_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/
r-vimp 2.3.6
Propagated dependencies: r-tibble@3.3.0 r-superlearner@2.0-29 r-rocr@1.0-11 r-rlang@1.1.6 r-mass@7.3-65 r-magrittr@2.0.4 r-dplyr@1.1.4 r-data-table@1.17.8 r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/v.scm (guix-cran packages v)
Home page: https://bdwilliamson.github.io/vimp/
Licenses: Expat
Synopsis: Perform Inference on Algorithm-Agnostic Variable Importance
Description:

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).

Total results: 1