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' <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.

Version:	2024.10.01
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)
Published:	2024-10-01
DOI:	10.32614/CRAN.package.gbm.auto
Author:	Simon Dedman [aut, cre]
Maintainer:	Simon Dedman <simondedman at gmail.com>
License:	MIT + file LICENSE
NeedsCompilation:	no
Language:	en-GB
Citation:	gbm.auto citation info
Materials:	README, NEWS
CRAN checks:	gbm.auto results

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