design-of-experiments
CommunityDOE to maximize information per experiment.
Authorjkitchin
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Design of Experiments (DOE) guidance for planning, executing, and analyzing experiments with classical designs, Bayesian optimization, model-driven designs, and active learning to maximize information per run.
Core Features & Use Cases
- Interactive DOE guidance: Question-driven recommendations for batch vs sequential designs.
- Classical DOE: Full/fractional factorials, CCD, Box-Behnken, and screening designs.
- Bayesian & active learning: Gaussian-process-based optimization and adaptive sampling.
Quick Start
Ask for a DOE plan to screen 6 factors in 20 runs, using a fractional factorial design, with follow-up optimization via Bayesian options.
Dependency Matrix
Required Modules
pyDOE3dexpypycsescikit-optimizestatsmodelsscipyscikit-learnmodALGPymatplotlibseaborn
Components
scriptsreferences
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: design-of-experiments Download link: https://github.com/jkitchin/skillz/archive/main.zip#design-of-experiments Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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