Species Distribution Model Selection
Plot Variable Importance
Predict ANN
Predict BRT
Predict Maxent
Predict Maxnet
Predict RF
SDMmodel2MaxEnt
SDMmodelCV
Combine Cross Validation models
Confusion Matrix
MaxEnt Thresholds
SDMmodel
Add Samples to Background
AICc
Artificial Neural Network
AUC
Boosted Regression Tree
Check Maxent Installation
Random Forest
Print Correlated Variables
Jackknife Test
Get Tunable Arguments
Grid Search
Maxent
Maxent Variable Importance
Maxnet
Merge SWD Objects
Model Report
Optimize Model
Plot Correlation
Plot Jackknife Test
Plot Presence Absence Map
Plot Prediction
Plot Response Curve
Plot ROC curve
Predict
Predict for Cross Validation
Prepare an SWD object
Create Random Folds
Random Search
Reduce Variables
SDMtune class
SDMtune: Species Distribution Model Selection
Sample With Data
SWD to csv
Thin Data
Thresholds
Train
Train, Validation and Test datasets
True Skill Statistics
Variable Importance
Variable Selection
User-friendly framework that enables the training and the evaluation of species distribution models (SDMs). The package implements functions for data driven variable selection and model tuning and includes numerous utilities to display the results. All the functions used to select variables or to tune model hyperparameters have an interactive real-time chart displayed in the 'RStudio' viewer pane during their execution.
Useful links