randomForestSRC - 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. 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.

Last updated 4 months ago

7.82 score 10 stars 13 packages 1.1k scripts 5.3k downloads

spikeslab - Prediction and Variable Selection Using Spike and Slab Regression

Spike and slab for prediction and variable selection in linear regression models. Uses a generalized elastic net for variable selection.

Last updated 3 years ago

2.53 score 1 stars 2 packages 42 scripts 1.3k downloads

boostmtree - Boosted Multivariate Trees for Longitudinal Data

Implements Friedman's gradient descent boosting algorithm for modeling longitudinal response using multivariate tree base learners. Longitudinal response could be continuous, binary, nominal or ordinal. A time-covariate interaction effect is modeled using penalized B-splines (P-splines) with estimated adaptive smoothing parameter. Although the package is design for longitudinal data, it can handle cross-sectional data as well. Implementation details are provided in Pande et al. (2017), Mach Learn <DOI:10.1007/s10994-016-5597-1>.

Last updated 3 years ago

1.00 score 9 scripts 368 downloads