A Python Package for intuitive design of experiments with user-friendly analysis of results. The aim is for this package to rival the DOE capabilities of commercial software such as JMP. Currently designs and analysis will be more geared towards investigations following the Response Surface Methodology.

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Implementation, analysis and benchmarking of optimization algorithms. Developed in Python and results showed in Jupyter Notebook

Response Surface Analysis Interactive Panel

Prudent Response Surface Models combine predictions with confidence scores and uncertainty levels, allowing their use in downstream analysis even for high-uncertainty or out-of-distribution inputs.

Multiple Linear Regression modelling for a sample data trucking.xlsx

This is a toy problem to show why our choices of Bayesian prior distributions and sample size are important in our knowledge of model response surface

Design of Experiments