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

## Documentation for package ‘miic’ version 1.5.3

- DESCRIPTION file.

## Help Pages

cosmicCancer | Genomic and ploidy alterations in breast tumors
cosmicCancer_stateOrder | Genomic and ploidy alterations in breast tumors
discretizeMDL | Discretize a real valued distribution
discretizeMutual | Iterative dynamic programming for (conditional) mutual information through optimized discretization.
getIgraph | Igraph plotting function for miic
hematoData | Early blood development: single cell binary gene expression data
miic | MIIC, causal network learning algorithm including latent variables
miic.export | Export miic result to different plotting methods
miic.write.network.cytoscape | GraphML converting function for miic graph
miic.write.style.cytoscape | Style writing function for the miic network
ohno | Tetraploidization in vertebrate evolution
ohno_stateOrder | Tetraploidization in vertebrate evolution
plot.miic | Basic plot function of a miic network inference result