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vimp (version 2.3.5)

Perform Inference on Algorithm-Agnostic Variable Importance

Description

Calculate point estimates of and valid confidence intervals for nonparametric, algorithm-agnostic variable importance measures in high and low dimensions, using flexible estimators of the underlying regression functions. For more information about the methods, please see Williamson et al. (Biometrics, 2020), Williamson et al. (JASA, 2021), and Williamson and Feng (ICML, 2020).

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Install

install.packages('vimp')

Monthly Downloads

273

Version

2.3.5

License

MIT + file LICENSE

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Repository

https://github.com/bdwilliamson/vimp

Homepage

https://bdwilliamson.github.io/vimp/

Maintainer

Brian D. Williamson

Last Published

July 23rd, 2025

Functions in vimp (2.3.5)

Return test-set only data

extract_sampled_split_predictions

Extract sampled-split predictions from a CV.SuperLearner object

estimate_type_predictiveness

Estimate Predictiveness Given a Type

Obtain the type of VIM to estimate using partial matching

format.predictiveness_measure

Format a predictiveness_measure object

Create Folds for Cross-Fitting

Format a vim object

Estimate the positive predictive value (PPV)

measure_average_value

Estimate the average value under the optimal treatment rule

Turn folds from 2K-fold cross-fitting into individual K-fold folds

Estimate the positive predictive value (NPV)

Estimate mean squared error

Run a Super Learner for the provided subset of features

process_arg_lst

Process argument list for Super Learner estimation of the EIF

Print vim objects

measure_r_squared

Estimate R-squared

measure_specificity

Estimate the specificity

measure_sensitivity

Estimate the sensitivity

print.predictiveness_measure

Print predictiveness_measure objects

Create necessary objects for SPVIMs

vimp: Perform Inference on Algorithm-Agnostic Intrinsic Variable Importance

Nonparametric Intrinsic Variable Importance Estimates: ANOVA

Influence function estimates for SPVIMs

Nonparametric Intrinsic Variable Importance Estimates: Classification accuracy

Compute bootstrap-based standard error estimates for variable importance

estimate.predictiveness_measure

Obtain a Point Estimate and Efficient Influence Function Estimate for a Given Predictiveness Measure

est_predictiveness_cv

Estimate a nonparametric predictiveness functional using cross-fitting

Estimate a Predictiveness Measure

Create complete-case outcome, weights, and Z

Check inputs to a call to vim, cv_vim, or sp_vim

est_predictiveness

Estimate a nonparametric predictiveness functional

check_fitted_values

Check pre-computed fitted values for call to vim, cv_vim, or sp_vim

Average multiple independent importance estimates

Nonparametric Intrinsic Variable Importance Estimates and Inference using Cross-fitting

estimate_eif_projection

Estimate projection of EIF on fully-observed variables

estimate_nuisances

Estimate nuisance functions for average value-based VIMs

get_cv_sl_folds

Get a numeric vector with cross-validation fold IDs from CV.SuperLearner

measure_accuracy

Estimate the classification accuracy

Estimate ANOVA decomposition-based variable importance.

Estimate area under the receiver operating characteristic curve (AUC)

measure_cross_entropy

Estimate the cross-entropy

predictiveness_measure

Construct a Predictiveness Measure

Merge multiple vim objects into one

measure_deviance

Estimate the deviance

Return an estimator on a different scale

Nonparametric Intrinsic Variable Importance Estimates: AUC

Nonparametric Intrinsic Variable Importance Estimates and Inference

Shapley Population Variable Importance Measure (SPVIM) Estimates and Inference

Confidence intervals for variable importance

Standard error estimate for SPVIM values