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First public release.
FINN is a differentiable forest gap model: a cohort-based dynamic vegetation model whose demographic processes (competition, growth, mortality, regeneration) can each be a mechanistic function, a neural network, or a mixture of the two, all calibrated end-to-end by gradient descent.
Highlights of the current interface:
finn() assembles a model from one process per
demographic component, each built with createProcess()
(mechanistic) or createHybrid() (neural network).simulateForest() runs a model forward;
fit() calibrates one to data and predict()
scores it, returning patch- and site-level results."mse"/"gaussian"), Poisson, negative
binomial, and a "binomial" likelihood for mortality that
takes a closed-cohort count pair (n_at_risk,
n_died).weights = "auto" (the default in fit())
scales each loss by its intercept-only baseline, so the six responses
are commensurable and every term reads as a fraction of its own null
deviance.makeObsData(),
resolveSiteIDs() and makeInitCohorts() turn a
raw tree list into FINN’s input tables.ALE(),
summary(), feature_importance() and
conditionalEffects().See the vignettes for a guided tour: Introduction to FINN, Plausible succession from a handful of species, Preparing your data for FINN, Fitting FINN to forest inventory data, and Mortality.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.