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(This article was first published on <strong><a href="http://andrewgelman.com/2018/01/09/three-new-domain-specific-embedded-languages-stan-backend/"> 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><b>One is an accident. Two is a coincidence. Three is a pattern.</b></p>
<p>Perhaps it’s no coincidence that there are three new interfaces that use Stan’s C++ implementation of adaptive Hamiltonian Monte Carlo (currently an updated version of the no-U-turn sampler).</p>
<ul>
<li><b>ScalaStan</b> embeds a Stan-like language in Scala. It’s a Scala package largely (if not entirely written by Joe Wingbermuehle.<br>
<a rel="nofollow" href="https://github.com/cibotech/ScalaStan" target="_blank">[GitHub link]</a>
<li> <b>tmbstan</b> lets you fit TMB models with Stan. It’s an R package listing Kasper Kristensen as author.<br>
<a rel="nofollow" href="https://cran.r-project.org/web/packages/tmbstan/tmbstan.pdf" target="_blank">[CRAN link]</a>
<li><b>SlicStan</b> is a “blockless” and self-optimizing version of Stan. It’s a standalone language coded in F# written by Maria Gorinova.<br>
<a rel="nofollow" href="https://homepages.inf.ed.ac.uk/s1207807/files/slicstan.pdf" target="_blank">[pdf language spec]</a>
</ul>
<p>These are in contrast with systems that entirely reimplement a version of the no-U-turn sampler, such as PyMC3, ADMB, and NONMEM.</p>
<p>The post <a rel="nofollow" href="http://andrewgelman.com/2018/01/09/three-new-domain-specific-embedded-languages-stan-backend/" target="_blank">Three new domain-specific (embedded) languages with a Stan backend</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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