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. |