Entry points to learn about mlr3.
The essential things neatly summarized. Perfectly printed out next to the keyboard or on a second monitor.
Recorded tutorials and lectures we have given.
Material from teaching at our universities.
A more scientific view on our the packages and the packages we depend on.
Bengtsson, Henrik. 2021.
“A Unifying Framework for Parallel and Distributed Processing in R Using Futures.” The R Journal 13 (2): 208–27.
https://doi.org/10.32614/RJ-2021-048.
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. 2017.
“checkmate: Fast Argument Checks for Defensive R Programming.” The R Journal 9 (1): 437–45.
https://doi.org/10.32614/RJ-2017-028.
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.
Lang, Michel, Bernd Bischl, and Dirk Surmann. 2017.
“Batchtools: Tools for R to Work on Batch Systems.” The Journal of Open Source Software, no. 10 (February).
https://doi.org/10.21105/joss.00135.
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.