Plot methods for genotype-phenotype networks. plot.gnbp and plot.dbn can be used to plot networks in which evidence has been absorbed and propagated. The beliefs or Jeffrey's signed information for conditional gaussian and phenotypic states and Fold Changes for discrete bayesian networks is mapped onto the network. plot.gpfit and plot.dbfit are plot methods for objects of class "gpfit" and "dbnfit" respectively.
gen.evidence
(Package: geneNetBP) :
Generate a sequence of evidence for a continuous node in a conditional gaussian bayesian network.
The evidence for a node in an RHugin domain is generated as a linear sequence within the specified standard deviation from the marginal mean of the node. The evidence can be given as an input to absorb.gnbp
The geneNetBP package implements algorithms to infer Conditional Gaussian Bayesian Networks (CG-BN) from genotype-phenotype or Quantitative Trait Loci (QTL) data, absorb evidence in the network and quantify and visualize the changes in network beliefs. Inference of Discrete Bayesian networks is also supported.
fit-methods
(Package: geneNetBP) :
Fit a Conditional Gaussian Bayesian Network or Discrete Bayesian Network to QTL data
Learn the structure of a genotype-phenotype network from quantitative trait loci (QTL) data and the conditional probability table for each node in the network.
Absorb a single piece or a spectrum of evidence for one or more phenotype nodes, propagate the beliefs, obtain the updated beliefs and quantify the effects.
providing 
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