.stash_last_result      Save most recent results to search path
.use_case_weights_with_yardstick
                        Determine if case weights should be passed on
                        to yardstick
augment.tune_results    Augment data with holdout predictions
autoplot.tune_results   Plot tuning search results
collect_predictions     Obtain and format results produced by tuning
                        functions
compute_metrics         Calculate and format metrics from tuning
                        functions
conf_mat_resampled      Compute average confusion matrix across
                        resamples
control_bayes           Control aspects of the Bayesian search process
control_last_fit        Control aspects of the last fit process
coord_obs_pred          Use same scale for plots of observed vs
                        predicted values
example_ames_knn        Example Analysis of Ames Housing Data
expo_decay              Exponential decay function
extract-tune            Extract elements of 'tune' objects
extract_model           Convenience functions to extract model
filter_parameters       Remove some tuning parameter results
finalize_model          Splice final parameters into objects
fit_best                Fit a model to the numerically optimal
                        configuration
fit_resamples           Fit multiple models via resampling
int_pctl.tune_results   Bootstrap confidence intervals for performance
                        metrics
last_fit                Fit the final best model to the training set
                        and evaluate the test set
message_wrap            Write a message that respects the line width
prob_improve            Acquisition function for scoring parameter
                        combinations
show_best               Investigate best tuning parameters
show_notes              Display distinct errors from tune objects
tune_bayes              Bayesian optimization of model parameters.
tune_grid               Model tuning via grid search
