glmmrOptim: Approximate Optimal Experimental Designs Using Generalised Linear Mixed Models

Optimal design analysis algorithms for any study design that can be represented or modelled as a generalised linear mixed model including cluster randomised trials, cohort studies, spatial and temporal epidemiological studies, and split-plot designs. See <> for a detailed manual on model specification. A detailed discussion of the methods in this package can be found in Watson, Hemming, and Girling (2023) <doi:10.1177/09622802231202379>.

Version: 0.3.5
Depends: R (≥ 3.4.0), Matrix, glmmrBase
Imports: methods, Rcpp (≥ 1.0.7), digest
LinkingTo: Rcpp (≥ 1.0.7), RcppEigen, RcppProgress, glmmrBase (≥ 0.4.6), SparseChol (≥ 0.2.1), BH, rminqa (≥ 0.2.2)
Suggests: testthat, CVXR
Published: 2024-06-02
DOI: 10.32614/CRAN.package.glmmrOptim
Author: Sam Watson [aut, cre], Yi Pan [aut]
Maintainer: Sam Watson <S.I.Watson at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
SystemRequirements: GNU make
CRAN checks: glmmrOptim results


Reference manual: glmmrOptim.pdf


Package source: glmmrOptim_0.3.5.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): glmmrOptim_0.3.5.tgz, r-oldrel (arm64): glmmrOptim_0.3.5.tgz, r-release (x86_64): glmmrOptim_0.3.5.tgz, r-oldrel (x86_64): glmmrOptim_0.3.5.tgz
Old sources: glmmrOptim archive


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