Skip to content


Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?

Latest commit


Git stats


Failed to load latest commit information.
Latest commit message
Commit time


R-CMD-check Codecov test coverage lifecycle Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public. CRAN checks Project Status: Active – The project has reached a stable, usable state and is being actively developed.

The goal of NetCoupler is to estimate potential causal links between a set of -omic (e.g. metabolomics, lipidomics) or other high-dimensional metabolic data as a conditional dependency network and either a disease outcome, an exposure, or both. These potential causal links are classified as direct, ambigious, or no effects. This algorithm is largely meant to be used with -omic style data to generate the networks and while theoretically non-omic data could be used, we have not tested it in that context. Given the algorithms nature, it’s primarily designed to be used for exploration of potential mechanisms and used to complement other analyses for a research question. It could also be used to confirm a pre-specified and explicit hypothesis, similar to how structural equation models are used. However, this might be a more niche use.

Overview of the NetCoupler algorithm.

Why or when might you want to use NetCoupler?

  1. You are interested in asking a research question on how some factor might influence another factor and how it might mediate through a metabolic network.
  2. If you want to explore how a factor might influence a metabolic network or how a metabolic network might influence a factor.
  3. You have an -omic dataset and want another method to explore how it relates to your variable of interest.

Basically, if you’re research question or objective has the general form of:

Type of questions or objectives that NetCoupler aims to help answer.

… So that you can ultimately have an answer that looks like:

General result that NetCoupler provides that might help answer your question.

There are a few vignettes available in this package:

  • Get Started (vignette("NetCoupler")) describes a simple overview of how and when to use NetCoupler, as well as a basic explanation of some of the components of NetCoupler.
  • Examples with different models (vignette("examples")) lists different models we’ve tested that work with NetCoupler. If you have tried a model out that isn’t listed and seen success, let us know by opening an Issue or submitting a Pull Request (see the contributing guidelines for instructions on doing this).


To install the official CRAN version, use:


To install the development version, use:

# install.packages("remotes")

Contributing and Code of Conduct

Checkout the guidelines for details on contributing. Please note that the ‘NetCoupler’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.


Inference of causal links between metabolomics and disease incidence



Unknown, MIT licenses found

Licenses found


Code of conduct