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.
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openmp
10.84 score 131 stars 15 dependents 1.3k scripts 8.0k downloadsvarPro - Model-Independent Variable Selection via the Rule-Based Variable Priority
A new framework of variable selection, which instead of generating artificial covariates such as permutation importance and knockoffs, creates release rules to examine the affect on the response for each covariate where the conditional distribution of the response variable can be arbitrary and unknown.
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openmp
5.03 score 3 stars 3 dependents 1 scripts 699 downloadsrandomForestSGT - Random Forest Super Greedy Trees
Super greedy trees (SGTs) with univariate and multivariate geometric cuts are obtained using lasso and coordinate descent.
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openmp
2.70 score 439 downloadsspikeslab - 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.
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2.62 score 1 stars 2 dependents 70 scripts 849 downloads