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)flevr_0.0.5.zip(r-4.6)flevr_0.0.5.zip(r-4.5)
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✨
card.svg |card.png
flevr/json (API)
NEWS

# 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:

biomarkers - Example biomarker data

On CRAN:

5.18 score 5 stars 2 scripts 138 downloads 19 exports 86 dependencies

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

Target	Result	Time
linux-devel-x86_64	OK	181
source / vignettes	OK	283
linux-release-x86_64	OK	181
macos-release-arm64	OK	168
macos-oldrel-arm64	OK	213
windows-devel	OK	129
windows-release	OK	134
windows-oldrel	OK	112
wasm-release	OK	138

Exports:extract_importance_glm extract_importance_glmnet extract_importance_mean extract_importance_polymars extract_importance_ranger extract_importance_SL extract_importance_SL_learner extract_importance_svm extract_importance_xgboost extrinsic_selection get_augmented_set get_base_set intrinsic_control intrinsic_selection pool_selected_sets pool_spvims SL_stabs_fitfun SL.ranger.imp spvim_vcov

Dependencies:bitops caret caTools class cli clock codetools cpp11 cvAUC data.table diagram digest dplyr e1071 farver foreach future future.apply gam generics ggplot2 globals glue gower gplots gtable gtools hardhat ipred isoband iterators kernlab KernSmooth labeling lattice lava lifecycle listenv lubridate magrittr MASS Matrix ModelMetrics mvtnorm nlme nnet nnls numDeriv parallelly pillar pkgconfig plyr pROC prodlim progressr proxy purrr R6 ranger RColorBrewer Rcpp RcppEigen recipes reshape2 rlang ROCR rpart S7 scales shape sparsevctrs SQUAREM stringi stringr SuperLearner survival tibble tidyr tidyselect timechange timeDate tzdb utf8 vctrs viridisLite withr

Extrinsic variable selection

Rendered fromextrinsic_selection.Rmdusingknitr::rmarkdownon Jun 03 2026.

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

Intrinsic variable selection

Rendered fromintrinsic_selection.Rmdusingknitr::rmarkdownon Jun 03 2026.

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

Introduction to flevr

Brian D. Williamson

Rendered fromintroduction_to_flevr.Rmdusingknitr::rmarkdownon Jun 03 2026.

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

Citation

Development and contributors

Readme and manuals

Help Manual

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