Intrinsic variable selectionFunctions for performing variable selection based on intrinsic variable importance. |
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Perform intrinsic, ensemble-based variable selection |
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Control parameters for intrinsic variable selection |
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Pool SPVIM Estimates Using Rubin's Rules |
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Extract a Variance-Covariance Matrix for SPVIM Estimates |
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Get an initial selected set based on intrinsic importance and a base method |
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Get an augmented set based on the next-most significant variables |
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Extrinsic variable selectionFunctions for performing variable selection based on extrinsic variable importance. |
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Perform extrinsic, ensemble-based variable selection |
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Extract the learner-specific importance from a glm object |
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Extract the learner-specific importance from a glmnet object |
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Extract the learner-specific importance from a mean object |
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Extract the learner-specific importance from a polymars object |
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Extract the learner-specific importance from a ranger object |
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Extract the learner-specific importance from a fitted SuperLearner algorithm |
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Extract extrinsic importance from a Super Learner object |
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Extract the learner-specific importance from an svm object |
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Extract the learner-specific importance from an xgboost object |
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Wrapper for using Super Learner-based extrinsic selection within stability selection |
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Super Learner wrapper for a ranger object with variable importance |
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Biomarker dataDataset for use in the vignettes and examples. |
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Example biomarker data |
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Package documentation homepageLanding page for R help. |
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flevr: Flexible, Ensemble-Based Variable Selection with Potentially Missing Data |
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Internal package utility functionsFunctions for internal convenience, used by other core functions. |
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Pool selected sets from multiply-imputed data |