Automated Boosted Regression Tree Modelling and Mapping Suite R package gbm.auto version 2024.10.01 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' <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. Software Depends: R (>= 3.5.0) Imports: beepr (>= 1.2), dismo (>= 1.3-14), dplyr (>= 1.0.9), gbm (>= 2.1.1), ggmap (>= 3.0.2), ggplot2 (>= 3.4.2), ggspatial (>= 1.1.9), lifecycle, lubridate (>= 1.9.2), mapplots (>= 1.5), Metrics (>= 0.1.4), readr (>= 2.1.4), sf (>= 0.9-7), stars (>= 0.6-3), starsExtra (>= 0.2.7), stats (>= 3.3.1), stringi (>= 1.6.1), tidyselect (>= 1.2.0), viridis (>= 0.6.4) Simon Dedman <simondedman@gmail.com> Comprehensive R Archive Network (CRAN) Simon Dedman [aut, cre] (ORCID: <https://orcid.org/0000-0002-9108-972X>) MIT + file LICENSE (https://CRAN.R-project.org/package=gbm.auto/LICENSE) 2024-10-01 application/tgz https://CRAN.R-project.org/package=gbm.auto doi:10.32614/CRAN.package.gbm.auto en-GB