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Project Status: Active - The project has reached a stable, usable state and is being actively developed. CRAN status

👋 About

This repository contains the code for the R package EMLN. EMLN standardizes workflows for creating, storing, and converting multilayer network data, and ships with a collection of empirical ecological multilayer datasets ready for analysis. It also provides interactive, browser-based visualization through its integration with MiRA, launched directly from R via plot_multilayer(). Although designed with ecological data in mind, EMLN is flexible and can handle data from other research domains.

📄 Paper and citing

Frydman N, Freilikhman S, Talpaz I, Pilosof S. Practical guidelines and the EMLN R package for handling ecological multilayer networks. Methods in Ecology and Evolution. 2023. DOI:10.1111/2041-210X.14225. Please cite the paper when implementing the guidelines we describe or when using the package, this helps us a lot!

📦 Installation

EMLN is available on CRAN. Installation is as follows:

install.packages("emln")

🌐 Website

Detailed explanations on workflows accompanied by examples for handling monolayer and multilayer data using emln are in: emln.ecomplab.com.

🕸️ Interactively visualizaing multilayer networks

EMLN integrates MiRA (Multilayer Interactive Rendering Application), a browser-based, installation-free visualizer launched from R via plot_multilayer(), or by exporting with multilayer_to_json() / multilayer_to_csv(). MiRA offers seven complementary modes — Network (3D), Map, Grid View, Layer View, Meta-Network, Dashboard, and Data — with interactive rotation, filtering, color/size mapping, and bipartite support, plus nine bundled empirical datasets.

If you use MiRA in your published research, please cite the MiRA preprint:

Nehoray SM, Bloch Y, Pilosof S (2026). Interactively visualizing biological multilayer networks using MiRA. arXiv:2605.09597 [cs.SI]. https://doi.org/10.48550/arXiv.2605.09597

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An R package for handling and manipulating multilayer network data, mainly for ecological networks

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