ProMises (Procrustes von Mises-Fisher) model

In this repository, you can find

1. A short tutorial about the application of the ProMises model using fMRI data;
2. The code used for the between-subjects classification of the Faces and Objects data (Code/FacesObjects);
3. The code used for the between-subjects classification of the Raiders data (Code/Raiders);
4. The code used for the group level inference analysis and functional connectivity analysis of the Auditory data (Code/Auditory);
5. The main function describing the ProMises model ProMisesModel.py;
6. The main function describing the Efficient ProMises model ProMisesModel.py;
7. Materials for the poster at OHBM conference, i.e., poster and video.

References

Andreella, A., & Finos, L. (2022). Procrustes analysis for high-dimensional data. psychometrika, 87(4), 1422-1438.

Andreella, A., Finos, L., & Lindquist, M. A. (2023). Enhanced hyperalignment via spatial prior information. Human Brain Mapping, 44(4), 1725-1740.

Gower, J. C., & Dijksterhuis, G. B. (2004). Procrustes problems (Vol. 30). OUP Oxford.

Haxby, J. V., Guntupalli, J. S., Connolly, A. C., Halchenko, Y. O., Conroy, B. R., Gobbini, M. I., ... & Ramadge, P. J. (2011). A common, high-dimensional model of the representational space in human ventral temporal cortex. Neuron, 72(2), 404-416.

Goodall, C. (1991). Procrustes methods in the statistical analysis of shape. Journal of the Royal Statistical Society: Series B (Methodological), 53(2), 285-321.

Did you find some bugs?

Please write to angela.andreella[\at]unive[\dot]it or insert a reproducible example using reprex on my issue github page.