Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC)
Imbalanced Two Class Problems
Impute Only Mode
Acquire Maximal Subtree Information
Acquire Partial Effect of a Variable
Find Interactions Between Pairs of Variables
Extract a Single Tree from a Forest and plot it on your browser
Hold out variable importance (VIMP)
Plots for Competing Risks
Plot Quantiles from Quantile Regression Forests
Plot Error Rate and Variable Importance from a RF-SRC analysis
Plot Subsampled VIMP Confidence Intervals
Plot of Survival Estimates
Plot Marginal Effect of Variables
Prediction for Random Forests for Survival, Regression, and Classifica...
Print Summary Output of a RF-SRC Analysis
Quantile Regression Forests
Fast Unified Random Forests for Survival, Regression, and Classificati...
Anonymous Random Forests
Fast Random Forests
Show the NEWS file
Fast Unified Random Forests for Survival, Regression, and Classificati...
sidClustering using SID (Staggered Interaction Data) for Unsupervised ...
Acquire Split Statistic Information
Subsample Forests for VIMP Confidence Intervals
Synthetic Random Forests
Tune Random Forest for the optimal mtry and nodesize parameters
Variable Selection
VIMP for Single or Grouped Variables
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.