R: Flexible, Ensemble-Based Variable Selection with Potentially Missing Data

# Flexible, Ensemble-Based Variable Selection with Potentially Missing Data

## Documentation for package ‘flevr’ version 0.0.5

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

biomarkers | Example biomarker data
extract_importance_glm | Extract the learner-specific importance from a glm object
extract_importance_glmnet | Extract the learner-specific importance from a glmnet object
extract_importance_mean | Extract the learner-specific importance from a mean object
extract_importance_polymars | Extract the learner-specific importance from a polymars object
extract_importance_ranger | Extract the learner-specific importance from a ranger object
extract_importance_SL | Extract extrinsic importance from a Super Learner object
extract_importance_SL_learner | Extract the learner-specific importance from a fitted SuperLearner algorithm
extract_importance_svm | Extract the learner-specific importance from an svm object
extract_importance_xgboost | Extract the learner-specific importance from an xgboost object
extrinsic_selection | Perform extrinsic, ensemble-based variable selection
get_augmented_set | Get an augmented set based on the next-most significant variables
get_base_set | Get an initial selected set based on intrinsic importance and a base method
intrinsic_control | Control parameters for intrinsic variable selection
intrinsic_selection | Perform intrinsic, ensemble-based variable selection
pool_selected_sets | Pool selected sets from multiply-imputed data
pool_spvims | Pool SPVIM Estimates Using Rubin's Rules
SL.ranger.imp | Super Learner wrapper for a ranger object with variable importance
SL_stabs_fitfun | Wrapper for using Super Learner-based extrinsic selection within stability selection
spvim_vcov | Extract a Variance-Covariance Matrix for SPVIM Estimates