{ "_id": "6a17eedbacfb0bcc41da5929", "Package": "PatientLevelPrediction", "Type": "Package", "Title": "Develop Clinical Prediction Models Using the Common Data Model", "Version": "6.6.0", "Date": "2026-03-09", "Authors@R": "c(\nperson(\"Egill\", \"Fridgeirsson\", email = \"e.fridgeirsson@erasmusmc.nl\", role = c(\"aut\", \"cre\")),\nperson(\"Jenna\", \"Reps\", email = \"jreps@its.jnj.com\", role = c(\"aut\")),\nperson(\"Martijn\", \"Schuemie\", role = c(\"aut\")),\nperson(\"Marc\", \"Suchard\", role = c(\"aut\")),\nperson(\"Patrick\", \"Ryan\", role = c(\"aut\")),\nperson(\"Peter\", \"Rijnbeek\", role = c(\"aut\")),\nperson(\"Observational Health Data Science and Informatics\", role = c(\"cph\")))", "Description": "A user friendly way to create patient level prediction\nmodels using the Observational Medical Outcomes Partnership\nCommon Data Model. Given a cohort of interest and an outcome of\ninterest, the package can use data in the Common Data Model to\nbuild a large set of features. These features can then be used\nto fit a predictive model with a number of machine learning\nalgorithms. This is further described in Reps (2017)\n.", "License": "Apache License 2.0", "URL": "https://ohdsi.github.io/PatientLevelPrediction/,\nhttps://github.com/OHDSI/PatientLevelPrediction", "BugReports": "https://github.com/OHDSI/PatientLevelPrediction/issues", "VignetteBuilder": "knitr", "RoxygenNote": "7.3.3", "Encoding": "UTF-8", "Config/testthat/edition": "3", "Roxygen": "list(markdown = TRUE)", "Config/pak/sysreqs": "make default-jdk libicu-dev libxml2-dev libx11-dev\nxz-utils", "Repository": "https://ohdsi.r-universe.dev", "Date/Publication": "2026-03-09 13:25:16 UTC", "RemoteUrl": "https://github.com/ohdsi/patientlevelprediction", "RemoteRef": "HEAD", "RemoteSha": "1439171b5c742a2d71e690a068035e8eff25d497", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-28 07:21:32 UTC", "User": "root" }, "Author": "Egill Fridgeirsson [aut, cre],\nJenna Reps [aut],\nMartijn Schuemie [aut],\nMarc Suchard [aut],\nPatrick Ryan [aut],\nPeter Rijnbeek [aut],\nObservational Health Data Science 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"createLearningCurve" ] }, { "page": "createLogSettings", "title": "Create the settings for logging the progression of the analysis", "topics": [ "createLogSettings" ] }, { "page": "createModelDesign", "title": "Specify settings for developing a single model", "topics": [ "createModelDesign" ] }, { "page": "createNormalizer", "title": "Create the settings for normalizing the data @param type The type of normalization to use, either \"minmax\" or \"robust\"", "topics": [ "createNormalizer" ] }, { "page": "createPlpResultTables", "title": "Create the results tables to store PatientLevelPrediction models and results into a database", "topics": [ "createPlpResultTables" ] }, { "page": "createPreprocessSettings", "title": "Create the settings for preprocessing the trainData.", "topics": [ "createPreprocessSettings" ] }, { "page": "createRandomForestFeatureSelection", "title": "Create the settings for random foreat based feature selection", "topics": [ "createRandomForestFeatureSelection" ] }, { "page": "createRareFeatureRemover", "title": "Create the settings for removing rare features", "topics": [ "createRareFeatureRemover" ] }, { "page": "createRestrictPlpDataSettings", "title": "createRestrictPlpDataSettings define extra restriction settings when calling getPlpData", "topics": [ "createRestrictPlpDataSettings" ] }, { "page": "createSampleSettings", "title": "Create the settings for defining how the trainData from 'splitData' are sampled using default sample functions.", "topics": [ "createSampleSettings" ] }, { "page": "createSimpleImputer", "title": "Create Simple Imputer settings", "topics": [ "createSimpleImputer" ] }, { "page": "createSklearnIterativeImputer", "title": "Create scikit-learn Iterative Imputer settings", "topics": [ "createSklearnIterativeImputer" ] }, { "page": "createSklearnModel", "title": "Plug an existing scikit learn python model into the PLP framework", "topics": [ "createSklearnModel" ] }, { "page": "createSplineSettings", "title": "Create 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"createValidationDesign - Define the validation design for external validation", "topics": [ "createValidationDesign" ] }, { "page": "createValidationSettings", "title": "createValidationSettings define optional settings for performing external validation", "topics": [ "createValidationSettings" ] }, { "page": "diagnoseMultiplePlp", "title": "Run a list of predictions diagnoses", "topics": [ "diagnoseMultiplePlp" ] }, { "page": "diagnosePlp", "title": "diagnostic - Investigates the prediction problem settings - use before training a model", "topics": [ "diagnosePlp" ] }, { "page": "evaluatePlp", "title": "evaluatePlp", "topics": [ "evaluatePlp" ] }, { "page": "externalValidateDbPlp", "title": "externalValidateDbPlp - Validate a model on new databases", "topics": [ "externalValidateDbPlp" ] }, { "page": "extractDatabaseToCsv", "title": "Exports all the results from a database into csv files", "topics": [ "extractDatabaseToCsv" ] }, { "page": "fitPlp", "title": "fitPlp", "topics": [ "fitPlp" ] }, { "page": "getCalibrationSummary", "title": "Get a sparse summary of the calibration", "topics": [ "getCalibrationSummary" ] }, { "page": "getCohortCovariateData", "title": "Extracts covariates based on cohorts", "topics": [ "getCohortCovariateData" ] }, { "page": "getDemographicSummary", "title": "Get a demographic summary", "topics": [ "getDemographicSummary" ] }, { "page": "getEunomiaPlpData", "title": "Create a plpData object from the Eunomia database'", "topics": [ "getEunomiaPlpData" ] }, { "page": "getPlpData", "title": "Extract the patient level prediction data from the server", "topics": [ "getPlpData" ] }, { "page": "getPredictionDistribution", "title": "Calculates the prediction distribution", "topics": [ "getPredictionDistribution" ] }, { "page": "getThresholdSummary", "title": "Calculate all measures for sparse ROC", "topics": [ "getThresholdSummary" ] }, { "page": "ici", "title": "Calculate the Integrated Calibration Index from Austin and Steyerberg https://onlinelibrary.wiley.com/doi/full/10.1002/sim.8281", "topics": [ "ici" ] }, { "page": "insertCsvToDatabase", "title": "Function to insert results into a database from csvs", "topics": [ "insertCsvToDatabase" ] }, { "page": "insertResultsToSqlite", "title": "Create sqlite database with the results", "topics": [ "insertResultsToSqlite" ] }, { "page": "listAppend", "title": "join two lists", "topics": [ "listAppend" ] }, { "page": "listCartesian", "title": "Cartesian product", "topics": [ "listCartesian" ] }, { "page": "loadPlpAnalysesJson", "title": "Load the multiple prediction json settings from a file", "topics": [ "loadPlpAnalysesJson" ] }, { "page": "loadPlpData", "title": "Load the plpData from a folder", "topics": [ "loadPlpData" ] }, { "page": "loadPlpModel", "title": "loads the plp model", "topics": [ "loadPlpModel" ] }, { "page": "loadPlpResult", "title": "Loads the evalaution dataframe", "topics": [ "loadPlpResult" ] }, { "page": "loadPlpShareable", "title": "Loads the plp result saved as json/csv files for transparent sharing", "topics": [ "loadPlpShareable" ] }, { "page": "loadPrediction", "title": "Loads the prediction dataframe to json", "topics": [ "loadPrediction" ] }, { "page": "MapIds", "title": "Map covariate and row Ids so they start from 1", "topics": [ "MapIds" ] }, { "page": "migrateDataModel", "title": "Migrate Data model", "topics": [ "migrateDataModel" ] }, { "page": "modelBasedConcordance", "title": "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/", "topics": [ "modelBasedConcordance" ] }, { "page": "outcomeSurvivalPlot", "title": "Plot the outcome incidence over time", "topics": [ "outcomeSurvivalPlot" ] }, { "page": "pfi", "title": "Permutation Feature Importance", "topics": [ "pfi" ] }, { "page": "plotDemographicSummary", "title": "Plot the Observed vs. expected incidence, by age and gender", "topics": [ "plotDemographicSummary" ] }, { "page": "plotF1Measure", "title": "Plot the F1 measure efficiency frontier using the sparse thresholdSummary data frame", "topics": [ "plotF1Measure" ] }, { "page": "plotGeneralizability", "title": "Plot the train/test generalizability diagnostic", "topics": [ "plotGeneralizability" ] }, { "page": "plotLearningCurve", "title": "plotLearningCurve", "topics": [ "plotLearningCurve" ] }, { "page": "plotNetBenefit", "title": "Plot the net benefit", "topics": [ "plotNetBenefit" ] }, { "page": "plotPlp", "title": "Plot all the PatientLevelPrediction plots", "topics": [ "plotPlp" ] }, { "page": "plotPrecisionRecall", "title": "Plot the precision-recall curve using the sparse thresholdSummary data frame", "topics": [ "plotPrecisionRecall" ] }, { "page": "plotPredictedPDF", "title": "Plot the Predicted probability density function, showing prediction overlap between true and false cases", "topics": [ "plotPredictedPDF" ] }, { "page": "plotPredictionDistribution", "title": "Plot the side-by-side boxplots of prediction distribution, by class", "topics": [ "plotPredictionDistribution" ] }, { "page": "plotPreferencePDF", "title": "Plot the preference score probability density function, showing prediction overlap between true and false cases #'", "topics": [ "plotPreferencePDF" ] }, { "page": "plotSmoothCalibration", "title": "Plot the smooth calibration as detailed in Calster et al. \"A calibration heirarchy for risk models was defined: from utopia to empirical data\" (2016)", "topics": [ "plotSmoothCalibration" ] }, { "page": "plotSparseCalibration", "title": "Plot the calibration", "topics": [ "plotSparseCalibration" ] }, { "page": "plotSparseCalibration2", "title": "Plot the conventional calibration", "topics": [ "plotSparseCalibration2" ] }, { "page": "plotSparseRoc", "title": "Plot the ROC curve using the sparse thresholdSummary data frame", "topics": [ "plotSparseRoc" ] }, { "page": "plotVariableScatterplot", "title": "Plot the variable importance scatterplot", "topics": [ "plotVariableScatterplot" ] }, { "page": "predictCyclops", "title": "Create predictive probabilities", "topics": [ "predictCyclops" ] }, { "page": "predictGlm", "title": "predict using a logistic regression model", "topics": [ "predictGlm" ] }, { "page": "predictPlp", "title": "predictPlp", "topics": [ "predictPlp" ] }, { "page": "preprocessData", "title": "A function that wraps around FeatureExtraction::tidyCovariateData to normalise the data and remove rare or redundant features", "topics": [ "preprocessData" ] }, { "page": "print.plpData", "title": "Print a plpData object", "topics": [ "print.plpData" ] }, { "page": "print.summary.plpData", "title": "Print a summary.plpData object", "topics": [ "print.summary.plpData" ] }, { "page": "recalibratePlp", "title": "recalibratePlp", "topics": [ "recalibratePlp" ] }, { "page": "recalibratePlpRefit", "title": "recalibratePlpRefit", "topics": [ "recalibratePlpRefit" ] }, { "page": "runMultiplePlp", "title": "Run a list of predictions analyses", "topics": [ "runMultiplePlp" ] }, { "page": "runPlp", "title": "runPlp - Develop and internally evaluate a model using specified settings", "topics": [ "runPlp" ] }, { "page": "savePlpAnalysesJson", "title": "Save the modelDesignList to a json file", "topics": [ "savePlpAnalysesJson" ] }, { "page": "savePlpData", "title": "Save the plpData to folder", "topics": [ "savePlpData" ] }, { "page": "savePlpModel", "title": "Saves the plp model", "topics": [ "savePlpModel" ] }, { "page": "savePlpResult", "title": "Saves the result from runPlp into the location directory", "topics": [ "savePlpResult" ] }, { "page": "savePlpShareable", "title": "Save the plp result as json files and csv files for transparent sharing", "topics": [ "savePlpShareable" ] }, { "page": "savePrediction", "title": "Saves the prediction dataframe to a json file", "topics": [ "savePrediction" ] }, { "page": "setAdaBoost", "title": "Create setting for AdaBoost with python DecisionTreeClassifier base estimator", "topics": [ "setAdaBoost" ] }, { "page": "setCoxModel", "title": "Create setting for lasso Cox model", "topics": [ "setCoxModel" ] }, { "page": "setDecisionTree", "title": "Create setting for the scikit-learn DecisionTree with python", "topics": [ "setDecisionTree" ] }, { "page": "setGradientBoostingMachine", "title": "Create setting for gradient boosting machine model using gbm_xgboost implementation", "topics": [ "setGradientBoostingMachine" ] }, { "page": "setIterativeHardThresholding", "title": "Create setting for Iterative Hard Thresholding model", "topics": [ "setIterativeHardThresholding" ] }, { "page": "setLassoLogisticRegression", "title": "Create modelSettings for lasso logistic regression", "topics": [ "setLassoLogisticRegression" ] }, { "page": "setLightGBM", "title": "Create setting for gradient boosting machine model using lightGBM (https://github.com/microsoft/LightGBM/tree/master/R-package).", "topics": [ "setLightGBM" ] }, { "page": "setMLP", "title": "Create setting for neural network model with python's scikit-learn. For bigger models, consider using 'DeepPatientLevelPrediction' package.", "topics": [ "setMLP" ] }, { "page": "setNaiveBayes", "title": "Create setting for naive bayes model with python", "topics": [ "setNaiveBayes" ] }, { "page": "setPythonEnvironment", "title": "Use the python environment created using configurePython()", "topics": [ "setPythonEnvironment" ] }, { "page": "setRandomForest", "title": "Create setting for random forest model using sklearn", "topics": [ "setRandomForest" ] }, { "page": "setRidgeRegression", "title": "Create modelSettings for ridge logistic regression", "topics": [ "setRidgeRegression" ] }, { "page": "setSVM", "title": "Create setting for the python sklearn SVM (SVC function)", "topics": [ "setSVM" ] }, { "page": "simulatePlpData", "title": "Generate simulated data", "topics": [ "simulatePlpData" ] }, { "page": "simulationProfile", "title": "A simulation profile for generating synthetic patient level prediction data", "topics": [ "simulationProfile" ] }, { "page": "sklearnFromJson", "title": "Loads sklearn python model from json", "topics": [ "sklearnFromJson" ] }, { "page": "sklearnToJson", "title": "Saves sklearn python model object to json in path", "topics": [ "sklearnToJson" ] }, { "page": "splitData", "title": "Split the plpData into test/train sets using a splitting settings of class 'splitSettings'", "topics": [ "splitData" ] }, { "page": "summary.plpData", "title": "Summarize a plpData object", "topics": [ "summary.plpData" ] }, { "page": "toSparseM", "title": "Convert the plpData in COO format into a sparse R matrix", "topics": [ "toSparseM" ] }, { "page": "validateExternal", "title": "validateExternal - Validate model performance on new data", "topics": [ "validateExternal" ] }, { "page": "validateMultiplePlp", "title": "externally validate the multiple plp models across new datasets", "topics": [ "validateMultiplePlp" ] }, { "page": "viewDatabaseResultPlp", "title": "open a local shiny app for viewing the result of a PLP analyses from a database", "topics": [ "viewDatabaseResultPlp" ] }, { "page": "viewMultiplePlp", "title": "open a local shiny app for viewing the result of a multiple PLP analyses", "topics": [ "viewMultiplePlp" ] }, { "page": "viewPlp", "title": "viewPlp - Interactively view the performance and model settings", "topics": [ "viewPlp" ] } ], "_readme": "https://github.com/ohdsi/patientlevelprediction/raw/HEAD/README.md", "_rundeps": [ "Andromeda", "backports", "bit", "bit64", "blob", "cachem", "checkmate", "cli", "clipr", "cpp11", "crayon", "Cyclops", "DatabaseConnector", "DBI", "dbplyr", "digest", "dplyr", "duckdb", "fastmap", "FeatureExtraction", "generics", "glue", "hms", "jsonlite", "lattice", "lifecycle", "magrittr", "Matrix", "memoise", "memuse", "ParallelLogger", "pillar", "pkgconfig", "prettyunits", "pROC", "progress", "PRROC", "purrr", "R6", "Rcpp", "RcppEigen", "readr", "rJava", "rlang", "RSQLite", "rstudioapi", "snow", "SqlRender", "stringi", "stringr", "survival", "tibble", "tidyr", "tidyselect", "triebeard", "tzdb", "urltools", "utf8", "vctrs", "vroom", "withr", "xml2", "zip" ], "_sysdeps": [ { "shlib": "libjvm", "package": "openjdk-21-jre-headless", "headers": "openjdk-21-jre-headless", "source": "openjdk", "version": "21.0.10+7-1~24.04", "name": "openjdk", "homepage": "https://openjdk.java.net/", "description": "OpenJDK Java runtime, using Hotspot JIT (headless)" } ], "_vignettes": [ { "source": "AddingCustomSplitting.Rmd", "filename": "AddingCustomSplitting.html", "title": "Adding Custom Data Splitting", "author": "Jenna Reps", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Data Splitting Function Code Structure", "Example", "Create function", "Implement function", "Acknowledgments" ], "created": "2022-03-11 14:13:13", "modified": "2025-02-06 10:15:34", "commits": 3 }, { "source": "AddingCustomFeatureEngineering.Rmd", "filename": "AddingCustomFeatureEngineering.html", "title": "Adding Custom Feature Engineering Functions", "author": "Jenna Reps, Egill Fridgeirsson", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Feature Engineering Function Code Structure", "Example", "Create function", "Implement function", "Acknowledgments" ], "created": "2022-03-11 14:13:13", "modified": "2025-02-06 10:15:34", "commits": 5 }, { "source": "AddingCustomModels.Rmd", "filename": "AddingCustomModels.html", "title": "Adding Custom Patient-Level Prediction Algorithms", "author": "Jenna Reps, Martijn J. Schuemie, Patrick B. Ryan, Peter R. Rijnbeek", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Algorithm Code Structure", "Set", "Fit", "Predict", "VarImp", "Algorithm Example", "Variable importance", "Acknowledgments" ], "created": "2022-03-11 14:13:13", "modified": "2026-03-09 13:25:16", "commits": 6 }, { "source": "AddingCustomSamples.Rmd", "filename": "AddingCustomSamples.html", "title": "Adding Custom Sampling Functions", "author": "Jenna Reps", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Sample Function Code Structure", "Example", "Create function", "Implement function", "Acknowledgments" ], "created": "2022-03-11 14:13:13", "modified": "2025-02-06 10:15:34", "commits": 3 }, { "source": "BuildingMultiplePredictiveModels.Rmd", "filename": "BuildingMultiplePredictiveModels.html", "title": "Automatically Build Multiple Patient-Level Predictive Models", "author": "Jenna Reps, Martijn J. Schuemie, Patrick B. Ryan, Peter R. Rijnbeek", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Creating a model design", "Model design example 1", "Model design example 2", "Model design example 3", "Running multiple models", "Validating multiple models", "Viewing the results", "Acknowledgments" ], "created": "2018-10-05 14:57:54", "modified": "2025-02-11 12:13:08", "commits": 9 }, { "source": "BenchmarkTasks.Rmd", "filename": "BenchmarkTasks.html", "title": "Benchmark Tasks", "author": "Jenna Reps, Ross Williams, Peter R. Rijnbeek", "engine": "knitr::rmarkdown", "headings": [ "Benchmark Tasks For Large-Scale Empirical Analyses" ], "created": "2023-10-12 13:04:56", "modified": "2025-02-06 10:15:34", "commits": 4 }, { "source": "BestPractices.Rmd", "filename": "BestPractices.html", "title": "Best Practice Research", "author": "Jenna Reps, Peter R. Rijnbeek", "engine": "knitr::rmarkdown", "headings": [ "Best practice publications using the OHDSI PatientLevelPrediction framework" ], "created": "2025-02-06 10:15:34", "modified": "2025-02-11 12:13:08", "commits": 2 }, { "source": "BuildingPredictiveModels.Rmd", "filename": "BuildingPredictiveModels.html", "title": "Building patient-level predictive models", "author": "Jenna Reps, Martijn J. Schuemie, Patrick B. Ryan, Peter R. Rijnbeek", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Study specification", "Problem definition 1: Stroke in atrial fibrilation patients", "Problem definition 2: Angioedema in ACE inhibitor users", "Study population definition", "Model development settings", "Example 1: Stroke in Atrial fibrillation patients", "Study Specification", "Study implementation", "Cohort instantiation", "ATLAS cohort builder", "Custom cohorts", "Study script creation", "Data extraction", "Additional inclusion criteria", "Splitting the data into training/validation/testing datasets", "Preprocessing the training data", "Model Development", "Example 2: Angioedema in ACE inhibitor users", "Spliting the data into training/validation/testing datasets", "Study package creation", "Internal validation", "Discrimination", "Smooth Calibration", "Other functionality", "Demos", "Acknowledgments", "Appendix 1: Study population settings details" ], "created": "2015-03-27 04:55:12", "modified": "2025-07-25 13:00:02", "commits": 38 }, { "source": "ClinicalModels.Rmd", "filename": "ClinicalModels.html", "title": "Clinical Models", "author": "Jenna Reps, Peter R. Rijnbeek", "engine": "knitr::rmarkdown", "headings": [ "Clinical models developed using the OHDSI PatientLevelPrediction framework" ], "created": "2025-02-06 10:15:34", "modified": "2025-02-11 12:13:08", "commits": 2 }, { "source": "ConstrainedPredictors.Rmd", "filename": "ConstrainedPredictors.html", "title": "Constrained Predictors", "author": "Jenna Reps", "engine": "knitr::rmarkdown", "headings": [ "Constrained Predictors", "How to use the PhenotypeLibrary R package", "The full set of predictor phenotypes" ], "created": "2023-10-12 13:04:56", "modified": "2025-02-06 10:15:34", "commits": 4 }, { "source": "CreatingLearningCurves.Rmd", "filename": "CreatingLearningCurves.html", "title": "Creating Learning Curves", "author": "Luis H. John, Jenna M. Reps, Peter R. 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Schuemie, Patrick B. Ryan, Peter R. Rijnbeek", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Useful publication", "Main steps for running a network study", "Step 1 – developing the study", "Step 2 – implementing the study part 1", "Step 3 – implementing the study part 2 (make sure the package is functioning as planned and the definitions are valid across sites)", "Step 4 – Publication", "Package Skeleton - File Structure" ], "created": "2018-05-14 12:05:31", "modified": "2025-02-06 10:15:34", "commits": 6 }, { "source": "InstallationGuide.Rmd", "filename": "InstallationGuide.html", "title": "Patient-Level Prediction Installation Guide", "author": "Jenna Reps, Peter R. Rijnbeek, Egill Fridgeirsson", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Software Prerequisites", "Windows Users", "Mac/Linux Users", "Installing the Package", "Installing PatientLevelPrediction using remotes", "Creating Python Reticulate Environment", "Installation issues", "Common issues", "python environment Mac/linux users:", "Acknowledgments" ], "created": "2018-05-21 14:00:07", "modified": "2025-02-11 12:13:08", "commits": 12 } ], "_score": 10.698065731043075, "_indexed": true, "_nocasepkg": "patientlevelprediction", "_universes": [ "ohdsi", "egillax" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "6.6.0", "date": "2026-05-28T07:24:53.000Z", "distro": "noble", "commit": "1439171b5c742a2d71e690a068035e8eff25d497", "fileid": "afcd082ea8a0fcd742c48e6b81b59227f42c78b5819550611a1fc3366dd7e310", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/ohdsi/actions/runs/26560503314" }, { "r": "4.6.0", "os": "linux", "version": "6.6.0", "date": "2026-05-28T07:26:37.000Z", "distro": "noble", "commit": "1439171b5c742a2d71e690a068035e8eff25d497", "fileid": "d63b594f0197d8f85ad99bb249c6289c4e507161ccaba1db3e45cc886ea73d56", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/ohdsi/actions/runs/26560503314" }, { "r": "4.5.3", "os": "mac", "version": "6.6.0", "date": "2026-05-28T07:25:50.000Z", "commit": "1439171b5c742a2d71e690a068035e8eff25d497", "fileid": "bb8846c07b42043b6e626e445ffecc96052a12ecfbc51318242697264d55ef03", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/ohdsi/actions/runs/26560503314" }, { "r": "4.6.0", "os": "mac", "version": "6.6.0", "date": "2026-05-28T07:25:57.000Z", "commit": "1439171b5c742a2d71e690a068035e8eff25d497", "fileid": "410456bc07e7f7336c9fed9105b60f9fbb428c20c8b43aff04e701c8bf0ee45c", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/ohdsi/actions/runs/26560503314" }, { "r": "4.6.0", "os": "wasm", "version": "6.6.0", "date": "2026-05-28T07:24:54.000Z", "commit": "1439171b5c742a2d71e690a068035e8eff25d497", "fileid": "7fa3d26552bdc8fe4b20ddb2120bcad8cd89eb70fcc16203e2bb6ab01dcb1ef4", "status": "success", "buildurl": "https://github.com/r-universe/ohdsi/actions/runs/26560503314" }, { "r": "4.7.0", "os": "win", "version": "6.6.0", "date": "2026-05-28T07:23:13.000Z", "commit": "1439171b5c742a2d71e690a068035e8eff25d497", "fileid": "46747254f7e2776c8c17262a8ae30199ca9e6b053a10217c5cc15c0fedf3f9cb", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/ohdsi/actions/runs/26560503314" }, { "r": "4.5.3", "os": "win", "version": "6.6.0", "date": "2026-05-28T07:23:13.000Z", "commit": "1439171b5c742a2d71e690a068035e8eff25d497", "fileid": "b0e290ea3f16a80da1e997d0b93b0a6c32c03a62f30c2d297b650a55f3576b6b", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/ohdsi/actions/runs/26560503314" }, { "r": "4.6.0", "os": "win", "version": "6.6.0", "date": "2026-05-28T07:23:09.000Z", "commit": "1439171b5c742a2d71e690a068035e8eff25d497", "fileid": "1152716f82ba49779ea675e0950ea6615a90a5dee3ea2d390a0b0a26617ee321", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/ohdsi/actions/runs/26560503314" } ] }