There are 10 repositories under design-of-experiments topic.
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.
python experiment management toolset
A modern Fortran statistical library.
Accelerate 2024 Workshop on Bayesian Optimization Recipes With BayBE
Simulation and Analysis Tool for TAP Reactor Systems
R package of comprehensive tools for designing and analyzing choice-based conjoint (cbc) experiments
Design of Experiments and Analysis
CRAN Task View: Design of Experiments (DoE) & Analysis of Experimental Data
A tool for remote experiment management
Open-source constructor of surrogates and metamodels
Simple implementation of Latin Hypercube Sampling.
ChemDesign: DWSIM Experiment Toolkit
Autonomously driving equation discovery, from the micro to the macro, from laptops to supercomputers.
Tools to create experiment designs
OPTIMEO is a web application doubled by a package that helps you optimize your experimental process by generating a Design of Experiment, generating new experiments using Bayesian Optimization (BO), and analyzing the results of your experiments using Machine Learning models.
Hammersley Sampling method For Design of Experiments (DOE) has been implemented in MATLAB
Optimization framework library fully written in C++
Code for calculating sample sizes for A/B tests based on power analysis.