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 <http://www.simondedman.com/> for published guides and papers using this package.

Version: 1.4.1
Depends: R (≥ 3.5.0)
Imports: gbm (≥ 2.1.1), dismo (≥ 1.0-15), beepr (≥ 1.2), mapplots (≥ 1.5), maptools (≥ 0.9-1), rgdal (≥ 1.1-10), rgeos (≥ 0.3-19), raster (≥ 2.5-8), sf (≥ 0.9-7), shapefiles (≥ 0.7), stats (≥ 3.3.1)
Published: 2021-02-24
Author: Simon Dedman [aut, cre], Hans Gerritsen [aut]
Maintainer: Simon Dedman <simondedman at gmail.com>
License: MIT + file LICENSE
NeedsCompilation: no
Language: en-GB
Materials: README NEWS
CRAN checks: gbm.auto results

Downloads:

Reference manual: gbm.auto.pdf
Package source: gbm.auto_1.4.1.tar.gz
Windows binaries: r-devel: gbm.auto_1.4.1.zip, r-release: gbm.auto_1.4.1.zip, r-oldrel: gbm.auto_1.4.1.zip
macOS binaries: r-release: gbm.auto_1.4.1.tgz, r-oldrel: gbm.auto_1.4.1.tgz
Old sources: gbm.auto archive

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