Forest Informed Neural Networks

Documentation for package ‘FINN’ version 0.1.0

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ALE	Accumulated local effect plots
apply_env_scaling	Apply stored z-standardization to environmental predictors
array2obsDF	Transform Arrays to Observation Data Table
averageConditionalEffects	Average conditional effects of a FINN model
BA_stand	Calculate the Basal Area of a Stand
BA_stem	Calculate the basal area of a tree given the diameter at breast height (dbh)
binomial_from_bernoulli	Draw binomial counts from per-trial Bernoulli probabilities
climateDF2array	Convert a climate data frame to a FINN environment array
CohortMat	Cohort Matrix Class
competition	Compute the fraction of available light (light) for each cohort based on the given parameters
compute_env_scaling	Learn z-standardization for environmental predictors
conditionalEffects	Conditional effects of a FINN model
createHybrid	Define a hybrid (deep-neural-network) demographic process for FINN
createProcess	Define a demographic process for FINN
dbh2ba	Convert DBH to basal area
feature_importance	Permutation feature importance for FINN demographic rates
finn	Forest Informed Neural Network
FINN.seed	Set Seed for Reproducibility in R and Torch
fit	Fit FINN
growth	Calculate growth
height	Calculate the height of a tree based on its diameter at breast height and an allometry parameter
makeInitCohorts	Make initial cohorts for FINN
makeObsData	Create observation data from trees
mortality	Mortality
np_runif	Generate random numbers from a uniform distribution
obsDF2arrays	Convert observation data frame to arrays
plot.FINNale	Plot ALE curves of a FINN model
pred2DF	Convert Prediction Arrays to Data Frames
predict.finn_class	Predict from a FINN model
regeneration	Calculate the regeneration of forest patches based on the input parameters
resolveSiteIDs	Resolve site, patch, and year indices for FINN inputs
rweibull_cohorts	Generate Cohorts Using Weibull Distribution
simulateForest	Simulate
summary.finn_class	Summarise a fitted FINN model