Description: 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 importance. Case-specific importance. Visualize trees on your Safari or Google Chrome browser. Anonymous random forests for data privacy.

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.

Suggests: glmnet, XML, survival, pec, prodlim, mlbench

Suggests: glmnet, survival, pec, prodlim, mlbench
Description: 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.  Now extended to include multivariate and unsupervised forests.  Also includes quantile regression forests for univariate and multivariate training/testing settings.  The package runs in both serial and parallel (OpenMP) modes.

Suggests: glmnet, survival, pec, prodlim, mlbench, RCurl

Suggests: glmnet, survival, pec, prodlim, mlbench, akima, caret
Description: Fast OpenMP parallel processing for Breiman's random forests for survival, competing risks, regression and classification based on Ishwaran and Kogalur's popular random survival forests (RSF) package.  Handles missing data and now includes multivariate, unsupervised forests and quantile regression.  New fast interface using subsampling.