Small Sample Size Species Distribution Modeling
Return scaled variables to the original scale using means and SDs
Density-ratio SDM estimation with Maxnet
Density-ratio SDM estimation with RuLSIF
Density-ratio SDM estimation with uLSIF
Generate ensemble predictions from S4DM range maps
Evaluate S4DM range map quality
Fit density-ratio distribution models in a plug-and-play framework.
Fit presence-background distribution models in a plug-and-play framewo...
Extract background data for SDM fitting.
Extract presence data for SDM fitting.
Internal function for getting available function names.
Generate Response Curves
Make a range map using plug-and-play modeling.
Internal function for fitting gaussian distributions in plug-and-play ...
Internal function for fitting KDE distributions in plug-and-play SDMs.
Internal function for fitting lobagoc distributions in plug-and-play S...
Internal function for returning empty pnp_estimate class models
Internal function for rangebagging in plug-and-play SDMs.
Internal function for fitting vine copula distributions in plug-and-pl...
Projects fitted density-ratio distribution models onto new covariates.
Projects fitted plug-and-play distribution models onto new covariates.
Rescale a dataset using vectors of means and SDs
Thresholds a continuous relative occurrence rate raster to create a bi...
Split data for k-fold spatially stratified cross validation
Split data for k-fold spatially stratified cross validation
Implements a set of distribution modeling methods that are suited to species with small sample sizes (e.g., poorly sampled species or rare species). While these methods can also be used on well-sampled taxa, they are united by the fact that they can be utilized with relatively few data points. More details on the currently implemented methodologies can be found in Drake and Richards (2018) <doi:10.1002/ecs2.2373>, Drake (2015) <doi:10.1098/rsif.2015.0086>, and Drake (2014) <doi:10.1890/ES13-00202.1>.