R package overview The functions of the spaceMap R package are perhaps best conceptualized in four categories. The model fitting category concerns functions that learn a network from input data. The model selection category encompasses functions that select the optimal tuning parameters and build robust networks through aggregation. The network analysis category contains tools to annotate and interpret inferred networks. The Utils category provides convenient functions such as evaluating simulation performance.
Model fitting Details on how to fit spaceMap and space (a special case of spaceMap) with pre-specified tuning parameters.
spacemap	Fit the spacemap model.
space	Fit the SPACE model.
Model selection
cvVote	Cross validation (CV.Vote) for spacemap and space models.
tuneVis	Visualize cross validation metrics
initFit	Identify scale of tuning parameters.
bootEnsemble	Ensemble networks through boostrapping.
bootVote	Model aggregation through Boot.Vote
Network Analysis Toolkit
adj2igraph	Convert adjacency matrix to igraph with annotations.
rankHub	Prioritize networks hubs by degree.
cisTrans	Identify x-y cis and trans edges.
reportHubs	Report top hub nodes with attributes.
xHubEnrich	Functional enrichment of x-hub neighborhood.
modEnrich	Module enrichment analysis.
Data
sim1	Toy Hub Network Simulation
bcpls	Network toolkit example input data
Utils
adjacency	Adjacency matrix from spacemap and spacemap models
reportPerf	Report performance of x-y or y-y edge detection
reportJointPerf	Report joint performance of (x-y,y-y) edge detection
cvPerf	CV.Vote Model Performance
algoFDR	False discovery rate
algoMCC	Matthew's Correlation Coefficient (MCC)
algoPower	Power function
nonZeroUpper	Count the number of y-y edges
nonZeroWhole	Count the number of x-y edges