| auc | Area under the ROC curve |
| auto_cor | Multicollinearity reduction via Pearson correlation |
| auto_vif | Multicollinearity reduction via Variance Inflation Factor |
| beowulf_cluster | Create a Beowulf cluster for parallel computing |
| case_weights | Generate case weights for imbalanced binary data |
| default_distance_thresholds | Default distance thresholds for spatial predictors |
| dot-vif_to_df | Convert VIF values to data frame |
| double_center_distance_matrix | Double-center a distance matrix |
| filter_spatial_predictors | Remove redundant spatial predictors |
| get_evaluation | Extract evaluation metrics from cross-validated model |
| get_importance | Extract variable importance from model |
| get_importance_local | Extract local variable importance from model |
| get_moran | Extract Moran's I test results for model residuals |
| get_performance | Extract out-of-bag performance metrics from model |
| get_predictions | Extract fitted predictions from model |
| get_residuals | Extract model residuals |
| get_response_curves | Extract response curve data for plotting |
| get_spatial_predictors | Extract spatial predictors from spatial model |
| is_binary | Check if variable is binary with values 0 and 1 |
| make_spatial_fold | Create spatially independent training and testing folds |
| make_spatial_folds | Create multiple spatially independent training and testing... |
| mem | Compute Moran's Eigenvector Maps from distance matrix |
| mem_multithreshold | Compute Moran's Eigenvector Maps across multiple distance... |
| moran | Moran's I test for spatial autocorrelation |
| moran_multithreshold | Moran's I test across multiple distance thresholds |
| normality | Normality test of a numeric vector |
| objects_size | Display sizes of objects in current R environment |
| optimization_function | Compute optimization scores for spatial predictor selection |
| pca | Compute Principal Component Analysis |
| pca_multithreshold | Compute Principal Component Analysis at multiple distance... |
| pipe | Pipe operator |
| plants_df | Plant richness and predictors for American ecoregions |
| plants_distance | Distance matrix between ecoregion edges |
| plants_predictors | Predictor variable names for plant richness examples |
| plants_response | Response variable name for plant richness examples |
| plants_rf | Example fitted random forest model |
| plants_rf_spatial | Example fitted spatial random forest model |
| plants_xy | Coordinates for plant richness data |
| plot_evaluation | Visualize spatial cross-validation results |
| plot_importance | Visualize variable importance scores |
| plot_moran | Plots a Moran's I test of model residuals |
| plot_optimization | Optimization plot of a selection of spatial predictors |
| plot_residuals_diagnostics | Plot residuals diagnostics |
| plot_response_curves | Plots the response curves of a model. |
| plot_response_surface | Plots the response surfaces of a random forest model |
| plot_training_df | Scatterplots of a training data frame |
| plot_training_df_moran | Moran's I plots of a training data frame |
| plot_tuning | Plots a tuning object produced by 'rf_tuning()' |
| prepare_importance_spatial | Prepares variable importance objects for spatial models |
| Custom print method for random forest models | |
| print_evaluation | Prints cross-validation results |
| print_importance | Prints variable importance |
| print_moran | Prints results of a Moran's I test |
| print_performance | print_performance |
| rank_spatial_predictors | Ranks spatial predictors |
| rescale_vector | Rescales a numeric vector into a new range |
| residuals_diagnostics | Normality test of a numeric vector |
| rf | Random forest models with Moran's I test of the residuals |
| rf_compare | Compares models via spatial cross-validation |
| rf_evaluate | Evaluates random forest models with spatial cross-validation |
| rf_importance | Contribution of each predictor to model transferability |
| rf_repeat | Fits several random forest models on the same data |
| rf_spatial | Fits spatial random forest models |
| rf_tuning | Tuning of random forest hyperparameters via spatial... |
| root_mean_squared_error | RMSE and normalized RMSE |
| select_spatial_predictors_recursive | Finds optimal combinations of spatial predictors |
| select_spatial_predictors_sequential | Sequential introduction of spatial predictors into a model |
| setup_parallel_execution | Setup parallel execution with automatic backend detection |
| standard_error | Standard error of the mean of a numeric vector |
| statistical_mode | Statistical mode of a vector |
| the_feature_engineer | Suggest variable interactions and composite features for... |
| thinning | Applies thinning to pairs of coordinates |
| thinning_til_n | Applies thinning to pairs of coordinates until reaching a... |
| weights_from_distance_matrix | Transforms a distance matrix into a matrix of weights |
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