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 
[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:
Downloads:
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