party: A Laboratory for Recursive Partytioning. A computational toolbox for recursive partitioning. The core of the package is ctree(), an implementation of conditional inference trees which embed tree-structured regression models into a well defined theory of conditional inference procedures. This non-parametric class of regression trees is applicable to all kinds of regression problems, including nominal, ordinal, numeric, censored as well as multivariate response variables and arbitrary measurement scales of the covariates. Based on conditional inference trees, cforest() provides an implementation of Breiman’s random forests. The function mob() implements an algorithm for recursive partitioning based on parametric models (e.g. linear models, GLMs or survival regression) employing parameter instability tests for split selection. Extensible functionality for visualizing tree-structured regression models is available.
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References in zbMATH (referenced in 9 articles )
Showing results 1 to 9 of 9.
- Hothorn, Torsten: partykit: a modular toolkit for recursive partytioning in $\mathsf R$ (2015)
- Nguyen, Thanh-Tung; Huang, Joshua Z.; Nguyen, Thuy Thi: Two-level quantile regression forests for bias correction in range prediction (2015)
- Zhou, Yan; McArdle, John J.: Rationale and applications of survival tree and survival ensemble methods (2015)
- Hapfelmeier, Alexander; Hothorn, Torsten; Ulm, Kurt; Strobl, Carolin: A new variable importance measure for random forests with missing data (2014)
- Kuhn, Max; Johnson, Kjell: Applied predictive modeling (2013)
- Zhao, Yanchang: R and data mining. Examples and case studies (2013)
- Hapfelmeier, A.; Hothorn, T.; Ulm, K.: Recursive partitioning on incomplete data using surrogate decisions and multiple imputation (2012)
- Hornik, Kurt; Buchta, Christian; Zeileis, Achim: Open-source machine learning: R meets Weka (2009)
- Bühlmann, Peter; Hothorn, Torsten: Boosting algorithms: regularization, prediction and model fitting (2007)