(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
There’s a new section of the Stan web site, with case studies meant to illustrate statistical methodologies, classes of models, application areas, statistical computation, and Stan programming.
The first ten or so are up, including a grab bag of education models from Daniel Furr at U.C. Berkeley:
- Hierarchical Two-Parameter Logistic Item Response Model
- Generalized Rating Scale Model with Latent Regression
- Generalized Partial Credit Model with Latent Regression
- Rating Scale Model with Latent Regression
- Partial Credit Model with Latent Regression
- Rasch Model with Latent Regression
- Two-Parameter Logistic Item Response Model
There’s also an introduction to the R package that Daniel and crew (Seung Yeon Lee, Joon-Ho Lee, and Sophia Rabe-Hesketh) have been working on to encapsulate commonly used Stan models in education:
- Two-Parameter Logistic Item Response Model
and three from me:
- Pooling with Hierarchical Models for Repeated Binary Trials
- Multiple Species-Site Occupancy Model
- Soil Carbon Modeling with RStan
That’s not all
Meanwhile, Hiroki ITÔ has added Stan models up through Chapter 13 for the Kéry and Schaub book on population analysis.
To make this offer completely irresistible
I was inspired to finally get my act together after Matthew McKay and John Stachurski showed me QuantEcon’s Jupyter notebook gallery.
I introduced them to Jim Savage (not that one, this one), and they talked Jim into writing a notebook for them introducing Bayesian methods in general and Stan in particular. I’ve read the draft and it’s something I’m going to be recommending to a lot of people—I’ll announce here when it’s up.
Yours can be next
There are instructions on how to contribute a case study on the top-level Stan Case Studies page. They can be knitr (R), or Jupyter (R, Python, or Julia).
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