Flexible, Ensemble-Based Variable Selection with Potentially Missing Data

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Documentation for package ‘flevr’ version 0.0.4

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

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