Flexible, Ensemble-Based Variable Selection with Potentially Missing Data
Extract the learner-specific importance from a glm object
Extract the learner-specific importance from a glmnet object
Extract the learner-specific importance from a mean object
Extract the learner-specific importance from a polymars object
Extract the learner-specific importance from a ranger object
Extract the learner-specific importance from a fitted SuperLearner alg...
Extract extrinsic importance from a Super Learner object
Extract the learner-specific importance from an svm object
Extract the learner-specific importance from an xgboost object
Perform extrinsic, ensemble-based variable selection
flevr: Flexible, Ensemble-Based Variable Selection with Potentially Mi...
Get an augmented set based on the next-most significant variables
Get an initial selected set based on intrinsic importance and a base m...
Control parameters for intrinsic variable selection
Perform intrinsic, ensemble-based variable selection
Pool selected sets from multiply-imputed data
Pool SPVIM Estimates Using Rubin's Rules
Wrapper for using Super Learner-based extrinsic selection within stabi...
Super Learner wrapper for a ranger object with variable importance
Extract a Variance-Covariance Matrix for SPVIM Estimates
Perform variable selection in settings with possibly missing data based on extrinsic (algorithm-specific) and intrinsic (population-level) variable importance. Uses a Super Learner ensemble to estimate the underlying prediction functions that give rise to estimates of variable importance. For more information about the methods, please see Williamson and Huang (2024) <doi:10.1515/ijb-2023-0059>.