Man pages for grf
Generalized Random Forests (Beta)

average_partial_effectEstimate average partial effects using a causal forest
average_treatment_effectEstimate average treatment effects using a causal forest
causal_forestCausal forest
create_dot_bodyWrites each node information If it is a leaf node: show it in...
custom_forestCustom forest
export_graphvizExport a tree in DOT format. This function generates a...
get_sample_weightsGiven a trained forest and test data, compute the training...
get_treeRetrieve a single tree from a trained forest object.
instrumental_forestIntrumental forest
local_linear_forestLocal Linear forest
plot.grf_treePlot a GRF tree object.
predict.causal_forestPredict with a causal forest
predict.custom_forestPredict with a custom forest.
predict.instrumental_forestPredict with an instrumental forest
predict.local_linear_forestPredict with a local linear forest
predict.quantile_forestPredict with a quantile forest
predict.regression_forestPredict with a regression forest
print.grfPrint a GRF forest object.
print.grf_treePrint a GRF tree object.
quantile_forestQuantile forest
regression_forestRegression forest
split_frequenciesCalculate which features the forest split on at each depth.
test_calibrationOmnibus evaluation of the quality of the random forest...
tune_causal_forestCausal forest tuning
tune_local_linear_forestLocal linear forest tuning
tune_regression_forestRegression forest tuning
variable_importanceCalculate a simple measure of 'importance' for each feature.
grf documentation built on Nov. 24, 2018, 9:04 a.m.