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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
2024.10.01 |
rolling source/ R- | gbm.auto_2024.10.01.tar.gz |
3.0 MiB |
2024.10.01 |
rolling linux/jammy R-4.5 | gbm.auto_2024.10.01.tar.gz |
3.6 MiB |
2024.10.01 |
latest source/ R- | gbm.auto_2024.10.01.tar.gz |
3.0 MiB |
2024.10.01 |
latest linux/jammy R-4.5 | gbm.auto_2024.10.01.tar.gz |
3.6 MiB |
2024.10.01 |
2026-04-23 source/ R- | gbm.auto_2024.10.01.tar.gz |
3.0 MiB |
2024.10.01 |
2026-04-09 windows/windows R-4.5 | gbm.auto_2024.10.01.zip |
3.6 MiB |
2024.10.01 |
2025-04-20 source/ R- | gbm.auto_2024.10.01.tar.gz |
3.0 MiB |
Dependencies (latest)
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)