randomForestSRC v2.10.1
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Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC)
Fast OpenMP parallel computing of Breiman's random forests for univariate, multivariate, unsupervised, survival, competing risks, class imbalanced classification and quantile regression. Extreme random forests and randomized splitting. Suite of imputation methods for missing data. Fast random forests using subsampling. Confidence regions and standard errors for variable importance. New improved holdout variable importance. Visualize trees on your Safari or Google Chrome browser.
Functions in randomForestSRC
| Name | Description | |
| breast | Wisconsin Prognostic Breast Cancer Data | |
| follic | Follicular Cell Lymphoma | |
| impute.rfsrc | Impute Only Mode | |
| hd | Hodgkin's Disease | |
| get.tree.rfsrc | Extract a Single Tree from a Forest and plot it on your browser | |
| housing | Ames Iowa Housing Data | |
| pbc | Primary Biliary Cirrhosis (PBC) Data | |
| max.subtree.rfsrc | Acquire Maximal Subtree Information | |
| imbalanced.rfsrc | Imbalanced Two Class Problems | |
| holdout.vimp.rfsrc | Hold out variable importance (VIMP) | |
| find.interaction.rfsrc | Find Interactions Between Pairs of Variables | |
| plot.quantreg.rfsrc | Plot Quantiles from Quantile Regression Forests | |
| plot.survival.rfsrc | Plot of Survival Estimates | |
| plot.subsample.rfsrc | Plot Subsampled VIMP Confidence Intervals | |
| plot.variable.rfsrc | Plot Marginal Effect of Variables | |
| predict.rfsrc | Prediction for Random Forests for Survival, Regression, and Classification | |
| plot.competing.risk.rfsrc | Plots for Competing Risks | |
| stat.split.rfsrc | Acquire Split Statistic Information | |
| plot.rfsrc | Plot Error Rate and Variable Importance from a RF-SRC analysis | |
| rfsrc.news | Show the NEWS file | |
| sidClustering.rfsrc | sidClustering using SID (Staggered Interaction Data) for Unsupervised Clustering | |
| rfsrc.fast | Fast Random Forests | |
| wihs | Women's Interagency HIV Study (WIHS) | |
| subsample.rfsrc | Subsample Forests for VIMP Confidence Intervals | |
| synthetic | Synthetic Random Forests | |
| vimp.rfsrc | VIMP for Single or Grouped Variables | |
| randomForestSRC-package | Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC) | |
| nutrigenomic | Nutrigenomic Study | |
| vdv | van de Vijver Microarray Breast Cancer | |
| rfsrc | Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC) | |
| partial.rfsrc | Acquire Partial Effect of a Variable | |
| wine | White Wine Quality Data | |
| print.rfsrc | Print Summary Output of a RF-SRC Analysis | |
| veteran | Veteran's Administration Lung Cancer Trial | |
| quantreg.rfsrc | Quantile Regression Forests | |
| tune.rfsrc | Tune Random Forest for the optimal mtry and nodesize parameters | |
| var.select.rfsrc | Variable Selection | |
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Details
| Date | 2021-02-09 |
| BugReports | https://github.com/kogalur/randomForestSRC/issues/new |
| License | GPL (>= 3) |
| URL | http://web.ccs.miami.edu/~hishwaran/ https://github.com/kogalur/randomForestSRC/ |
| NeedsCompilation | yes |
| Packaged | 2021-02-09 22:45:32 UTC; kogalur |
| Repository | CRAN |
| Date/Publication | 2021-02-10 15:00:10 UTC |
| depends | , R (>= 3.6.0) |
| suggests | akima , caret , cluster , imbalance , mlbench , pec , prodlim , survival |
| imports | data.tree , DiagrammeR , parallel |
| Contributors | Hemant Ishwaran, Udaya Kogalur |
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[](http://www.rdocumentation.org/packages/randomForestSRC)