biased-dot-bn-dot-fit: Random network with a biased degree distribution
In ddgraph: Distinguish direct and indirect interactions with Graphical Modelling

Description Usage Arguments Value Examples

A version of random.bn.fit which generates a graph based on degree distribution and beta distribution for probabilities

1	biased.bn.fit(nodes, beta.est, in.degree.distr, bn.graph)

`nodes`	character vector of node names
`beta.est`	the beta distribution parameters for different degrees of a node. Should be a list where [[2]] corresponds to 2-dimenstional contingency table (i.e. one parent, one output). It contains a data.frame with columns shape1, shape2 for the beta distribution, and rows are degrees of freedom (in this case 2, when P(Out=0\|Parent=0) and P(Out=0\|Parent=1))
`in.degree.distr`	a vector with degree distribution for all the nodes in the network (names are ignored, and degree is randomly sampled from this vector)
`bn.graph`	if the graph structure is already available, then the graph structure in object of class "bn"

a list of two elements: bn - a bn object which contains the structure and bn.fit - a bn.fit object with filled in conditional probabilities

# nodes, conditional probability distribution, an indegree distribution
nodes = letters[1:5]
beta.est = list(data.frame(shape1=2,shape2=3), data.frame(shape1=c(2,4), shape2=c(5,2)), data.frame(shape1=c(1,2,3,4), shape2=c(3,2,1,2)))
in.degree.distr = c(0, 1, 1, 2, 2)
# make a random graph using these parameters
biased.bn.fit(nodes, beta.est, in.degree.distr)