The goal of envSDM is to help automate the preparation, tuning and prediction of species distribution models. envSDM attempts to make decisions at each of these steps that are robust(ish) for running SDMs for many, many taxa.

Preparation includes generating:

• optional generation of a (possibly buffered) minimum convex polygon around presences to limit the rest of the process
• density raster of presences
• spatially thickened background points against density raster
• balanced spatial folds from the presences and background points
• environmental data for presences and background points (byo environmental rasters)
• environmental layers that are correlated at presences

Tuning includes:

• three possible algorithms:
  • randomForest::randomForest()
    • always using the randomForest() sampsize argument downsample to the minimum number of presences
  • maxnet::maxnet()
  • predicts::envelope()
• ability to use multiple metrics for choosing a 'best' tune

Prediction includes choice of prediction to any combination of:

• full extent of environmental layers
• only within the minimum convex polygon around the presences
• only within another specified area of interest

Installation

envSDM is not on CRAN.

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("acanthiza/envSDM")