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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 Maximization (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)
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. 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, causal discovery algorithms.
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