As a surprise welcome to 2017, our paper on how the Stan language works along with an overview of how the MCMC and optimization algorithms work hit the stands this week. Bob Carpenter, Andrew Gelman, Matthew D. Hoffman, Daniel Lee, Ben Goodrich, Michael Betancourt, Marcus Brubaker, Jiqiang Guo, Peter Li, and Allen Riddell. 2017. Stan: […]

## Stan 2.14 released for R and Python; fixes bug with sampler

Stan 2.14 is out and it fixes the sampler bug in Stan versions 2.10 through 2.13. Critical update It’s critical to update to Stan 2.14. See: RStan 2.14.1 PyStan 2.14.0.0 CmdStan 2.14.0 The other interfaces will update when you udpate CmdStan. The process After Michael Betancourt diagnosed the bug, it didn’t take long for him […]

## Michael found the bug in Stan’s new sampler

Gotcha! Michael found the bug! That was a lot of effort, during which time he produced ten pages of dense LaTeX to help Daniel and me understand the algorithm enough to help debug (we’re trying to write a bunch of these algorithmic details up for a more general audience, so stay tuned). So what was […]

## Stan 2.10 through Stan 2.13 produce biased samples

[Update: bug found! See the follow-up post, Michael found the bug in Stan’s new sampler] [Update: rolled in info from comments.] After all of our nagging of people to use samplers that produce unbiased samples, we are mortified to have to announce that Stan versions 2.10 through 2.13 produce biased samples. The issue Thanks to […]

## Mathematica, now with Stan

Vincent Picaud developed a Mathematica interface to Stan: MathematicaStan You can find everything you need to get started by following the link above. If you have questions, comments, or suggestions, please let us know through the Stan user’s group or the GitHub issue tracker. MathematicaStan interfaces to Stan through a CmdStan process. Stan programs are […]

## A book on RStan in Japanese: *Bayesian Statistical Modeling Using Stan and R* (Wonderful R, Volume 2)

Wonderful, indeed, to have an RStan book in Japanese: Kentarou Matsuura. 2016. Bayesian Statistical Modeling Using Stan and R. Wonderful R Series, Volume 2. Kyoritsu Shuppan Co., Ltd. Google translate makes the following of the description posted on Amazon Japan (linked from the title above): In recent years, understanding of the phenomenon by fitting a […]

## Stan users group hits 2000 registrations

Of course, there are bound to be duplicate emails, dead emails, and people who picked up Stan, joined the list, and never came back. But still, that’s a lot of people who’ve expressed interest! It’s been an amazing ride that’s only going to get better as we learn more and continue to improve Stan’s speed […]

## Who owns your code and text and who can use it legally? Copyright and licensing basics for open-source

I am not a lawyer (“IANAL” in web-speak); but even if I were, you should take this with a grain of salt (same way you take everything you hear from anyone). If you want the straight dope for U.S. law, see the U.S. government Copyright FAQ; it’s surprisingly clear for government legalese. What is copyrighted? […]

## Free workshop on Stan for pharmacometrics (Paris, 22 September 2016); preceded by (non-free) three day course on Stan for pharmacometrics

So much for one post a day… Workshop: Stan for Pharmacometrics Day If you are interested in a free day of Stan for pharmacometrics in Paris on 22 September 2016, see the registration page: Stan for Pharmacometrics Day (free workshop) Julie Bertrand (statistical pharmacologist from Paris-Diderot and UCL) has finalized the program: When Who What […]

## Stan Course up North (Anchorage, Alaska) 23–24 Aug 2016

Daniel Lee’s heading up to Anchorage, Alaska to teach a two-day Stan course at the Alaska chapter of the American Statistical Association (ASA) meeting in Anchorage. Here’s the rundown: Information and Free Registration I hear Alaska’s beautiful in the summer—16 hour days in August and high temps of 17 degrees celsius. Plus Stan! More Upcoming […]

## Stan 2.11 Good, Stan 2.10 Bad

Stan 2.11 is available for all interfaces We are happy to announce that all of the interfaces have been updated to Stan 2.11. There was a subtle bug introduced in 2.10 where a probabilistic acceptance condition was being checked twice. Sorry about that and thanks for your patience. We’ve added some additional tests to catch […]

## One-day workshop on causal inference (NYC, Sat. 16 July)

James Savage is teaching a one-day workshop on causal inference this coming Saturday (16 July) in New York using RStanArm. Here’s a link to the details: One-day workshop on causal inference Here’s the course outline: How do prices affect sales? What is the uplift from a marketing decision? By how much will studying for an […]

## Reproducible Research with Stan, R, knitr, Docker, and Git (with free GitLab hosting)

Jon Zelner recently developed a neat Docker packaging of Stan, R, and knitr for fully reproducible research. The first in his series of posts (with links to the next parts) is here: * Reproducibility, part 1 The post on making changes online and auto-updating results using GitLab’s continuous integration service is here: * GitLab continuous […]

## Log Sum of Exponentials for Robust Sums on the Log Scale

This is a public service announcement in the interest of more robust numerical calculations. Like matrix inverse, exponentiation is bad news. It’s prone to overflow or underflow. Just try this in R: > exp(-800) > exp(800) That’s not rounding error you see. The first one evaluates to zero (underflows) and the second to infinity (overflows). […]

## Betancourt Binge (Video Lectures on HMC and Stan)

Even better than binging on Netflix, catch up on Michael Betancourt’s updated video lectures, just days after their live theatrical debut in Tokyo. Scalable Bayesian Inference with Hamiltonian Monte Carlo (YouTube, 1 hour) Some Bayesian Modeling Techniques in Stan (YouTube, 1 hour 40 minutes) His previous videos have received very good reviews and they’re only […]

## A Primer on Bayesian Multilevel Modeling using PyStan

Chris Fonnesbeck contributed our first PyStan case study (I wrote the abstract), in the form of a very nice Jupyter notebook. Daniel Lee and I had the pleasure of seeing him present it live as part of a course we were doing at Vanderbilt last week. A Primer on Bayesian Multilevel Modeling using PyStan This […]

## Beautiful Graphs for Baseball Strike-Count Performance

This post is by Bob. I have no idea what Andrew will make of these graphs; I’ve been hoping to gather enough comments from him to code up a ggplot theme. Shravan, you can move along, there’s nothing here but baseball. Jim Albert created some great graphs for strike-count performance in a series of two […]

## Stan Coding Corner: O(N) Change-Point Program with Clever Forward-Backward Calculation

It’s so much fun to work in open source. Luke Wiklendt sent along this improved code for a change-point model calculation in Stan. With N data points in the time series, the version in the manual is O(N2), whereas the improved version is O(N). In practice, Luke says [the new code] results in a dramatic […]

*Stan Case Studies* Launches

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. Stan Case Studies 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 […]