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randomForestSRC (version 3.3.1)

Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC)

Description

Fast OpenMP parallel computing of Breiman's random forests for univariate, multivariate, unsupervised, survival, competing risks, class imbalanced classification and quantile regression. New Mahalanobis splitting for correlated outcomes. 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 importance. Case-specific importance. Minimal depth variable importance. Visualize trees on your Safari or Google Chrome browser. Anonymous random forests for data privacy.

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Version

Install

install.packages('randomForestSRC')

Monthly Downloads

5,102

Version

3.3.1

License

GPL (>= 3)

Maintainer

Last Published

July 25th, 2024

Functions in randomForestSRC (3.3.1)

find.interaction.rfsrc

Find Interactions Between Pairs of Variables
plot.quantreg.rfsrc

Plot Quantiles from Quantile Regression Forests
plot.subsample.rfsrc

Plot Subsampled VIMP Confidence Intervals
plot.competing.risk.rfsrc

Plots for Competing Risks
nutrigenomic

Nutrigenomic Study
plot.rfsrc

Plot Error Rate and Variable Importance from a RF-SRC analysis
partial.rfsrc

Acquire Partial Effect of a Variable
pbc

Primary Biliary Cirrhosis (PBC) Data
plot.survival.rfsrc

Plot of Survival Estimates
peakVO2

Systolic Heart Failure Data
plot.variable.rfsrc

Plot Marginal Effect of Variables
rfsrc.fast

Fast Random Forests
predict.rfsrc

Prediction for Random Forests for Survival, Regression, and Classification
rfsrc.news

Show the NEWS file
stat.split.rfsrc

Acquire Split Statistic Information
randomForestSRC-package

Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC)
sidClustering.rfsrc

sidClustering using SID (Staggered Interaction Data) for Unsupervised Clustering
rfsrc

Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC)
rfsrc.anonymous

Anonymous Random Forests
wine

White Wine Quality Data
vdv

van de Vijver Microarray Breast Cancer
print.rfsrc

Print Summary Output of a RF-SRC Analysis
quantreg.rfsrc

Quantile Regression Forests
wihs

Women's Interagency HIV Study (WIHS)
veteran

Veteran's Administration Lung Cancer Trial
var.select.rfsrc

Variable Selection
subsample.rfsrc

Subsample Forests for VIMP Confidence Intervals
vimp.rfsrc

VIMP for Single or Grouped Variables
tune.rfsrc

Tune Random Forest for the optimal mtry and nodesize parameters
synthetic

Synthetic Random Forests
impute.rfsrc

Impute Only Mode
breast

Wisconsin Prognostic Breast Cancer Data
hd

Hodgkin's Disease
get.tree.rfsrc

Extract a Single Tree from a Forest and plot it on your browser
max.subtree.rfsrc

Acquire Maximal Subtree Information
imbalanced.rfsrc

Imbalanced Two Class Problems
holdout.vimp.rfsrc

Hold out variable importance (VIMP)
housing

Ames Iowa Housing Data
follic

Follicular Cell Lymphoma