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(This article was first published on <strong><a href="http://andrewgelman.com/2017/08/11/stan-weekly-roundup-11-august-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>This week, more Stan!</p>
<ul>
<li><b>Charles Margossian</b> is rock star of the week, finishing off the algebraic solver math library fixture and getting all plumbed through Stan and documented. Now you can solve nonlinear sets of equations and get derivatives with the implicit function theory all as part of defining your log density. There is a chapter in the revised manual

<li>The entire <b>Stan Development Team</b>, spearheaded by <b>Ben Goodrich</b> needing fixes for RStan, is about to roll out Stan 2.17 along with the interfaces. Look for that to begin trickling out next week. This will fix some further install and error message reporting issues as well as include the algebraic solver. We are also working on moving things toward Stan 3 behind the scenes. We won’t just keep incrementing 2.x forever!

<li><b>Ben Goodrich</b> fixed the inlining declarations in C++ to allow multiple Stan models to be linked or built in a single translation unit. This will be part of the 2.17 release.

<li><b>Sean Talts</b> is still working on build issues after Travis changed some config and compilers changed everywhere disrupting our continuous integration.

<li><b>Sean</b> is working with <b>Michael Betancourt</b> on the Cook-Gelman-Rubin diagnostic and have gotten to the bottom of the quantization errors (usingly on 1000 posterior draws and splitting into too many bins).

<li><b>Imad Ali</b> is looking ahead to spatio-temporal models as he wraps up the spatial models in RStanArm.

<li><b>Yuling Yao</b> and <b>Aki Vehtari</b> finished the stacking paper (like model averaging), adding references, etc.

<li><b>Yuling</b> has also made some updates to the loo package that are coming soon.

<li><b>Andrew Gelman</b> wrote papers (with me and <b>Michael</b>) about R-hat and effective sample size and a paper on how priors can only be understood in the context of the likelihood.
<li><b>Jonah Gabry</b>, <b>Ben</b> and <b>Imad</b> have been working on the paper for priors based on $R^2$

<li><b>Andrew</b>, <b>Breck Baldwin</b> and I are trying to put together some educational proposals for NSF to teach intro stats, and we’re writing more grants now that it’s grant season. All kinds of interesting things at NSF and NIH with spatial modeling.

<li><b>Jonah Gabry</b> continues the ggplot-ification of the new Gelman and Hill book.

<li><b>Ben Goodrich</b> has been working on multivariate effective sample size calculations.

<li><b>Breck Baldwin</b> has been working with <b>Michael</b> on courses before Stan (and elsewhere).

<li><b>Jonah Gabry</b> has been patching all the packagedown references for our doc and generally cleaning things up with cross-references and organization.

<li><b>Mitzi Morris</b> finished adding a data qualifier to function arguments to allow propagation of data-only-ness as required by our ODE functions and now the algebraic solver.

<li><b>Dootika Vats</b> was visiting <b>Michael</b>; she’s been working on multivariate effective sample sizes, which considers how bad things can be with linear combinations of parameters; scaling is quadratic, so there are practical issues. She has previously worked on efficient and robust effective sample size calculations which look promising for inclusion in Stan.

<li><b>Bob Carpenter</b> has been working on exposing properties of variables in a Stan program in the C++ model at runtime and statically.
</ul>
<p>The post <a rel="nofollow" href="http://andrewgelman.com/2017/08/11/stan-weekly-roundup-11-august-2017/" target="_blank">Stan Weekly Roundup, 11 August 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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