as.table.sa_categorical
                        The contingency table a categorical comparison
                        was run on
center_by_control       Centre every feature on the control group
cluster_dbscan          Cluster by finding the dense regions
cluster_hclust          Cluster by building a tree and cutting it
cluster_kmeans          Cluster by moving centres until they stop
cluster_snn             Cluster by how many neighbours points have in
                        common
coef.sa_fit             Coefficients, summary and predictions from the
                        model inside a fit
coef.sa_model           Coefficients of a fitted model
compare_categorical_groups
                        Test a contingency table with every applicable
                        test at once
compare_factorial_groups
                        Analyse a crossed-factor design as one model
compare_multiple_groups
                        Run every applicable multi-group test at once
compare_one_sample      Compare one sample against a hypothesised value
compare_two_groups      Run every applicable two-group test at once
diagnose_distribution   Check the assumptions a comparison rests on
draw_butterfly_hist     Draw a butterfly histogram of one feature
                        across two groups
draw_corrplot           Draw a correlation matrix, with the cells that
                        failed the test left blank
draw_dim_reduction_plot
                        Draw a reduction as a scatter of its points
draw_forest_plot        Draw a forest plot of a comparison result
draw_grouped_barplot    Draw a grouped barplot of a descriptive summary
draw_grouped_boxplot    Draw a grouped boxplot across several features
draw_heatmap            Draw a clustered heatmap of features by samples
draw_interaction_plot   Draw an interaction plot of a factorial
                        comparison
draw_mosaic_plot        Draw a mosaic plot of a contingency table
draw_prediction_plot    Draw predicted against observed for an
                        evaluated regression
draw_roc_curve          Draw the ROC curves of an evaluated
                        classification
draw_volcano_plot       Draw a volcano plot
estimate_categorical_significance
                        Reduce a contingency table to significance
                        verdicts
estimate_significance   Reduce a comparison to one significance verdict
                        per feature
evaluate_classification_models
                        Score fitted classifications on held-out rows
evaluate_regression_models
                        Score fitted regressions on held-out rows
fit_elastic_net         Fit an elastic net, lasso or ridge regression
fit_linear_regression   Fit a linear regression
fit_logistic_regression
                        Fit a logistic regression
fit_rf                  Fit a random forest
fit_svm                 Fit a support vector machine
make_block_cor          Build a block correlation matrix
perform_pca             Reduce many features to a few components
perform_rfe             Select the predictors worth keeping
perform_stepwise        Stepwise feature selection by information
                        criteria
perform_tsne            Embed samples or features with t-SNE
perform_umap            Embed samples or features with UMAP
plot.sa_performance     Draw an evaluation result
predict.sa_model        Predict from a fitted model on rows it was not
                        fitted to
print.sa_categorical    Print a categorical comparison
print.sa_categorical_significance
                        Print a categorical significance verdict
print.sa_cluster        Print a clustering
print.sa_comparison     Print a comparison result
print.sa_diagnosis      Print a distribution diagnosis
print.sa_model          Print a fitted model
print.sa_performance    Print an evaluation result
print.sa_reduction      Print a dimensionality reduction
print.sa_selection      Print a feature selection
print.sa_significance   Print a significance verdict
print.sa_split          Print a train/test split
screen_outliers         Flag candidate outliers without removing them
simulate_categorical_groups
                        Simulate a contingency table whose association
                        is known
simulate_classification
                        Simulate a two-class outcome whose coefficients
                        are known
simulate_factorial_groups
                        Simulate a crossed-factor experiment whose
                        answer is known
simulate_multiple_groups
                        Simulate a control-versus-treatments experiment
                        whose answer is known
simulate_regression     Simulate a regression whose coefficients are
                        known
simulate_two_groups     Simulate a two-group experiment whose answer is
                        known
split_data              Split data into training and test sets
summarize_association_stats
                        Correlation between every pair of features,
                        with all three coefficients
summarize_descriptive_stats
                        Descriptive summary of several features,
                        optionally split by group
