Scholary papers describing the methodology

The party package implements conditional inference trees (function ctree(), Hothorn, Hornik, and Zeileis, 2006), model-based recursive partitioning (function mob(), Zeileis, Hothorn, and Hornik, 2008), and conditional inference forests (function cforest(), Hothorn, Bühlmann, Dudoit, Molinaro, and van der Laan, 2006). Variable importances for conditional inference forests are implemented in function varimp() as described in Strobl, Boulesteix, Zeileis, and Hothorn (2007) and Strobl, Boulesteix, Kneib, Augustin, and Zeileis (2008). An introduction to trees and forests was published by Strobl, Malley, and Tutz (2009).

References

[1] T. Hothorn, P. Bühlmann, S. Dudoit, et al. “Survival Ensembles”. In: Biostatistics 7.3 (Jul. 2006), pp. 355–373. DOI: 10.1093/biostatistics/kxj011.

[2] 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.

[3] C. Strobl, A. Boulesteix, A. Zeileis, et al. “Bias in Random Forest Variable Importance Measures: Illustrations, Sources and a Solution”. In: BMC Bioinformatics 8 (2007). highly accessed, p. 25. DOI: 10.1186/1471-2105-8-25. URL: http://www.biomedcentral.com/1471-2105/8/25/abstract.

[4] C. Strobl, A. Boulesteix, T. Kneib, et al. “Conditional Variable Importance for Random Forests”. In: BMC Bioinformatics 9.1 (2008), p. 307. DOI: 10.1186/1471-2105-9-307. URL: http://www.biomedcentral.com/1471-2105/9/307.

[5] 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.

[6] C. Strobl, J. Malley, and G. Tutz. “An Introduction to Recursive Partitioning: Rationale, Application, and Characteristics of Classification and Regression Trees, Bagging, and Random forests”. In: Psychological Methods 14.4 (2009), pp. 323–348. DOI: 10.1037/a0016973.