fs_bayes                Bayesian feature selection for model
                        optimization
fs_boruta               Feature selection using Boruta
fs_chi                  Chi-square feature selection for categorical
                        features
fs_correlation          Correlation-based feature selection
fs_elastic              Elastic Net Feature Selection and Model
                        Training
fs_infogain             Feature Selection via 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 and held-out
                        evaluation
fs_recursivefeature     Recursive feature elimination with held-out
                        evaluation
fs_stepwise             Stepwise linear-regression feature selection
                        via AIC
fs_supervised           Supervised Filter-Based Feature Selection
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-Based Feature Selection
print.fs_result         Print a featR result
selected                Extract the selected features from a featR
                        result
summary.fs_result       Summarize a featR result
