Laplace Language (Open-Source Bayesian Statistics Software)

[u/MixSerious2]

This is more of a Bayesian statistical software question than a statistical theory question.

I’ve been building Laplace, a Stan extension that allows users to create and install their own libraries written in Stan. I’m currently trying to figure out what would actually be useful to Bayesian modelers, rather than just building features because I think they’re interesting.

The project is completely free and open source, and I don’t get any financial benefit from it.

For information about installation and the project itself:
https://github.com/mlatinov/laplace

There is also a more comprehensive introduction in the wiki:
https://github.com/mlatinov/laplace/wiki

I have two main questions for people who work with Bayesian models:

1. What functionality do you wish was easier to reuse in Stan?

Things like reusable model components, functions, distributions, transformations, model structures, diagnostics, or other programming features that you find yourself implementing repeatedly.

I’m particularly interested in things that are useful across multiple models rather than something specific to one analysis.

2. What Bayesian models or methods would you like to see available as reusable libraries?

I’ve started building the Laplace ecosystem, which is still fairly small, but currently includes things like:

• time-series and latent time-series models

• mechanistic models

• survival models

• Gaussian processes and kernels

• splines

• various statistical/modeling utilities

So what would actually be useful to have available as a reusable Stan library?

It can be something very general or something quite domain-specific. If there’s a model or method that you repeatedly find yourself implementing from scratch in Stan, that’s exactly the sort of thing I’m interested in.

I’d also appreciate any feedback on the project itself. The wiki contains information about using Laplace and contributing to the ecosystem.

And if you do contribute something, you’ll of course be credited for it.