randomForestSRC: Random Forests for Survival, Regression and Classification (RF-SRC). A unified treatment of Breiman’s random forests for survival, regression and classification problems based on Ishwaran and Kogalur’s random survival forests (RSF) package. The package runs in both serial and parallel (OpenMP) modes. Now extended to include multivariate and unsupervised forests.
Keywords for this software
References in zbMATH (referenced in 11 articles )
Showing results 1 to 11 of 11.
- Cansu Alakus, Denis Larocque, Aurelie Labbe: RFpredInterval: An R Package for Prediction Intervals with Random Forests and Boosted Forests (2021) arXiv
- Calhoun, Peter; Hallett, Melodie J.; Su, Xiaogang; Cafri, Guy; Levine, Richard A.; Fan, Juanjuan: Random forest with acceptance-rejection trees (2020)
- Haider, Humza; Hoehn, Bret; Davis, Sarah; Greiner, Russell: Effective ways to build and evaluate individual survival distributions (2020)
- Sage, Andrew J.; Genschel, Ulrike; Nettleton, Dan: Tree aggregation for random forest class probability estimation (2020)
- Alireza S. Mahani; Mansour T.A. Sharabiani: Bayesian, and Non-Bayesian, Cause-Specific Competing-Risk Analysis for Parametric and Nonparametric Survival Functions: The R Package CFC (2019) not zbMATH
- 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)
- Steingrimsson, Jon Arni; Diao, Liqun; Strawderman, Robert L.: Censoring unbiased regression trees and ensembles (2019)
- Dazard, Jean-Eudes; Ishwaran, Hemant; Mehlotra, Rajeev; Weinberg, Aaron; Zimmerman, Peter: Ensemble survival tree models to reveal pairwise interactions of variables with time-to-events outcomes in low-dimensional setting (2018)
- Moradian, Hoora; Larocque, Denis; Bellavance, François: (L_1) splitting rules in survival forests (2017)
- Ishwaran, Hemant: The effect of splitting on random forests (2015)
- John Ehrlinger: ggRandomForests: Visually Exploring a Random Forest for Regression (2015) arXiv