seegSDM-package: Streamlined functions for species distribution modelling in...

In SEEG-Oxford/seegSDM: Streamlined Functions for Species Distribution Modelling in the SEEG Research Group

Description

The present focus is on ensemble boosted regression tree methods such as those used in Bhatt et al. (2013). This will be developed to incorporate other statistical models, methods for selection of pseudo-absence data and other modelling choices.

Details

Package:	seegSDM
Type:	Package
Version:	0.1-9
Date:	2016-03-14
License:	GPL(>=2)

Functions are provided to assist with the seeg SDM workflow:

1. quality control of occurrence and covariate data

2. generation of pseudo-absence data

3. fitting ensembles of models in parallel

4. combining model ensembles to produce final predictions, uncertainty estimates and model summaries

Author(s)

Nick Golding, with some functions based on code by Samir Bhatt

References

Bhatt et al. (2013) The global distribution and burden of dengue. Nature http://www.nature.com/nature/journal/v496/n7446/full/nature12060.html

See Also

Functions for quality control:

checkOccurrence, checkRasters

Functions for manipulating rasters and shapefiles:

biasGrid, featureDensity, gaussWindow, osgb36, wgs84, en2os, extent2poly, importRasters, maxExtent, percCover, buildSP, nearestLand, extractAdmin, getGAUL, occurrence2SPDF

Functions for generating pseudo-absences:

bgDistance, bgSample

Functions for running and assessing BRT ensembles:

runBRT, getEffectPlots, getRelInf, combinePreds

Miscellaneous functions:

rmse, sdWeighted, splitIdx, subsample

Synthetic data for testing and examples: occurrence

SEEG-Oxford/seegSDM documentation built on May 9, 2019, 11:08 a.m.