HDI_quantregforest: Implements HDI RF prediction interval method in Zhu 2019....
In piRF: Prediction Intervals for Random Forests

This function implements an HDI RF prediction interval method.

HDI_quantregforest(
  formula = NULL,
  train_data = NULL,
  test_data = NULL,
  alpha = NULL,
  num_tree = NULL,
  mtry = NULL,
  min_node_size = NULL,
  max_depth = NULL,
  replace = TRUE,
  verbose = FALSE,
  num_threads = NULL
)

`formula`	Object of class formula or character describing the model to fit. Interaction terms supported only for numerical variables.
`train_data`	Training data of class data.frame, matrix, dgCMatrix (Matrix) or gwaa.data (GenABEL). Matches ranger() requirements.
`test_data`	Test data of class data.frame, matrix, dgCMatrix (Matrix) or gwaa.data (GenABEL). Utilizes ranger::predict() to get prediction intervals for test data.
`alpha`	Significance level for prediction intervals.
`num_tree`	Number of trees.
`mtry`	Number of variables to randomly select from at each split.
`min_node_size`	Minimum number of observations before split at a node.
`max_depth`	maximum depth of each tree in RF. ranger parameter.
`replace`	Sample with replacement, or not. Utilized for the two different variants outlined in Ghosal, Hooker 2018. Currently variant 2 not implemented.
`num_threads`	The number of threads to use in parallel. Default is the current number of cores.