Published October 18, 2021 | Version 3.0
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pyeee: Parameter screening using Efficient/Sequential Elementary Effects, an extension of Morris' method

  • 1. Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement - INRAE, Nancy, France
  • 2. University of Waterloo, ON, Canada


pyeee is a Python library for performing parameter screening of computational models. It uses Efficient or Sequential Elementary Effects, an extension of Morris' method of Elementary Effects, published by:

    Cuntz M, Mai J, Zink M, Thober S, Kumar R, Schäfer D, Schrön M, Craven J, Rakovec O, Spieler D, Prykhodko V, Dalmasso G, Musuuza J, Langenberg B, Attinger A, and Samaniego L (2015) Computationally inexpensive identification of noninformative model parameters by sequential screening, Water Resources Research 51, 6417–6441, doi:10.1002/2015WR016907

pyeee can be used with Python functions as well as external executables using wrappers provided, for example, by partialwrap. Function evaluations can be distributed with Python's multiprocessing or via MPI.

Full documentation is available from Read The Docs

A similar package (EEE) using a combination of bash and Python scripts is presented here:

The current version 3.0 uses the pyjams package now. All modules, functions, tests, and docs that are now in the pyjams package were removed.

pyjams tries to bundle all routines that are used in several projects.

Version 3.0 also made the move from to for continuous integration.



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  • Cuntz, Mai et al. (2015) Computationally inexpensive identification of noninformative model parameters by sequential screening, Water Resources Research 51, 6417-6441, doi:10.1002/2015WR016907