gbm.auto: Automated Boosted Regression Tree Modelling and Mapping Suite
Automates delta log-normal boosted regression tree abundance prediction. Loops through parameters provided (LR (learning rate), TC (tree complexity), BF (bag fraction)), chooses best, simplifies, & generates line, dot & bar plots, & outputs these & predictions & a report, makes predicted abundance maps, and Unrepresentativeness surfaces. Package core built around 'gbm' (gradient boosting machine) functions in 'dismo' (Hijmans, Phillips, Leathwick & Jane Elith, 2020 & ongoing), itself built around 'gbm' (Greenwell, Boehmke, Cunningham & Metcalfe, 2020 & ongoing, originally by Ridgeway). Indebted to Elith/Leathwick/Hastie 2008 'Working Guide' tools:::Rd_expr_doi("10.1111/j.1365-2656.2008.01390.x") ; workflow follows Appendix S3. See https://www.simondedman.com/ for published guides and papers using this package. package
Maintainer : Simon Dedman simondedman@gmail.com (ORCID)
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