blip.learn.tw: Learns a BN with a treewidth bound
In r.blip: Bayesian Network Learning Improved Project

Description Usage Arguments Details Value Examples

View source: R/main.R

Fully learns a Bayesian networks with a treewidth bound.

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blip.learn.tw(dat, scorer.method = "is", solver.method = "kmax",
  treewidth = 5, time = 3600, allocated = 80,
  scorefunction = "bic", alpha = 1, cores = 1, verbose = 0)

`dat`	dataframe from which to learn the parent sets.(required)
`scorer.method`	Method to be used for scoring the parent sets. Possible values: "is" (independence selection), "sq" (sequential selection). (default: is)
`solver.method`	Method to be used for bounded-treewidth structure exploration. Possible values: "kmax", "kg", "ka". (default: kmax)
`treewidth`	Maximum treewidth (default: 4)
`time`	Execution time (default: 3600)
`allocated`	Percentage of the total execution time dedicated to parent set exploration (default: 80)
`scorefunction`	Chosen score function. Possible choices: BIC, BDeu (default: bic)
`alpha`	(if BDeu is chosen) equivalent sample size parameter (default: 1.0)
`cores`	Number of machine cores to use. If 0, all are used. (default: 1)
`verbose`	Verbose level (default: 0)

The input data is required to be complete and discrete. Accordingly missing values in the input data.frame will be ignored, and all numeric values will be converted to integers.

The learned Bayesian network in the bnlearn format.