Squiggle is a minimalist programming language for probabilistic estimation. It's meant for intuitively-driven quantitative estimation instead of data analysis or data-driven statistical techniques.
The basics of Squiggle are fairly straightforward. This can be enough for many models. The more advanced functionality can take some time to learn.
Say you're trying to estimate the number of piano tuners in New York City. You can build a simple model of this, like so.
This editor is interactive! Try changing the code.
Now let's take this a bit further. Let's imagine that you think that NYC will grow over time, and you'd like to estimate the number of piano tuners for every point in time for the next few years.
You can currently interact with Squiggle in a few ways:
Squiggle Hub (opens in a new tab)
Squiggle Hub is a platform for the creation and sharing of code written in Squiggle. It's a great way to get started with Squiggle or to share your models with others.
Visual Studio Code Extension (opens in a new tab)
There's a simple VS Code extension (opens in a new tab) for running and visualizing Squiggle code. We find that VS Code is a useful editor for managing larger Squiggle setups.
Typescript Library (opens in a new tab)
Squiggle is built using Typescript (opens in a new tab), and is accessible via a simple Typescript library. You can use this library to either run Squiggle code in full, or to call select specific functions within Squiggle.
React Components Library (opens in a new tab)
All of the components used in the playground and documentation are available in a separate component NPM repo. You can see the full Storybook of components here (opens in a new tab).
Observable (opens in a new tab)
You can use Squiggle Components in Observable notebooks. Sam Nolan put together an exportable Observable Notebook (opens in a new tab) of the key components that you can directly import and use in your Observable notebooks.
- A simple programming language for doing math with probability distributions.
- A tool to encode functions as forecasts that can be embedded in other applications.
- A complete replacement for enterprise Risk Analysis tools. (See Crystal Ball (opens in a new tab), @Risk (opens in a new tab), Lumina Analytica (opens in a new tab))
- A probabilistic programming language (opens in a new tab). Squiggle does not support Bayesian inference.
- A tool for substantial data analysis. (See programming languages like Python (opens in a new tab) or Julia (opens in a new tab))
- A programming language for anything other than estimation.
- A visually-driven tool. (See Guesstimate (opens in a new tab) and Causal (opens in a new tab))
- Simple and readable syntax, especially for dealing with probabilistic math.
- Fast for relatively small models. Strong for rapid prototyping.
- Optimized for using some numeric and symbolic approaches, not just Monte Carlo.
- Free and open-source.
- Limited scientific capabilities.
- Much slower than serious probabilistic programming languages on sizeable models.
- Can't do Bayesian backwards inference.
- Essentially no support for libraries or modules (yet).
- Still very new, so a tiny ecosystem.
- Still very new, so there are likely math bugs.
- Generally not as easy to use as Guesstimate or Causal, especially for non programmers.
Squiggle is one of the main projects of The Quantified Uncertainty Research Institute (opens in a new tab). QURI is a nonprofit funded primarily by Effective Altruist (opens in a new tab) donors.