Find the equivalence class and the v-structures of a Bayesian network, construct its moral graph, or create a consistent extension of an equivalent class.
cpdag(x, wlbl = FALSE, debug = FALSE)
cextend(x, strict = TRUE, debug = FALSE)
moral(x, debug = FALSE)cextend.all(x, debug = FALSE)
colliders(x, arcs = FALSE, debug = FALSE)
shielded.colliders(x, arcs = FALSE, debug = FALSE)
unshielded.colliders(x, arcs = FALSE, debug = FALSE)
vstructs(x, arcs = FALSE, debug = FALSE)
cpdag() returns an object of class bn, representing the
equivalence class. moral on the other hand returns the moral graph.
See bn-class for details.
cextend() returns an object of class bn, representing a DAG that
is the consistent extension of x.
cextend.all() returns an object of class bn or a list of objects
of class bn.
vstructs(), colliders(), shielded.colliders() and
unshielded.colliders() return a matrix with either 2 or 3 columns,
according to the value of the arcs argument.
an object of class bn or bn.fit (with the exception of
cextend(), which only accepts objects of class bn).
a boolean value. If TRUE the arcs that are part of at least
one v-structure are returned instead of the v-structures themselves.
a boolean value. If TRUE arcs whose directions have been
fixed by a whitelist or a by blacklist are preserved when constructing
the CPDAG.
a boolean value. If no consistent extension is possible and
strict is TRUE, an error is generated; otherwise a partially
extended graph is returned with a warning.
a boolean value. If TRUE a lot of debugging output is
printed; otherwise the function is completely silent.
Marco Scutari
Note that arcs whose directions are dictated by the parametric assumptions of
the network are preserved as directed arcs in cpdag(). This means
that, in a conditional Gaussian network, arcs from discrete nodes to
continuous nodes will be treated as whitelisted in their only valid direction.
cextend.all() returns all possible consistent extensions of a CPDAG,
whereas cextend() returns only one.
Dor D (1992). A Simple Algorithm to Construct a Consistent Extension of a Partially Oriented Graph. UCLA, Cognitive Systems Laboratory.
Koller D, Friedman N (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press.
Pearl J (2009). Causality: Models, Reasoning and Inference. Cambridge University Press, 2nd edition.
Andersson SA, Madigan D, Perlman MD (1997). "A Characterization of Markov Equivalence Classes for Acyclic Digraphs." The Annals of Statistics, 25(2):505--541.
Wienobst M, Luttermann M, Bannach M, Liskiewicz M (2023). "Efficient Enumeration of Markov Equivalent DAGs." Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 12313--12320.
data(learning.test)
dag = hc(learning.test)
cpdag(dag)
vstructs(dag)
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