The data | Initial cohorts | Fit a Process-FINN | Replace growth with a neural network (Hybrid-FINN) | Training convergence | What the model learned: summary() | Assess the fit: in-sample and on held-out sites | Interpreting the growth process with ALE | Environmental niches | Which drivers matter: feature_importance() | Response to size and light: did the network recover the mechanistic form?

Mortality is a proportion, not a rate | Why binomial and not mse | Two models: mechanistic mortality, and mortality as a network | Scoring mortality: why not Spearman | What the network learned: ALE

How FINN works | Installation | Run the model | Species parameters | Environment and disturbances | Simulate

From Ellenberg to FINN parameters | Simulate succession | The successional pattern | Ecological plausibility of the successional pattern | Adding a climate gradient | References

What FINN needs | Start from a raw tree list | Acquiring raw data (example: US FIA) | Define status flags | Build obs_dt with makeObsData() | Environmental data | Resolve IDs across tables | Initial cohorts | Checklist before fitting