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glmmrOptim
View on CRAN: Click
here
Download and install glmmrOptim package within the R console
Install from CRAN:
install.packages("glmmrOptim")
Install from Github:
library("remotes")
install_github("cran/glmmrOptim") Install by package version:
library("remotes")
install_version("glmmrOptim", "0.3.6") Attach the package and use:
library("glmmrOptim")
Maintained by
Sam Watson
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-01-20
Latest Update: 2024-12-17
Description:
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 and Pan (2022) .
How to cite:
Sam Watson (2023). glmmrOptim: Approximate Optimal Experimental Designs Using Generalised Linear Mixed Models. R package version 0.3.6, https://cran.r-project.org/web/packages/glmmrOptim. Accessed 12 Aug. 2026.
Previous versions and publish date:
(2026-07-09 07:44), 0.2.2 (2023-01-20 11:20), 0.2.3 (2023-02-24 19:30), 0.2.4 (2023-04-20 11:20), 0.2.5 (2023-07-04 11:10), 0.3.1 (2023-08-18 18:42), 0.3.2 (2023-09-11 12:40), 0.3.3 (2023-11-22 10:10), 0.3.4 (2024-03-12 09:10), 0.3.5 (2024-06-02 14:10), 0.3.6 (2024-12-17 18:00), 0.3.7 (2026-03-31 15:10)
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Other R packages that glmmrOptim depends,
imports, suggests or enhances
Complete documentation for glmmrOptim
Functions, R codes and Examples using
the glmmrOptim R package
Some associated functions: DesignSpace . GradRobustStep . apportion .
Some associated R codes: R6designspace.R . RcppExports.R . apportion.R . Full glmmrOptim package functions and examples
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