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Capabilities of included engines

As outlined by Fletcher et al. (2019), there are many different forms of integration such as through [ensemble] modelling, adding [offsets], predictors (e.g. [add_predictor_range()] ) or [priors] and through full integration of different likelihoods (See (Data integration) ). Not all of these options are available for every engine supported by the ibis.iSDM package and the table below shows the currently implemented engines and various types of integrations supported by them.

Stating the name and function call of each engine and its supported model complexity with linear (ln) and non-linear (nl) formulations, although it should be noted that linear models can approximate non-linearity by including transformations (as with Maxent, e.g. hinge/product/quadratic). Not every engine supports the different types of integration via ensembles, offsets, priors, joint likelihood estimation and ensemble compositing of models using separate datasets of the same species. When multiple biodiversity datasets are added to an engine that does not support joint likelihood estimation, the parameter method_integration in [train()] determines how the different predictions are integrated. Available options for integration are via predictors, offsets, interactions, priors or weights (see the help file of [train()] for more information).

Name Complexity Engine Offsets Priors Weights Joint likel.
Generalized linear model (GLM) ln [engine_glm()] x x
Regularized elastic net regression (GLMNET) ln [engine_glmnet()] x GLMNETPrior() x
Bayesian additive regression trees (BART) nl [engine_bart()] (x) BARTPrior() x
Bayesian regularized regression (BREG) ln [engine_breg()] BREGPrior() x
Approximate point modelling (SCAMPR) ln [engine_scampr()] x x
Gradient descent boosting (GDB) ln/nl [engine_gdb()] x GDBPrior() x
Integrated Nested Laplace approximation (INLA) ln [engine_inla()] x INLAPrior() x x
Integrated Nested Laplace approximation (INLABRU) ln [engine_inlabru()] x INLAPrior() x x
Bayesian regressions (Stan) ln [engine_stan()] x STANPrior() x (x)
eXtreme Gradient Boosting (XGBOOST) ln/nl [engine_xgboost()] x XGBPrior() x