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_pkgdown.yml
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_pkgdown.yml
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template:
params:
bootswatch: cosmo
home:
links:
- text: Ask a question
href: http://forums.ohdsi.org
navbar:
structure:
left:
- home
- intro
- videos
- reference
- articles
- tutorial
- bestpractice
- 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
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
- title: "Settings for designing a prediction models"
desc: >
Design settings required when developing a model.
contents:
- createStudyPopulationSettings
- createDefaultSplitSetting
- createSampleSettings
- createFeatureEngineeringSettings
- createPreprocessSettings
- title: "Execution settings when developing a model"
desc: >
Execution settings required when developing a model.
contents:
- createLogSettings
- createExecuteSettings
- 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
- 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
- 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
- title: "Saving results into database"
desc: >
Functions for saving the prediction model and performances into a database.
contents:
- createPlpResultTables
- populatePlpResultTables
- 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
- 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