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https://github.com/itsdfish/sequentialsamplingmodels.jl

A unified interface for simulating and evaluating sequential sampling models in Julia.
https://github.com/itsdfish/sequentialsamplingmodels.jl

attentional-drift-diffusion circular-drift-diffusion drift-diffusion-model ex-gaussian julia julia-lang julia-language lba leaky-competing-accumulator linear-ballistic-accumulator lnr lognormal-race-model multi-attribute-decision-field-theory multi-attribute-linear-ballistic-accumulator poisson-race racing-diffusion shifted-lognormal shifted-wald

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A unified interface for simulating and evaluating sequential sampling models in Julia.

Host: GitHub
URL: https://github.com/itsdfish/sequentialsamplingmodels.jl
Owner: itsdfish
License: mit
Created: 2020-12-10T15:53:20.000Z (almost 4 years ago)
Default Branch: master
Last Pushed: 2024-10-13T19:38:13.000Z (24 days ago)
Last Synced: 2024-10-19T05:18:33.508Z (19 days ago)
Topics: attentional-drift-diffusion, circular-drift-diffusion, drift-diffusion-model, ex-gaussian, julia, julia-lang, julia-language, lba, leaky-competing-accumulator, linear-ballistic-accumulator, lnr, lognormal-race-model, multi-attribute-decision-field-theory, multi-attribute-linear-ballistic-accumulator, poisson-race, racing-diffusion, shifted-lognormal, shifted-wald
Language: Julia
Homepage: https://itsdfish.github.io/SequentialSamplingModels.jl/dev/
Size: 25.5 MB
Stars: 27
Watchers: 5
Forks: 4
Open Issues: 8
Metadata Files:
- Readme: README.md
- License: LICENSE

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README

        [![](docs/logo/logo.png)](https://itsdfish.github.io/SequentialSamplingModels.jl/dev/)

[![](https://img.shields.io/badge/docs-latest-blue.svg)](https://itsdfish.github.io/SequentialSamplingModels.jl/dev/) [![CI](https://github.com/itsdfish/SequentialSamplingModels.jl/actions/workflows/CI.yml/badge.svg)](https://github.com/itsdfish/SequentialSamplingModels.jl/actions/workflows/CI.yml)

# SequentialSamplingModels

This package provides a unified interface for simulating and evaluating sequential sampling models (SSMs) in Julia. SSMs describe decision making as a stochastic and dynamic evidence accumulation process in which a decision is triggered by the option whose evidence hits a decision treshold first. 

![](docs/src/assets/rdm.gif)

# Feature Overview

A summary of the core features is provided below. Please see the [documentation](https://itsdfish.github.io/SequentialSamplingModels.jl/dev/) for more information.

## Supported Models

The following SSMs are supported:

### Single Choice Models 

- [Ex-Gaussian](https://itsdfish.github.io/SequentialSamplingModels.jl/dev/ex_gaussian/)

- [Shifted Log Normal](https://itsdfish.github.io/SequentialSamplingModels.jl/dev/shifted_lognormal/)

- [Wald](https://itsdfish.github.io/SequentialSamplingModels.jl/dev/wald/) 

- [Wald Mixture](https://itsdfish.github.io/SequentialSamplingModels.jl/dev/wald_mixture/)

### Multi-Choice Models 

#### Single Attribute

- [Attentional Drift Diffusion](https://itsdfish.github.io/SequentialSamplingModels.jl/dev/aDDM/)

- [Leaky Competing Accumulator](https://itsdfish.github.io/SequentialSamplingModels.jl/dev/lca/)

- [Drift Diffusion](https://itsdfish.github.io/SequentialSamplingModels.jl/dev/DDM/)

- [Linear Ballistic Accumulator](https://itsdfish.github.io/SequentialSamplingModels.jl/dev/lba/) 

- [Log Normal Race](https://itsdfish.github.io/SequentialSamplingModels.jl/dev/lnr/) 

- [Poisson Race](https://itsdfish.github.io/SequentialSamplingModels.jl/dev/poisson_race)

- [Racing Diffusion](https://itsdfish.github.io/SequentialSamplingModels.jl/dev/rdm/) 

#### Multi-Attribute 

- [Multi-attribute Attentional Drift Diffusion](https://itsdfish.github.io/SequentialSamplingModels.jl/dev/maaDDM/)

- [Multi-attribute Decision Field Theory](https://itsdfish.github.io/SequentialSamplingModels.jl/dev/mdft/)

- [Multi-attribute Linear Ballistic Accumulator](https://itsdfish.github.io/SequentialSamplingModels.jl/dev/mlba/)

### Alternative Geometries 

- [Circular Drift Diffusion](https://itsdfish.github.io/SequentialSamplingModels.jl/dev/cddm/) 

## API

The core API consists of the following 

- rand: generate simulated data 

- pdf: evaluate the probability density of the data

- logpdf: evaluate the log probability density of the data

- simulate: generate samples from the internal evidence accumulation process

## Ecosystem Integration

SSMs work with the following packages (and possibly more):

- [Distributions.jl](https://github.com/JuliaStats/Distributions.jl): functions for probability distributions

- [Pigeons.jl](http://pigeons.run/dev/): Bayesian parameter estimation and Bayes factors

- [Plots.jl](https://github.com/JuliaPlots/Plots.jl): extended plotting tools for SSMs

- [Turing.jl](https://turinglang.org/dev/docs/using-turing/get-started): Bayesian parameter estimation

## Installation

You can install a stable version of *SequentialSamplingModels* by running the following in the Julia REPL:

```julia

] add SequentialSamplingModels

```

## Quick Example

In the example below, we instantiate a Linear Ballistic Accumulator (LBA) model, and generate data from it.

```julia

using SequentialSamplingModels

# Create LBA distribution with known parameters

dist = LBA(; ν=[2.75,1.75], A=0.8, k=0.5, τ=0.25)

# Sample 1000 random data points from this distribution

choice, rt = rand(dist, 1000)

```