Package: glmmrOptim 0.3.5
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, Hemming, and Girling (2023) <doi:10.1177/09622802231202379>.
Authors:
glmmrOptim_0.3.5.tar.gz
glmmrOptim_0.3.5.tar.gz(r-4.5-noble)glmmrOptim_0.3.5.tar.gz(r-4.4-noble)
glmmrOptim_0.3.5.tgz(r-4.4-emscripten)glmmrOptim_0.3.5.tgz(r-4.3-emscripten)
glmmrOptim.pdf |glmmrOptim.html✨
glmmrOptim/json (API)
# Install 'glmmrOptim' in R: |
install.packages('glmmrOptim', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/samuel-watson/glmmroptim/issues
Last updated 6 months agofrom:ddc5acddc5. Checks:OK: 2. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 31 2024 |
R-4.5-linux-x86_64 | OK | Oct 31 2024 |
Exports:apportionDesignSpacesetParallelOptim
Dependencies:abindbackportsBHcallrcheckmateclicolorspacedescdigestdistributionalfansifarvergenericsggplot2glmmrBasegluegridExtragtableinlineisobandlabelinglatticelifecycleloomagrittrMASSMatrixmatrixStatsmgcvmunsellnlmenumDerivpillarpkgbuildpkgconfigposteriorprocessxpsQuickJSRR6RColorBrewerRcppRcppEigenRcppParallelRcppProgressrlangrminqarstanrstantoolsscalesSparseCholStanHeaderstensorAtibbleutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Approximate Optimal Experimental Designs Using Generalised Linear Mixed Models | glmmrOptim-package glmmrOptim |
Generate exact designs from approximate weights | apportion |
A GLMM Design Space | DesignSpace |
Disable or enable parallelised computing | setParallelOptim |