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.

References in zbMATH (referenced in 28 articles )

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  1. Genuer, Robin; Poggi, Jean-Michel: Random forests with R (2020)
  2. Gupta, Bhisham C.; Guttman, Irwin; Jayalath, Kalanka P.: Statistics and probability with applications for engineers and scientists using MINITAB, R and JMP (2020)
  3. Daniel Conn, Tuck Ngun, Gang Li, Christina M. Ramirez: Fuzzy Forests: Extending Random Forest Feature Selection for Correlated, High-Dimensional Data (2019) not zbMATH
  4. Jaeger, Byron C.; Long, D. Leann; Long, Dustin M.; Sims, Mario; Szychowski, Jeff M.; Min, Yuan-I; McClure, Leslie A.; Howard, George; Simon, Noah: Oblique random survival forests (2019)
  5. Steingrimsson, Jon Arni; Diao, Liqun; Strawderman, Robert L.: Censoring unbiased regression trees and ensembles (2019)
  6. Eun-Kyung Lee: PPtreeViz: An R Package for Visualizing Projection Pursuit Classification Trees (2018) not zbMATH
  7. Olshen, Adam B.; Strawderman, Robert L.; Ryslik, Gregory; Lostritto, Karen; Arnold, Alice M.; Molinaro, Annette M.: Novel aggregate deletion/substitution/addition learning algorithms for recursive partitioning (2018)
  8. Janitza, Silke; Tutz, Gerhard; Boulesteix, Anne-Laure: Random forest for ordinal responses: prediction and variable selection (2016)
  9. Waelchli, Boris: A proximity based macro stress testing framework (2016)
  10. Hothorn, Torsten: partykit: a modular toolkit for recursive partytioning in \textttR (2015)
  11. Nguyen, Thanh-Tung; Huang, Joshua Z.; Nguyen, Thuy Thi: Two-level quantile regression forests for bias correction in range prediction (2015)
  12. Sauerbrei, Willi; Buchholz, Anika; Boulesteix, Anne-Laure; Binder, Harald: On stability issues in deriving multivariable regression models (2015)
  13. Zhou, Yan; McArdle, John J.: Rationale and applications of survival tree and survival ensemble methods (2015)
  14. Hapfelmeier, Alexander; Hothorn, Torsten; Ulm, Kurt; Strobl, Carolin: A new variable importance measure for random forests with missing data (2014)
  15. Hapfelmeier, A.; Ulm, K.: Variable selection by random forests using data with missing values (2014)
  16. Hapfelmeier, A.; Ulm, K.: A new variable selection approach using random forests (2013)
  17. Kuhn, Max; Johnson, Kjell: Applied predictive modeling (2013)
  18. Zhao, Yanchang: R and data mining. Examples and case studies (2013)
  19. Bettina Grün; Ioannis Kosmidis; Achim Zeileis: Extended Beta Regression in R: Shaken, Stirred, Mixed, and Partitioned (2012) not zbMATH
  20. Drăghici, Sorin: Statistics and data analysis for microarrays using R and Bioconductor. With CD-ROM. (2012)

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