A unified framework for valid statistical inference on algorithm-agnostic measures of intrinsic variable importance. You provide the data, a method for estimating the conditional mean of the outcome given the covariates, choose a variable importance measure, and specify variable(s) of interest; 'vimp' takes care of the rest.
Maintainer: Brian Williamson https://bdwilliamson.github.io/ Contributor: Jean Feng https://www.jeanfeng.com
Brian D. Williamson
Peter B. Gilbert
Noah R. Simon
doi: 10.1111/biom.13392 (R-squared-based variable importance)
doi: 10.1111/biom.13389 (Rejoinder to discussion on R-squared-based variable importance article)
http://proceedings.mlr.press/v119/williamson20a.html (general Shapley-based variable importance)
doi: 10.1080/01621459.2021.2003200 (general variable importance)
Other useful links:
Report bugs at https://github.com/bdwilliamson/vimp/issues
The packages that we import either make the internal code nice (dplyr, magrittr, tibble, rlang, MASS, data.table), are directly relevant to estimating the conditional mean (SuperLearner) or predictiveness measures (ROCR), or are necessary for hypothesis testing (stats) or confidence intervals (boot, only for bootstrap intervals).
We suggest several other packages: xgboost, ranger, gam, glmnet, polspline, and quadprog allow a flexible library of candidate learners in the Super Learner; ggplot2 and cowplot help with plotting variable importance estimates; testthat and covr help with unit tests; and knitr, rmarkdown, and tidyselect help with the vignettes and examples.
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