featR: A Unified Toolkit for Feature Selection
Filter, wrapper, and embedded feature-selection methods behind a
consistent set of functions that share one calling convention and one
return type: univariate threshold filters, correlation and chi-squared
filters, information gain, the least absolute shrinkage and selection
operator (LASSO) and elastic net, Bayesian model comparison, Boruta,
recursive feature elimination, random forest importance, multivariate
adaptive regression splines, support vector machine recursive feature
elimination, stepwise selection, and principal component / singular
value decomposition helpers. The implemented methods follow
Tibshirani (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>,
Zou and Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>,
Friedman (1991) <doi:10.1214/aos/1176347963>,
Breiman (2001) <doi:10.1023/A:1010933404324>,
Guyon, Weston, Barnhill and Vapnik (2002) <doi:10.1023/A:1012487302797>,
Kursa and Rudnicki (2010) <doi:10.18637/jss.v036.i11>, and
Vehtari, Gelman and Gabry (2017) <doi:10.1007/s11222-016-9696-4>.
Heavy modeling engines are optional and only required by the functions
that use them.
| Version: |
0.2.0 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
data.table, methods, parallel, stats, utils, withr (≥ 2.4.0) |
| Suggests: |
bigstatsr, Boruta, brms, caret, doParallel, e1071, earth, foreach, furrr, future, ggplot2, glmnet, kernlab, knitr, loo, MASS, Matrix, MLmetrics, pbapply, polycor, pROC, PRROC, randomForest, rmarkdown, RSpectra, testthat (≥ 3.0.0) |
| Published: |
2026-11-09 |
| DOI: |
10.32614/CRAN.package.featR |
| Author: |
Justin Chase [aut, cre, cph] |
| Maintainer: |
Justin Chase <jchase.msu at gmail.com> |
| BugReports: |
https://github.com/elkronos/featR/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/elkronos/featR,
https://elkronos.github.io/featR/ |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Materials: |
README, NEWS |
| CRAN checks: |
featR results |
Documentation:
Downloads:
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