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Estimating high dimensional ODE models from convoluted observations with an application to fMRI

Project description

This repo is under development, do not download

Estimating high dimensional ODE models from convoluted observations with an application to fMRI

scdn is a Python-based package implementing sparse causal dynamic network analysis for convoluted observations, particular for Functional magnetic resonance imaging (fMRI) in our research. It aims to provide a sparse dynamic network estimation not only for fMRI data but for other possible convoluted observations. The introduciton and explaination of parameters and ODE models can be found in (1).

Getting Started

This package supports both python 2.7 and python 3.6.

Example provided in the repo has been tested in mac os and Linux environment.

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

This package is also published in pypi. For a quick installation, try

pip install scdn


What things you need to install the software and how to install them

See for details of packages requirements. 

Installing from GitHub

Download the packages by using git clone

python install

If you experience problems related to installing the dependency Matplotlib on OSX, please see

Intro to our package

After installing our package locally, try to import scdn in your python environment and learn about package's function.


The examples subfolder includes a basic analysis of our sample data.

Running the tests

The test is going to be added in the future.

Built With

  • Python 2.7


  • python 2.7
  • python 3.6



This project is licensed under the MIT License - see the LICENSE file for details

Project details

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