The covariates used in the regression.

The outcome.

Fraction of the data used to build each tree. Note: If honesty = TRUE, these subsamples will further be cut by a factor of honesty.fraction.

Number of variables tried for each split.

Number of trees grown in the forest. Note: Getting accurate confidence intervals generally requires more trees than getting accurate predictions.

Number of threads used in training. If set to NULL, the software automatically selects an appropriate amount.

A target for the minimum number of observations in each tree leaf. Note that nodes with size smaller than min.node.size can occur, as in the original randomForest package.

Whether to use honest splitting (i.e., sub-sample splitting).

The fraction of data that will be used for determining splits if honesty = TRUE. Corresponds to set J1 in the notation of the paper. When using the defaults (honesty = TRUE and honesty.fraction = NULL), half of the data will be used for determining splits

A tuning parameter that controls the maximum imbalance of a split.

A tuning parameter that controls how harshly imbalanced splits are penalized.

The seed for the C++ random number generator.

Vector of integers or factors specifying which cluster each observation corresponds to.

If sampling by cluster, the number of observations to be sampled from each cluster when training a tree. If NULL, we set samples_per_cluster to the size of the smallest cluster. If some clusters are smaller than samples_per_cluster, the whole cluster is used every time the cluster is drawn. Note that clusters with less than samples_per_cluster observations get relatively smaller weight than others in training the forest, i.e., the contribution of a given cluster to the final forest scales with the minimum of the number of observations in the cluster and samples_per_cluster.