featR

A unified R package of feature-selection methods: statistical filters, regularization, model-based wrappers, and dimensionality reduction — all behind one calling convention and one return type.

Heavy modeling engines (brms, caret, glmnet, randomForest, ...) are optional Suggests; each function checks for what it needs and tells you what to install.

Installation

# install.packages("devtools")
devtools::install_github("elkronos/featR")

The convention

Every selection function takes the data first and the target column second, and returns the same object:

fs_<method>(data, target, ..., seed = NULL, verbose = FALSE, n_cores = 1L)

library(featR)

res <- fs_lasso(mtcars, "mpg", nfolds = 5, seed = 1)

res               # <fs_result> lasso (regression), with the selected features
selected(res)     # character vector of chosen features
res$scores        # per-feature scores, comparable within a method
res$model         # the fitted model, when the method produced one and you asked for it
res$details       # everything method-specific
summary(res)      # ranked score table, selected features marked

Because the shape is shared, methods are interchangeable:

methods <- list(lasso = fs_lasso, boruta = fs_boruta, rfe = fs_recursivefeature)
lapply(methods, function(f) selected(f(mtcars, "mpg", seed = 1)))

Functions are sequential by default (parallelism is opt-in and capped), never seed the RNG unless you pass seed, and signal progress with suppressible message()s.

Functions

Filters

Function	What it does
fs_chi()	Chi-square tests of association between categorical features and a categorical target, with p-value adjustment and Monte-Carlo fallback for sparse tables.
fs_correlation()	Flags variable pairs above a correlation threshold. prune = TRUE (default) returns the reduced non-redundant set; prune = FALSE returns every flagged pair member.
fs_infogain()	Information gain w.r.t. a target, for numeric (binned), categorical, and date features. normalize = "gain_ratio" corrects the bias toward many-leveled predictors.
fs_supervised()	Threshold filter: absolute correlation (numeric target) or ANOVA F (factor target).
fs_unsupervised()	Target-free threshold filter: variance, MAD, IQR, range, missing proportion, distinct-value count.

Regularization

Function	What it does
fs_lasso()	Cross-validated LASSO (glmnet). Scores are standardized coefficients; raw-scale ones are in details.
fs_elastic()	Elastic net over an alpha grid via caret + glmnet, with optional PCA fit inside each resample.

Wrappers and model-based

Function	What it does
fs_bayes()	Bayesian model comparison over predictor subsets (brms), ranked by LOO with a 1-SE parsimony rule.
fs_boruta()	Boruta all-relevant selection, with importance-aware correlation pruning.
fs_randomforest()	Random-forest permutation importance with held-out evaluation.
fs_recursivefeature()	caret RFE on a training split, honestly evaluated on held-out rows.
fs_stepwise()	Stepwise linear regression via MASS::stepAIC.
fs_svm()	SVM pipeline with true SVM-RFE (linear-kernel weight ranking, refit per elimination step); random-forest screening available via select_method = "rf_rfe".
fs_mars()	MARS (earth) with tuning and optional ROC/PR AUC.

Dimensionality reduction

These reduce dimensions rather than select features, so they return their own decomposition structure rather than an fs_result.

Function	What it does
fs_pca()	PCA (prcomp, or bigstatsr for large data) with loadings, scores, variance explained, optional plot.
fs_svd()	Exact or approximate (RSpectra) truncated SVD. Pass n_singular_values < min(dim(x)) to enable the approximate solver.

Notes on honesty

Several methods can silently flatter themselves. featR tries not to:

• Class upsampling happens inside resampling folds (fs_mars(), fs_svm()), never before.
• fs_recursivefeature() evaluates on rows the selection never saw, and its final model trains on training rows only.
• fs_elastic() fits PCA per fold; fs_lasso() refuses to mean-impute by default (impute = "none") because full-data imputation leaks across folds.
• fs_randomforest()'s feature_select hook runs on the training split only.
• Post-selection statistics (fs_stepwise() p-values) are labeled as invalid for inference rather than presented as if they were not.

License

MIT (c) Justin Chase. See LICENSE.md.