Man pages for ck37/ckTools
Chris Kennedy's R Toolkit
| auc_inference | Estimate AUC and 95 CI |
| auc_table | Generate a table of AUCs by learner |
| auc_table.CV.SuperLearner | Table of cross-validated AUCs from CV.SuperLearner result |
| auc_table.SuperLearner | Table of cross-validated AUCs from SuperLearner result |
| brier_score | Calculate Brier score from cross-validation results |
| brier_table | Generate a table of Brier scores by learner |
| brier_table.CV.SuperLearner | Table of cross-validated Brier scores from CV.SuperLearner... |
| categoricals_to_factors | Convert categorical numerics to factors |
| create_dataset | Create a dataset |
| cvsl_auc | Calculate cross-validated AUC from CV.SuperLearner result |
| cvsl_auc_table | Deprecated, use auc_table() instead. |
| cvsl_plot_roc | Deprecated, please use plot_roc() instead. |
| cvsl_weights | Create a table of meta-weights from a CV.SuperLearner |
| factors_to_indicators | Convert factors to indicator variables. |
| gen_superlearner | Setup a SuperLearner() based on parallel configuration. |
| h2o_init_multinode | Starts an h2o cluster on multiple nodes |
| import_csvs | Import all CSV files in a given directory and save them to a... |
| impute_missing_values | Impute missing values in a dataframe and add missingness... |
| ipa_measure | Calculate IPA measure from cross-validation results |
| ipa_table | Generate a table of IPA scores by learner |
| ipa_table.CV.SuperLearner | Table of cross-validated IPA measures from CV.SuperLearner... |
| 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... |
| Mode | Compute the mode of a vector (can be multiple results). |
| parallelize | Setup parallel processing using snow. |
| plot_roc | Plot the ROC curve for an ensemble object. |
| plot_roc.CV.SuperLearner | Plot a ROC curve from cross-validated AUC from... |
| plot_roc.SuperLearner | Plot a ROC curve from cross-validated AUC from SuperLearner |
| plot.SuperLearner | Plot estimated risk and confidence interval for each learner |
| plot_table | Plot a table of values using ggplot |
| plot_tree | Plot rpart decision tree with pretty good defaults. |
| prauc | Calculate PR-AUC from cross-validation results |
| prauc_table | Generate a table of PR-AUCs by learner |
| prauc_table.CV.SuperLearner | Table of cross-validated PR-AUCs from CV.SuperLearner result |
| prauc_table.SuperLearner | Table of PR-AUCs from SuperLearner result |
| predict.SL.stratified | predict() for SL.stratified |
| rf_count_terminal_nodes | Count the terminal nodes in each tree from a random forest |
| rmse | Calculate RMSE from cross-validation results |
| rmse_table | Generate a table of RMSEs by learner |
| rmse_table.SuperLearner | Table of RMSEs from SuperLearner result |
| setup_parallel_tmle | Setup TMLE to run in parallel |
| sl_auc_table | Deprecated, use auc_table() instead. |
| SL.glmnet2 | Elastic net regression, including lasso and ridge |
| SL.h2o_auto | Automatic machine learning using h2o |
| SL.mgcv | Wrapper for mgcv's gam implementation |
| sl_plot_roc | Deprecated, please use plot_roc() instead. |
| sl_stderr | Calculate the SE of individual SL learners |
| SL.stratified | Stratified estimator |
| SL.xgboost_cv | XGBoost SuperLearner wrapper with internal cross-validation... |
| standardize | Rescale variables, possibly excluding some columns |
| stop_cluster | Stop the cluster if snow::makeCluster() was used, but nothing... |
| summarize_vars | Summary table of variables |
| tmle_parallel | Modify TMLE to support parallel computation for g and Q. |
| vim_corr | Correlation analysis |
