{ "_id": "6a1dbdda1d7bb097a0a56817", "Package": "familiar", "Title": "End-to-End Automated Machine Learning and Model Evaluation", "Version": "2.0.2", "Authors@R": "c(\nperson(\"Alex\", \"Zwanenburg\",\nemail = \"alexander.zwanenburg@nct-dresden.de\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-0342-9545\")),\nperson(\"Steffen\", \"Löck\", role=\"aut\"),\nperson(\"German Cancer Research Center (DKFZ)\", role=\"cph\"),\nperson(\"Technische Universität Dresden\", role=\"cph\"))", "Description": "Single unified interface for end-to-end modelling of\nregression, categorical and time-to-event (survival) outcomes.\nModels created using familiar are self-containing, and their\nuse does not require additional information such as baseline\nsurvival, feature clustering, or feature transformation and\nnormalisation parameters. Model performance, calibration, risk\ngroup stratification, (permutation) variable importance,\nindividual conditional expectation, partial dependence, and\nmore, are assessed automatically as part of the evaluation\nprocess and exported in tabular format and plotted, and may\nalso be computed manually using export and plot functions.\nWhere possible, metrics and values obtained during the\nevaluation process come with confidence intervals.", "URL": "https://github.com/oncoray/familiar", "BugReports": "https://github.com/oncoray/familiar/issues", "License": "EUPL", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "VignetteBuilder": "knitr", "Collate": "'FamiliarS4Classes.R' 'FamiliarS4Generics.R'\n'BatchNormalisation.R' 'BootstrapConfidenceInterval.R'\n'CheckArguments.R' 'CheckHyperparameters.R' 'CheckPackages.R'\n'ClassBalance.R' 'ClusteringMethod.R' 'Clustering.R'\n'ClusterRepresentation.R' 'Normalisation.R'\n'CombatNormalisation.R' 'LearnerS4Naive.R' 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'NoveltyDetectorS4IsolationTree.R'\n'NoveltyDetectorS4NoneNoveltyDetector.R' 'OutcomeInfo.R'\n'PairwiseSimilarity.R' 'ParallelFunctions.R' 'ParseData.R'\n'ParseSettings.R' 'PlotAUCcurves.R' 'PlotAll.R'\n'PlotCalibration.R' 'PlotColours.R' 'PlotConfusionMatrix.R'\n'PlotDecisionCurves.R' 'PlotFamiliarPlot.R'\n'PlotFeatureRanking.R' 'PlotFeatureSimilarity.R' 'PlotGTable.R'\n'PlotICE.R' 'PlotInputArguments.R' 'PlotKaplanMeier.R'\n'PlotModelPerformance.R' 'PlotPermutationVariableImportance.R'\n'PlotSampleClustering.R' 'PlotShapDependence.R'\n'PlotShapForce.R' 'PlotShapSummary.R' 'PlotShapWaterfall.R'\n'PlotUnivariateImportance.R' 'PlotUtilities.R'\n'PredictS4Methods.R' 'ProcessTimeUtilities.R' 'Random.R'\n'RankBordaAggregation.R' 'RankMain.R' 'RankSimpleAggregation.R'\n'RankStabilityAggregation.R' 'SocketServer.R'\n'StringUtilities.R' 'TaskEvaluate.R' 'TaskFeatureInfo.R'\n'TaskLearn.R' 'TaskLearnerHyperparameters.R' 'TaskMain.R'\n'TaskNoveltyDetector.R' 'TaskNoveltyDetectorHyperparameters.R'\n'TaskVimp.R' 'TaskVimpHyperparameters.R' 'TestDataCreators.R'\n'TestFeatureInfo.R' 'TestFunctions.R' 'TestTrain.R'\n'TestTrainNovelty.R' 'TestVimp.R' 'TrimUtilities.R'\n'Utilities.R' 'UtilitiesS4.R' 'VimpMain.R'\n'VimpS4Concordance.R' 'VimpS4CoreLearn.R' 'VimpS4Correlation.R'\n'VimpS4MutualInformation.R' 'VimpS4OtherMethods.R'\n'VimpS4Regression.R' 'VimpTable.R' 'aaa.R'", "Config/testthat/parallel": "true", "Config/testthat/edition": "3", "Config/roxygen2/version": "8.0.0", "Repository": "https://oncoray.r-universe.dev", "Date/Publication": "2026-06-01 16:02:55 UTC", "RemoteUrl": "https://github.com/oncoray/familiar", "RemoteRef": "HEAD", "RemoteSha": "a7398744ac98cb66f6d4f19c40e5bf54070f638f", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-01 16:55:57 UTC", "User": "root" }, "Author": "Alex Zwanenburg [aut, cre] (ORCID:\n),\nSteffen Löck [aut],\nGerman Cancer Research Center (DKFZ) [cph],\nTechnische Universität Dresden [cph]", 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between features.", "topics": [ "export_feature_similarity", "export_feature_similarity,ANY-method", "export_feature_similarity,familiarCollection-method" ] }, { "page": "export_fs_vimp-methods", "title": "Extract and export feature selection variable importance.", "topics": [ "export_fs_vimp", "export_fs_vimp,ANY-method", "export_fs_vimp,familiarCollection-method" ] }, { "page": "export_hyperparameters-methods", "title": "Extract and export model hyperparameters.", "topics": [ "export_hyperparameters", "export_hyperparameters,ANY-method", "export_hyperparameters,familiarCollection-method" ] }, { "page": "export_ice_data-methods", "title": "Extract and export individual conditional expectation data.", "topics": [ "export_ice_data", "export_ice_data,ANY-method", "export_ice_data,familiarCollection-method" ] }, { "page": "export_model_performance-methods", "title": "Extract and export metrics for model performance.", "topics": [ "export_model_performance", 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"export_prediction_data,familiarCollection-method" ] }, { "page": "export_risk_stratification_data-methods", "title": "Extract and export sample risk group stratification and associated tests.", "topics": [ "export_risk_stratification_data", "export_risk_stratification_data,ANY-method", "export_risk_stratification_data,familiarCollection-method" ] }, { "page": "export_risk_stratification_info-methods", "title": "Extract and export cut-off values for risk group stratification.", "topics": [ "export_risk_stratification_info", "export_risk_stratification_info,ANY-method", "export_risk_stratification_info,familiarCollection-method" ] }, { "page": "export_sample_similarity-methods", "title": "Extract and export mutual correlation between features.", "topics": [ "export_sample_similarity", "export_sample_similarity,ANY-method", "export_sample_similarity,familiarCollection-method" ] }, { "page": "export_shap-methods", "title": "Extract and export individual conditional expectation data.", "topics": [ "export_shap", "export_shap,ANY-method", "export_shap,familiarCollection-method" ] }, { "page": "export_univariate_analysis_data-methods", "title": "Extract and export univariate analysis data of features.", "topics": [ "export_univariate_analysis_data", "export_univariate_analysis_data,ANY-method", "export_univariate_analysis_data,familiarCollection-method" ] }, { "page": "familiar", "title": "familiar: Fully Automated Machine Learning with Interpretable Analysis of Results", "topics": [ "familiar-package", "familiar" ] }, { "page": "familiarCollection-class", "title": "Collection of familiar data.", "topics": [ "familiarCollection-class" ] }, { "page": "familiarData-class", "title": "Dataset obtained after evaluating models on a dataset.", "topics": [ "familiarData-class" ] }, { "page": "familiarDataElement-class", "title": "Data container for evaluation data.", "topics": [ "familiarDataElement-class" ] }, { "page": "familiarEnsemble-class", "title": "Ensemble of familiar 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