pyabc

Spec sheet

Purpose

pyABC performs likelihood-free parameter inference from a black-box simulator and observed or synthetic data.Read 12 Oct 2026 · pyabc.readthedocs.io

Deployment

desktopRead 24 Sep 2026 · pyabc.readthedocs.io

Free plan

YesRead 24 Sep 2026 · pyabc.readthedocs.io

Integrations

Optional interfaces are available for R via rpy2 and Julia via PyJulia.Read 12 Oct 2026 · pyabc.readthedocs.io

Support

The project invites users needing help to contact the maintainers and suggests filing general-interest issues on GitHub.Read 12 Oct 2026 · pyabc.readthedocs.io

License

The package is licensed under a permissive 3-clause BSD license.Read 12 Oct 2026 · pyabc.readthedocs.io

Bayesian analysis

YesRead 24 Sep 2026 · pyabc.readthedocs.io

Customization

Users can customize algorithm aspects by implementing custom classes alongside the package’s default choices.Read 12 Oct 2026 · pyabc.readthedocs.io

Data exports

Its database export utility supports CSV, feather, JSON, msgpack, HTML, HDF, and Stata formats.Read 12 Oct 2026 · pyabc.readthedocs.io

Data storage

pyABC stores simulation and logging data in SQL databases for later analysis and sharing.Read 12 Oct 2026 · pyabc.readthedocs.io

Distributed backends

Supported backend modes include Dask.distributed, a Redis based broker, and IPython parallel.Read 12 Oct 2026 · pyabc.readthedocs.io

Extensibility

Users can experiment with ABC-SMC schemes by customizing algorithm components through classes.Read 12 Oct 2026 · pyabc.readthedocs.io

Inference method

It uses Approximate Bayesian Computation with a Sequential Monte Carlo scheme for Bayesian posterior estimation.Read 12 Oct 2026 · pyabc.readthedocs.io

Installation

The package can be installed with pip, including a recommended per-user installation using `pip install --user pyabc`.Read 12 Oct 2026 · pyabc.readthedocs.io

Language interfaces

The documentation includes interfaces for using R through rpy2 and Julia through PyJulia.Read 12 Oct 2026 · pyabc.readthedocs.io

Method

It implements Approximate Bayesian Computation using Sequential Monte Carlo for Bayesian posterior estimation.Read 12 Oct 2026 · pyabc.readthedocs.io

No likelihood required

Users do not need to provide an analytical likelihood function.Read 12 Oct 2026 · pyabc.readthedocs.io

Operating systems

The package is continuously tested on Linux; the documentation says many parts should work on Windows, while noting that its multiprocessing depends on forking and its main development focus is Linux.Read 12 Oct 2026 · pyabc.readthedocs.io

Parallel computing

It is designed for multicore and distributed environments, including scientific cluster and cloud deployments.Read 12 Oct 2026 · pyabc.readthedocs.io

Parallel execution

pyABC supports multicore and distributed execution, including SGE environments and cloud deployment.Read 12 Oct 2026 · pyabc.readthedocs.io

Requirements and platforms

pyABC requires Python 3.11 or later, is continuously tested on Linux and partly on iOS, and its main development focus is Linux.Read 12 Oct 2026 · pyabc.readthedocs.io

Scientific integrations

Documented examples cover PEtab import, yaml2sbml, and COPASI through BasiCO.Read 12 Oct 2026 · pyabc.readthedocs.io

Scripting support

YesRead 24 Sep 2026 · pyabc.readthedocs.io

Storage

pyABC stores simulation and logging data in SQL databases for later analysis and sharing.Read 12 Oct 2026 · pyabc.readthedocs.io

Support and license

The maintainers invite users to contact them for help and suggest GitHub issues for matters of general interest; the package uses a permissive 3-clause BSD license.Read 12 Oct 2026 · pyabc.readthedocs.io

Visualization

It includes plotting and analysis routines plus a Dash based visualization server for running and completed jobs.Read 12 Oct 2026 · pyabc.readthedocs.io

Web server status

The documentation marks its Flask-based visualization server as deprecated and no longer maintained.Read 12 Oct 2026 · pyabc.readthedocs.io

When to use

The documentation recommends considering a likelihood based inference method when a likelihood can be written down, describing pyABC as intended for the harder likelihood-free case.Read 12 Oct 2026 · pyabc.readthedocs.io

Windows limitation

Many parts may work on Windows, but multicore parallelization relies on forking, which Windows does not support, so those samplers may not work as expected.Read 12 Oct 2026 · pyabc.readthedocs.io

Where Specifiction places it

• Statistical Analysis Software#55 of 1067.7