# Resources

## General resources

- Stan reference manual: the Bible of the Stan modelling language. There is a 90% probability it has a solution to your problem (if only you could find where).
- Stan forums: the official Stan discourse. Active and friendly community with relatively fast response times.
- Stan case studies: many helpful and detailed demonstrations of Stan use cases. Be sure to look at the robust workflow examples for PyStan, RStan, and cmdStanpy.
- Stan prior recommendations: recommendations for priors from Stan team. Always being updated (for better or worse).
- Stan example models: a veritable treasure trove of prewritten Stan models across a wide array of statistical problems.
- ShinyStan: helpful visualization tools compatible with RStan.
- ArviZ: helpful visualization tools compatible with PyStan.
- An Introduction to Stan by Michael Betancourt.

## Stan for cognitive science

- Stan for cognitive science: aims to centralize all types of work in cognitive science that use the software Stan. Has some useful tutorials and links to papers using Stan.
- Book (in progress): An Introduction to Bayesian Data Analysis for Cognitive Science, CRC Press
- rlssm: a Python package by Laura Fontanesi for fitting reinforcement learning (RL) models, sequential sampling models (DDM, RDM, LBA, ALBA, and ARDM), and combinations of the two, using Bayesian parameter estimation.

## Bayesian Inference and more foundational knowledge

- More of Michael Betancourtâ€™s blog: lots of useful posts from intro to probability theory, to modelling, and some others..
- An Introduction To Bayesian Inference (II): Inference Of Parameters And Models: a recorded lecture by David MacKay.
- Statistical-rethinking: a Bayesian Statistics book with examples in Stan.

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