(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
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.
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