partykit

partykit: A Toolkit for Recursive Partytioning. A toolkit with infrastructure for representing, summarizing, and visualizing tree-structured regression and classification models. This unified infrastructure can be used for reading/coercing tree models from different sources (rpart, RWeka, PMML) yielding objects that share functionality for print/plot/predict methods. Furthermore, new and improved reimplementations of conditional inference trees (ctree) and model-based recursive partitioning (mob) from the party package are provided based on the new infrastructure.


References in zbMATH (referenced in 22 articles , 1 standard article )

Showing results 1 to 20 of 22.
Sorted by year (citations)

1 2 next

  1. Gero Szepannek: An Overview on the Landscape of R Packages for Credit Scoring (2020) arXiv
  2. Liu, Nan-Ting; Lin, Feng-Chang; Shih, Yu-Shan: Count regression trees (2020)
  3. Madan Gopal Kundu, Samiran Ghosh: Survival trees for right-censored data based on score based parameter instability test (2020) arXiv
  4. Sage, Andrew J.; Genschel, Ulrike; Nettleton, Dan: Tree aggregation for random forest class probability estimation (2020)
  5. Heidi Seibold, Achim Zeileis, Torsten Hothorn: model4you: An R Package for Personalised Treatment Effect Estimation. (2019) not zbMATH
  6. Seibold, Heidi; Hothorn, Torsten; Zeileis, Achim: Generalised linear model trees with global additive effects (2019)
  7. Asfha, Huruy Debessay; Kilinc, Betul Kan: Appraisal of performance of three tree-based classification methods (2018)
  8. Au, Timothy C.: Random forests, decision trees, and categorical predictors: the “absent levels” problem (2018)
  9. Eun-Kyung Lee: PPtreeViz: An R Package for Visualizing Projection Pursuit Classification Trees (2018) not zbMATH
  10. Peter Calhoun; Xiaogang Su;Martha Nunn; Juanjuan Fan: Constructing Multivariate Survival Trees: The MST Package for R (2018) not zbMATH
  11. Ting Wang, Edgar C. Merkle: merDeriv: Derivative Computations for Linear Mixed Effects Models with Application to Robust Standard Errors (2018) not zbMATH
  12. Alvarez-Iglesias, Alberto; Hinde, John; Ferguson, John; Newell, John: An alternative pruning based approach to unbiased recursive partitioning (2017)
  13. Baumer, Benjamin S.; Kaplan, Daniel T.; Horton, Nicholas J.: Modern data science with R (2017)
  14. Marjolein Fokkema: pre: An R Package for Fitting Prediction Rule Ensembles (2017) arXiv
  15. Reto Bürgin; Gilbert Ritschard: Coefficient-Wise Tree-Based Varying Coefficient Regression with vcrpart (2017) not zbMATH
  16. Hothorn, Torsten: partykit: a modular toolkit for recursive partytioning in \textttR (2015)
  17. Zhou, Yan; McArdle, John J.: Rationale and applications of survival tree and survival ensemble methods (2015)
  18. Doove, L. L.; van Buuren, S.; Dusseldorp, E.: Recursive partitioning for missing data imputation in the presence of interaction effects (2014)
  19. Falk, Michael; Hain, Johannes; Marohn, Frank; Fischer, Hans; Michel, René: Statistics in theory and practice. With applications in R (2014)
  20. Thomas Grubinger; Achim Zeileis; Karl-Peter Pfeiffer: evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in R (2014) not zbMATH

1 2 next