The mlr3 ecosystem is a collection of R packages for machine learning. The base package mlr3 only provides the basic building blocks for machine learning. The extensions packages extent mlr3 with functionality for additional task types, learning algorithms, tuning algorithms, feature selection strategies, visualizations or preprocessing capabilities. The packages are listed bellow with a short description. For more information about the packages, check out their respective homepages. The dot next to the package name indicates the lifecycle stage. Packages with a green dot are stable. Experimental packages are marked with an orange dot . Planned packages are marked with a red dot .
Graph of Extension Packages
If you use our packages in your research, please cite our articles on mlr3 (Lang et al. 2019), mlr3proba (Sonabend et al. 2021) or mlr3pipelines (Binder et al. 2021). To get the citation information of other packages, call
Binder, Martin, Florian Pfisterer, Michel Lang, Lennart Schneider, Lars Kotthoff, and Bernd Bischl. 2021. “mlr3pipelines - Flexible Machine Learning Pipelines in R.”Journal of Machine Learning Research 22 (184): 1–7. https://jmlr.org/papers/v22/21-0281.html.
Lang, Michel, Martin Binder, Jakob Richter, Patrick Schratz, Florian Pfisterer, Stefan Coors, Quay Au, Giuseppe Casalicchio, Lars Kotthoff, and Bernd Bischl. 2019. “mlr3: A Modern Object-Oriented Machine Learning Framework in R.”Journal of Open Source Software, December. https://doi.org/10.21105/joss.01903.
Sonabend, Raphael, Franz J Király, Andreas Bender, Bernd Bischl, and Michel Lang. 2021. “mlr3proba: An R Package for Machine Learning in Survival Analysis.”Bioinformatics, February. https://doi.org/10.1093/bioinformatics/btab039.