fs_bayes                Bayesian predictor-subset selection with brms
                        and LOO
fs_boruta               All-relevant feature selection with Boruta
fs_chi                  Chi-squared tests for categorical features
fs_correlation          Find and prune highly correlated variables
fs_elastic              Elastic-net feature selection with tuned alpha
                        and lambda
fs_infogain             Rank features by information gain
fs_lasso                Lasso feature selection with cross-validation
fs_mars                 MARS (earth) feature selection
fs_pca                  Principal component analysis with tidy results
                        and optional plotting
fs_randomforest         Random forest importance with a held-out
                        evaluation
fs_recursivefeature     Recursive feature elimination with held-out
                        evaluation
fs_stepwise             Stepwise linear-regression feature selection by
                        AIC
fs_supervised           Supervised filter that scores each feature
                        against a target
fs_svd                  Singular value decomposition with optional
                        scaling and truncation
fs_svm                  Train and evaluate an SVM, with optional
                        SVM-RFE feature selection
fs_unsupervised         Unsupervised filter that scores each feature
                        without a target
print.fs_result         Print a featR result
selected                Extract the selected features from a featR
                        result
summary.fs_result       Summarize a featR result
