Other packages > Find by keyword >

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", "2.0.4")



Attach the package and use:
library("miic")
Maintained by
Franck Simon
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-10-09
Latest Update: 2024-09-17
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:
Franck Simon (2017). miic: Learning Causal or Non-Causal Graphical Models Using Information Theory. R package version 2.0.4, https://cran.r-project.org/web/packages/miic. Accessed 08 Oct. 2026.
Previous versions and publish date:
(2026-09-14 22:50), 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), 1.5.3 (2020-10-14 01:50), 2.0.3 (2024-09-18 00:30)
Other packages that cited miic R package
View miic citation profile
Other R packages that miic depends, imports, suggests or enhances
Complete documentation for miic
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

nextGenShinyApps  
Craft Exceptional 'R Shiny' Applications and Dashboards with Novel Responsive Tools
Nove responsive tools for designing and developing 'Shiny' dashboards and applications. The scripts ...
Download / Learn more Package Citations See dependency  
abc.data  
Data Only: Tools for Approximate Bayesian Computation (ABC)
Contains data which are used by functions of the 'abc' package. ...
Download / Learn more Package Citations See dependency  
dineR  
Differential Network Estimation in R
An efficient and convenient set of functions to perform differential network estimation through the ...
Download / Learn more Package Citations See dependency  
colorfindr  
Extract Colors from Windows BMP, JPEG, PNG, TIFF, and SVG Format Images
Extracts colors from various image types, returns customized reports and plots treemaps and 3D scat ...
Download / Learn more Package Citations See dependency  
climwin  
Climate Window Analysis
Contains functions to detect and visualise periods of climate sensitivity (climate windows) for a g ...
Download / Learn more Package Citations See dependency  
metafor  
Meta-Analysis Package for R
A comprehensive collection of functions for conducting meta-analyses in R. The package includes func ...
Download / Learn more Package Citations See dependency  

28,905

R Packages

247,686

Dependencies

76,495

Author Associations

28,906

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA