FINN
0.1.0Forest Informed Neural Networks
Overview
A hybrid dynamic forest (gap) model (FINN) that can be configured as a fully mechanistic, process-based model, like classic forest gap models, or with its demographic processes (growth, mortality, regeneration) replaced by deep neural networks (DNNs), or any combination of the two. Provides functions to define a model and its mechanistic or empirical components, calibrate it to forest inventory data, and interpret the calibrated processes. FINN is implemented with the 'torch' package, which supplies GPU support and the automatic differentiation used to calibrate the model by stochastic gradient descent; no knowledge of 'torch' is required. The hybrid modeling approach is described in Pichler and Käber (2026) doi:10.1111/2041-210x.70347.
Install
Health
- OK2026-08-108 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 99%
- Return-value docs
- 100%
- References docs
- 8%
Downloads
Repository
Repository practices
Repository github.com/finnverse/finn is linked, but repository-practices checks have not run for it yet.
Dependencies
Nothing depends on this yet.
Code & Tests
People & History
1 release. R releases are shown for context.
- 0.1.0Latest2026-08-09 · current release
- RR 4.6.0 released · 2026-04-24
Package metadata
- First published
- 2026-08-09
- Total releases
- 1 / 1 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 4.1.0
- Download size
- not tracked yet
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("FINN")This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-08-11, which the citation names so these numbers can be found later. More on citing and the projects behind them.