random.bn.fit

a vector of desired node names (basis for partial ordering)

nodes

expected number of neighbours per node in the random graph

num.neigh

the probability distribution function to use

prob.distr

the <code>bn</code> object with an already laid out graph, if not supplied will be generated

bn.graph

Generate a random Bayesian network using package <code>bnlearn</code>. The nodes specify the partial ordering
of the graph, and the conditional probabilities are sampled from given distribution. The network is
generated to have on average given number of neighbours (i.e. both in-going and out-going edges)

Distinguish direct from indirect interactions in gene
regulation and infer combinatorial code from highly correlated
variables such as transcription factor binding profiles. The
package implements the Neighbourhood Consistent PC algorithm
(NCPC) and draws Direct Dependence Graphs to represent
dependence structure around a target variable. The package also
provides a unified interface to other Graphical Modelling
(Bayesian Network) packages for distinguishing direct and
indirect interactions.

random.bn.fit: Generate a random `bn.fit` network

Description

Usage

Arguments

Value

Examples