hybrid: Hybrid structure learning algorithms

Description Usage Arguments Value Note Author(s) See Also

Description

Learn the structure of a Bayesian network with Max-Min Hill Climbing (MMHC), Hybrid HPC (H2PC), and the more general 2-phase Restricted Maximization (RSMAX2) hybrid algorithms.

Usage

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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)
h2pc(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 structure learning and the documentation of each algorithm for details.

maximize

a character string, the score-based algorithm to be used in the “maximize” phase. Possible values are hc and tabu. See structure learning for details.

restrict.args

a list of arguments to be passed to the algorithm specified by restrict, such as test or alpha.

maximize.args

a list of arguments to be passed to the algorithm specified by maximize, such as restart for hill-climbing or tabu for tabu search.

debug

a boolean value. If TRUE a lot of debugging output is printed; otherwise the function is completely silent.

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. Similarly, h2pc is simply rsmax2() with restrict set to hpcand maximize set to hc.

See structure learning for a complete list of structure learning algorithms with the respective references.

Author(s)

Marco Scutari

See Also

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


bnlearn documentation built on Sept. 7, 2021, 1:07 a.m.