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

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)
  • 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)

pip install pyDOE

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

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