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FINN
View on CRAN: Click
here
Download and install FINN package within the R console
Install from CRAN:
install.packages("FINN")
Install from Github:
library("remotes")
install_github("cran/FINN") Install by package version:
library("remotes")
install_version("FINN", "0.1.0") Attach the package and use:
library("FINN")
Maintained by
Yannek Käber
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[Scholar Profile | Author Map]
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First Published: 2026-08-09
Latest Update: 2026-08-09
Description:
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>.
How to cite:
Yannek Käber (2026). FINN: Forest Informed Neural Networks. R package version 0.1.0, https://cran.r-project.org/web/packages/FINN. Accessed 03 Oct. 2026.
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