new to networks | overview books | academic articles | networks & conflict | model-specific

the netify object: a base r object with attributes | extracting parts | tidy interop | predicates and descriptors | object-level validation | open-cohort panels with actor_pds | the actor_pds roster, step by step | density and per-period actor sets | na versus zero in weighted networks | writing a custom graph-level statistic | writing a custom actor-level statistic | reading the dyad_data nested list | writing a custom exporter (to_* function) | performance characteristics | see also

1. cross-sectional pipeline | before you fit: three sanity checks | dyadic edge covariates: the _e suffix | 2. longitudinal pipeline (per-time fits) | 3. multilayer pipeline (per-layer fits) | 4. round-tripping simulated networks back to netify | tl;dr | references

1. build | 2. explore | 3. test (basic inferential) | 4. bridge | tl;dr | a note for non-time use cases

new to networks | overview books | academic articles | networks & conflict | model-specific

the netify object: a base r object with attributes | extracting parts | tidy interop | predicates and descriptors | object-level validation | open-cohort panels with actor_pds | the actor_pds roster, step by step | density and per-period actor sets | na versus zero in weighted networks | writing a custom graph-level statistic | writing a custom actor-level statistic | reading the dyad_data nested list | writing a custom exporter (to_* function) | performance characteristics | see also

1. cross-sectional pipeline | before you fit: three sanity checks | dyadic edge covariates: the _e suffix | 2. longitudinal pipeline (per-time fits) | 3. multilayer pipeline (per-layer fits) | 4. round-tripping simulated networks back to netify | tl;dr | references

1. build | 2. explore | 3. test (basic inferential) | 4. bridge | tl;dr | a note for non-time use cases