Introduction | Quick workflow example | Data | Tie-oriented model | Preparing the event history | Computing statistics | Memory | Event types and consider_type | Tie-oriented model with case-control sampling | Actor-oriented model

Estimation approaches: Frequentist and Bayesian | Let's get started (loading the remstimate package) | Modeling frameworks | Tie-Oriented Modeling framework | The likelihood function | A toy example on the tie oriented modeling framework | Estimating a model with remstimate() in 3 steps | Frequentist approach | Maximum Likelihood Estimation (MLE) | print( ) | summary( ) | Information Criteria | diagnostics( ) | plot( ) | Bayesian approach | Hamiltonian Monte Carlo (HMC) | Actor-Oriented Modeling framework | Sender activity rate model | Receiver choice model | A toy example on the actor oriented modeling framework | References

Definition of risk set | The full risk set | Visualizing the risk set | The active, active_saturated, and manual risk set | The active risk set | active dyads: those observed in randomREHsmall | The manual risk set | Specifying a manual risk set | The processed risk set

Aim | Input | edgelist | directed | ordinal | model | thin | actors | riskset | manual.riskset | event_type | origin | time.units | attach_riskset | riskset_decode | riskset_max_decode | event_covariates | ncores | Running the example | Output | M | E | N | C | D and activeD | intereventTime | edgelist_id | meta | ids | index | omit_dyad | riskset_info | Actor-oriented model output | Methods | print() and summary() | dim() | plot()