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 |