ForLion: 'ForLion' Algorithms to Find Optimal Experimental Designs with Mixed Factors

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>. Lin, S., Huang, Y., & Yang, J. (2025) <doi:10.48550/arXiv.2505.00629>.

Version: 0.2.0
Imports: psych, stats, cubature
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0)
Published: 2025-06-10
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_0.2.0.tar.gz
Windows binaries: r-devel: ForLion_0.2.0.zip, r-release: ForLion_0.2.0.zip, r-oldrel: ForLion_0.2.0.zip
macOS binaries: r-release (arm64): ForLion_0.2.0.tgz, r-oldrel (arm64): ForLion_0.2.0.tgz, r-release (x86_64): ForLion_0.1.0.tgz, r-oldrel (x86_64): ForLion_0.1.0.tgz
Old sources: ForLion archive

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