prune.control: prune.control
In hhcartr: HHCART(G) - A Reflected Feature Space for CART

This internal function is used to validate the parameters specified on the prune_control parameter.

prune.control(
  prune_type = "ccp",
  prune_stochastic_max_nodes = 10,
  prune_stochastic_max_depth = 10,
  prune_stochastic_samples = 100,
  ...
)

`prune_type`	The prune type required, valid values are 'all', 'ccp' and stochastic'. The default value is 'all'.
`prune_stochastic_max_nodes`	The prune_stochastic_max_nodes parameter specifies the number of internal nodes to randomly sample on each prune_stochastic_samples. The value specified must be an even number as an equal number of left and right internal nodes will form the sample. The prune_stochastic_max_nodes parameter can have a value of any even integer in the range two to twenty-four. The default value is 10.
`prune_stochastic_max_depth`	When sampling internal nodes, the prune_stochastic_max_depth parameter specifies the maximum decision tree depth to select internal nodes from. Internal nodes occurring in the original decision tree at depths greater than prune_stochastic_max_depth are not eligible for sampling. Any positive integer in the range two to the maximum depth of the current tree is accepted. The default value is 12.
`prune_stochastic_samples`	The prune_stochastic_samples parameter specifies the number of times internal nodes will be sampled from the current decision tree. The number of internal nodes to be sampled each iteration is determined by the prune_stochastic_max_nodes parameter, the internal nodes eligible to be sampled is determined by the prune_stochastic_max_depth parameter. The prune_stochastic_samples parameter can have any positive integer value greater than zero. The default value is 100.
`...`	parameter catch-all.