The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

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: 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>.

Version: 1.5.3
Imports: ppcor, Rcpp, scales, stats
LinkingTo: Rcpp
Suggests: igraph, grDevices, ggplot2 (≥ 3.3.0), gridExtra
Published: 2020-10-13
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 at curie.fr>
BugReports: https://github.com/miicTeam/miic_R_package/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/miicTeam/miic_R_package
NeedsCompilation: yes
SystemRequirements: C++14
In views: Omics
CRAN checks: miic results

Documentation:

Reference manual: miic.pdf

Downloads:

Package source: miic_1.5.3.tar.gz
Windows binaries: r-devel: miic_1.5.3.zip, r-release: miic_1.5.3.zip, r-oldrel: miic_1.5.3.zip
macOS binaries: r-release (arm64): miic_1.5.3.tgz, r-oldrel (arm64): miic_1.5.3.tgz, r-release (x86_64): miic_1.5.3.tgz, r-oldrel (x86_64): miic_1.5.3.tgz
Old sources: miic archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=miic to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.