{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/18"},{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/10"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/9"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/22"},{"label":"Utilities","url":"https://skillfed.io/packages/category/utilities/11"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/3"},{"label":"Physics","url":"https://skillfed.io/packages/category/scientific-engineering-physics"},{"label":"Education","url":"https://skillfed.io/packages/category/education"}],"enrichment":{"capability":"pyDOE3 provides functions to construct experimental designs for scientific and engineering research, including factorial, response-surface, randomized, low-discrepancy, sampling, Taguchi, optimal, and sparse grid designs.","skillfed_tags":["experimental-design","doe","optimization"],"use_cases":["Generate full or fractional factorial designs to explore all factor combinations or their subsets in experimental studies","Create Latin hypercube samples for efficient exploration of high-dimensional parameter spaces with uniform coverage","Design response-surface experiments (Box-Behnken, central-composite) to model and optimize system behavior","Apply Taguchi design methods to identify robust parameter settings that minimize sensitivity to noise","Construct low-discrepancy sequences (Sobol, Halton) for quasi-random sampling in Monte Carlo simulations","Use optimal design algorithms with various criteria (D, A, G, etc.) to maximize information from limited experimental runs"],"what_it_does":"pyDOE3 is a Python package for constructing experimental designs used in scientific research, engineering, and statistics. It provides a comprehensive suite of design methods spanning factorial designs (full and fractional), response-surface designs (Box-Behnken, central-composite), randomized sampling (Latin hypercube, random k-means), low-discrepancy sequences (Sobol, Halton, Korobov), Taguchi designs for robust optimization, and advanced optimal design algorithms with multiple search strategies. The package depends on numpy and scipy for numerical computation.\n\nScientists and engineers use pyDOE3 to systematically plan experiments that explore parameter spaces efficiently, reduce the number of required trials, or optimize designs according to statistical criteria. It is typically used in the planning phase of research to generate design matrices that guide data collection.","worth_installing":"Yes, if you need to construct experimental designs for research or engineering. The package is mature (marked Production/Stable), has low install friction, and covers a broad range of design methods. However, the archived repository and abandoned maintenance status mean you should not expect active bug fixes or compatibility updates; use it for stable, well-tested design generation but plan to fork or maintain locally if you encounter issues with future Python or dependency versions."},"id":"pydoe3","links":{"html":"https://skillfed.io/packages/pydoe3","md":"https://skillfed.io/packages/pydoe3.md","pypi":"https://pypi.org/project/pydoe3/"},"maintenance":{"status":"abandoned"},"meta":{"latest_release":"2026-01-12","license_spdx":"BSD-3-Clause","license_treatment":"permissive","name":"pyDOE3","python_support":"unspecified","summary":"Design of experiments for Python"},"popularity":{"monthly_downloads":78258,"position":14455,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.6.2"}