dec_tree.fit: Bayesian Decision Tree Fit
In ebridge2/badmf: Bayesian Decision-Making Forests (BaD-MFs)

Fit a Bayesian Decision Tree with a stats-like formula frontend interface.

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dec_tree.fit(formuler, data = NULL, d = NULL, alpha = NULL,
  method = "classification", depth.max = 5L, size = 1L,
  debug = FALSE, ...)

`formuler`	ravioli ravioli give me the formuoli.
`data`	the data associated with the formuler. Note: if you want an intercept, you must add it ahead of time.
`d`	the number of features to subsample at each node. Defaults to `NULL`, which tries every feature.
`alpha`	the prior parameters for the feature probabilities. A `[p]` vector. If `NULL`, samples uniformly. Defaults to `NULL`.
`method`	whether you want "classification" or "regression". Defaults to `"classification"`.
`depth.max`	the maximum allowed tree depth. Defaults to `5L`.
`size`	the minimum allowed number of samples for an individual node. Defaults to `1L`.
`debug`	whether to save the predictors and responses that are categorized. Defaults to `FALSE`.
`...`	trailing arguments.

an object of class dec.tree.class containing the following:

`tree`	the decision tree.
`X`	The training predictors.
`Y`	the training responses.
`d`	d the number of features subsampled at each node.
`alpha`	the sampling distribution for the features. A `[p]` vector.
`depth.max`	the maximum allowed tree depth.
`size`	the maximum allowed tree depth.
`debug`	whether to save the predictors and responses that are categorized.