constraint-based algorithms | R Documentation |

Learn the equivalence class of a directed acyclic graph (DAG) from data using the PC, Grow-Shrink (GS), Incremental Association (IAMB), Fast Incremental Association (Fast-IAMB), Interleaved Incremental Association (Inter-IAMB), Incremental Association with FDR (IAMB-FDR), Max-Min Parents and Children (MMPC), Semi-Interleaved HITON-PC or Hybrid Parents and Children (HPC) constraint-based algorithms.

```
pc.stable(x, cluster, whitelist = NULL, blacklist = NULL, test = NULL,
alpha = 0.05, B = NULL, max.sx = NULL, debug = FALSE, undirected = FALSE)
gs(x, cluster, whitelist = NULL, blacklist = NULL, test = NULL,
alpha = 0.05, B = NULL, max.sx = NULL, debug = FALSE, undirected = FALSE)
iamb(x, cluster, whitelist = NULL, blacklist = NULL, test = NULL,
alpha = 0.05, B = NULL, max.sx = NULL, debug = FALSE, undirected = FALSE)
fast.iamb(x, cluster, whitelist = NULL, blacklist = NULL, test = NULL,
alpha = 0.05, B = NULL, max.sx = NULL, debug = FALSE, undirected = FALSE)
inter.iamb(x, cluster, whitelist = NULL, blacklist = NULL, test = NULL,
alpha = 0.05, B = NULL, max.sx = NULL, debug = FALSE, undirected = FALSE)
iamb.fdr(x, cluster, whitelist = NULL, blacklist = NULL, test = NULL,
alpha = 0.05, B = NULL, max.sx = NULL, debug = FALSE, undirected = FALSE)
mmpc(x, cluster, whitelist = NULL, blacklist = NULL, test = NULL,
alpha = 0.05, B = NULL, max.sx = NULL, debug = FALSE, undirected = TRUE)
si.hiton.pc(x, cluster, whitelist = NULL, blacklist = NULL, test = NULL,
alpha = 0.05, B = NULL, max.sx = NULL, debug = FALSE, undirected = TRUE)
hpc(x, cluster, whitelist = NULL, blacklist = NULL, test = NULL,
alpha = 0.05, B = NULL, max.sx = NULL, debug = FALSE, undirected = TRUE)
```

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

`cluster` |
an optional cluster object from package parallel. |

`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. |

`test` |
a character string, the label of the conditional independence
test to be used in the algorithm. If none is specified, the default test
statistic is the |

`alpha` |
a numeric value, the target nominal type I error rate. |

`B` |
a positive integer, the number of permutations considered for each
permutation test. It will be ignored with a warning if the conditional
independence test specified by the |

`max.sx` |
a positive integer, the maximum allowed size of the conditioning sets used in conditional independence tests. The default is that there is no limit on size. |

`debug` |
a boolean value. If |

`undirected` |
a boolean value. If |

An object of class `bn`

.
See `bn-class`

for details.

Note that even when `undirected`

is set to `FALSE`

there is no
guarantee that all arcs in the returned network will be directed; some arc
directions are impossible to learn just from data due to score equivalence.
`cextend()`

provides a consistent extension of partially directed
networks into directed acyclic graphs, which can then be used (for instance)
for parameter learning.

See `structure learning`

for a complete list of structure learning
algorithms with the respective references. All algorithms accept incomplete
data, which they handle by computing individual conditional independence tests
on locally complete observations.

Marco Scutari

`independence tests`

, local discovery algorithms,
score-based algorithms, hybrid algorithms, cextend.

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