This repository provides code and computational templates to implement and run the conditional functional graphical model (cFGM) algorithm for applications in neuroscience. For a detailed explanation of the methodology and the computational workflow, please refer to the README.pdf.
Scripts for running the cFGM algorithm both sequentially and in parallel. Refer to the README.pdf for a step-by-step walkthrough of the workflow.
Include the code from: Zhao, B., Zhai, P. S., Wang, Y. S., & Kolar, M. (2024). High-dimensional functional graphical model structure learning via neighborhood selection approach. Electronic Journal of Statistics, 18(1), 1042-1129.
Include the code to run the algorithm of the simulations implemented by Laura (algorithm execution and performance evaluation scripts).