Hyperparameter Optimization for 'mlr3' R package mlr3tuning version 1.6.1 Hyperparameter optimization package of the 'mlr3' ecosystem. It features highly configurable search spaces via the 'paradox' package and finds optimal hyperparameter configurations for any 'mlr3' learner. 'mlr3tuning' works with several optimization algorithms e.g. Random Search, Iterated Racing, Bayesian Optimization (in 'mlr3mbo') and Hyperband (in 'mlr3hyperband'). Moreover, it can automatically optimize learners and estimate the performance of optimized models with nested resampling. Software Depends: mlr3 (>= 0.23.0), paradox (>= 1.0.1), R (>= 3.3.0) Imports: bbotk (>= 1.9.0), checkmate (>= 2.0.0), cli, data.table, lgr, mlr3misc (>= 0.15.1), R6 Suggests: adagio, future, GenSA, irace (>= 4.1.0), knitr, mirai, mlflow, mlr3learners (>= 0.14.0), mlr3pipelines (>= 0.5.2), nloptr, processx, redux, rush (>= 1.2.0), rmarkdown, rpart, testthat (>= 3.0.0), xgboost (>= 3.1.2.1) Marc Becker <marcbecker@posteo.de> Comprehensive R Archive Network (CRAN) Marc Becker [cre, aut] (ORCID: <https://orcid.org/0000-0002-8115-0400>), Michel Lang [aut] (ORCID: <https://orcid.org/0000-0001-9754-0393>), Jakob Richter [aut] (ORCID: <https://orcid.org/0000-0003-4481-5554>), Bernd Bischl [aut] (ORCID: <https://orcid.org/0000-0001-6002-6980>), Daniel Schalk [aut] (ORCID: <https://orcid.org/0000-0003-0950-1947>) LGPL-3 2026-07-26 application/tgz https://CRAN.R-project.org/package=mlr3tuning doi:10.32614/CRAN.package.mlr3tuning