pydoe 0.9.7
Design of Experiments for Python
Requires Python: >=3.10
Keywords: DOE, design of experiments, experimental design, optimal design, optimization, python, sparse grids, statistics, taguchi design

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)