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The Stata Journal
Volume 17 Number 2: pp. 330-342



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Introducing the StataStan interface for fast, complex Bayesian modeling using Stan

Robert L. Grant
BayesCamp
Croydon, UK
robert@bayescamp.com
Bob Carpenter
Columbia University
New York, NY
Daniel C. Furr
University of California at Berkeley
Berkeley, CA
Andrew Gelman
Columbia University
New York, NY
Abstract.  In this article, we present StataStan, an interface that allows simulation-based Bayesian inference in Stata via calls to Stan, the flexible, open-source Bayesian inference engine. Stan is written in C++, and Stata users can use the commands stan and windowsmonitor to run Stan programs from within Stata. We provide a brief overview of Bayesian algorithms, details of the commands available from Statistical Software Components, considerations for users who are new to Stan, and a simple example. Stan uses a different algorithm than bayesmh, BUGS, JAGS, SAS, and MLwiN. This algorithm provides considerable improvements in efficiency and speed. In a companion article, we give an extended comparison of StataStan and bayesmh in the context of item response theory models.
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View all articles by these authors: Robert L. Grant, Bob Carpenter, Daniel C. Furr, Andrew Gelman

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