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.
[aut, cre],
David S. Watson [aut]