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It enables the identification of sequentialexperimentation orders for factorial designs that jointly reduce bias and the number of level changes. The method used is that presented by Conto et al. (2025), known as the Assignment-Expansion method, which consists of adapting the linear programming assignment problem to generate balanced experimentation orders. The properties identified are then generalized to designs with a larger number of factors and levels using the expansion method proposed by Correa et al. (2009) and later generalized by Bhowmik et al. (2017). For more details see Conto et al. (2025) <doi:10.1016/j.cie.2024.110844>, Correa et al. (2009) <doi:10.1080/02664760802499337> and Bhowmik et al. (2017) <doi:10.1080/03610926.2016.1152490>.
Version: | 0.1.0 |
Imports: | FMC, minimalRSD |
Published: | 2025-04-22 |
DOI: | 10.32614/CRAN.package.rob |
Author: | Romario Conto |
Maintainer: | Romario Conto <racontol at unal.edu.co> |
BugReports: | https://github.com/RomarioContoL/rob/issues |
License: | MIT + file LICENSE |
URL: | https://github.com/RomarioContoL/rob |
NeedsCompilation: | no |
Materials: | README |
CRAN checks: | rob results |
Reference manual: | rob.pdf |
Package source: | rob_0.1.0.tar.gz |
Windows binaries: | r-devel: not available, r-release: rob_0.1.0.zip, r-oldrel: rob_0.1.0.zip |
macOS binaries: | r-release (arm64): rob_0.1.0.tgz, r-oldrel (arm64): rob_0.1.0.tgz, r-release (x86_64): rob_0.1.0.tgz, r-oldrel (x86_64): rob_0.1.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.