Bayesian Optimization and Design of Experiments
Design-of-experiment (DOE) generator for science, engineering, and statistics
Design of Experiment Generator. Read the docs at: https://doepy.readthedocs.io/en/latest/
Framework for Data-Driven Design & Analysis of Structures & Materials (F3DASM)
Generates and evaluates D, I, A, Alias, E, T, G, and custom optimal designs. Supports generation and evaluation of mixture and split/split-split/N-split plot designs. Includes parametric and Monte Carlo power evaluation functions. Provides a framework to evaluate power using functions provided in other packages or written by the user.
Experimental design and Bayesian optimization library in Python/PyTorch
A design-of-experiments platform for evaluating compound AI systems - find which technique drives quality, by how much, and whether the difference is real.
Curated list of resources for the Design of Experiments (DOE)
Design of Experiments in Julia
BASM - 2017 Spring
Flexible and accessible design of experiments in Python. Provides industry with an easy package to create designs based with limited expert knowledge. Provides researchers with the ability to easily create new criteria and design structures.
python experiment management toolset
Python library for Design and Analysis of Experiments
Python toolkit for analysis of industrial process data; multivariate analysis, designed experiments, process monitoring.
A modern Fortran statistical library.
Python package for flexible generation of D-optimal experimental designs
Simulation and Analysis Tool for TAP Reactor Systems
R package of comprehensive tools for designing and analyzing choice-based conjoint (cbc) experiments
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