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 29 articles , 1 standard article )

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  1. Dutang, Christophe; Guibert, Quentin: An explicit split point procedure in model-based trees allowing for a quick fitting of GLM trees and GLM forests (2022)
  2. Arevalillo, Jorge M.: Ensemble learning from model based trees with application to differential price sensitivity assessment (2021)
  3. Zhao, Xin; Barber, Stuart; Taylor, Charles C.; Milan, Zoka: Interval forecasts based on regression trees for streaming data (2021)
  4. Chatla, Suneel Babu; Shmueli, Galit: A tree-based semi-varying coefficient model for the COM-Poisson distribution (2020)
  5. Gero Szepannek: An Overview on the Landscape of R Packages for Credit Scoring (2020) arXiv
  6. Jones, Payton J.; Mair, Patrick; Simon, Thorsten; Zeileis, Achim: Network trees: a method for recursively partitioning covariance structures (2020)
  7. Liu, Nan-Ting; Lin, Feng-Chang; Shih, Yu-Shan: Count regression trees (2020)
  8. Madan Gopal Kundu, Samiran Ghosh: Survival trees for right-censored data based on score based parameter instability test (2020) arXiv
  9. Sage, Andrew J.; Genschel, Ulrike; Nettleton, Dan: Tree aggregation for random forest class probability estimation (2020)
  10. Heidi Seibold, Achim Zeileis, Torsten Hothorn: model4you: An R Package for Personalised Treatment Effect Estimation. (2019) not zbMATH
  11. Seibold, Heidi; Hothorn, Torsten; Zeileis, Achim: Generalised linear model trees with global additive effects (2019)
  12. Asfha, Huruy Debessay; Kilinc, Betul Kan: Appraisal of performance of three tree-based classification methods (2018)
  13. Au, Timothy C.: Random forests, decision trees, and categorical predictors: the “absent levels” problem (2018)
  14. Eun-Kyung Lee: PPtreeViz: An R Package for Visualizing Projection Pursuit Classification Trees (2018) not zbMATH
  15. Peter Calhoun; Xiaogang Su;Martha Nunn; Juanjuan Fan: Constructing Multivariate Survival Trees: The MST Package for R (2018) not zbMATH
  16. Philipp, Michel; Rusch, Thomas; Hornik, Kurt; Strobl, Carolin: Measuring the stability of results from supervised statistical learning (2018)
  17. Ting Wang, Edgar C. Merkle: merDeriv: Derivative Computations for Linear Mixed Effects Models with Application to Robust Standard Errors (2018) not zbMATH
  18. Alvarez-Iglesias, Alberto; Hinde, John; Ferguson, John; Newell, John: An alternative pruning based approach to unbiased recursive partitioning (2017)
  19. Baumer, Benjamin S.; Kaplan, Daniel T.; Horton, Nicholas J.: Modern data science with R (2017)
  20. Marjolein Fokkema: pre: An R Package for Fitting Prediction Rule Ensembles (2017) arXiv

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