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miic  

Learning Causal or Non-Causal Graphical Models Using Information Theory
View on CRAN: Click here


Download and install miic package within the R console
Install from CRAN:
install.packages("miic")

Install from Github:
library("remotes")
install_github("cran/miic")

Install by package version:
library("remotes")
install_version("miic", "1.5.3")



Attach the package and use:
library("miic")
Maintained by
Vincent Cabeli
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-10-09
Latest Update: 2020-10-13
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 , Verny et al. PLoS Comp. Bio. 2017 .
How to cite:
Vincent Cabeli (2017). miic: Learning Causal or Non-Causal Graphical Models Using Information Theory. R package version 1.5.3, https://cran.r-project.org/web/packages/miic
Previous versions and publish date:
0.1 (2017-10-09 17:54), 1.0.1 (2017-12-05 14:26), 1.0.3 (2018-02-02 15:29), 1.0 (2017-11-22 17:57), 1.4.0 (2020-07-22 23:10), 1.4.2 (2020-07-31 10:50), 1.5.0 (2020-09-11 11:40), 1.5.1 (2020-09-18 10:00), 1.5.2 (2020-09-24 01:50)
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