Learning Causal or Non-Causal Graphical Models Using Information
Theory
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Documentation for package ‘miic’ version 1.5.3
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 |