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:

The following individuals forked and worked on pyDOE2:

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.

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