Perform Inference on Algorithm-Agnostic Variable Importance

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. (arXiv, 2020+) , and Williamson and Feng (ICML, 2020).


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


2.2.5 by Brian D. Williamson, 2 months ago,

Report a bug at

Browse source code at

Authors: Brian D. Williamson [aut, cre] , Jean Feng [ctb] , Noah Simon [ths] , Marco Carone [ths]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports SuperLearner, stats, dplyr, magrittr, ROCR, tibble, rlang, MASS, boot, data.table

Suggests knitr, rmarkdown, gam, xgboost, glmnet, ranger, polspline, quadprog, covr, testthat, ggplot2, cowplot, cvAUC, tidyselect

See at CRAN