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. (JASA, 2021), and Williamson and Feng (ICML, 2020).


Reference manual

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install.packages("vimp")

2.3.6 by Brian D. Williamson, a year ago


https://bdwilliamson.github.io/vimp/, https://github.com/bdwilliamson/vimp, http://bdwilliamson.github.io/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] (ORCID: , Jean Feng [ctb] , Charlie Wolock [ctb] , Noah Simon [ths] , Marco Carone [ths]


Documentation:   PDF Manual  


MIT + file LICENSE license


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

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


Imported by lvimp.

Suggested by flevr, tidyhte.


See at CRAN