Population Experimental Design (PopED) in R

Optimal Design of a Synchronous Reluctance Motor Using BioGeography-Based Optimization

R package of comprehensive tools for designing and analyzing choice-based conjoint (cbc) experiments

Code for FLEX, a fast, adaptive and flexible model-based reinforcement learning exploration algorithm.

A simple code for running ANSYS Electronics Desktop (ANSYS Maxwell) from MATLAB.

An optimal design of experiments library written in pure Rust. Read-only mirror of https://git.sr.ht/~maunke/odesign. This repository does not accept pull requests. Please send contributions to https://lists.sr.ht/~maunke/odesign-devel.

Package providing functions to calculate pseudo-ranks and (pseudo)-rank based nonparametric test statistics.

Code to compute Optimal Experimental Design as in Balietti, Klein & Riedl (2020)

Pythonic Aerospace Vehicle Designer - A highly modular conceptual Aircraft designer, coupling geometric global optimization with Raymer, Roskam, Levis and other cool workflows

A Github Repo for Competitive Swarm Optimizer with Mutated Agents.

Torque Ripple Minimization for a Switch Reluctance Motor Using the Ant Lion Optimization Algorithm

R package for sample size calculation for the Wilcoxon-Mann-Whitney test.

Computing approximate optimal designs for multivariate polynomial regressions

Source Code of NestedPSO Algorithm for Finding Standardized Maximin D-optimal Design for Enzyme Inhibition Models

Computational Aerosciences and Optimization : Theory & Applications

Assessing the validity and reproducibility of genome-scale predictions

Shiny app to compute the sample size for optimal designs for univariate norming based on the methods derived in Innocenti et al. (2023, Psychological Methods, 28(1), 89–106)

Selection of the optimal configuration for the toroidal-lattice communication network.

R scripts of Innocenti et al. (2021, Statistical Methods in Medical Research, 30(2), 357-375)

No description provided.

Shiny app to compute the sample size for optimal designs for multivariate norming based on the methods developed in Innocenti et al. (2024, Journal of Educational and Behavioral Statistics, 49(5), 817-847)

This GitHub is an implementation of the paper 'A Model-based Approach to Designing Developmental Toxicology Experiments using Sea Urchin Embryos' and the associated R Shiny app.