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_pkgdown.yml
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_pkgdown.yml
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template:
params:
bootswatch: cosmo
development:
mode: auto
development: docs/dev
home:
links:
- text: Ask a question
href: http://forums.ohdsi.org
navbar:
structure:
left:
- home
- intro
- videos
- reference
- articles
- tutorial
- benchmarks
- predictors
- bestpractice
- clinicalmodels
- news
right: [hades, github]
components:
home:
icon: fa-home fa-lg
href: index.html
reference:
text: Reference
href: reference/index.html
intro:
text: Get started
href: articles/InstallationGuide.html
videos:
text: Videos
href: articles/Videos.html
bestpractice:
text: Best Practices
href: articles/BestPractices.html
clinicalmodels:
text: Clinical Models
href: articles/ClinicalModels.html
benchmarks:
text: Benchmarks
href: articles/BenchmarkTasks.html
predictors:
text: Predictors
href: articles/ConstrainedPredictors.html
news:
text: Changelog
href: news/index.html
tutorial:
text: Tutorial
href: https://www.ohdsi.org/past-events/patient-level-prediction/
github:
icon: fa-github fa-lg
href: https://github.com/OHDSI/PatientLevelPrediction
hades:
text: hadesLogo
href: https://ohdsi.github.io/Hades
reference:
- title: "Extracting data from the OMOP CDM database"
desc: >
Functions for getting the necessary data from the database in Common Data Model and saving/loading.
contents:
- createDatabaseDetails
- createRestrictPlpDataSettings
- getPlpData
- savePlpData
- loadPlpData
- getCohortCovariateData
- title: "Settings for designing a prediction models"
desc: >
Design settings required when developing a model.
contents:
- createStudyPopulationSettings
- createDefaultSplitSetting
- createSampleSettings
- createFeatureEngineeringSettings
- createPreprocessSettings
- title: "Optional design settings"
desc: >
Settings for optional steps that can be used in the PLP pipeline
contents:
- createCohortCovariateSettings
- createRandomForestFeatureSelection
- createUnivariateFeatureSelection
- createSplineSettings
- createStratifiedImputationSettings
- title: "External validation"
contents:
- createValidationDesign
- validateExternal
- createValidationSettings
- recalibratePlp
- recalibratePlpRefit
- title: "Execution settings when developing a model"
desc: >
Execution settings required when developing a model.
contents:
- createLogSettings
- createExecuteSettings
- createDefaultExecuteSettings
- title: "Binary Classification Models"
desc: >
Functions for setting binary classifiers and their hyper-parameter search.
contents:
- setAdaBoost
- setDecisionTree
- setGradientBoostingMachine
- setKNN
- setLassoLogisticRegression
- setMLP
- setNaiveBayes
- setRandomForest
- setSVM
- setIterativeHardThresholding
- setLightGBM
- title: "Survival Models"
desc: >
Functions for setting survival models and their hyper-parameter search.
contents:
- setCoxModel
- title: "Single Patient-Level Prediction Model"
desc: >
Functions for training/evaluating/applying a single patient-level-prediction model
contents:
- runPlp
- externalValidateDbPlp
- savePlpModel
- loadPlpModel
- savePlpResult
- loadPlpResult
- diagnosePlp
- title: "Multiple Patient-Level Prediction Models"
desc: >
Functions for training mutliple patient-level-prediction model in an efficient way.
contents:
- createModelDesign
- runMultiplePlp
- validateMultiplePlp
- savePlpAnalysesJson
- loadPlpAnalysesJson
- diagnoseMultiplePlp
- title: "Individual pipeline functions"
desc: >
Functions for running parts of the PLP workflow
contents:
- createStudyPopulation
- splitData
- preprocessData
- fitPlp
- predictPlp
- evaluatePlp
- covariateSummary
- title: "Saving results into database"
desc: >
Functions for saving the prediction model and performances into a database.
contents:
- insertResultsToSqlite
- createPlpResultTables
- addMultipleRunPlpToDatabase
- addRunPlpToDatabase
- createDatabaseSchemaSettings
- createDatabaseList
- addDiagnosePlpToDatabase
- addMultipleDiagnosePlpToDatabase
- extractDatabaseToCsv
- insertCsvToDatabase
- insertModelDesignInDatabase
- migrateDataModel
- title: "Shiny Viewers"
desc: >
Functions for viewing results via a shiny app
contents:
- viewPlp
- viewMultiplePlp
- viewDatabaseResultPlp
- title: "Plotting"
desc: >
Functions for various performance plots
contents:
- plotPlp
- plotSparseRoc
- plotSmoothCalibration
- plotSparseCalibration
- plotSparseCalibration2
- plotDemographicSummary
- plotF1Measure
- plotGeneralizability
- plotPrecisionRecall
- plotPredictedPDF
- plotPreferencePDF
- plotPredictionDistribution
- plotVariableScatterplot
- outcomeSurvivalPlot
- title: "Learning Curves"
desc: >
Functions for creating and plotting learning curves
contents:
- createLearningCurve
- plotLearningCurve
- title: "Simulation"
desc: >
Functions for simulating cohort method data objects.
contents:
- simulatePlpData
- plpDataSimulationProfile
- title: "Data manipulation functions"
desc: >
Functions for manipulating data
contents:
- toSparseM
- MapIds
- title: "Helper/utility functions"
contents:
- listAppend
- listCartesian
- createTempModelLoc
- configurePython
- setPythonEnvironment
- title: "Evaluation measures"
contents:
- accuracy
- averagePrecision
- brierScore
- calibrationLine
- computeAuc
- f1Score
- falseDiscoveryRate
- falseNegativeRate
- falseOmissionRate
- falsePositiveRate
- ici
- modelBasedConcordance
- negativeLikelihoodRatio
- negativePredictiveValue
- positiveLikelihoodRatio
- positivePredictiveValue
- sensitivity
- specificity
- computeGridPerformance
- diagnosticOddsRatio
- getCalibrationSummary
- getDemographicSummary
- getThresholdSummary
- getThresholdSummary_binary
- getPredictionDistribution
- getPredictionDistribution_binary
- title: "Saving/loading models as json"
desc: >
Functions for saving or loading models as json
contents:
- sklearnFromJson
- sklearnToJson
- title: "Load/save for sharing"
desc: >
Functions for loading/saving objects for sharing
contents:
- savePlpShareable
- loadPlpShareable
- loadPrediction
- savePrediction
- title: "Feature importance"
contents:
- pfi
- title: "Other functions"
contents:
- predictCyclops