Cross-sectional APIMs | The distinguishable APIM | Residual random-effects structure | Fitting the distinguishable APIM with glmmTMB | The exchangeable APIM | Modeling the residual random-effects structure | Fitting the restricted exchangeable APIM with glmmTMB | Testing distinguishability | Checking partner dependence | Intensive longitudinal APIMs | Concurrent ILD Gaussian APIM for distinguishable dyads | Adding role-specific residual AR(1) | Random slopes | Concurrent ILD Gaussian APIM for exchangeable dyads | Adding pooled residual AR(1) | Extension to exchangeable random slopes | Testing random-effect constraints | When carryover is the research question | References

Cross-Sectional Gaussian DIM | Example DIM Model | Model interpretation | Demonstrating model equivalence to APIM | Why Are These Models Equivalent? Exploring the Reparameterization | Random-effect transformation | Checking partner dependence | Intensive Longitudinal DIM | Interpretation of concurrent ILD DIM coefficients | APIM and DIM parameterizations in ILD | Including Random Slopes | Transforming DIM random slopes to APIM slopes | Dynamic ILD models | References

Preparing DSM Data | Cross-Sectional Gaussian DSM | Interpreting the DSM paths | $$\operatorname{Cov}(r_{\mathrm{m}},r_{\mathrm{d}}) | $$\operatorname{Cov}(e_{\mathrm{F}},e_{\mathrm{M}}) | Reversing the coding | Relationship to the APIM and DIM | Fixed-effect transformation | Random-effect transformation | $$\begin | Checking partner dependence | Intensive longitudinal DSM | References

Installation | About this vignette | Prerequisites | Data preparation for distinguishable dyads | Data preparation for exchangeable dyads | Generating DIM and DSM columns | Intensive longitudinal dyadic data | Preparing lagged predictors | Working with multiple dyad compositions | Keeping only selected dyad compositions (filtering) | Setting distinguishable dyads to be treated as exchangeable | Pooling different dyad compositions | Citation | References

A contrasting example | Separate the two levels | Inspect the correlation matrices | Plot the two levels | Extract results for further work | Add uncertainty when needed