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Design of Experiments for Python

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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.

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)
    • 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)

Installation

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)

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