pyabc
The facts below are what the maker publishes about pyabc, checked and dated. Our editors have not written about it yet.
The facts
Each one read from pyabc's own pages, with the date it was checked.
What it does
- Bayesian analysis
- Yeschecked 24 Sep 2026
- Customization
- Users can customize algorithm aspects by implementing custom classes alongside the package’s default choices.checked 12 Oct 2026
- Data exports
- Its database export utility supports CSV, feather, JSON, msgpack, HTML, HDF, and Stata formats.checked 12 Oct 2026
- Data storage
- pyABC stores simulation and logging data in SQL databases for later analysis and sharing.checked 12 Oct 2026
- Deployment
- desktopchecked 24 Sep 2026
- Distributed backends
- Supported backend modes include Dask.distributed, a Redis based broker, and IPython parallel.checked 12 Oct 2026
- Extensibility
- Users can experiment with ABC-SMC schemes by customizing algorithm components through classes.checked 12 Oct 2026
- Inference method
- It uses Approximate Bayesian Computation with a Sequential Monte Carlo scheme for Bayesian posterior estimation.checked 12 Oct 2026
- Installation
- The package can be installed with pip, including a recommended per-user installation using `pip install --user pyabc`.checked 12 Oct 2026
- Integrations
- Optional interfaces are available for R via rpy2 and Julia via PyJulia.checked 12 Oct 2026
- Language interfaces
- The documentation includes interfaces for using R through rpy2 and Julia through PyJulia.checked 12 Oct 2026
- License
- The package is licensed under a permissive 3-clause BSD license.checked 12 Oct 2026
- Method
- It implements Approximate Bayesian Computation using Sequential Monte Carlo for Bayesian posterior estimation.checked 12 Oct 2026
- No likelihood required
- Users do not need to provide an analytical likelihood function.checked 12 Oct 2026
- 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.checked 12 Oct 2026
- Parallel computing
- It is designed for multicore and distributed environments, including scientific cluster and cloud deployments.checked 12 Oct 2026
- Parallel execution
- pyABC supports multicore and distributed execution, including SGE environments and cloud deployment.checked 12 Oct 2026
- Purpose
- pyABC performs likelihood-free parameter inference from a black-box simulator and observed or synthetic data.checked 12 Oct 2026
- Scientific integrations
- Documented examples cover PEtab import, yaml2sbml, and COPASI through BasiCO.checked 12 Oct 2026
- Scripting support
- Yeschecked 24 Sep 2026
- Storage
- pyABC stores simulation and logging data in SQL databases for later analysis and sharing.checked 12 Oct 2026
- Support
- The project invites users needing help to contact the maintainers and suggests filing general-interest issues on GitHub.checked 12 Oct 2026
Money
- Free plan
- Yeschecked 24 Sep 2026
Where it runs
- 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.checked 12 Oct 2026
