BFSlevel | Build (generalized) hierarchy by Breath-First Search |
BoutrosRNAi2002 | RNAi data on Drosophila innate immune response |
BoutrosRNAiDiscrete | RNAi data on Drosophila innate immune response |
BoutrosRNAiExpression | RNAi data on Drosophila innate immune response |
bum.dalt | internal functions |
bum.EM | internal functions |
bum.histogram | internal functions |
bum.mle | internal functions |
bum.negLogLik | internal functions |
bum.palt | internal functions |
bum.qalt | internal functions |
bum.ralt | internal functions |
connectModules | internal functions |
CONTmLL | Marginal likelihood of a phenotypic hierarchy with continuous data |
dbum | internal functions |
enumerate.models | Exhaustive enumeration of models |
filterEGenes | Automatic selection of most relevant S-genes |
fitBUM | internal functions |
FULLmLL | Full marginal likelihood of a phenotypic hierarchy |
getComponent | internal functions |
getDensityMatrix | Calculate density matrix from raw p-value matrix |
getRelevantEGenes | Automatic selection of most relevant S-genes |
internal | internal functions |
inv.logit | internal functions |
local.model.prior | Computes a prior to be used for edge-wise model inference |
logit | internal functions |
mLL | Marginal likelihood of a phenotypic hierarchy |
moduleNetwork | Infers a phenotypic hierarchy using the module network |
moduleNetwork.aux | internal functions |
nem | Nested Effects Models - main function |
nem.cont.preprocess | Calculate classification probabilities of perturbation data according to control experiments |
nem.discretize | Discretize perturbation data according to control experiments |
nem.greedy | Infers a phenotypic hierarchy using a greedy search strategy |
nemModelSelection | model selection for nested effect models |
network.AIC | AIC criterion for network graph |
pairwise.posterior | Infers a phenotypic hierarchy edge by edge |
pbum | internal functions |
PhiDistr | Computes the marginal likelihood of phenotypic hierarchies |
plot.effects | Plots data according to a phenotypic hierarchy |
plot.ModuleNetwork | plot nested effect model |
plot.nem | plot nested effect model |
plot.pairwise | plot nested effect model |
plot.score | plot nested effect model |
plot.triples | plot nested effect model |
print.ModuleNetwork | Infers a phenotypic hierarchy using the module network |
print.nem.greedy | Infers a phenotypic hierarchy using a greedy search strategy |
print.pairwise | Infers a phenotypic hierarchy edge by edge |
print.score | Computes the marginal likelihood of phenotypic hierarchies |
print.triples | Infers a phenotypic hierarchy from triples |
prune.graph | Prunes spurious edges in a phenotypic hierarchy |
qbum | internal functions |
qqbum | internal functions |
rbum | internal functions |
SCCgraph | Combines Strongly Connected Components into single nodes |
score | Computes the marginal likelihood of phenotypic hierarchies |
selectEGenes | Automatic selection of most relevant S-genes |
subsets | Subsets |
transitive.closure | Computes the transitive closure of a directed graph |
transitive.reduction | Computes the transitive reduction of a graph |
triples.posterior | Infers a phenotypic hierarchy from triples |