## Usage

## nodes
mb(x, node)
nbr(x, node)
parents(x, node)
parents(x, node, debug = FALSE) <- value
children(x, node)
children(x, node, debug = FALSE) <- value
spouses(x, node)
ancestors(x, node)
descendants(x, node)
in.degree(x, node)
out.degree(x, node)
root.nodes(x)
leaf.nodes(x)
nnodes(x)## arcs
arcs(x)
arcs(x, check.cycles = TRUE, check.illegal = TRUE, debug = FALSE) <- value
directed.arcs(x)
undirected.arcs(x)
incoming.arcs(x, node)
outgoing.arcs(x, node)
incident.arcs(x, node)
compelled.arcs(x)
reversible.arcs(x)
narcs(x)

## adjacency matrix
amat(x)
amat(x, check.cycles = TRUE, check.illegal = TRUE, debug = FALSE) <- value

## graphs
nparams(x, data, effective = FALSE, debug = FALSE)
ntests(x)
whitelist(x)
blacklist(x)

## shared with the graph package.
# these used to be a simple nodes(x) function.
# S4 method for bn
nodes(object)
# S4 method for bn.fit
nodes(object)
# these used to be a simple degree(x, node) function.
# S4 method for bn
degree(object, Nodes)
# S4 method for bn.fit
degree(object, Nodes)
# re-label the nodes.
# S4 method for bn
nodes(object) <- value
# S4 method for bn.fit
nodes(object) <- value

## Arguments

x,object

an object of class `bn`

or `bn.fit`

. The replacement
form of `parents`

, `children`

, `arcs`

and `amat`

requires an object of class `bn`

.

node,Nodes

a character string, the label of a node.

value

either a vector of character strings (for `parents`

and
`children`

), an adjacency matrix (for `amat`

) or a data frame with
two columns (optionally labeled "from" and "to", for `arcs`

).

data

a data frame containing the data the Bayesian network was learned
from. It's only needed if `x`

is an object of class `bn`

.

check.cycles

a boolean value. If `FALSE`

the returned network will
not be checked for cycles.

check.illegal

a boolean value. If `TRUE`

arcs that break the
parametric assumptions of `x`

, such as those from continuous to
discrete nodes in conditional Gaussian networks, cause an error.

effective

a boolean value. If `TRUE`

the number of non-zero free
parameters is returned, that is, the effective degrees of freedom of the
network; otherwise the theoretical number of parameters is returned.

debug

a boolean value. If `TRUE`

a lot of debugging output is
printed; otherwise the function is completely silent.