R: Perform Inference on Algorithm-Agnostic Variable Importance

# Perform Inference on Algorithm-Agnostic Variable Importance

## Documentation for package ‘vimp’ version 2.3.6

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS

## Help Pages

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_npv | Estimate the positive predictive value (NPV)
measure_ppv | Estimate the positive predictive value (PPV)
measure_r_squared | Estimate R-squared
measure_sensitivity | Estimate the sensitivity
measure_specificity | Estimate the specificity
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