There are also various analysis facilities for experimental data, e.g. treatment comparison procedures and several non-parametric tests, but also some quite specialized possibilities for specific types of experiments. It supports planning of lattice designs, factorial designs, randomized complete block designs, completely randomized designs, (Graeco-)Latin square designs, balanced incomplete block designs and alpha designs. It offers extensive functionality on experimental design especially for agricultural and plant breeding experiments, which can also be useful for other purposes. Package agricolae is by far the most-used package from this task view (status: October 2017). Experimental designs for agricultural and plant breeding experiments Volunteers for co-maintaining are welcome. You may also notice that the maintainers’ experience is mainly from industrial experimentation (in a broad sense), which may explain a somewhat biased view on things. Of course, the division into fields is not always clear-cut, and some packages from the more specialized sections can also be applied in general contexts. Subsequently, it covers the most general packages, continues with specific sections on industrial experimentation, computer experiments, and experimentation in the clinical trials contexts (this section is going to be removed eventually experimental design packages for clinical trials will be integrated into the clinical trials task view), and closes with a section on various special experimental design packages that have been developed for other specific purposes. This task view starts out with a section on the historically earliest application area, agricultural experimentation.
#Latin hypercube design of experiments free#
Please feel free to suggest enhancements, and please send information on new packages or major package updates if you think they belong here, either via e-mail to the maintainers or by submitting an issue or pull request in the GitHub repository linked above.Įxperimental design is applied in many areas, and methods have been tailored to the needs of various fields. Packages that focus on analysis only and do not make relevant contributions for design creation are not considered in the scope of this task view. This task view collects information on R packages for experimental design and analysis of data from experiments. See the CRAN Task View Initiative for more details. For example, ctv::install.views("ExperimentalDesign", coreOnly = TRUE) installs all the core packages or ctv::update.views("ExperimentalDesign") installs all packages that are not yet installed and up-to-date. The packages from this task view can be installed automatically using the ctv package. CRAN Task View: Design of Experiments (DoE) & Analysis of Experimental Data. Ulrike Groemping, Tyler Morgan-Wall (2022). For further details see the Contributing guide. Suggestions and improvements for this task view are very welcome and can be made through issues or pull requests on GitHub or via e-mail to the maintainer address. CRAN Task View: Design of Experiments (DoE) & Analysis of Experimental Data CRAN Task View: Design of Experiments (DoE) & Analysis of Experimental Data Maintainer: All these designs can be downloaded from the website. We thus construct a database of approximate maximin and Audze-Eglais Latin hypercube designs for up to ten dimensions and for up to 300 design points. (2005), we obtain new results which we compare to existing results. Using periodic designs and the Enhanced Stochastic Evolutionary algorithm of Jin et al. Up to now only several two-dimensional designs and a few higher dimensional designs for these classes have been published. In this paper the classes of maximin and Audze-Eglais Latin hypercube designs are considered. ĪB - In the area of computer simulation, Latin hypercube designs play an important role.
N2 - In the area of computer simulation, Latin hypercube designs play an important role. T1 - Space-Filling Latin Hypercube Designs For Computer Experiments (Revision of CentER DP 2006-18)
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