Scholary papers describing the methodology

The partykit package provides a flexible toolkit for learning, representing, summarizing, and visualizing a wide range of tree-structured regression and classification models (Hothorn and Zeileis, 2015). In addition, it implements conditional inference trees (function ctree(), Hothorn, Hornik, and Zeileis, 2006), and model-based recursive partitioning (function mob(), Zeileis, Hothorn, and Hornik, 2008).

References

[1] T. Hothorn, K. Hornik, and A. Zeileis. “Unbiased Recursive Partitioning: A Conditional Inference Framework”. In: Journal of Computational and Graphical Statistics 15.3 (2006), pp. 651–674. DOI: 10.1198/106186006X133933.

[2] A. Zeileis, T. Hothorn, and K. Hornik. “Model-based Recursive Partitioning”. In: Journal of Computational and Graphical Statistics 17.2 (2008), pp. 492–514. DOI: 10.1198/106186008X319331.

[3] T. Hothorn and A. Zeileis. “partykit: A Modular Toolkit for Recursive Partytioning in R”. In: Journal of Machine Learning Research 16 (2015), pp. 3905–3909. URL: http://jmlr.org/papers/v16/hothorn15a.html.