{ "_id": "6a197464acfb0bcc41df03d8", "Package": "miic", "Title": "Learning Causal or Non-Causal Graphical Models Using Information\nTheory", "Version": "2.0.4", "Authors@R": "c(person(given = \"Franck\",\nfamily = \"Simon\",\nrole = c(\"aut\", \"cre\"),\nemail = \"franck.simon@curie.fr\"),\nperson(given = \"Ali\",\nfamily = \"Chemkhi\",\nrole = \"aut\",\nemail = \"ali.chemkhi@curie.fr\"),\nperson(given = \"Tiziana\",\nfamily = \"Tocci\",\nrole = \"aut\",\nemail = \"tiziana.tocci@curie.fr\"),\nperson(given = \"Nikita\",\nfamily = \"Lagrange\",\nrole = \"aut\",\nemail = \"nikita.lagrange@curie.fr\"),\nperson(given = \"Orianne\",\nfamily = \"Debeaupuis\",\nrole = \"aut\",\nemail = \"orianne.debeaupuis@curie.fr\"),\nperson(given = \"Louise\",\nfamily = \"Dupuis\",\nrole = \"aut\",\nemail = \"louise.dupuis@curie.fr\"),\nperson(given = \"Vincent\",\nfamily = \"Cabeli\",\nrole = \"aut\"),\nperson(given = \"Honghao\",\nfamily = \"Li\",\nrole = \"aut\"),\nperson(given = \"Marcel\",\nfamily = \"Ribeiro Dantas\",\nrole = \"aut\"),\nperson(given = \"Nadir\",\nfamily = \"Sella\",\nrole = \"aut\"),\nperson(given = \"Louis\",\nfamily = \"Verny\",\nrole = \"aut\"),\nperson(given = \"Severine\",\nfamily = \"Affeldt\",\nrole = \"aut\"),\nperson(given = \"Hervé\",\nfamily = \"Isambert\",\nrole = \"aut\",\nemail = \"herve.isambert@curie.fr\"))", "Description": "Multivariate Information-based Inductive Causation, better\nknown by its acronym MIIC, is a causal discovery method, based\non information theory principles, which learns a large class of\ncausal or non-causal graphical models from purely observational\ndata, while including the effects of unobserved latent\nvariables. Starting from a complete graph, the method\niteratively removes dispensable edges, by uncovering\nsignificant information contributions from indirect paths, and\nassesses edge-specific confidences from randomization of\navailable data. The remaining edges are then oriented based on\nthe signature of causality in observational data. The recent\nmore interpretable MIIC extension (iMIIC) further distinguishes\ngenuine causes from putative and latent causal effects, while\nscaling to very large datasets (hundreds of thousands of\nsamples). Since the version 2.0, MIIC also includes a temporal\nmode (tMIIC) to learn temporal causal graphs from stationary\ntime series data. MIIC has been applied to a wide range of\nbiological and biomedical data, such as single cell gene\nexpression data, genomic alterations in tumors, live-cell\ntime-lapse imaging data (CausalXtract), as well as medical\nrecords of patients. MIIC brings unique insights based on\ncausal interpretation and could be used in a broad range of\nother data science domains (technology, climatology, economy,\n...). For more information, you can refer to: Simon et al.,\neLife 2024, , Ribeiro-Dantas et\nal., iScience 2024, , Cabeli et\nal., NeurIPS 2021,\n, Cabeli et\nal., Comput. Biol. 2020, , Li\net al., NeurIPS 2019,\n,\nVerny et al., PLoS Comput. Biol. 2017,\n, Affeldt et al., UAI 2015,\n. Changes\nfrom the previous 1.5.3 release on CRAN are available at\n.", "License": "GPL (>= 2)", "URL": "https://github.com/miicTeam/miic_R_package", "BugReports": "https://github.com/miicTeam/miic_R_package/issues", "LazyData": "true", "Encoding": "UTF-8", "RoxygenNote": "7.3.2", "Repository": "https://miicteam.r-universe.dev", "Date/Publication": "2026-01-29 12:17:54 UTC", "RemoteUrl": "https://github.com/miicteam/miic_r_package", "RemoteRef": "HEAD", "RemoteSha": "1d7747366f4020e92c13a7d66a6817b0b8858a4b", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-29 09:39:21 UTC", "User": "root" }, "Author": "Franck Simon [aut, cre],\nAli Chemkhi [aut],\nTiziana Tocci [aut],\nNikita Lagrange [aut],\nOrianne Debeaupuis [aut],\nLouise Dupuis [aut],\nVincent Cabeli [aut],\nHonghao Li [aut],\nMarcel Ribeiro Dantas [aut],\nNadir Sella [aut],\nLouis Verny [aut],\nSeverine Affeldt [aut],\nHervé Isambert [aut]", "Maintainer": "Franck Simon ", "MD5sum": "1278df25d55e7ce12102022efb84648c", 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