The package provides a platform-independent GUI for design of experiments. It is implemented as a plugin to the R-Commander, which is a more general graphical user interface for statistics in R based on tcl/tk. DoE functionality can be accessed through the menu Design that is added to the R-Commander menus.

This package creates various kinds of designs for (industrial) experiments. It uses, and sometimes enhances, design generation routines from other packages. So far, response surface designs from package rsm, latin hypercube samples from packages lhs and DiceDesign, and D-optimal designs from package AlgDesign have been implemented.

gsDesign is a package that derives group sequential designs and describes their properties.

Provides functions to generate response-surface designs, fit first- and second-order response-surface models, make surface plots, obtain the path of steepest ascent, and do canonical analysis.

This package creates full factorial experimental designs and designs based on orthogonal arrays for (industrial) experiments. Additionally, it provides some utility functions used also by other DoE packages.

This package creates regular and non-regular Fractional Factorial designs. Furthermore, analysis tools for Fractional Factorial designs with 2-level factors are offered (main effects and interaction plots for all factors simultaneously, cube plot for looking at the simultaneous effects of three factors, full or half normal plot, alias structure in a more readable format than with the built-in function alias). The package is still under development. While much of the intended functionality is already available, some changes and improvements are still to be expected. Suggestions are welcome.

Bayesian nonstationary, semiparametric nonlinear regression and design by treed Gaussian processes (GPs) with jumps to the limiting linear model (LLM). Special cases also implemented include Bayesian linear models, CART, treed linear models, stationary separable and isotropic GPs, and GP single-index models. Provides 1-d and 2-d plotting functions (with projection and slice capabilities) and tree drawing, designed for visualization of tgp-class output. Sensitivity analysis and multi-resolution models are supported. Sequential experimental design and adaptive sampling functions are also provided, including ALM, ALC, and expected improvement. The latter supports derivative-free optimization of noisy black-box functions.

Tools for the design of QTL experiments

This package provides a number of methods for creating and augmenting Latin Hypercube Samples

R package for design of experiments for design of genome-wide association studies. Version 2 incorporating quantitative traits and case-control studies. The Bayes factor should be chosen large enough to give respectable posterior odds. This requires Bayes factors of the order of 10^6 in genome-wide association studies where prior odds are low. Sample sizes needed to get this strength of evidence are substantially higher than those from traditional power calculations. The corresponding threshold for p-values is substantially lower than commonly used. For quantitative traits ldDesign uses an existing deterministic power calculation for detection of linkage disequilibrium between a bi-allelic QTL and a bi-allelic marker, together with the Spiegelhalter and Smith Bayes factor to generate designs with power to detect effects with a given Bayes factor. For case- control studies an asymptotic approximate Bayes factor is used to derive an analytical power calculation in dominant, recessive, additive and general genetic models.