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miic
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]
[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:
Other packages that cited miic R package
View miic citation profile
Other R packages that miic depends,
imports, suggests or enhances
Functions, R codes and Examples using
the miic R package
Some associated functions: cosmicCancer . cosmicCancer_stateOrder . discretizeMDL . discretizeMutual . getIgraph . hematoData . miic.export . miic . miic.write.network.cytoscape . miic.write.style.cytoscape . ohno . ohno_stateOrder . plot.miic .
Some associated R codes: RcppExports.R . data.R . discretizeMDL.R . discretizeMutual.R . miic.R . miic.export.R . miic.reconstruct.R . miic.utils.R . parseResults.R . write.cytoscape.R . write.style.R . Full miic package functions and examples
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