End-to-End Automated Machine Learning and Model Evaluation R package familiar version 2.0.2 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. Software Depends: R (>= 4.0.0) 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) Alex Zwanenburg <alexander.zwanenburg@nct-dresden.de> Comprehensive R Archive Network (CRAN) 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] EUPL 2026-06-01 application/tgz https://CRAN.R-project.org/package=familiar doi:10.32614/CRAN.package.familiar