  Many ecological and spatial processes are complex in nature and are not
accurately modeled by linear models. Regression trees promise to handle the
high-order interactions that are present in ecological and spatial datasets,
but fail to produce physically realistic characterizations of the underlying
landscape. The "autocart" (autocorrelated regression trees) R package extends
the functionality of previously proposed spatial regression tree methods
through a spatially aware splitting function and novel adaptive inverse
distance weighting method in each terminal node. The efficacy of these autocart
models, including an autocart extension of random forest, is demonstrated on
multiple datasets. This highlights the ability of autocart to model complex
interactions between spatial variables while still providing physically
realistic representations of the landscape.
