glmmrOptim: Approximate Optimal Experimental Designs Using GeneralisedLinear 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 <https://github.com/samuel-watson/glmmrBase/blob/master/README.md> 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: | 0.3.6 |
| 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: | 2024-12-17 |
| DOI: | 10.32614/CRAN.package.glmmrOptim |
| Author: | Sam Watson [aut, cre], Yi Pan [aut] |
| Maintainer: | Sam Watson <S.I.Watson at bham.ac.uk> |
| BugReports: | https://github.com/samuel-watson/glmmrOptim/issues |
| License: | GPL-2 |GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/samuel-watson/glmmrOptim |
| NeedsCompilation: | yes |
| SystemRequirements: | GNU make |
| CRAN checks: | glmmrOptim results |
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