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scientific-computingresearch
design-of-experiments
Expert guidance for Design of Experiments (DOE) in Python - interactive goal-driven design selection, classical DOE (factorial, response surface, screening), Bayesian optimization with Gaussian processes, model-driven optimal designs, active learning, and sequential experimentation; includes pyDOE3, pycse, BoTorch, Ax, scikit-optimize, statsmodels
maintainer
jkitchin
更新於 3/7/2026
星標
24
分支
5
quick start
Installation and usage
Expert guidance for Design of Experiments (DOE) in Python - interactive goal-driven design selection, classical DOE (factorial, response surface, screening), Bayesian optimization with Gaussian processes, model-driven optimal designs, active learning, and sequential experimentation; includes pyDOE3, pycse, BoTorch, Ax, scikit-optimize, statsmodels
安裝
$ install --globalskills.sh
使用
安裝後，您可以透過在終端機執行以下指令來使用此技能：
skills use design-of-experiments