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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install.packages("vimp")

2.1.0 by Brian D. Williamson, 4 months ago


https://github.com/bdwilliamson/vimp


Report a bug at https://github.com/bdwilliamson/vimp/issues


Browse source code at https://github.com/cran/vimp


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