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' ; workflow follows Appendix S3. See <> for published guides and papers using this package.


Reference manual

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1.4.1 by Simon Dedman, 7 months ago

Browse source code at

Authors: Simon Dedman [aut, cre] , Hans Gerritsen [aut]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports gbm, dismo, beepr, mapplots, maptools, rgdal, rgeos, raster, sf, shapefiles, stats

See at CRAN