| 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. |
| Mode | Compute the mode of a vector (can be multiple results). |
| 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. |