Package: flevr 0.0.4

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

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 (2023+) <arxiv:2202.12989>.

Authors:Brian D. Williamson [aut, cre]

flevr_0.0.4.tar.gz
flevr_0.0.4.zip(r-4.5)flevr_0.0.4.zip(r-4.4)flevr_0.0.4.zip(r-4.3)
flevr_0.0.4.tgz(r-4.4-any)flevr_0.0.4.tgz(r-4.3-any)
flevr_0.0.4.tar.gz(r-4.5-noble)flevr_0.0.4.tar.gz(r-4.4-noble)
flevr_0.0.4.tgz(r-4.4-emscripten)flevr_0.0.4.tgz(r-4.3-emscripten)
flevr.pdf |flevr.html✨
flevr/json (API)
NEWS

# Install 'flevr' in R:
install.packages('flevr', repos = c('https://bdwilliamson.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/bdwilliamson/flevr/issues

Datasets:

On CRAN:

5.48 score 5 stars 2 scripts 156 downloads 19 exports 88 dependencies

Last updated 9 months agofrom:e100b88e6d. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 04 2024
R-4.5-winOKNov 04 2024
R-4.5-linuxOKNov 04 2024
R-4.4-winOKNov 04 2024
R-4.4-macOKNov 04 2024
R-4.3-winOKNov 04 2024
R-4.3-macOKNov 04 2024

Exports:extract_importance_glmextract_importance_glmnetextract_importance_meanextract_importance_polymarsextract_importance_rangerextract_importance_SLextract_importance_SL_learnerextract_importance_svmextract_importance_xgboostextrinsic_selectionget_augmented_setget_base_setintrinsic_controlintrinsic_selectionpool_selected_setspool_spvimsSL_stabs_fitfunSL.ranger.impspvim_vcov

Dependencies:bitopscaretcaToolsclasscliclockcodetoolscolorspacecpp11cvAUCdata.tablediagramdigestdplyre1071fansifarverforeachfuturefuture.applygamgenericsggplot2globalsgluegowergplotsgtablegtoolshardhatipredisobanditeratorskernlabKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmgcvModelMetricsmunsellmvtnormnlmennetnnlsnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6rangerRColorBrewerRcppRcppEigenrecipesreshape2rlangROCRrpartscalesshapeSQUAREMstringistringrSuperLearnersurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr

Extrinsic variable selection

Rendered fromextrinsic_selection.Rmdusingknitr::rmarkdownon Nov 04 2024.

Last update: 2023-11-23
Started: 2021-06-16

Intrinsic variable selection

Rendered fromintrinsic_selection.Rmdusingknitr::rmarkdownon Nov 04 2024.

Last update: 2023-11-22
Started: 2021-06-16

Introduction to flevr

Brian D. Williamson

Rendered fromintroduction_to_flevr.Rmdusingknitr::rmarkdownon Nov 04 2024.

Last update: 2021-06-16
Started: 2021-06-16

Readme and manuals

Help Manual

Help pageTopics
Example biomarker databiomarkers
Extract the learner-specific importance from a glm objectextract_importance_glm
Extract the learner-specific importance from a glmnet objectextract_importance_glmnet
Extract the learner-specific importance from a mean objectextract_importance_mean
Extract the learner-specific importance from a polymars objectextract_importance_polymars
Extract the learner-specific importance from a ranger objectextract_importance_ranger
Extract extrinsic importance from a Super Learner objectextract_importance_SL
Extract the learner-specific importance from a fitted SuperLearner algorithmextract_importance_SL_learner
Extract the learner-specific importance from an svm objectextract_importance_svm
Extract the learner-specific importance from an xgboost objectextract_importance_xgboost
Perform extrinsic, ensemble-based variable selectionextrinsic_selection
flevr: Flexible, Ensemble-Based Variable Selection with Potentially Missing Dataflevr
Get an augmented set based on the next-most significant variablesget_augmented_set
Get an initial selected set based on intrinsic importance and a base methodget_base_set
Control parameters for intrinsic variable selectionintrinsic_control
Perform intrinsic, ensemble-based variable selectionintrinsic_selection
Pool selected sets from multiply-imputed datapool_selected_sets
Pool SPVIM Estimates Using Rubin's Rulespool_spvims
Wrapper for using Super Learner-based extrinsic selection within stability selectionSL_stabs_fitfun
Super Learner wrapper for a ranger object with variable importanceSL.ranger.imp
Extract a Variance-Covariance Matrix for SPVIM Estimatesspvim_vcov