Package: miic
Title: Learning Causal or Non-Causal Graphical Models Using Information
        Theory
Version: 1.5.3
Authors@R: 
    c(person(given = "Vincent",
             family = "Cabeli",
             role = c("aut", "cre"),
             email = "vincent.cabeli@curie.fr"),
      person(given = "Honghao",
             family = "Li",
             role = "aut",
             email = "honghao.li@curie.fr"),
      person(given = "Marcel",
             family = "Ribeiro Dantas",
             role = "aut",
             email = "marcel.ribeiro-dantas@curie.fr"),
      person(given = "Nadir",
             family = "Sella",
             role = "aut",
             email = "nadir.sella@curie.fr"),
      person(given = "Louis",
             family = "Verny",
             role = "aut"),
      person(given = "Severine",
             family = "Affeldt",
             role = "aut"),
      person(given = "Hervé",
             family = "Isambert",
             role = "aut",
             email = "Herve.Isambert@curie.fr"))
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:
    Cabeli et al. PLoS Comp. Bio. 2020 <doi:10.1371/journal.pcbi.1007866>,
    Verny et al. PLoS Comp. Bio. 2017 <doi:10.1371/journal.pcbi.1005662>.
License: GPL (>= 2)
URL: https://github.com/miicTeam/miic_R_package
BugReports: https://github.com/miicTeam/miic_R_package/issues
Imports: ppcor, Rcpp, scales, stats,
Suggests: igraph, grDevices, ggplot2 (>= 3.3.0), gridExtra
LinkingTo: Rcpp
SystemRequirements: C++14
LazyData: true
Encoding: UTF-8
RoxygenNote: 7.1.1
NeedsCompilation: yes
Packaged: 2020-10-09 13:30:22 UTC; vcabeli
Author: Vincent Cabeli [aut, cre],
  Honghao Li [aut],
  Marcel Ribeiro Dantas [aut],
  Nadir Sella [aut],
  Louis Verny [aut],
  Severine Affeldt [aut],
  Hervé Isambert [aut]
Maintainer: Vincent Cabeli <vincent.cabeli@curie.fr>
Repository: CRAN
Date/Publication: 2020-10-13 23:50:11 UTC
Built: R 4.1.3; x86_64-w64-mingw32; 2023-04-17 15:40:33 UTC; windows
Archs: i386, x64
