MapIds                  Map covariate and row Ids so they start from 1
averagePrecision        Calculate the average precision
brierScore              brierScore
calibrationLine         calibrationLine
computeAuc              Compute the area under the ROC curve
computeAuprc            Compute the area under the Precision-Recall
                        curve
computeGridPerformance
                        Computes grid performance for a hyperparameter
                        combination (backwards compatible)
configurePython         Sets up a python environment to use for PLP
                        (can be conda or venv)
covariateSummary        covariateSummary
createCohortCovariateSettings
                        Extracts covariates based on cohorts
createDatabaseDetails   Create a setting that holds the details about
                        the cdmDatabase connection for data extraction
createDatabaseSchemaSettings
                        Create the PatientLevelPrediction database
                        result schema settings
createDefaultExecuteSettings
                        Creates default list of settings specifying
                        what parts of runPlp to execute
createDefaultSplitSetting
                        Create the settings for defining how the
                        plpData are split into test/validation/train
                        sets using default splitting functions (either
                        random stratified by outcome, time or subject
                        splitting)
createExecuteSettings   Creates list of settings specifying what parts
                        of runPlp to execute
createExistingSplitSettings
                        Create the settings for defining how the
                        plpData are split into test/validation/train
                        sets using an existing split - good to use for
                        reproducing results from a different run
createFeatureEngineeringSettings
                        Create the settings for defining any feature
                        engineering that will be done
createGlmModel          createGlmModel
createHyperparameterSettings
                        Create Hyperparameter Settings
createIterativeImputer
                        Create Iterative Imputer settings
createLearningCurve     createLearningCurve
createLogSettings       Create the settings for logging the progression
                        of the analysis
createModelDesign       Specify settings for developing a single model
createNormalizer        Create the settings for normalizing the data
                        @param type The type of normalization to use,
                        either "minmax" or "robust"
createPlpResultTables   Create the results tables to store
                        PatientLevelPrediction models and results into
                        a database
createPreprocessSettings
                        Create the settings for preprocessing the
                        trainData.
createRandomForestFeatureSelection
                        Create the settings for random foreat based
                        feature selection
createRareFeatureRemover
                        Create the settings for removing rare features
createRestrictPlpDataSettings
                        createRestrictPlpDataSettings define extra
                        restriction settings when calling getPlpData
createSampleSettings    Create the settings for defining how the
                        trainData from 'splitData' are sampled using
                        default sample functions.
createSimpleImputer     Create Simple Imputer settings
createSklearnIterativeImputer
                        Create scikit-learn Iterative Imputer settings
createSklearnModel      Plug an existing scikit learn python model into
                        the PLP framework
createSplineSettings    Create the settings for adding a spline for
                        continuous variables
createStratifiedImputationSettings
                        Create the settings for using stratified
                        imputation.
createStudyPopulation   Create a study population
createStudyPopulationSettings
                        create the study population settings
createTempModelLoc      Create a temporary model location
createTuningMetric      Create a tuning metric descriptor
createUnivariateFeatureSelection
                        Create the settings for defining any feature
                        selection that will be done
createValidationDesign
                        createValidationDesign - Define the validation
                        design for external validation
createValidationSettings
                        createValidationSettings define optional
                        settings for performing external validation
diagnoseMultiplePlp     Run a list of predictions diagnoses
diagnosePlp             diagnostic - Investigates the prediction
                        problem settings - use before training a model
evaluatePlp             evaluatePlp
externalValidateDbPlp   externalValidateDbPlp - Validate a model on new
                        databases
extractDatabaseToCsv    Exports all the results from a database into
                        csv files
fitPlp                  fitPlp
getCalibrationSummary   Get a sparse summary of the calibration
getCohortCovariateData
                        Extracts covariates based on cohorts
getDemographicSummary   Get a demographic summary
getEunomiaPlpData       Create a plpData object from the Eunomia
                        database'
getPlpData              Extract the patient level prediction data from
                        the server
getPredictionDistribution
                        Calculates the prediction distribution
getThresholdSummary     Calculate all measures for sparse ROC
ici                     Calculate the Integrated Calibration Index from
                        Austin and Steyerberg
                        https://onlinelibrary.wiley.com/doi/full/10.1002/sim.8281
insertCsvToDatabase     Function to insert results into a database from
                        csvs
insertResultsToSqlite   Create sqlite database with the results
listAppend              join two lists
listCartesian           Cartesian product
loadPlpAnalysesJson     Load the multiple prediction json settings from
                        a file
loadPlpData             Load the plpData from a folder
loadPlpModel            loads the plp model
loadPlpResult           Loads the evalaution dataframe
loadPlpShareable        Loads the plp result saved as json/csv files
                        for transparent sharing
loadPrediction          Loads the prediction dataframe to json
migrateDataModel        Migrate Data model
modelBasedConcordance   Calculate the model-based concordance, which is
                        a calculation of the expected discrimination
                        performance of a model under the assumption the
                        model predicts the "TRUE" outcome as detailed
                        in van Klaveren et al.
                        https://pubmed.ncbi.nlm.nih.gov/27251001/
outcomeSurvivalPlot     Plot the outcome incidence over time
pfi                     Permutation Feature Importance
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
plotLearningCurve       plotLearningCurve
plotNetBenefit          Plot the net benefit
plotPlp                 Plot all the PatientLevelPrediction plots
plotPrecisionRecall     Plot the precision-recall curve using the
                        sparse thresholdSummary data frame
plotPredictedPDF        Plot the Predicted probability density
                        function, showing prediction overlap between
                        true and false cases
plotPredictionDistribution
                        Plot the side-by-side boxplots of prediction
                        distribution, by class
plotPreferencePDF       Plot the preference score probability density
                        function, showing prediction overlap between
                        true and false cases #'
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
plotSparseRoc           Plot the ROC curve using the sparse
                        thresholdSummary data frame
plotVariableScatterplot
                        Plot the variable importance scatterplot
predictCyclops          Create predictive probabilities
predictGlm              predict using a logistic regression model
predictPlp              predictPlp
preprocessData          A function that wraps around
                        FeatureExtraction::tidyCovariateData to
                        normalise the data and remove rare or redundant
                        features
print.plpData           Print a plpData object
print.summary.plpData   Print a summary.plpData object
recalibratePlp          recalibratePlp
recalibratePlpRefit     recalibratePlpRefit
runMultiplePlp          Run a list of predictions analyses
runPlp                  runPlp - Develop and internally evaluate a
                        model using specified settings
savePlpAnalysesJson     Save the modelDesignList to a json file
savePlpData             Save the plpData to folder
savePlpModel            Saves the plp model
savePlpResult           Saves the result from runPlp into the location
                        directory
savePlpShareable        Save the plp result as json files and csv files
                        for transparent sharing
savePrediction          Saves the prediction dataframe to a json file
setAdaBoost             Create setting for AdaBoost with python
                        DecisionTreeClassifier base estimator
setCoxModel             Create setting for lasso Cox model
setDecisionTree         Create setting for the scikit-learn
                        DecisionTree with python
setGradientBoostingMachine
                        Create setting for gradient boosting machine
                        model using gbm_xgboost implementation
setIterativeHardThresholding
                        Create setting for Iterative Hard Thresholding
                        model
setLassoLogisticRegression
                        Create modelSettings for lasso logistic
                        regression
setLightGBM             Create setting for gradient boosting machine
                        model using lightGBM
                        (https://github.com/microsoft/LightGBM/tree/master/R-package).
setMLP                  Create setting for neural network model with
                        python's scikit-learn. For bigger models,
                        consider using 'DeepPatientLevelPrediction'
                        package.
setNaiveBayes           Create setting for naive bayes model with
                        python
setPythonEnvironment    Use the python environment created using
                        configurePython()
setRandomForest         Create setting for random forest model using
                        sklearn
setRidgeRegression      Create modelSettings for ridge logistic
                        regression
setSVM                  Create setting for the python sklearn SVM (SVC
                        function)
simulatePlpData         Generate simulated data
simulationProfile       A simulation profile for generating synthetic
                        patient level prediction data
sklearnFromJson         Loads sklearn python model from json
sklearnToJson           Saves sklearn python model object to json in
                        path
splitData               Split the plpData into test/train sets using a
                        splitting settings of class 'splitSettings'
summary.plpData         Summarize a plpData object
toSparseM               Convert the plpData in COO format into a sparse
                        R matrix
validateExternal        validateExternal - Validate model performance
                        on new data
validateMultiplePlp     externally validate the multiple plp models
                        across new datasets
viewDatabaseResultPlp   open a local shiny app for viewing the result
                        of a PLP analyses from a database
viewMultiplePlp         open a local shiny app for viewing the result
                        of a multiple PLP analyses
viewPlp                 viewPlp - Interactively view the performance
                        and model settings
