Three new domain-specific (embedded) languages with a Stan backend

January 9, 2018
By

(This article was first published on R – Statistical Modeling, Causal Inference, and Social Science, and kindly contributed to R-bloggers)

One is an accident. Two is a coincidence. Three is a pattern.

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).

  • ScalaStan embeds a Stan-like language in Scala. It’s a Scala package largely (if not entirely written by Joe Wingbermuehle.
    [GitHub link]

  • tmbstan lets you fit TMB models with Stan. It’s an R package listing Kasper Kristensen as author.
    [CRAN link]

  • SlicStan is a “blockless” and self-optimizing version of Stan. It’s a standalone language coded in F# written by Maria Gorinova.
    [pdf language spec]

These are in contrast with systems that entirely reimplement a version of the no-U-turn sampler, such as PyMC3, ADMB, and NONMEM.

The post Three new domain-specific (embedded) languages with a Stan backend appeared first on Statistical Modeling, Causal Inference, and Social Science.

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