SSMS (Sequential Sampling Model Simulators)

Python Package which collects simulators for Sequential Sampling Models.

Find the package documentation here.

Quick Start

The ssms package serves two purposes.

1. Easy access to fast simulators of sequential sampling models
2. Support infrastructure to construct training data for various approaches to likelihood / posterior amortization

A number of tutorial notebooks are available under the /notebooks directory.

Installation

pip install ssm-simulators

Note

Building from source or developing this package requires a C compiler (such as GCC). On Linux, you can install GCC with:

sudo apt-get install build-essential

Most users installing from PyPI wheels do not need to install GCC.

Command Line Interface

The package exposes a command-line tool, generate, for creating training data from a YAML configuration file.

generate --config-path <path/to/config.yaml> --output <output/directory> [--log-level INFO]

• --config-path: Path to your YAML configuration file (required).
• --output: Directory where generated data will be saved (required).
• --log-level: (Optional) Set the logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL). Default is WARNING.

Below is a sample YAML configuration you can use with the generate command:

MODEL: 'ddm'
N_SAMPLES: 2000
N_PARAMETER_SETS: 100
DELTA_T: 0.001
N_TRAINING_SAMPLES_BY_PARAMETER_SET: 200
N_SUBRUNS: 20
GENERATOR_APPROACH: 'lan'

Example:

generate --config-path myconfig.yaml --output ./output --log-level INFO

This will generate training data according to your configuration and save it in the specified output directory.

Tutorial

Check the basic tutorial here.

Advanced: Dependency Management with uv

We use uv for fast and efficient dependency management. To get started:

1. Install uv:

curl -LsSf https://astral.sh/uv/install.sh | sh

2. Install dependencies (including development):

uv sync --all-groups  # Installs all dependency groups