vimp: 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).
| 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. Williamson
 [aut, cre],
Jean Feng [ctb],
Charlie Wolock [ctb],
Noah Simon [ths],
Marco Carone
[ths] |
| Maintainer: | Brian D. Williamson <brian.d.williamson at kp.org> |
| BugReports: | https://github.com/bdwilliamson/vimp/issues |
| License: | MIT + file LICENSE |
| URL: | https://bdwilliamson.github.io/vimp/, https://github.com/bdwilliamson/vimp, http://bdwilliamson.github.io/vimp/ |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | vimp results |
Documentation:
| Reference manual: | vimp.pdf |
| Vignettes: |
Introduction to 'vimp' Variable importance with coarsened data Using precomputed regression function estimates in 'vimp' Types of VIMs |
Downloads:
| Package source: | vimp_2.3.3.tar.gz |
| Windows binaries: | r-devel: vimp_2.3.3.zip, r-release: vimp_2.3.3.zip, r-oldrel: vimp_2.3.3.zip |
| macOS binaries: | r-release (arm64): vimp_2.3.3.tgz, r-oldrel (arm64): vimp_2.3.3.tgz, r-release (x86_64): vimp_2.3.3.tgz, r-oldrel (x86_64): vimp_2.3.3.tgz |
| Old sources: | vimp archive |
Reverse dependencies:
| Reverse suggests: | flevr, tidyhte |
Linking:
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