rpart

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.

Keywords for this software

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References in zbMATH (referenced in 131 articles )

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  1. Bénard, Clément; Biau, Gérard; Da Veiga, Sébastien; Scornet, Erwan: SIRUS: stable and interpretable RUle set for classification (2021)
  2. Beaulac, Cédric; Rosenthal, Jeffrey S.: BEST: a decision tree algorithm that handles missing values (2020)
  3. Benjamin G. Stokell, Rajen D. Shah, Ryan J. Tibshirani: Modelling High-Dimensional Categorical Data Using Nonconvex Fusion Penalties (2020) arXiv
  4. Blanquero, Rafael; Carrizosa, Emilio; Molero-Río, Cristina; Romero Morales, Dolores: Sparsity in optimal randomized classification trees (2020)
  5. Bommert, Andrea; Sun, Xudong; Bischl, Bernd; Rahnenführer, Jörg; Lang, Michel: Benchmark for filter methods for feature selection in high-dimensional classification data (2020)
  6. Genuer, Robin; Poggi, Jean-Michel: Random forests with R (2020)
  7. Gupta, Bhisham C.; Guttman, Irwin; Jayalath, Kalanka P.: Statistics and probability with applications for engineers and scientists using MINITAB, R and JMP (2020)
  8. Han, Sunwoo; Kim, Hyunjoong; Lee, Yung-Seop: Double random forest (2020)
  9. Madan Gopal Kundu, Samiran Ghosh: Survival trees for right-censored data based on score based parameter instability test (2020) arXiv
  10. Nicolas R. Lauve, Stuart J. Nelson, S. Stanley Young, Robert L. Obenchain, Christophe G. Lambert: LocalControl: An R Package for Comparative Safety and Effectiveness Research (2020) not zbMATH
  11. Ribeiro, Rita P.; Moniz, Nuno: Imbalanced regression and extreme value prediction (2020)
  12. Sambasivan, Rajiv; Das, Sourish; Sahu, Sujit K.: A Bayesian perspective of statistical machine learning for big data (2020)
  13. Sayan Putatunda, Dayananda Ubrangala, Kiran Rama, Ravi Kondapalli: DriveML: An R Package for Driverless Machine Learning (2020) arXiv
  14. Bui, Anh Tuan; Apley, Daniel W.: An exploratory analysis approach for understanding variation in stochastic textured surfaces (2019)
  15. 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)
  16. Denuit, Michel; Mesfioui, Mhamed; Trufin, Julien: Concordance-based predictive measures in regression models for discrete responses (2019)
  17. Lee, Jeong Eun; Nicholls, Geoff K.; Ryder, Robin J.: Calibration procedures for approximate Bayesian credible sets (2019)
  18. Plaia, Antonella; Sciandra, Mariangela: Weighted distance-based trees for ranking data (2019)
  19. Steingrimsson, Jon Arni; Diao, Liqun; Strawderman, Robert L.: Censoring unbiased regression trees and ensembles (2019)
  20. Asfha, Huruy Debessay; Kilinc, Betul Kan: Appraisal of performance of three tree-based classification methods (2018)

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