Creates full factorial experimental designs and designs based on orthogonal arrays for (industrial) experiments. Provides diverse quality criteria. Provides utility functions for the class design, which is also used by other packages for designed experiments.

'CRAN' packages 'DoE.base' and 'Rmosek' and non-'CRAN' package 'gurobi' are enhanced with functionality for the creation of optimized arrays for experimentation, where optimization is in terms of generalized minimum aberration. It is also possible to optimally extend existing arrays to larger run size. The package writes 'MPS' (Mathematical Programming System) files for use with any mixed integer optimization software that can process such files. If at least one of the commercial products 'Gurobi' or 'Mosek' (free academic licenses available for both) is available, the package also creates arrays by optimization. For installing 'Gurobi' and its R package 'gurobi', follow instructions at <https://support.gurobi.com/hc/en-us/articles/14462206790033-How-do-I-install-Gurobi-for-R>. For installing 'Mosek' and its R package 'Rmosek', follow instructions at <https://www.mosek.com/downloads/> and <https://docs.mosek.com/8.1/rmosek/install-interface.html>, or use the functionality in the stump CRAN R package 'Rmosek'.

Various kinds of designs for
 (industrial) experiments can be created. The package 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.

Performs analysis of popular experimental designs used in the field of biological research. The designs covered are completely randomized design, randomized complete block design, factorial completely randomized design, factorial randomized complete block design, split plot design, strip plot design and latin square design. The analysis include analysis of variance, coefficient of determination, normality test of residuals, standard error of mean, standard error of difference and multiple comparison test of means. The package has functions for transformation of data and yield data conversion. Some datasets are also added in order to facilitate examples.