A general test for conditional independence in supervised learning algorithms as proposed by Watson & Wright (2021) <doi:10.1007/s10994-021-06030-6>. Implements a conditional variable importance measure which can be applied to any supervised learning algorithm and loss function. Provides statistical inference procedures without parametric assumptions and applies equally well to continuous and categorical predictors and outcomes.
Version: 0.1.5 Imports: foreach, mlr3, lgr, knockoff Suggests: mlr3learners, ranger, glmnet, testthat (≥ 3.0.0), knitr, rmarkdown, doParallel Published: 2024-11-25 DOI: 10.32614/CRAN.package.cpi Author: Marvin N. Wright [aut, cre], David S. Watson [aut] Maintainer: Marvin N. Wright <cran at wrig.de> BugReports: https://github.com/bips-hb/cpi/issues License: GPL (≥ 3) URL: https://github.com/bips-hb/cpi, https://bips-hb.github.io/cpi/ NeedsCompilation: no Citation: cpi citation info Materials: README, NEWS CRAN checks: cpi results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=cpi to link to this page.
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