Why cograph | Plotting | Specialized plots | Input formats | Wrangling | Selecting | Weights | Structure and editing | Centrality | Network properties | Community detection | Motifs | Robustness | Disparity filter | Multi-cluster visualization | Multilayer networks | Higher-order networks | TNA integration | Palettes | Further reading

How to read this catalogue | Notation | Catalogue index | Argument catalogue

Setup | Form 1 — Named list of per-actor frames | Form 2 — Single combined frame with actor_type | Form 3 — Single combined frame with node_groups | 3a. Named list of code vectors | 3b. Two-column data frame (codebook) | All forms produce the same network | Handling code-label collisions with disambiguate = | Splitting into cohorts with group = | Cheat sheet

Overview | Bar chart | Study labels on bars | Multi-value columns | Adjusting label space | Stacked / grouped bar chart | Waffle chart | Donut / pie chart | Co-occurrence heatmap | UpSet plot | Alluvial plot | Treemap | Year trend | World map | Summary table | Handling missing data | Drop NAs, percentages of total (default) | Drop NAs, percentages of reported only | Keep NAs, percentages of total | Keep NAs with a custom label, percentages of reported | Using NA options in plots | Custom study ID column | Using summarize_data() directly | Palette

1. Building a heterogeneous network | 2. Plotting the network | 3. Per-actor sequence plots | 4. Centralities | 5. Bootstrap | 6. Centrality stability | 7. Split-half reliability | 8. Comparing two networks | 9. Permutation test | 10. Path patterns

Data preparation | Groups from a pre-existing variable | Comparing groups | Permutation test | Subsequence comparison | Groups from sequence clustering | Summarizing the cluster-specific models | Pruning with bootstrap | Centrality measures | Subsequence comparison across clusters

Tutorial of TNA with R | Building tna Model | Plotting and interpreting tna models | Interpretation of the model | Pruning and retaining edges that "matter" | Patterns | Centralities | Node-level measures | Edge-level measures | Community finding | Network inference | Bootstrapping | Permutation | Interpreting the Results of the Case-Dropping Bootstrap for Centrality Indices