Fluctuating ecological networks: a synthesis of maximum-entropy approaches for pattern detection and process inference - Computer codes and Data examples

<p>This repository contains: </p> <p><br></p> <p>- the Python script “Fit_BiCM.py” to fit a binary configuration model to a bipartite network </p> <p>- the Python script “Fit_WBiCM.py” to fit a weighted, bipartite configuration model but that can also account for information on the degree sequence </p> <p>- the R code Bipartite.R to analyse the data contained in the folder "samples" and generated by Fit_BiCM.py applied to the data in "bicm_mat.csv"</p> <p>- the R Code Bipartite_W.R to analyse the data contained in the folder "samples_W" and generated by Fit_WBiCM.py applied to the data in "bicm_matW.csv"</p> <p>- the Guideline document "Bipartite Binary Configuration model.docx" that explains the flowork to fit a binary configuration model to a bipartite network using the code Fit_BiCM.py, which uses the existing (and tested) Python package bicm2 (https://pypi.org/project/bicm/) </p> <p>- The Guideline document "Bipartite Weighted Configuration model.docx" that explains the flowork to fit a weighted configuration model to a weighted bipartite network using the code  “Fit_WBiCM.py”, which uses the existing (and tested) Python package NEMtropy (https://pypi.org/project/NEMtropy/).</p> <p><br></p> <p>For a formal and open source description of the fundamental routines used by the codes uploaded here, and for testing the routines, check  https://pypi.org/project/bicm/ and https://pypi.org/project/NEMtropy/</p> <p><br></p> <p>Further details are also available in the Maximum Entropy Hub at https://meh.imtlucca.it/codes.</p> <p><br></p> <p><br></p> <p><br></p> <p><br></p> <p><br></p>