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>.

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Type
Package
Title
Learning Causal or Non-Causal Graphical Models Using Information Theory
Version
1.0.1
Date
2017-11-11
Package
miic
Description
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) .
Maintainer
Nadir Sella
Imports
MASS, igraph, bnlearn, ppcor, stats, Rcpp
License
GPL (>= 2)
NeedsCompilation
yes
Encoding
UTF-8
LazyData
true
RoxygenNote
6.0.1
LinkingTo
Rcpp
Packaged
2017-12-05 13:12:16 UTC; nadir
Author
Nadir Sella [aut, cre], Louis Verny [aut], Severine Affeldt [aut], Hervé Isambert [aut]
Repository
CRAN
Date/Publication
2017-12-05 13:26:25 UTC

install.packages('miic')

1.0.1

a month ago

Nadir Sella

GPL (>= 2)

Imports

MASS, igraph, bnlearn, ppcor, stats, Rcpp

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