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 <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 and Pan (2022) <doi:10.48550/arXiv.2207.09183>.
| Version: |
0.3.4 |
| 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: |
2024-03-12 |
| 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 |
Documentation:
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