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
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.
A modern Fortran statistical library.
Python library for Design and Analysis of Experiments
python experiment management toolset
Python package for flexible generation of D-optimal experimental designs
Accelerate 2024 Workshop on Bayesian Optimization Recipes With BayBE
Design of Experiments and Analysis
Simulation and Analysis Tool for TAP Reactor Systems
Blocking and randomization for experimental design
R package of comprehensive tools for designing and analyzing choice-based conjoint (cbc) experiments
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