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: |
1.5.0 |
| 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: |
2021-10-01 |
| 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 |
Documentation:
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