| average_vim | Average multiple independent importance estimates | 
| bootstrap_se | Compute bootstrap-based standard error estimates for variable importance | 
| check_fitted_values | Check pre-computed fitted values for call to vim, cv_vim, or sp_vim | 
| check_inputs | Check inputs to a call to vim, cv_vim, or sp_vim | 
| create_z | Create complete-case outcome, weights, and Z | 
| cv_vim | Nonparametric Intrinsic Variable Importance Estimates and Inference using Cross-fitting | 
| estimate | Estimate a Predictiveness Measure | 
| estimate.predictiveness_measure | Obtain a Point Estimate and Efficient Influence Function Estimate for a Given Predictiveness Measure | 
| estimate_eif_projection | Estimate projection of EIF on fully-observed variables | 
| estimate_nuisances | Estimate nuisance functions for average value-based VIMs | 
| estimate_type_predictiveness | Estimate Predictiveness Given a Type | 
| est_predictiveness | Estimate a nonparametric predictiveness functional | 
| est_predictiveness_cv | Estimate a nonparametric predictiveness functional using cross-fitting | 
| extract_sampled_split_predictions | Extract sampled-split predictions from a CV.SuperLearner object | 
| format.predictiveness_measure | Format a 'predictiveness_measure' object | 
| format.vim | Format a 'vim' object | 
| get_cv_sl_folds | Get a numeric vector with cross-validation fold IDs from CV.SuperLearner | 
| get_full_type | Obtain the type of VIM to estimate using partial matching | 
| get_test_set | Return test-set only data | 
| make_folds | Create Folds for Cross-Fitting | 
| make_kfold | Turn folds from 2K-fold cross-fitting into individual K-fold folds | 
| measure_accuracy | Estimate the classification accuracy | 
| measure_anova | Estimate ANOVA decomposition-based variable importance. | 
| measure_auc | Estimate area under the receiver operating characteristic curve (AUC) | 
| measure_average_value | Estimate the average value under the optimal treatment rule | 
| measure_cross_entropy | Estimate the cross-entropy | 
| measure_deviance | Estimate the deviance | 
| measure_mse | Estimate mean squared error | 
| measure_r_squared | Estimate R-squared | 
| merge_vim | Merge multiple 'vim' objects into one | 
| predictiveness_measure | Construct a Predictiveness Measure | 
| print.predictiveness_measure | Print 'predictiveness_measure' objects | 
| print.vim | Print 'vim' objects | 
| process_arg_lst | Process argument list for Super Learner estimation of the EIF | 
| run_sl | Run a Super Learner for the provided subset of features | 
| sample_subsets | Create necessary objects for SPVIMs | 
| scale_est | Return an estimator on a different scale | 
| spvim_ics | Influence function estimates for SPVIMs | 
| spvim_se | Standard error estimate for SPVIM values | 
| sp_vim | Shapley Population Variable Importance Measure (SPVIM) Estimates and Inference | 
| vim | Nonparametric Intrinsic Variable Importance Estimates and Inference | 
| vimp_accuracy | Nonparametric Intrinsic Variable Importance Estimates: Classification accuracy | 
| vimp_anova | Nonparametric Intrinsic Variable Importance Estimates: ANOVA | 
| vimp_auc | Nonparametric Intrinsic Variable Importance Estimates: AUC | 
| vimp_ci | Confidence intervals for variable importance | 
| vimp_deviance | Nonparametric Intrinsic Variable Importance Estimates: Deviance | 
| vimp_hypothesis_test | Perform a hypothesis test against the null hypothesis of delta importance | 
| vimp_regression | Nonparametric Intrinsic Variable Importance Estimates: ANOVA | 
| vimp_rsquared | Nonparametric Intrinsic Variable Importance Estimates: R-squared | 
| vimp_se | Estimate variable importance standard errors | 
| vrc01 | Neutralization sensitivity of HIV viruses to antibody VRC01 |