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.
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)a data frame containing the variables in the model.
a data frame with two columns (optionally labeled "from" and "to"), containing a set of arcs to be included in the graph.
a data frame with two columns (optionally labeled "from" and "to"), containing a set of arcs not to be included in the graph.
a character string, the constraint-based or local search
algorithm to be used in the “restrict” phase. See
bnlearn-package and the documentation of each algorithm for
details.
a character string, the score-based algorithm to be used in
the “maximize” phase. Possible values are hc and tabu.
See bnlearn-package for details.
a list of arguments to be passed to the algorithm
specified by restrict, such as test or alpha.
a list of arguments to be passed to the algorithm
specified by maximize, such as restart for hill-climbing or
tabu for tabu search.
a boolean value. If TRUE a lot of debugging output is
printed; otherwise the function is completely silent.
An object of class bn. See bn-class for details.
Tsamardinos I, Brown LE, Aliferis CF (2006). "The Max-Min Hill-Climbing Bayesian Network Structure Learning Algorithm". Machine Learning, 65(1):31--78.
local discovery algorithms, score-based algorithms, constraint-based algorithms.