https://github.com/pydoe/pydoe

Design of Experiments for Python
https://github.com/pydoe/pydoe

design-of-experiments python

Last synced: 4 months ago
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Design of Experiments for Python

• Host: GitHub
• URL: https://github.com/pydoe/pydoe
• Owner: pydoe
• License: bsd-3-clause
• Created: 2013-08-27T17:42:03.000Z (almost 13 years ago)
• Default Branch: master
• Last Pushed: 2026-02-16T05:30:21.000Z (4 months ago)
• Last Synced: 2026-02-16T12:07:19.211Z (4 months ago)
• Topics: design-of-experiments, python
• Language: Python
• Homepage: https://pydoe.github.io/pydoe/
• Size: 3.18 MB
• Stars: 299
• Watchers: 24
• Forks: 117
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Contributing: docs/contributing.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

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README

          
PyDOE: An Experimental Design Package for Python

================================================

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PyDOE is a Python package for design of experiments (DOE), enabling scientists, engineers, and statisticians to efficiently construct experimental designs.

- **Website:** https://pydoe.github.io/pydoe/

- **Documentation:** https://pydoe.github.io/pydoe/reference/factorial/

- **Source code:** https://github.com/pydoe/pydoe

- **Contributing:** https://pydoe.github.io/pydoe/contributing/

- **Bug reports:** https://github.com/pydoe/pydoe/issues

Overview

--------

The package provides extensive support for design-of-experiments (DOE) methods and is capable of creating designs for any number of factors.

It provides:

- **Factorial Designs**

  - General Full-Factorial (``fullfact``)

  - 2-level Full-Factorial (``ff2n``)

  - 2-level Fractional Factorial (``fracfact``, ``fracfact_aliasing``, ``fracfact_by_res``, ``fracfact_opt``, ``alias_vector_indices``)

  - Plackett-Burman (``pbdesign``)

  - Generalized Subset Designs (``gsd``)

  - Fold-over Designs (``fold``)

- **Response-Surface Designs**

  - Box-Behnken (``bbdesign``)

  - Central-Composite (``ccdesign``)

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

  - Star Designs (``star``)

  - Union Designs (``union``)

  - Repeated Center Points (``repeat_center``)

- **Space-Filling Designs**

  - Latin-Hypercube (``lhs``)

  - 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``)

- **Clustering Designs**

  - Random K-Means (``random_k_means``)

- **Sensitivity Analysis Designs**

  - Morris Method (``morris_sampling``)

  - Saltelli Sampling (``saltelli_sampling``)

- **Taguchi Designs**

  - Orthogonal arrays and robust design utilities (``taguchi_design``, ``compute_snr``, ``get_orthogonal_array``, ``list_orthogonal_arrays``, ``TaguchiObjective``)

- **Optimal Designs**

  - Advanced optimal design algorithms (``optimal_design``)

  - Optimality criteria (``a_optimality``, ``c_optimality``, ``d_optimality``, ``e_optimality``, ``g_optimality``, ``i_optimality``, ``s_optimality``, ``t_optimality``, ``v_optimality``)

  - Efficiency measures (``a_efficiency``, ``d_efficiency``)

  - Search algorithms (``sequential_dykstra``, ``simple_exchange_wynn_mitchell``, ``fedorov``, ``modified_fedorov``, ``detmax``)

  - Design utilities (``criterion_value``, ``information_matrix``, ``build_design_matrix``, ``build_uniform_moment_matrix``, ``generate_candidate_set``)

- **Sparse Grid Designs**

  - Sparse Grid Design (``doe_sparse_grid``)

  - Sparse Grid Dimension (``sparse_grid_dimension``)

Installation

------------

```bash

pip install pydoe

```

Credits

-------

For more info see: https://pydoe.github.io/pydoe/credits/

License

-------

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