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) .


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2.1.0 by Brian D. Williamson, 4 months ago

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Authors: Brian D. Williamson [aut, cre] , Noah Simon [aut] , Marco Carone [aut]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports SuperLearner, stats, dplyr, magrittr, ROCR, tibble, rlang, MASS

Suggests knitr, rmarkdown, gam, xgboost, glmnet, ranger, polspline, quadprog, covr, testthat, ggplot2, cowplot, RCurl, forcats

See at CRAN