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(This article was first published on <strong><a href="http://andrewgelman.com/2017/07/21/stan-weekly-roundup-21-july-2017/"> R – Statistical Modeling, Causal Inference, and Social Science</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>It was another productive week in Stan land. The big news is that</p>
<ul>
<li><b>Jonathan Auerbach</b> reports that<br>
<blockquote><p>
A team of Columbia students (mostly Andrew’s, including myself) recently won first place in a competition predicting elementary school enrollment. I heard 192 entered, and there were 5 finalists….Of course, we used Stan (RStan specifically). … Thought it might be Stan news worthy.
</p>
</blockquote>
<p>I’d say that’s newsworthy. Jon also provided a link to the “challenge” page, a New York City government sponsored “call for innovations”: <a rel="nofollow" href="http://www.nyc.gov/html/cfi/html/DOE/index.html" target="_blank">Enhancing School Zoning Efforts by Predicting Population Change</a>. They took home a US$20K paycheck for their efforts! Stan’s seeing quite a lot of use these days among demographers and others looking to predict forward from time series data. Jonathan’s been very active using government data sets (see his StanCon 2017 presentation with Rob Trangucci, <a rel="nofollow" href="http://mc-stan.org/events/stancon2017-notebooks/stancon2017-auerbach-trangucci-twelve-cities.pdf" target="_blank">Twelve Cities: Does lowering speed limits save pedestrian lives?</a>). Hopefully they’ll share their code—maybe they already have. I really like to see this combination of government data and careful statistical analysis.</p>
</ul>
<p>In other big news coming up soon,</p>
<ul>
<li><b>Women in ML</b> are sponosoring <a rel="nofollow" href="https://www.meetup.com/NYC-Women-in-Machine-Learning-Data-Science/events/240870777/" target="_blank">a Stan workshop this coming Saturday</a> to be presented by <b>Michael Betancourt</b> and <b>Mitzi Morris</b>. A bunch of the Stan gang will be there to help out.
</ul>
<p>In other news,</p>
<ul>
<li><b>Andrew Gelman</b>‘s been driving a lot of rethinking of our interfaces because he’s revising his and Jennifer Hill’s regression book (the revision will be two books). Specifically, he’s thinking a lot about workflow and how we can manage model expansion by going from fixed to modeled parameters. Right now, the process is onerous in that you have to move data variables into the parameters block and keep updating their priors. Andrew wants to be able to do this from the outside, but Michael Betancourt and I both expressed a lot of skepticism in terms of it breaking a lot of our fundamental abstractions (like a Stan program defining the density that’s fit!). More to come on this hot topic. Any ideas on how to manage developing a model would be appreciated. This goes back to the very first thing Matt Hoffman, Michael Malecki and I worked on with Andrew when he hired us before we’d conceived Stan. You’d think we’d have better advice on this after all this time. I’ve seen people do everything from use the C++ preprocessor to write elaborate program generation code in R.

<li><b>Breck Baldwin</b> has been working on governance and we’re converging on a workable model that we’ll share with everyone soon. The goal’s to make the governance clear and less of a smoke-filled room job by those of us who happen to go to lunch after the weekly meetings.

<li><b>Jonah Gabry</b> is taking on the ggplot2-ification of the new regression book and trying to roll everything into a combination of RStanArm and BayesPlot. No word yet if the rest of the tidyverse is to follow. Andrew said, “I’ll see what Jonah comes up with” or something to that effect.

<li><b>Jonah</b> has alos been working on priors for multilevel regression and poststratification with <b>Yajuan Si</b> (former postdoc of Andrew’s, now at U. Wisconsin); the trick is doing somethign reasonable when you have lots of interactions.

<li><b>Ben Goodrich</b> has been working on the next release of RStanArm. It’s beginning to garner a lot of contributions. Remember that the point has been to convert a lot of common point estimation packages to Bayesian versions and supply them with familiar R interfaces. Ben’s particularly been working on survival analysis lately.

<li>Our <b>high school student intern</b> (don’t know if I can mention names online—the rest of our developers are adults!) is working on applying the Cook-Gelman-Rubin metric to evaluating various models. We’re doing much more of this method and it needs a less verbose and more descriptive name!

<li><b>Mitzi Morris</b> submitted a pull request for the Stan repo to add line numbers to error messages involving compound declare-and-define statements.
<li>A joint effort by <b>Mitzi Morris</b>, <b>Dan Simpson</b>, <b>Imad Ali</b>, and <b>Miguel A Martinez Beneito</b> has led to convergence of models and fits among BUGS, Stan, and INLA for intrinsic conditional autorgression (ICAR) models. Imad’s building the result in RStanArm and has figured out how to extend the loo (leave one out cross-validation) package to deal with spatial models. Look for it in a Stan package near you soon. Mitzi’s working on the case study, which has been updated in the example-models repo.

<li><b>Charles Margossian</b> knocked off a paper on the mixed ODE solver he’s been working on, with a longer paper promised that goes through all the code details. Not sure if that’s on arXiv or not. He’s also been training <b>Bill Gillespie</b> to code in C++, which is great news for the project since Charles has to contend with his first year of grad school next year (whereas Bill’s facing a pleasant retirement of tracking down memory leaks). Charles is also working on getting the algebraic and fixed state solvers integrated into Torsten before the fall.

<li><b>Krzysztof Sakrejda</b> has a new paper out motivating a custom density he wrote in Stan for tracking dealys in diseseas incidence in a countrywide analysis for Thailand. I’m not sure where he put it.

<li><b>Yuling Yao</b> is revising the stacking paper (for a kind of improved model averaging). I believe this is going into the loo package (or is maybe already there). So much going on with Stan I can’t keep up!

<li><b>Yuling</b> also revised the simulated tempering paper, which is some custom code on top of Stan to fit models with limited multimodality. There was some discussion about realistic examples with limited multimodality and we hope to have a suite of them to go along with the paper.

<li><b>Sebastian Weber</b>, <b>Bob Carpenter</b>, and <b>Micahel Betancourt</b>, and <b>Charles Margossian</b> had a long and productive meeting to design the MPI interface. It’s very tricky trying to find an interface that’ll fit into the Stan language and let you ship off data once and reuse it. I think we’re almost there. The design discussion’s on Discourse.

<li><b>Rob Trangucci</b> is finishing the GP paper (after revising the manual chapter) with Dan Simpson, Aki Vehtari, and Michael Betancourt.
</ul>
<p>I’m sure I’m missing a lot of goings on, especially among people not at our weekly meetings. If you know of things that should be on this list, please let me know.</p>
<p>The post <a rel="nofollow" href="http://andrewgelman.com/2017/07/21/stan-weekly-roundup-21-july-2017/" target="_blank">Stan Weekly Roundup, 21 July 2017</a> appeared first on <a rel="nofollow" href="http://andrewgelman.com/" target="_blank">Statistical Modeling, Causal Inference, and Social Science</a>.</p>
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