A Python library for coordinate- and image-based meta-analysis.
- Coordinate-based methods (
nimare.meta.cbma)- Kernel-based methods
- Activation likelihood estimation (ALE)
- Specific coactivation likelihood estimation (SCALE)
- Multilevel kernel density analysis (MKDA)
- Kernel density analysis (KDA)
- Model-based methods (
nimare.meta.cbma.model)- Bayesian hierarchical cluster process model (BHICP)
- Hierarchical Poisson/Gamma random field model (HPGRF)
- Spatial Bayesian latent factor regression (SBLFR)
- Spatial binary regression (SBR)
- Kernel-based methods
- Image-based methods (
nimare.meta.ibma)- Mixed effects general linear model (MFX-GLM)
- Random effects general linear model (RFX-GLM)
- Fixed effects general linear model (FFX-GLM)
- Stouffer's meta-analysis
- Random effects Stouffer's meta-analysis
- Weighted Stouffer's meta-analysis
- Fisher's meta-analysis
- Database extraction (
nimare.dataset.extract)- NeuroVault
- Neurosynth
- Brainspell
- PubMed abstract extraction
- Functional characterization analysis (
nimare.decode)- BrainMap decoding
- Neurosynth correlation-based decoding
- Neurosynth MKDA-based decoding
- Generalized correspondence latent Dirichlet allocation (GCLDA)
python setup.py install
To build the Docker image:
docker build -t test/nimare .
To run the Docker container:
docker run -it -v `pwd`:/home/neuro/code/NiMARE -p8888:8888 test/nimare bash
Once inside the container, you can install NiMARE:
python /home/neuro/code/NiMARE/setup.py develop
Please see our contributing guidelines for more information on contributing to NiMARE.
We ask that all contributions to NiMARE respect our code of conduct.