CRAN/E | gbm.auto

gbm.auto

Automated Boosted Regression Tree Modelling and Mapping Suite

Installation

About

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 for published guides and papers using this package.

Citation gbm.auto citation info

Key Metrics

Version 2023.08.31
R ≥ 3.5.0
Published 2023-09-01 210 days ago
Needs compilation? no
License MIT
License File
CRAN checks gbm.auto results
Language en-GB

Downloads

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Maintainer

Maintainer

Simon Dedman

simondedman@gmail.com

Authors

Simon Dedman

aut / cre

Material

README
NEWS
Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

gbm.auto archive

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