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featR  

A Unified Toolkit for Feature Selection
View on CRAN: Click here


Download and install featR package within the R console
Install from CRAN:
install.packages("featR")

Install from Github:
library("remotes")
install_github("cran/featR")

Install by package version:
library("remotes")
install_version("featR", "0.1.0")



Attach the package and use:
library("featR")
Maintained by
Justin Chase
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-09-14
Latest Update: 2026-09-14
Description:
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.
How to cite:
Justin Chase (2026). featR: A Unified Toolkit for Feature Selection. R package version 0.1.0, https://cran.r-project.org/web/packages/featR. Accessed 29 Sep. 2026.
Previous versions and publish date:
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Complete documentation for featR
Functions, R codes and Examples using the featR R package
Full featR package functions and examples
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