View source: R/frontendlearning.R
scorebased algorithms  R Documentation 
Learn the structure of a Bayesian network using a hillclimbing (HC) or a Tabu search (TABU) greedy search.
hc(x, start = NULL, whitelist = NULL, blacklist = NULL, score = NULL, ...,
debug = FALSE, restart = 0, perturb = 1, max.iter = Inf, maxp = Inf, optimized = TRUE)
tabu(x, start = NULL, whitelist = NULL, blacklist = NULL, score = NULL, ...,
debug = FALSE, tabu = 10, max.tabu = tabu, max.iter = Inf, maxp = Inf, optimized = TRUE)
x 
a data frame containing the variables in the model. 
start 
an object of class 
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. 
score 
a character string, the label of the network score to be used in
the algorithm. If none is specified, the default score is the Bayesian
Information Criterion for both discrete and continuous data sets. See

... 
additional tuning parameters for the network score. See

debug 
a boolean value. If 
restart 
an integer, the number of random restarts. 
tabu 
a positive integer number, the length of the tabu list used in the

max.tabu 
a positive integer number, the iterations tabu search can perform without improving the best network score. 
perturb 
an integer, the number of attempts to randomly insert/remove/reverse an arc on every random restart. 
max.iter 
an integer, the maximum number of iterations. 
maxp 
the maximum number of parents allowed for a node in any network
that is considered in the search, including that that is returned. The
default value is 
optimized 
a boolean value. If 
An object of class bn
. See bnclass
for details.
See structure learning
for a complete list of structure learning
algorithms with the respective references.
Marco Scutari
network scores
, constraintbased algorithms,
hybrid algorithms, local discovery algorithms,
alpha.star.
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