Function reference
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applySparrowBonferroni() - Applies Bonferroni Correction to a Sparrow Network Object
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applySparrowFDR() - Applies FDR Correction to a Sparrow Network Object
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aracne() - This function applies ARACNE on the data
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arbitrarySparsity() - This function Builds an arbitary sparse network
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buildConsensus() - This function builds the Consensus Network from the component network
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c3netWrapper() - This function wraps c3net implementation
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calcHyperPval() - Compute Hypergeometric PValue
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compute(<LocalModularity>) - Find Local Modularity (NQ)
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compute(<Modularity>) - Find Global Modularity (Q)
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compute(<ModularityDensity>) - Find Global Modularity (Qds)
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compute(<ModuleQualityMetric>) - Find Modularity Quality
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computeBICcurve() - This Function Computes a Network BIC Curve
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computeDriverDistance() - Compute Graph Distance
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computeDriverDistancePvalue() - Compute Driver Distance P-Value
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correlation() - Compute a Correlation Matrix
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covarianceSelection() - Covariance Selection with Graphical Lasso
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covarianceSelectionBisection() - Covariance Selection with Bisection Optimization
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covarianceSelectionMBPath() - Covariance based on Neighborhood Selection
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covarianceSelectionPath() - Covariance Selection with Path BIC Selection
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deployNetworkSparsity() - This function deploys arbitary sparsity on a list of networks
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fastlm() - Fast Linear Modeling
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fastlmbic() - Fast Linear Modeling BIC
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fdrThres() - FDR Threshold
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findModules.CFinder() - Find Modules with CFinder
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findModules.CFinder.once() - This function tries to get modules from network adjacency matrix using Link communities algorithm.
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findModules.GANXiS() - Find Modules with GANXiS
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findModules.GANXiS.once() - Find Modules using GANXIS
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findModules.consensusCluster() - Finds Consensus Clusters
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findModules.consensusKmeans() - Finds Moduless With Kmeans Clustering
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findModules.edge_betweenness() - Find Modules with Network Adjacency
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findModules.edge_betweenness.once() - Find Modules with Network Adjacency Single iteration
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findModules.fast_greedy() - Find Modules with iGraph Fast and Greedy algorithm
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findModules.fast_greedy.once() - Find Modules Fast and Greedy
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findModules.hclust() - Find Modules with Hierarchical Clustering
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findModules.infomap() - Find Modules using graph adjacency
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findModules.infomap.once() - Find Modules using graph adjacency
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findModules.label_prop() - Wraps Finding Modules with Graph Adjacency
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findModules.label_prop.once() - Find Modules with Graph Adjacency
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findModules.leading_eigen() - Find Modules with Leading Eigen Edges
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findModules.leading_eigen.once() - Find Modules with Leading Eigen Edges
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findModules.linkcommunities() - Find Modules with Network Adjacency Matrix Using Link Communities Algorithm
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findModules.linkcommunities.once() - Find Modules with Network Adjacency Matrix Using Link Communities Algorithm
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findModules.louvain() - Find Modules with Network Adjacency Matrix Using Louvain Clustering
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findModules.louvain.once() - Find Modules with Network Adjacency Matrix Using Louvain Clustering
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findModules.megena() - Find Modules with Megena Clustering
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findModules.spinglass() - Find Modules with Network Adjacency Matrix Using Spinglass Clustering
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findModules.spinglass.once() - Find Modules with Network Adjacency Matrix Using spinglass Clustering
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findModules.walktrap() - Find Modules with Network Adjacency Matrix Using Walktrap Clustering
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findModules.walktrap.once() - Find Modules with Network Adjacency Matrix Using Walktrap Clustering
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fisherEnrichment() - Fishers Enrichment Analysis
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genie3() - Run genie3
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installAracne() - This function applies ARACNE on the data
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lassoAIC() - Grab AIC Solution for Lasso
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lassoBIC() - Grab BIC Solution for Lasso
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lassoCV1se() - Grab CV1se Solution for Lasso Regression
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lassoCVmin() - Grab CV1min Solution for Lasso Regression
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metaNet-package - metaNet: Statistical network exploration toolkit
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mpiWrapper() - Runs Sparrow Regression
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mrnetWrapper() - Implements mrnet
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multiplot() - Multiple plot function
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rankConsensus() - Ranks Consensus networks
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rankConsensus2() - Ranks Consensus networks with ties.
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rankedEdgeList() - Rank edges of a coexpression matrix
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regulatorAnalysis.directed() - Function to Identify Network Regulators from Directed Networks
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regulatorAnalysis.directed_weighted() - Function to Identify Network Regulators from Directed Weighted Networks
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regulatorAnalysis.undirected() - Function to Identify Network Regulators from Undirected Networks
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regulatorAnalysis.undirected_weighted() - Function to Identify Network Regulators from Undirected Weighted Networks
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ridgeAIC() - Grab AIC Solution for Ridge Regression
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ridgeBIC() - Grab BIC Solution for Ridge Regression
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ridgeCV1se() - Grab CV1se Solution for Ridge Regression
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ridgeCVmin() - Grab CV1min Solution for Ridge Regression
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run.consensus.cluster() - Runs Consensus Clustering Algorithm
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score.nodes() - Scores Nodes From Regulator Discovery
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simulateNetworkData() - Simulate Network Data
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sparrow2Z() - Runs vbsr with a 2Z cutoff for a gene across a matrix
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sparrowNetwork() - Runs wraps vbsr across a matrix
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sparrowZ() - Runs vbsr gene across a matrix
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synGetFiles() - This function pulls files from a synapse project
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tigress() - Runs tigress on an expression matrix
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wgcnaSoftThreshold() - Runs WGCNA
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wgcnaTOM() - Runs WGCNA TOMsimilarity
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winsorizeData() - Winsorize a Gene Expression Matrix