Package: familiar
Title: End-to-End Automated Machine Learning and Model Evaluation
Version: 2.0.2
Authors@R: c(
    person("Alex", "Zwanenburg",
           email = "alexander.zwanenburg@nct-dresden.de",
           role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-0342-9545")),
    person("Steffen", "Löck", role="aut"),
    person("German Cancer Research Center (DKFZ)", role="cph"),
    person("Technische Universität Dresden", role="cph"))
Description: Single unified interface for end-to-end modelling of
        regression, categorical and time-to-event (survival) outcomes.
        Models created using familiar are self-containing, and their
        use does not require additional information such as baseline
        survival, feature clustering, or feature transformation and
        normalisation parameters. Model performance, calibration, risk
        group stratification, (permutation) variable importance,
        individual conditional expectation, partial dependence, and
        more, are assessed automatically as part of the evaluation
        process and exported in tabular format and plotted, and may
        also be computed manually using export and plot functions.
        Where possible, metrics and values obtained during the
        evaluation process come with confidence intervals.
URL: https://github.com/oncoray/familiar
BugReports: https://github.com/oncoray/familiar/issues
Depends: R (>= 4.0.0)
License: EUPL
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
VignetteBuilder: knitr
Imports: data.table, methods, rlang (>= 1.0.0), rstream, survival
Suggests: BART, callr (>= 3.4.3), cluster, CORElearn, coro,
        dynamicTreeCut, e1071 (>= 1.7.5), fastcluster, fastglm, ggplot2
        (>= 4.0.0), glmnet, gtable, harmonicmeanp, isotree (>= 0.6.0),
        knitr, labeling, laGP, maxstat, microbenchmark, nnet,
        paletteer, power.transform, praznik, proxy, randomForestSRC,
        ranger, rmarkdown, scales, testthat (>= 3.0.0), xml2, xgboost
        (>= 3.0.0)
Collate: 'FamiliarS4Classes.R' 'FamiliarS4Generics.R'
        'BatchNormalisation.R' 'BootstrapConfidenceInterval.R'
        'CheckArguments.R' 'CheckHyperparameters.R' 'CheckPackages.R'
        'ClassBalance.R' 'ClusteringMethod.R' 'Clustering.R'
        'ClusterRepresentation.R' 'Normalisation.R'
        'CombatNormalisation.R' 'LearnerS4Naive.R' 'DataObject.R'
        'DataParameterChecks.R' 'DataPreProcessing.R'
        'DataProcessing.R' 'DataServerBackend.R' 'ErrorMessages.R'
        'ExperimentData.R' 'ExperimentSetup.R' 'Familiar.R'
        'FamiliarCollection.R' 'FamiliarCollectionExport.R'
        'FamiliarData.R' 'FamiliarDataComputation.R'
        'FamiliarDataComputationPredictionData.R' 'PredictionTable.R'
        'FamiliarDataComputationAUCCurves.R'
        'FamiliarDataComputationCalibrationData.R'
        'FamiliarDataComputationCalibrationInfo.R'
        'FamiliarDataComputationConfusionMatrix.R'
        'FamiliarDataComputationDecisionCurveAnalysis.R'
        'FamiliarDataComputationFeatureExpression.R'
        'FamiliarDataComputationFeatureSimilarity.R'
        'FamiliarDataComputationHyperparameters.R'
        'FamiliarDataComputationICE.R'
        'FamiliarDataComputationModelPerformance.R'
        'FamiliarDataComputationPermutationVimp.R'
        'FamiliarDataComputationRiskStratificationData.R'
        'FamiliarDataComputationRiskStratificationInfo.R'
        'FamiliarDataComputationSHAP.R'
        'FamiliarDataComputationSampleSimilarity.R'
        'FamiliarDataComputationUnivariateAnalysis.R'
        'FamiliarDataComputationUtilities.R'
        'FamiliarDataComputationVimp.R' 'FamiliarDataElement.R'
        'FamiliarEnsemble.R' 'FamiliarHyperparameterLearner.R'
        'FamiliarModel.R' 'FamiliarNoveltyDetector.R'
        'FamiliarObjectConversion.R' 'Transformation.R'
        'FamiliarObjectUpdate.R' 'FamiliarSharedS4Methods.R'
        'FamiliarVimpMethod.R' 'FeatureInfo.R'
        'FeatureInfoParameters.R' 'FunctionWrapperUtilities.R'
        'HyperparameterOptimisation.R'
        'HyperparameterOptimisationMetaLearners.R'
        'HyperparameterOptimisationUtilities.R'
        'HyperparameterS4BayesianAdditiveRegressionTrees.R'
        'HyperparameterS4GaussianProcess.R'
        'HyperparameterS4RandomSearch.R' 'HyperparameterS4Ranger.R'
        'Imputation.R' 'Iterations.R' 'LearnerMain.R'
        'LearnerRecalibration.R' 'LearnerRiskStratification.R'
        'LearnerS4Cox.R' 'LearnerS4GLM.R' 'LearnerS4GLMnet.R'
        'LearnerS4KNN.R' 'LearnerS4MBoost.R' 'LearnerS4NaiveBayes.R'
        'LearnerS4RFSRC.R' 'LearnerS4Ranger.R' 'LearnerS4SVM.R'
        'LearnerS4SurvivalRegression.R' 'LearnerS4XGBoost.R'
        'LearnerSurvivalProbability.R' 'Logger.R' 'MetricS4.R'
        'MetricS4AUC.R' 'MetricS4Brier.R' 'MetricS4ConcordanceIndex.R'
        'MetricS4ConfusionMatrixMetrics.R' 'MetricS4Regression.R'
        'NoveltyDetectorMain.R' 'NoveltyDetectorS4IsolationTree.R'
        'NoveltyDetectorS4NoneNoveltyDetector.R' 'OutcomeInfo.R'
        'PairwiseSimilarity.R' 'ParallelFunctions.R' 'ParseData.R'
        'ParseSettings.R' 'PlotAUCcurves.R' 'PlotAll.R'
        'PlotCalibration.R' 'PlotColours.R' 'PlotConfusionMatrix.R'
        'PlotDecisionCurves.R' 'PlotFamiliarPlot.R'
        'PlotFeatureRanking.R' 'PlotFeatureSimilarity.R' 'PlotGTable.R'
        'PlotICE.R' 'PlotInputArguments.R' 'PlotKaplanMeier.R'
        'PlotModelPerformance.R' 'PlotPermutationVariableImportance.R'
        'PlotSampleClustering.R' 'PlotShapDependence.R'
        'PlotShapForce.R' 'PlotShapSummary.R' 'PlotShapWaterfall.R'
        'PlotUnivariateImportance.R' 'PlotUtilities.R'
        'PredictS4Methods.R' 'ProcessTimeUtilities.R' 'Random.R'
        'RankBordaAggregation.R' 'RankMain.R' 'RankSimpleAggregation.R'
        'RankStabilityAggregation.R' 'SocketServer.R'
        'StringUtilities.R' 'TaskEvaluate.R' 'TaskFeatureInfo.R'
        'TaskLearn.R' 'TaskLearnerHyperparameters.R' 'TaskMain.R'
        'TaskNoveltyDetector.R' 'TaskNoveltyDetectorHyperparameters.R'
        'TaskVimp.R' 'TaskVimpHyperparameters.R' 'TestDataCreators.R'
        'TestFeatureInfo.R' 'TestFunctions.R' 'TestTrain.R'
        'TestTrainNovelty.R' 'TestVimp.R' 'TrimUtilities.R'
        'Utilities.R' 'UtilitiesS4.R' 'VimpMain.R'
        'VimpS4Concordance.R' 'VimpS4CoreLearn.R' 'VimpS4Correlation.R'
        'VimpS4MutualInformation.R' 'VimpS4OtherMethods.R'
        '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: 2026-06-11 08:52:48 UTC
RemoteUrl: https://github.com/oncoray/familiar
RemoteRef: HEAD
RemoteSha: ebe07ce4b70ed2a621b07036fe352e11aaade97b
NeedsCompilation: no
Packaged: 2026-06-11 10:38:25 UTC; root
Author: Alex Zwanenburg [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-0342-9545>),
  Steffen Löck [aut],
  German Cancer Research Center (DKFZ) [cph],
  Technische Universität Dresden [cph]
Maintainer: Alex Zwanenburg <alexander.zwanenburg@nct-dresden.de>