Perform Inference on Algorithm-Agnostic Variable Importance R package vimp version 2.3.6 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). Software Depends: R (>= 3.1.0) 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 Brian D. Williamson <brian.d.williamson@kp.org> Comprehensive R Archive Network (CRAN) Brian D. Williamson [aut, cre] (ORCID: <https://orcid.org/0000-0002-7024-548X>), Jean Feng [ctb], Charlie Wolock [ctb], Noah Simon [ths] (ORCID: <https://orcid.org/0000-0002-8985-2474>), Marco Carone [ths] (ORCID: <https://orcid.org/0000-0003-2106-0953>) MIT + file LICENSE (https://CRAN.R-project.org/package=vimp/LICENSE) 2025-08-28 application/tgz https://CRAN.R-project.org/package=vimp doi:10.32614/CRAN.package.vimp