Partykit: a modular toolkit for recursive partytioning in R
By Torsten Hothorn, Achim Zeileis. 2015
871 citations recorded by the source corpus.
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Citations in the corpus
- Invariant Causal Prediction for Nonlinear Models (2017, background): “Appendix B.6 for further details. Another interesting possibility for future work would be to devise a conditional independence test based on model-based recursive partitioning (Zeileis et al., 2008; Hothorn and Zeileis, 2015). Non-trivial, assumption-free conditional independence tests with a valid”
- Random Forests, Decision Trees, and Categorical Predictors: The "Absent Levels" Problem (2017, methodology): “Interestingly, the partykit R package (Hothorn and Zeileis, 2015) appears to recognize the possibility of absent levels occurring, and handles them as if they were missing—its reference manual states that “Factors in test samples whose levels were empty in the learning sample are treated as missing…”
- Hyperparameters and tuning strategies for random forest (2018, methodology): “(2006) and implemented in the R package party and in the newer package partykit (Hothorn and Zeileis, 2015) allow to avoid this variable selection bias by selecting the variable with the smallest p-value in a global test (i.”
- Residues of currently used pesticides in soils and earthworms: A silent threat? (2021, methodology): “2 using the following packages: partykit (Hothorn and Zeileis, 2015), pgirmess (Giraudoux, 2018), and car (Fox et al.”
- pre: An R Package for Fitting Prediction Rule Ensembles (2017, methodology): “Function ctree() from package partykit (version 1.2.0; Hothorn and Zeileis 2015) was used to fit single conditional inference”