Documentation
Introduction

Introduction

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.

A Simple Example

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.

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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.

Using Squiggle

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.

Playground
The Squiggle Playground is a nice tool for working with small models and making prototypes. You can make simple shareable links, but you can't save models that change over time.

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.

Squiggle Vs. Other Tools

What Squiggle Is

  • A simple programming language for doing math with probability distributions.
  • An embeddable language that can be used in Javascript applications.
  • A tool to encode functions as forecasts that can be embedded in other applications.

What Squiggle Is Not

Strengths

  • 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.
  • Embeddable in Javascript.
  • Free and open-source.

Weaknesses

  • 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.

Organization

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.

Distribution Creation