bnlearn (version 5.2)

hybrid algorithms: Hybrid structure learning algorithms

Description

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

Usage

rsmax2(x, whitelist, blacklist, restrict = "si.hiton.pc",
  maximize = "hc", restrict.args = list(), maximize.args = list(), debug = FALSE)
mmhc(x, whitelist, blacklist, restrict.args = list(),
  maximize.args = list(), debug = FALSE)
h2pc(x, whitelist, blacklist, restrict.args = list(),
  maximize.args = list(), debug = FALSE)

Value

An object of class bn. See bn-class for details.

Arguments

x

a data frame containing the variables in the model.

whitelist

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

blacklist

a data frame with two columns (optionally labelled "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.

Author

Marco Scutari

See Also

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