If you're new to drift diffusion modeling (DDM), you can watch a short video lecture on the topic by clicking here. The slides to this talk are also available in this repository (slides/Intro to DDM CPC Zurich 2021.pdf).
You can find a conceptual overview of the model-fitting process in the slides entitled slides/Carney Modeling Workshop 2021 HDDM Demo.pdf.
Sample code and additional self-learning material can be found in the Jupyter notebook entitled hddm_tutorial_2022.ipynb. In order to run this code, you will need to install HDDM.
In this repository's Wiki, you can find how-to guides for installing HDDM in various ways. If you're just playing around with HDDM, I highly recommend using Google Colab. If you're wanting to use HDDM for more intensive research purposes, I also have guides for installing HDDM on your personal computer or university's high-performance computing cluster. Please let me know if you see any errors that need correction.
The HDDM GitHub page can be found here.
The online documentation is as good as a textbook.
If you need help, the mailing list is very responsive.
Here are some example of studies I've run using HDDM:
Manuscript: https://www.nature.com/articles/s41598-019-48050-2
Data and scripts: https://osf.io/8ka47/