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glmmrOptim  

Approximate Optimal Experimental Designs Using Generalised Linear Mixed Models
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", "0.3.5")



Attach the package and use:
library("glmmrOptim")
Maintained by
Sam Watson
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-01-20
Latest Update: 2023-12-06
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 0.3.5, https://cran.r-project.org/web/packages/glmmrOptim. Accessed 21 Nov. 2024.
Previous versions and publish date:
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)
Other packages that cited glmmrOptim R package
View glmmrOptim citation profile
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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