DESCRIPTION

Package: miic
Title: Learning Causal or Non-Causal Graphical Models Using Information Theory
Version: 2.0.3
Authors@R:
    c(person(given = "Franck",
             family = "Simon",
             role = c("aut", "cre"),
             email = "franck.simon@curie.fr"),
      person(given = "Tiziana",
             family = "Tocci",
             role = "aut",
             email = "tiziana.tocci@curie.fr"),
      person(given = "Nikita",
             family = "Lagrange",
             role = "aut",
             email = "nikita.lagrange@curie.fr"),
      person(given = "Orianne",
             family = "Debeaupuis",
             role = "aut",
             email = "orianne.debeaupuis@curie.fr"),
      person(given = "Louise",
             family = "Dupuis",
             role = "aut",
             email = "louise.dupuis@curie.fr"),
      person(given = "Vincent",
             family = "Cabeli",
             role = "aut"),
      person(given = "Honghao",
             family = "Li",
             role = "aut"),
      person(given = "Marcel",
             family = "Ribeiro Dantas",
             role = "aut"),
      person(given = "Nadir",
             family = "Sella",
             role = "aut"),
      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: Multivariate Information-based Inductive Causation, better known 
    by its acronym MIIC, is a causal discovery method, based on information 
    theory principles, which learns a large class of causal or non-causal 
    graphical models from purely observational data, while including the effects 
    of unobserved latent variables. 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. The recent more 
    interpretable MIIC extension (iMIIC) further distinguishes genuine causes 
    from putative and latent causal effects, while scaling to very large 
    datasets (hundreds of thousands of samples). Since the version 2.0, MIIC 
    also includes a temporal mode (tMIIC) to learn temporal causal graphs from 
    stationary time series data. MIIC has been applied to a wide range of 
    biological and biomedical data, such as single cell gene expression data, 
    genomic alterations in tumors, live-cell time-lapse imaging data 
    (CausalXtract), as well as medical records of patients. MIIC brings unique 
    insights based on causal interpretation and could be used in a broad range 
    of other data science domains (technology, climatology, economy, ...). 
    For more information, you can refer to: 
    Simon et al., eLife 2024, <doi:10.1101/2024.02.06.579177>, 
    Ribeiro-Dantas et al., iScience 2024, <doi:10.1016/j.isci.2024.109736>, 
    Cabeli et al., NeurIPS 2021, <https://why21.causalai.net/papers/WHY21_24.pdf>, 
    Cabeli et al., Comput. Biol. 2020, <doi:10.1371/journal.pcbi.1007866>, 
    Li et al., NeurIPS 2019, <https://papers.nips.cc/paper/9573-constraint-based-causal-structure-learning-with-consistent-separating-sets>, 
    Verny et al., PLoS Comput. Biol. 2017, <doi:10.1371/journal.pcbi.1005662>, 
    Affeldt et al., UAI 2015, <https://auai.org/uai2015/proceedings/papers/293.pdf>. 
    Changes from the previous 1.5.3 release on CRAN are available at 
    <https://github.com/miicTeam/miic_R_package/blob/master/NEWS.md>.
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
LazyData: true
Encoding: UTF-8
RoxygenNote: 7.3.2