Man pages for bdwilliamson/nova
Perform Inference on Algorithm-Agnostic Variable Importance
| average_vim | Average multiple independent importance estimates |
| bootstrap_se | Compute bootstrap-based standard error estimates for variable... |
| check_fitted_values | Check pre-computed fitted values for call to vim, cv_vim, or... |
| 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... |
| estimate | Estimate a Predictiveness Measure |
| estimate_eif_projection | Estimate projection of EIF on fully-observed variables |
| estimate_nuisances | Estimate nuisance functions for average value-based VIMs |
| estimate.predictiveness_measure | Obtain a Point Estimate and Efficient Influence Function... |
| estimate_type_predictiveness | Estimate Predictiveness Given a Type |
| est_predictiveness | Estimate a nonparametric predictiveness functional |
| est_predictiveness_cv | Estimate a nonparametric predictiveness functional using... |
| extract_sampled_split_predictions | Extract sampled-split predictions from a CV.SuperLearner... |
| 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... |
| 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... |
| measure_accuracy | Estimate the classification accuracy |
| measure_anova | Estimate ANOVA decomposition-based variable importance. |
| measure_auc | Estimate area under the receiver operating characteristic... |
| 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 |
| sp_vim | Shapley Population Variable Importance Measure (SPVIM)... |
| spvim_ics | Influence function estimates for SPVIMs |
| spvim_se | Standard error estimate for SPVIM values |
| vim | Nonparametric Intrinsic Variable Importance Estimates and... |
| vimp | vimp: Perform Inference on Algorithm-Agnostic Intrinsic... |
| vimp_accuracy | Nonparametric Intrinsic Variable Importance Estimates:... |
| 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:... |
| vimp_hypothesis_test | Perform a hypothesis test against the null hypothesis of... |
| vimp_regression | Nonparametric Intrinsic Variable Importance Estimates: ANOVA |
| vimp_rsquared | Nonparametric Intrinsic Variable Importance Estimates:... |
| vimp_se | Estimate variable importance standard errors |
| vrc01 | Neutralization sensitivity of HIV viruses to antibody VRC01 |
