The general workflow of Exploratory Graph Analysis (EGA; Golino & Epskamp, 2017; Golino et al., 2020) should at minimum take the following order of analysis: |
| EGA | EGA is the core function of the package: it estimates a network from your data and applies a community detection algorithm to recover the number of dimensions. |
| UVA | Before a network is estimated at all, it's worth asking whether any items are redundant with one another — measuring such similar content that they behave more like duplicates than distinct variables. |
| bootEGA | A single EGA result is one estimate from one sample. |
| Invariance | An EGA structure is only as useful as its comparability across the people you'd like to apply it to. |
| Hierarchical EGA | Some constructs aren't just multidimensional — they're multidimensional and organized under a broader, more general factor. |
| Dynamic EGA | Most of {EGAnet} treats each row of data as one independent observation. |