setAdaBoost()

Create setting for AdaBoost with python 
setDeepNN()

Create setting for DeepNN model 
setDecisionTree()

Create setting for DecisionTree with python 
setGradientBoostingMachine()

Create setting for gradient boosting machine model using gbm_xgboost implementation 
setKNN()

Create setting for knn model 
setLassoLogisticRegression()

Create setting for lasso logistic regression 
setLRTorch()

Create setting for logistics regression model with python 
setMLP()

Create setting for neural network model with python 
setMLPTorch()

Create setting for neural network model with python 
setNaiveBayes()

Create setting for naive bayes model with python 
setRandomForest()

Create setting for random forest model with python (very fast) 
plotPlp()

Plot all the PatientLevelPrediction plots 
plotSparseRoc()

Plot the ROC curve using the sparse thresholdSummary data frame 
plotSmoothCalibration()

Plot the smooth calibration as detailed in Calster et al. "A calibration heirarchy for risk models
was defined: from utopia to empirical data" (2016) 
plotSparseCalibration()

Plot the calibration 
plotSparseCalibration2()

Plot the conventional calibration 
plotDemographicSummary()

Plot the Observed vs. expected incidence, by age and gender 
plotF1Measure()

Plot the F1 measure efficiency frontier using the sparse thresholdSummary data frame 
plotGeneralizability()

Plot the train/test generalizability diagnostic 
plotPrecisionRecall()

Plot the precisionrecall curve using the sparse thresholdSummary data frame 
plotPredictedPDF()

Plot the Predicted probability density function, showing prediction overlap between true and false cases 
plotPreferencePDF()

Plot the preference score probability density function, showing prediction overlap between true and false cases
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plotPredictionDistribution()

Plot the sidebyside boxplots of prediction distribution, by class#' 
plotVariableScatterplot()

Plot the variable importance scatterplot 