.vif_to_df              Convert VIF values to data frame
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
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 folds
mem                     Compute Moran's Eigenvector Maps from distance
                        matrix
mem_multithreshold      Compute Moran's Eigenvector Maps across
                        multiple distance thresholds
moran                   Moran's I test for spatial autocorrelation
moran_multithreshold    Moran's I test across multiple distance
                        thresholds
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 thresholds
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
print.rf                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
residuals_test          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 cross-validation
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 random forest models
thinning                Applies thinning to pairs of coordinates
thinning_til_n          Applies thinning to pairs of coordinates until
                        reaching a given n
weights_from_distance_matrix
                        Transforms a distance matrix into a matrix of
                        weights
