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: 2023.08.31
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: 2023-09-01
Author: Simon Dedman ORCID iD [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

Documentation:

Reference manual: gbm.auto.pdf

Downloads:

Package source: gbm.auto_2023.08.31.tar.gz
Windows binaries: r-devel: gbm.auto_2023.08.31.zip, r-release: gbm.auto_2023.08.31.zip, r-oldrel: gbm.auto_2023.08.31.zip
macOS binaries: r-release (arm64): gbm.auto_2023.08.31.tgz, r-oldrel (arm64): gbm.auto_2023.08.31.tgz, r-release (x86_64): gbm.auto_2023.08.31.tgz
Old sources: gbm.auto archive

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