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")

2.1.6 by Brian D. Williamson, 9 days ago


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

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


See at CRAN