R: Learning Causal or Non-Causal Graphical Models Using Information Theory

# Learning Causal or Non-Causal Graphical Models Using Information Theory

## Documentation for package ‘miic’ version 2.0.3

- DESCRIPTION file

## Help Pages

computeMutualInfo | Compute (conditional) mutual information
computeThreePointInfo | Compute (conditional) three-point information
cosmicCancer | Genomic and ploidy alterations in breast tumors
cosmicCancer_stateOrder | Genomic and ploidy alterations in breast tumors
covidCases | Covid cases
discretizeMDL | Discretize a real valued distribution
discretizeMutual | Iterative dynamic programming for (conditional) mutual information through optimized discretization.
estimateTemporalDynamic | Estimation of the temporal causal discovery parameters
export | Export miic result for plotting (with igraph)
hematoData | Early blood development: single cell binary gene expression data
miic | MIIC, causal network learning algorithm including latent variables
plot.miic | Basic plot function of a miic network inference result
plot.tmiic | Basic plot function of a temporal miic (tmiic) network inference result
writeCytoscapeNetwork | GraphML converting function for miic graph
writeCytoscapeStyle | Style writing function for the miic network