Added functions sp_vim, sample_subsets, spvim_ics, spvim_se; these allow computation of Shapely Population Variable Importance (SPVIM)
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sp_vim and helper functions run_sl, sample_subsets, spvim_ics, spvim_se; these will be added in a future releasecv_vim_nodonsker, since cv_vim supersedes this functionsp_vim and helper functions run_sl, sample_subsets, spvim_ics, spvim_se; these functions allow computation of the Shapley Population Variable Importance Measure (SPVIM)cv_vim and vim now use an outer layer of sample splitting for hypothesis testingvimp_auc, vimp_accuracy, vimp_deviance, vimp_rsquaredvimp_regression is now deprecated; use vimp_anova insteadvim; each variable importance function is now a wrapper function around vim with the type argument filled incv_vim_nodonsker is now deprecated; use cv_vim insteadvimp_anova)vimp_anova)None
gam package update by switching library to SL.xgboost, SL.step, and SL.meanNone
gam package update in unit testsNone
cv_vim andcv_vim_nodonsker now return the cross-validation folds used within the functionNone
family for the top-level SuperLearner if run_regression = TRUE; in call cases, the second-stage SuperLearner uses a gaussian familySL.mean as the best-fitting algorithm, the second-stage regression is now run using the original outcome, rather than the first-stage fitted valuescv_vim_nodonsker, which computes the cross-validated naive estimator and the update on the same, single, validation fold. This does not allow for relaxation of the Donsker class conditions.None
two_validation_set_cv, which sets up folds for V-fold cross-validation with two validation sets per foldcv_vim: now, the cross-validated naive estimator is computed on a first validation set, while the update for the corrected estimator is computed using the second validation set (both created from two_validation_set_cv); this allows for relaxation of the Donsker class conditions necessary for asymptotic convergence of the corrected estimator, while making sure that the initial CV naive estimator is not biased high (due to a higher R^2 on the training data)None
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cv_vim: now, the cross-validated naive estimator is computed on the training data for each fold, while the update for the corrected cross-validated estimator is computed using the test data; this allows for relaxation of the Donsker class conditions necessary for asymptotic convergence of the corrected estimatorvim, replaced with individual-parameter functionsvimp_regression to match Python packagecv_vim now can compute regression estimatorsvimp_ci, vimp_se, vimp_update, onestep_based_estimatorNone
Bugfixes etc.