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: Cabeli et al. PLoS Comp. Bio. 2020 , Verny et al. PLoS Comp. Bio. 2017 .


Reference manual

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1.5.3 by Vincent Cabeli, a year ago


Report a bug at https://github.com/miicTeam/miic_R_package/issues

Browse source code at https://github.com/cran/miic

Authors: Vincent Cabeli [aut, cre] , Honghao Li [aut] , Marcel Ribeiro Dantas [aut] , Nadir Sella [aut] , Louis Verny [aut] , Severine Affeldt [aut] , Hervé Isambert [aut]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports ppcor, Rcpp, scales, stats

Suggests igraph, grDevices, ggplot2, gridExtra

Linking to Rcpp

System requirements: C++14

See at CRAN