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

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) <doi:10.1371/journal.pcbi.1005662>.

Version: 1.0.1
Imports: MASS, igraph, bnlearn, ppcor, stats, Rcpp
LinkingTo: Rcpp
Published: 2017-12-05
Author: Nadir Sella [aut, cre], Louis Verny [aut], Severine Affeldt [aut], Hervé Isambert [aut]
Maintainer: Nadir Sella <nadir.sella at curie.fr>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: miic results

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Reference manual: miic.pdf
Package source: miic_1.0.1.tar.gz
Windows binaries: r-devel: miic_1.0.1.zip, r-release: miic_1.0.1.zip, r-oldrel: miic_1.0.1.zip
OS X El Capitan binaries: r-release: miic_1.0.1.tgz
OS X Mavericks binaries: r-oldrel: miic_1.0.1.tgz
Old sources: miic archive

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