pyDOE3: An experimental design package for python

pyDOE3 is a fork of the pyDOE2 package that is designed to help the scientist, engineer, statistician, etc., to construct appropriate experimental designs.

This fork came to life to solve bugs and issues that remained unsolved in the original package.

Capabilities

The package currently includes functions for creating designs for any number of factors:

• Factorial Designs
  • General Full-Factorial (fullfact)
  • 2-level Full-Factorial (ff2n)
  • 2-level Fractional Factorial (fracfact)
  • Plackett-Burman (pbdesign)
  • Generalized Subset Designs (gsd)
• Response-Surface Designs
  • Box-Behnken (bbdesign)
  • Central-Composite (ccdesign)
• Randomized Designs
  • Latin-Hypercube (lhs)

See Documentation.

Requirements

• NumPy
• SciPy

Installation

pip install pyDOE3

Credits

pyDOE original code was originally converted from code by the following individuals for use with Scilab:

• Copyright (C) 2012 - 2013 - Michael Baudin

• Copyright (C) 2012 - Maria Christopoulou

• Copyright (C) 2010 - 2011 - INRIA - Michael Baudin

• Copyright (C) 2009 - Yann Collette

• Copyright (C) 2009 - CEA - Jean-Marc Martinez

• Website: forge.scilab.org/index.php/p/scidoe/sourcetree/master/macros

pyDOE was converted to Python by the following individual:

• Copyright (c) 2014, Abraham D. Lee

The following individuals forked and worked on pyDOE2:

• Copyright (C) 2018 - Rickard Sjögren and Daniel Svensson

License

This package is provided under the BSD License (3-clause)

References

• Factorial designs
• Plackett-Burman designs
• Box-Behnken designs
• Central composite designs
• Latin-Hypercube designs
• Surowiec, Izabella, Ludvig Vikström, Gustaf Hector, Erik Johansson, Conny Vikström, and Johan Trygg. “Generalized Subset Designs in Analytical Chemistry.” Analytical Chemistry 89, no. 12 (June 20, 2017): 6491–97. https://doi.org/10.1021/acs.analchem.7b00506.