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ForLion: 'ForLion' Algorithm to Find D-Optimal Designs for Experiments

Designing experimental plans that involve both discrete and continuous factors with general parametric statistical models using the 'ForLion' algorithm and 'EW ForLion' algorithm. The algorithms will search for locally optimal designs and EW optimal designs under the D-criterion. Reference: Huang, Y., Li, K., Mandal, A., & Yang, J., (2024)<doi:10.1007/s11222-024-10465-x>.

Version: 0.1.0
Imports: psych, stats, cubature
Suggests: knitr, rmarkdown
Published: 2025-02-11
DOI: 10.32614/CRAN.package.ForLion
Author: Yifei Huang [aut], Siting Lin [aut, cre], Jie Yang [aut]
Maintainer: Siting Lin <slin95 at uic.edu>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README
CRAN checks: ForLion results

Documentation:

Reference manual: ForLion.pdf
Vignettes: Introduction to ForLion package (source, R code)

Downloads:

Package source: ForLion_0.1.0.tar.gz
Windows binaries: r-devel: ForLion_0.1.0.zip, r-release: ForLion_0.1.0.zip, r-oldrel: ForLion_0.1.0.zip
macOS binaries: r-devel (arm64): ForLion_0.1.0.tgz, r-release (arm64): ForLion_0.1.0.tgz, r-oldrel (arm64): ForLion_0.1.0.tgz, r-devel (x86_64): ForLion_0.1.0.tgz, r-release (x86_64): ForLion_0.1.0.tgz, r-oldrel (x86_64): ForLion_0.1.0.tgz

Linking:

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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.