Mode                    Compute the mode of a vector (can be multiple
                        results).
cvsl_auc                Calculate cross-validated AUC from
                        CV.SuperLearner result
cvsl_plot_roc           Plot a ROC curve from cross-validated AUC from
                        CV.SuperLearner
cvsl_weights            Create a table of meta-weights from a
                        CV.SuperLearner
gen_superlearner        Setup a SuperLearner() based on parallel
                        configuration.
import_csvs             Import all CSV files in a given directory and
                        save them to a list.
impute_missing_values   Impute missing values in a dataframe and add
                        missingness indicators.
load_all_code           Load all R files in a library directory.
load_packages           Load a list of packages.
missingness_indicators
                        Return matrix of missingness indicators for a
                        dataframe or matrix.
parallelize             Setup parallel processing, either multinode or
                        multicore.
plot.SuperLearner       Plot estimated risk and confidence interval for
                        each learner
rf_count_terminal_nodes
                        Count the terminal nodes in each tree from a
                        random forest
setup_parallel_tmle     Setup TMLE to run in parallel
sl_auc_table            Table of cross-validated AUCs from SuperLearner
                        result
sl_plot_roc             Plot a ROC curve from cross-validated AUC from
                        SuperLearner
sl_stderr               Calculate the SE of individual SL learners
standardize             Rescale variables, possibly excluding some
                        columns
stop_cluster            Stop the cluster if snow::makeCluster() was
                        used, but nothing needed if doMC was used.
tmle_parallel           Modify TMLE to support parallel computation for
                        g and Q.
