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.
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)
- General Full-Factorial (
- Response-Surface Designs
- Box-Behnken (
bbdesign) - Central-Composite (
ccdesign)
- Box-Behnken (
- Randomized Designs
- Latin-Hypercube (
lhs)
- Latin-Hypercube (
See the original pyDOE homepage for details on usage and other notes.
- NumPy
- SciPy
Through pip:
pip install pyDOE3
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 work on pyDOE2:
- Copyright (C) 2018 - Rickard Sjögren and Daniel Svensson
This package is provided under the BSD License (3-clause)
- 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.