Random Forests, Linear Trees, and Gradient Boosting for Inference and Interpretability
addTrees-forestry
compute lp distances
Cpp to R translator
Checks if forestry object has valid pointer for C++ object.
forestry class
forestry
getOOB-forestry
getOOBpreds-forestry
getVI-forestry
Honest Random Forest
Feature imputation using random forests neighborhoods
load RF
make_savable
visualize a tree
predict-forestry
predictInfo-forestry
preprocess_testing
preprocess_training
relink CPP ptr
save RF
scale_center
Test data check
Training data check
unscale_uncenter
Provides fast implementations of Honest Random Forests, Gradient Boosting, and Linear Random Forests, with an emphasis on inference and interpretability. Additionally contains methods for variable importance, out-of-bag prediction, regression monotonicity, and several methods for missing data imputation. Soren R. Kunzel, Theo F. Saarinen, Edward W. Liu, Jasjeet S. Sekhon (2019) <arXiv:1906.06463>.
Useful links