vimp: Perform Inference on Algorithm-Agnostic Variable Importance (original) (raw)

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).

Version: 2.3.3
Depends: R (≥ 3.1.0)
Imports: SuperLearner, stats, dplyr, magrittr, ROCR, tibble, rlang, MASS, boot, data.table
Suggests: knitr, rmarkdown, gam, xgboost, glmnet, ranger, polspline, quadprog, covr, testthat, ggplot2, cowplot, cvAUC, tidyselect, WeightedROC, purrr
Published: 2023-08-28
DOI: 10.32614/CRAN.package.vimp
Author: Brian D. WilliamsonORCID iD [aut, cre], Jean Feng [ctb], Charlie Wolock [ctb], Noah Simon ORCID iD [ths], Marco Carone ORCID iD [ths]
Maintainer: Brian D. Williamson <brian.d.williamson at kp.org>
BugReports: https://github.com/bdwilliamson/vimp/issues
License: MIT + file
URL: https://bdwilliamson.github.io/vimp/,https://github.com/bdwilliamson/vimp,http://bdwilliamson.github.io/vimp/
NeedsCompilation: no
Materials: NEWS
CRAN checks: vimp results

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