R package rpart: Recursive Partitioning. Recursive partitioning and regression trees. Recursive partitioning for classification, regression and survival trees. An implementation of most of the functionality of the 1984 book by Breiman, Friedman, Olshen and Stone.

References in zbMATH (referenced in 117 articles )

Showing results 1 to 20 of 117.
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  1. Benjamin G. Stokell, Rajen D. Shah, Ryan J. Tibshirani: Modelling High-Dimensional Categorical Data Using Nonconvex Fusion Penalties (2020) arXiv
  2. Blanquero, Rafael; Carrizosa, Emilio; Molero-Río, Cristina; Romero Morales, Dolores: Sparsity in optimal randomized classification trees (2020)
  3. Gupta, Bhisham C.; Guttman, Irwin; Jayalath, Kalanka P.: Statistics and probability with applications for engineers and scientists using MINITAB, R and JMP (2020)
  4. Madan Gopal Kundu, Samiran Ghosh: Survival trees for right-censored data based on score based parameter instability test (2020) arXiv
  5. Sayan Putatunda, Dayananda Ubrangala, Kiran Rama, Ravi Kondapalli: DriveML: An R Package for Driverless Machine Learning (2020) arXiv
  6. Bui, Anh Tuan; Apley, Daniel W.: An exploratory analysis approach for understanding variation in stochastic textured surfaces (2019)
  7. Casalicchio, Giuseppe; Bossek, Jakob; Lang, Michel; Kirchhoff, Dominik; Kerschke, Pascal; Hofner, Benjamin; Seibold, Heidi; Vanschoren, Joaquin; Bischl, Bernd: \textttOpenML: an \textttRpackage to connect to the machine learning platform openml (2019)
  8. Denuit, Michel; Mesfioui, Mhamed; Trufin, Julien: Concordance-based predictive measures in regression models for discrete responses (2019)
  9. Lee, Jeong Eun; Nicholls, Geoff K.; Ryder, Robin J.: Calibration procedures for approximate Bayesian credible sets (2019)
  10. Plaia, Antonella; Sciandra, Mariangela: Weighted distance-based trees for ranking data (2019)
  11. Steingrimsson, Jon Arni; Diao, Liqun; Strawderman, Robert L.: Censoring unbiased regression trees and ensembles (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. Henckaerts, Roel; Antonio, Katrien; Clijsters, Maxime; Verbelen, Roel: A data driven binning strategy for the construction of insurance tariff classes (2018)
  16. Muñoz, Mario A.; Villanova, Laura; Baatar, Davaatseren; Smith-Miles, Kate: Instance spaces for machine learning classification (2018)
  17. Peter Calhoun; Xiaogang Su;Martha Nunn; Juanjuan Fan: Constructing Multivariate Survival Trees: The MST Package for R (2018) not zbMATH
  18. Quan, Zhiyu; Valdez, Emiliano A.: Predictive analytics of insurance claims using multivariate decision trees (2018)
  19. Valliant, Richard; Dever, Jill A.; Kreuter, Frauke: Practical tools for designing and weighting survey samples (2018)
  20. Zhao, Xin; Barber, Stuart; Taylor, Charles C.; Milan, Zoka: Classification tree methods for panel data using wavelet-transformed time series (2018)

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