Package: flevr 0.0.5

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 (2024) <doi:10.1515/ijb-2023-0059>.

Authors:Brian D. Williamson [aut, cre]

flevr_0.0.5.tar.gz
flevr_0.0.5.zip(r-4.7-any)flevr_0.0.5.zip(r-4.6-any)flevr_0.0.5.zip(r-4.5-any)
flevr_0.0.5.tgz(r-4.6-any)flevr_0.0.5.tgz(r-4.5-any)
flevr_0.0.5.tar.gz(r-4.7-any)flevr_0.0.5.tar.gz(r-4.6-any)
flevr_0.0.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
flevr/json (API)

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

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

Datasets:

On CRAN:

Conda:

5.18 score 5 stars 2 scripts 322 downloads 19 exports 86 dependencies

Last updated from:9c4fc952ca. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK211
source / vignettesOK275
linux-releaseOK179
macos-releaseOK174
macos-oldrelOK176
windows-develOK148
windows-releaseOK106
windows-oldrelOK110
wasm-releaseOK147

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:bitopscaretcaToolsclasscliclockcodetoolscpp11cvAUCdata.tablediagramdigestdplyre1071farverforeachfuturefuture.applygamgenericsggplot2globalsgluegowergplotsgtablegtoolshardhatipredisobanditeratorskernlabKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixModelMetricsmvtnormnlmennetnnlsnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6rangerRColorBrewerRcppRcppEigenrecipesreshape2rlangROCRrpartS7scalesshapesparsevctrsSQUAREMstringistringrSuperLearnersurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr

Extrinsic variable selection
Introduction | Extrinsic selection with missing data
Rendered fromextrinsic_selection.Rmdusingknitr::rmarkdown

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

Intrinsic variable selection
Introduction | Intrinsic selection with missing data
Rendered fromintrinsic_selection.Rmdusingknitr::rmarkdown

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

Introduction to flevr
Introduction | Installation | Quick start | Extrinsic variable selection | Intrinsic variable selection
Rendered fromintroduction_to_flevr.Rmdusingknitr::rmarkdown

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
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