Function reference • shine

Models
`model.sw()`	Simulate network via small world model
`model.ff()`	Simulate network via forest fire model
`model.er()`	Simulate network via erdos-renyi model
`model.mer()`	Simulate network via modular erdos-renyi model
`model.pa()`	Simulate network via preferential attachment model
`model.mpa()`	Simulate network via modular preferential attachment model
`model.hpa()`	Simulate diverging models through preferential attachment
`model.kd()`	Plot model degree distribution
`model.sim()`	Simulate multivariate Gaussian data for a model
`model.layout()`	Spring-embedded layout
`model.simplify()`	Removes loops, multi-edges, and isolated vertices from graph
`model.plot()`	Plot model
`models.plot()`	Plot one or more models highlighting differences
`model.similarity()`	Szymkiewicz–Simpson coefficient of edges
`models.similarity()`	Edge similarity of two or more graphs
Filtering
`keep.var()`	Keep variables with non-zero variance within subtypes
`rank.var()`	Rank variables by median absolute deviation across one or more subtypes
Constraints
`mods.detect()`	Wrapper for weighted gene co-expression analysis
`me.get()`	Module eigengenes from the kth principal component for all modules
`mm.get()`	Module memberships by correlation with eigengene for a module
`mm.merged()`	Wrapper to merge module memberships for the first two eigengenes
`mod.plot()`	Plot membership of all genes for one module
`mods.plot()`	Plot membership of all genes for all modules
`fuzzy.mods()`	Classify fuzzy modules with quadratic disciminant analysis
`fuzzy.plot()`	Visualize fuzzy module classification
`fuzzy.predict()`	Classify fuzzy membership for a module with quadratic discriminant analysis
Learning
`bdg.estimate()`	Wrapper for estimating posterior probabilities with bdgraph
`bdg.islands()`	Estimate connected components independently
`bdg.metanet()`	Estimate a meta-network from module eigengenes
`blanket.new()`	Create a new blanket covering the entire graph search space
`blanket.lift()`	Lifts the blanket from within or between modules
`blanket.cred()`	Computes complexity reduction of the blanket
`blanket.inform()`	Adds prior information where the blanket is lifted
Datasets
`toy`	Toy Data

Reference

Models

Filtering

Constraints

Learning

Datasets