featR: A Unified Toolkit for Feature Selection

featR collects filter, regularization, wrapper, and dimensionality-reduction methods for choosing predictors. Every selection method is one function call on a data frame and returns the same kind of object, so switching from fs_lasso() to fs_boruta() changes the function name and its method-specific options and leaves the surrounding code alone.

The package website has longer guides. Start with Getting started, Choosing a method, and Validation and leakage, which explains which reported numbers are held-out and which are optimistic.

Calling convention

Selection functions are called as fs_<method>(data, target, ...). data holds the observations (a data frame, or a data.table or matrix where the method accepts one), and target is the name of the outcome column inside data, never a separate vector. Method-specific options follow, and housekeeping arguments (seed, verbose, n_cores) come last wherever a method has them.

Four functions take no target. fs_unsupervised() and fs_correlation() score features without an outcome, and fs_pca() and fs_svd() decompose a numeric table.

The result object

Every selection function returns an fs_result, a list of seven elements:

selected: Character vector of the chosen feature names, possibly empty.
scores: Per-feature scores: usually a named numeric vector covering every candidate feature, a data frame for a few methods, or NULL when the method has no comparable per-feature score. What a score means depends on the method; see each function's Value section.
method: A single string naming what ran, for example "supervised_correlation", "lasso", or "svm_linear".
task: "classification", "regression", or NA when the method has no task.
model: The fitted model when the method fits one, otherwise NULL.
details: A named list of method-specific extras, documented on each function's help page.
call: The matched call.

Results have print() and summary() methods, and selected() extracts the chosen names. fs_pca() and fs_svd() are the exceptions: they build new components rather than choosing columns, and each returns its own plain list. The fs_result guide tabulates what scores holds for each function.

Methods

Filters: No model is fitted, so they are cheap and scale to wide data. fs_supervised() scores each feature by absolute Pearson correlation or one-way ANOVA F against the target; fs_unsupervised() by variance, MAD, IQR, range, missingness, or number of distinct values; fs_chi() by a chi-squared test for categorical features; and fs_infogain() by information gain or gain ratio. fs_correlation() drops redundant members of highly correlated groups.
Regularization and embedded importance: One model fit whose own structure picks the survivors. fs_lasso() uses an L1 penalty, fs_elastic() an L1/L2 mixture, fs_randomforest() permutation importance with held-out evaluation, and fs_mars() keeps the predictors an earth model retains.
Wrappers: A model is refitted over candidate subsets. These are the most expensive methods and the most closely tied to the model you intend to use. fs_recursivefeature() runs caret recursive feature elimination; fs_svm() tunes an SVM, optionally after SVM-RFE or random-forest screening; fs_boruta() tests each feature against shuffled shadow copies; fs_stepwise() runs AIC stepwise regression; and fs_bayes() compares brms models with loo.
Dimensionality reduction: New components replace the original columns. fs_pca() returns principal components with tidy output and an optional plot, and fs_svd() a truncated singular value decomposition with an optional approximate solver.

Dependencies

The hard dependencies are data.table, methods, parallel, stats, utils, and withr. Every modeling engine (caret, glmnet, kernlab, randomForest, Boruta, earth, MASS, brms, RSpectra, and the rest) is a suggested package, loaded only on the code path that needs it. If one is missing, the call stops with an error that names the missing packages and gives the install.packages() call to run.

Reproducibility and parallelism

featR never seeds the random number generator on its own. Functions that use randomness take seed = NULL; a supplied seed applies to that call only, and the caller's RNG state is restored on exit. Every function runs sequentially by default. Parallelism is opt-in through n_cores (or parallel = TRUE), worker requests are capped at the detected core count, and any cluster featR starts is stopped when the call returns. See Reproducibility and parallelism.

Author

Maintainer: Justin Chase jchase.msu@gmail.com [copyright holder]