Prepare data for the analysis | Acquire environmental variables | Prepare presence and background locations | Create an SWD object | Explore the SWD object | Save an SWD object | Train a model | Train a model with default settings | Explore an SDMmodel object | Train a model changing the default settings | Make prediction | Create a distribution map | Plot a distribution map | Plot a presence/absence map | Evaluate a model | AUC | TSS | AICc | Training and testing | Cross validation | Spatial Cross Validation | References

Training, validation and testing split | Check the effect of varying one hyperparameter | Tune hyperparameters | Random search | Optimize Model | Evaluate final model | Hyperparameters tuning with cross validation

Train the model | Tune model hyperparameters | Evaluate the final model | References

Load data and train model | Variable importance | Permutation importance | Jackknife test for variable importance | Response curves | Model report | Data-driven variable selection | Remove highly correlated variables | Remove variables with low importance | References