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", "2.0.3")



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 2.0.3, https://cran.r-project.org/web/packages/miic. Accessed 16 Jul. 2026.
Previous versions and publish date:
(2026-07-09 06:30), 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)
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

bbdetection  
Identification of Bull and Bear States of the Market
Implements two algorithms of detecting Bull and Bear markets in stock prices: the algorithm of Pagan ...
Download / Learn more Package Citations See dependency  
tibble  
Simple Data Frames
Provides a 'tbl_df' class (the 'tibble') with stricter checking and better formatting than the tradi ...
Download / Learn more Package Citations See dependency  
stevedata  
Steve's Toy Data for Teaching About a Variety of Methodological, Social, and Political Topics
This is a collection of various kinds of data with broad uses for teaching. My students, and academ ...
Download / Learn more Package Citations See dependency  
Rnmr1D  
Perform the Complete Processing of a Set of Proton Nuclear Magnetic Resonance Spectra
Perform the complete processing of a set of proton nuclear magnetic resonance spectra from the free ...
Download / Learn more Package Citations See dependency  
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  
wordnet  
WordNet Interface
An interface to WordNet using the Jawbone Java API to WordNet. WordNet (< ...
Download / Learn more Package Citations See dependency  

27,806

R Packages

239,283

Dependencies

73,837

Author Associations

27,807

Publication Badges

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