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: correlation and chi-squared filters, information gain, 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: |
0.1.0 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
data.table, parallel, stats, utils, withr |
| 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: |
2026-09-14 |
| DOI: |
10.32614/CRAN.package.featR (may not be active yet) |
| 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 |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Materials: |
README, NEWS |
| CRAN checks: |
featR results |
Documentation:
Downloads:
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