Nistats is being retired.
This repository is now archived.
It will not receive any further development or bug fixes. Its functionality has now been incorporated into Nilearn's stats, datasets, and reporting modules.
It will be available in Nilearn 0.7.0 onwards, set to release in late 2020. Please file issues and pull requests in Nilearn, from now on.
Credit for the various Pull Requests that have been merged into Nistats are now visible in Nilearn. Open issues have been moved into Nilearn. Open PRs will need to be redone.
Nistats is a Python module for fast and easy modeling and statistical analysis of functional Magnetic Resonance Imaging data.
It leverages the nilearn Python toolbox for neuroimaging data manipulation (data downloading, visualization, masking).
This work is made available by a community of people, amongst which the INRIA Parietal Project Team and D'esposito lab at Berkeley.
It is based on developments initiated in the nipy nipy project.
- Official source code repo: https://github.com/nistats/nistats/
- HTML documentation (stable release): http://nistats.github.io/
The required dependencies to use the software are:
- Python >= 2.7
- Numpy >= 1.11
- SciPy >= 0.17
- Nibabel >= 2.0.2
- Nilearn >= 0.4.0
- Pandas >= 0.18.0
- Sklearn >= 0.18.0
If you are using nilearn plotting functionalities or running the examples, matplotlib >= 1.5.1 is required.
If you want to run the tests, you need nose >= 1.2.1 and coverage >= 3.7.
If you want to download openneuro datasets Boto3 >= 1.2 is required
The installation instructions are found on the webpage: https://nistats.github.io/
You can check the latest sources with the command:
git clone git://github.com/nistats/nistats
or if you have write privileges:
git clone email@example.com:nistats/nistats