pyDOE: The experimental design package for python
pyDOE package is designed to help the
scientist, engineer, statistician, etc., to construct appropriate
The package currently includes functions for creating designs for any number of factors:
#. General Full-Factorial (
#. 2-level Full-Factorial (
#. 2-level Fractional Factorial (
#. Plackett-Burman (
#. Box-Behnken (
#. Central-Composite (
#. Latin-Hypercube (
package homepage_ for details on usage and other notes
In this release, an incorrect indexing variable in the internal LHS function
_pdist has been corrected so point-distances are now calculated accurately.
package homepage_ for helpful hints relating to downloading
and installing pyDOE.
The latest, bleeding-edge but working
documentation source <https://github.com/tisimst/pyDOE/tree/master/doc/> are
on GitHub <https://github.com/tisimst/pyDOE/>_.
Any feedback, questions, bug reports, or success stores should
be sent to the
author_. I'd love to hear from you!
This code was originally published 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
Much thanks goes to these individuals.
And thanks goes out to the following for finding and offering solutions for bugs:
This package is provided under two licenses:
Central composite designs_
.. _author: mailto:email@example.com .. _Factorial designs: http://en.wikipedia.org/wiki/Factorial_experiment .. _Box-Behnken designs: http://en.wikipedia.org/wiki/Box-Behnken_design .. _Central composite designs: http://en.wikipedia.org/wiki/Central_composite_design .. _Plackett-Burman designs: http://en.wikipedia.org/wiki/Plackett-Burman_design .. _Latin-Hypercube designs: http://en.wikipedia.org/wiki/Latin_hypercube_sampling .. _package homepage: http://pythonhosted.org/pyDOE .. _lhs documentation: http://pythonhosted.org/pyDOE/randomized.html#latin-hypercube