With the official publication of the Dmipy journal paper, we hereby also release the mature version of the Dmipy codebase. The reference is the following:
Fick, Rutger HJ, Demian Wassermann, and Rachid Deriche. "The Dmipy Toolbox: Diffusion MRI Multi-Compartment Modeling and Microstructure Recovery Made Easy." Frontiers in Neuroinformatics 13 (2019): 64.
Dmipy's modular, on-the-fly model generation implementation allows for the implementation and exploration of most imaginable PGSE dMRI-based multi-compartment modeling approaches and variants, that are available in the literature. Moreover, Dmipy goes beyond the state-of-the-art by facilitating the creation of cross-framework or iterative MC-models (that have multiple, separate optimization steps), and generalizing multi-tissue MC-modeling for all MC-model variants. To demonstrate, the examples page explicitly implements tens of MC-models and demonstrates their use.