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hybrid algorithms {bnlearn} | R Documentation |

## Hybrid structure learning algorithms

### Description

Learn the structure of a Bayesian network with the Max-Min Hill Climbing (MMHC) and the more general 2-phase Restricted Maximization (RSMAX2) hybrid algorithms.

### Usage

rsmax2(x, whitelist = NULL, blacklist = NULL, restrict = "si.hiton.pc", maximize = "hc", restrict.args = list(), maximize.args = list(), debug = FALSE) mmhc(x, whitelist = NULL, blacklist = NULL, restrict.args = list(), maximize.args = list(), debug = FALSE)

### Arguments

`x` |
a data frame containing the variables in the model. |

`whitelist` |
a data frame with two columns (optionally labeled "from" and "to"), containing a set of arcs to be included in the graph. |

`blacklist` |
a data frame with two columns (optionally labeled "from" and "to"), containing a set of arcs not to be included in the graph. |

`restrict` |
a character string, the constraint-based or local search algorithm to be used in the
“restrict” phase. See |

`maximize` |
a character string, the score-based algorithm to be used in the “maximize” phase. Possible
values are |

`restrict.args` |
a list of arguments to be passed to the algorithm specified by |

`maximize.args` |
a list of arguments to be passed to the algorithm specified by |

`debug` |
a boolean value. If |

### Value

An object of class `bn`

. See `bn-class`

for details.

### Note

`mmhc()`

is simply `rsmax2()`

with `restrict`

set to `mmpc`

and
`maximize`

set to `hc`

.

### Author(s)

Marco Scutari

### References

Tsamardinos I, Brown LE, Aliferis CF (2006). "The Max-Min Hill-Climbing Bayesian Network Structure Learning
Algorithm". *Machine Learning*, **65**(1):31–78.

### See Also

local discovery algorithms, score-based algorithms, constraint-based algorithms.

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