pyDOE3 1.6.2
Design of experiments for Python
Keywords: DOE, design of experiments, experimental design, optimal design, optimization, python, sparse grids, statistics, taguchi design

pyDOE3: An experimental design package for python
=================================================

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This package is designed to help the scientist, engineer, statistician, etc., to
construct appropriate experimental designs.

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``)
  - Doehlert Design (``doehlert_shell_design``, ``doehlert_simplex_design``)

- **Randomized Designs**
  - Latin-Hypercube (``lhs``)
  - Random K-Means (``random_k_means``)
  - Random Uniform (``random_uniform``)

- **Low-Discrepancy Sequences**
  - Sukharev Grid (``sukharev_grid``)
  - Sobol’ Sequence (``sobol_sequence``)
  - Halton Sequence (``halton_sequence``)
  - Rank-1 Lattice Design (``rank1_lattice``)
  - Korobov Sequence (``korobov_sequence``)
  - Cranley-Patterson Randomization (``cranley_patterson_shift``)

- **Sampling Designs**
  - Morris Method (``morris_sampling``)
  - Saltelli Sampling (``saltelli_sampling``)

- **Taguchi Designs**
  - Orthogonal arrays and robust design utilities (``taguchi_design``, ``compute_snr``)

- **Optimal Designs**
  - Advanced optimal design algorithms (``optimal_design``)
  - Optimality criteria (``A``, ``C``, ``D``, ``E``, ``G``, ``I``, ``S``, ``T``, ``V``)
  - Search algorithms (``Sequential (Dykstra)``, ``Simple Exchange (Wynn-Mitchell)``, ``Fedorov``, ``Modified Fedorov``, ``DETMAX``)

- **Sparse Grid Designs**
  - Sparse Grid Design (``doe_sparse_grid``)
  - Sparse Grid Dimension (``sparse_grid_dimension``)

See [Documentation](https://pydoe3.readthedocs.io).

Installation
------------

```bash
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

`pyDOE` was converted to Python by the following individual:

- Copyright (c) 2014, Abraham D. Lee

The following individuals forked `pyDOE` and worked on `pyDOE2`:

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

`pyDOE3` is a fork of the [`pyDOE2`](https://github.com/clicumu/pyDOE2). 
It came to life to solve bugs and issues that remained unsolved in the
`pyDOE2` package.

License
-------

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

References
----------

- [Factorial designs](http://en.wikipedia.org/wiki/Factorial_experiment)
- [Plackett-Burman designs](http://en.wikipedia.org/wiki/Plackett-Burman_design)
- [Box-Behnken designs](http://en.wikipedia.org/wiki/Box-Behnken_design)
- [Central composite designs](http://en.wikipedia.org/wiki/Central_composite_design)
- [Doehlert Design](https://academic.oup.com/jrsssc/article/19/3/231/6882590)
- [Latin-Hypercube designs](http://en.wikipedia.org/wiki/Latin_hypercube_sampling)
- [Halton sequence](http://en.wikipedia.org/wiki/Halton_sequence)
- [Sobol sequence](http://en.wikipedia.org/wiki/Sobol_sequence)
- [Low-Discrepancy sequences](http://en.wikipedia.org/wiki/Low-discrepancy_sequence)
- [Taguchi designs](http://en.wikipedia.org/wiki/Taguchi_methods)
- [Generalized Subset Designs](https://doi.org/10.1021/acs.analchem.7b00506)
- [Optimal experimental design](https://en.wikipedia.org/wiki/Optimal_experimental_design)
- [Sparse grid](https://en.wikipedia.org/wiki/Sparse_grid)