miic: Learning Causal or Non-Causal Graphical Models Using Information Theory
Version 1.0.3

We report an information-theoretic method which learns a large class of causal or non-causal graphical models from purely observational data, while including the effects of unobserved latent variables, commonly found in many datasets. Starting from a complete graph, the method iteratively removes dispensable edges, by uncovering significant information contributions from indirect paths, and assesses edge-specific confidences from randomization of available data. The remaining edges are then oriented based on the signature of causality in observational data. This approach can be applied on a wide range of datasets and provide new biological insights on regulatory networks from single cell expression data, genomic alterations during tumor development and co-evolving residues in protein structures. For more information you can refer to: Verny et al. Plos Comput Biol. (2017) .

Getting started

Package details

AuthorNadir Sella [aut, cre], Louis Verny [aut], Severine Affeldt [aut], Hervé Isambert [aut]
Date of publication2018-02-02 14:29:57 UTC
MaintainerNadir Sella <[email protected]>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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miic documentation built on Feb. 2, 2018, 5:03 p.m.