Type: Package
Title: Multivariate Information Inductive Causation
Version: 1.0
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) <doi:10.1371/journal.pcbi.1005662>.
Authors@R: c(person("Nadir", "Sella", role = c("aut","cre"), email = "nadir.sella@curie.fr"),
           person("Louis", "Verny",role = "aut"),
           person("Severine", "Affeldt", role = "aut"),
           person("Hervé", "Isambert", role = c("aut"), email = "Herve.Isambert@curie.fr"))
Maintainer: Nadir Sella <nadir.sella@curie.fr>
Imports: MASS, igraph, bnlearn, ppcor, stats, Rcpp
License: GPL (>= 2)
NeedsCompilation: yes
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
LinkingTo: Rcpp
Packaged: 2017-11-22 16:47:43 UTC; nadir
Author: Nadir Sella [aut, cre],
  Louis Verny [aut],
  Severine Affeldt [aut],
  Hervé Isambert [aut]
Repository: CRAN
Date/Publication: 2017-11-22 16:57:01 UTC
