Learn the equivalence class of a directed acyclic graph (DAG) from data using the PC, Grow-Shrink (GS), Incremental Association (IAMB), Fast Incremental Association (Fast-IAMB), Interleaved Incremental Association (Inter-IAMB), Max-Min Parents and Children (MMPC) or Semi-Interleaved HITON-PC constraint-based algorithms.
pc.stable(x, cluster = NULL, whitelist = NULL, blacklist = NULL, test = NULL,
alpha = 0.05, B = NULL, max.sx = NULL, debug = FALSE, undirected = FALSE)
gs(x, cluster = NULL, whitelist = NULL, blacklist = NULL, test = NULL,
alpha = 0.05, B = NULL, max.sx = NULL, debug = FALSE, optimized = FALSE,
strict = FALSE, undirected = FALSE)
iamb(x, cluster = NULL, whitelist = NULL, blacklist = NULL, test = NULL,
alpha = 0.05, B = NULL, max.sx = NULL, debug = FALSE, optimized = FALSE,
strict = FALSE, undirected = FALSE)
fast.iamb(x, cluster = NULL, whitelist = NULL, blacklist = NULL, test = NULL,
alpha = 0.05, B = NULL, max.sx = NULL, debug = FALSE, optimized = FALSE,
strict = FALSE, undirected = FALSE)
inter.iamb(x, cluster = NULL, whitelist = NULL, blacklist = NULL, test = NULL,
alpha = 0.05, B = NULL, max.sx = NULL, debug = FALSE, optimized = FALSE,
strict = FALSE, undirected = FALSE)
mmpc(x, cluster = NULL, whitelist = NULL, blacklist = NULL, test = NULL,
alpha = 0.05, B = NULL, max.sx = NULL, debug = FALSE, optimized = FALSE,
strict = FALSE, undirected = TRUE)
si.hiton.pc(x, cluster = NULL, whitelist = NULL, blacklist = NULL, test = NULL,
alpha = 0.05, B = NULL, max.sx = NULL, debug = FALSE, optimized = FALSE,
strict = FALSE, undirected = TRUE)a data frame containing the variables in the model.
an optional cluster object from package parallel.
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 label of the conditional independence
test to be used in the algorithm. If none is specified, the default test
statistic is the mutual information for categorical variables, the
Jonckheere-Terpstra test for ordered factors and the linear
correlation for continuous variables. See bnlearn-package
for details.
a numeric value, the target nominal type I error rate.
a positive integer, the number of permutations considered for each
permutation test. It will be ignored with a warning if the conditional
independence test specified by the test argument is not a
permutation test.
a positive integer, the maximum allowed size of the conditioning sets used in conditional independence tests. The default is that there is no limit on size.
a boolean value. If TRUE a lot of debugging output is
printed; otherwise the function is completely silent.
a boolean value. See bnlearn-package for
details.
a boolean value. If TRUE conflicting results in the
learning process generate an error; otherwise they result in a warning.
a boolean value. If TRUE no attempt will be made to
determine the orientation of the arcs; the returned (undirected) graph
will represent the underlying structure of the Bayesian network.
An object of class bn.
See bn-class for details.
for PC:
Colombo D, Maathuis MH (2014). "Order-Independent Constraint-Based Causal Structure Learning". Journal of Machine Learning Research, 15:3921--3962.
for GS:
Margaritis D (2003). Learning Bayesian Network Model Structure from Data. Ph.D. thesis, School of Computer Science, Carnegie-Mellon University, Pittsburgh, PA.
for IAMB:
Tsamardinos I, Aliferis CF, Statnikov A (2003). "Algorithms for Large Scale Markov Blanket Discovery". Proceedings of the Sixteenth International Florida Artificial Intelligence Research Society Conference, pp. 376--381.
for Fast-IAMB and Inter-IAMB:
Yaramakala S, Margaritis D (2005). "Speculative Markov Blanket Discovery for Optimal Feature Selection". Proceedings of the Fifth IEEE International Conference on Data Mining, pp. 809--812.
for MMPC:
Tsamardinos I, Aliferis CF, Statnikov A (2003). "Time and Sample Efficient Discovery of Markov Blankets and Direct Causal Relations". Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 673--678.
Tsamardinos I, Brown LE, Aliferis CF (2006). "The Max-Min Hill-Climbing Bayesian Network Structure Learning Algorithm". Machine Learning, 65(1):31--78.
for the Semi-Interleaved HITON-PC:
Aliferis FC, Statnikov A, Tsamardinos I, Subramani M, Koutsoukos XD (2010). "Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation". Journal of Machine Learning Research, 11:171--234.
local discovery algorithms, score-based algorithms, hybrid algorithms.