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# CITATION.cff file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "FINN" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'FINN: Forest Informed Neural Networks'
version: 0.2.0
doi: 10.32614/CRAN.package.FINN
abstract: 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) <https://doi.org/10.1111/2041-210x.70347>.
authors:
- family-names: Käber
  given-names: Yannek
  email: y.kaeber@posteo.de
  orcid: https://orcid.org/0000-0002-7041-9849
- family-names: Pichler
  given-names: Maximilian
  email: maximilian.pichler@biologie.uni-regensburg.de
  orcid: https://orcid.org/0000-0003-2252-8327
repository: https://finnverse.r-universe.dev
repository-code: https://github.com/FINNverse/FINN
commit: 609a30c72e5936012cf2121a81981858f8f07633
url: https://finnverse.github.io/FINN/
date-released: '2026-09-22'
contact:
- family-names: Käber
  given-names: Yannek
  email: y.kaeber@posteo.de
  orcid: https://orcid.org/0000-0002-7041-9849