Version 0.2.0
Additional significant changes include:
- Now activate parallel processing by setting parallel=TRUE, and change number of cores used with numCores.
- Added a "models" slot in ENMevaluation object class to hold Maxent model objects. This allows the user to access the lambda values and original results table generated by Maxent, and use the dismo::predict() function to create logistic predictions and project the model to new areas and/or time periods.
- Fixed a bug that allowed only a single categorical variable; now multiple categorical variables work.
- Added an argument in the ENMevaluate function to turn off raster prediction generation to save time (default is rasterPreds=TRUE).