Package: glmmrOptim 0.5.1

Sam Watson

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:Sam Watson [aut, cre], Yi Pan [aut]

glmmrOptim_0.5.1.tar.gz
glmmrOptim_0.5.1.zip(r-4.7)glmmrOptim_0.5.1.zip(r-4.6)glmmrOptim_0.5.1.zip(r-4.5)
glmmrOptim_0.5.1.tgz(r-4.6-x86_64)glmmrOptim_0.5.1.tgz(r-4.6-arm64)glmmrOptim_0.5.1.tgz(r-4.5-x86_64)glmmrOptim_0.5.1.tgz(r-4.5-arm64)
glmmrOptim_0.5.1.tar.gz(r-4.7-arm64)glmmrOptim_0.5.1.tar.gz(r-4.7-x86_64)glmmrOptim_0.5.1.tar.gz(r-4.6-arm64)glmmrOptim_0.5.1.tar.gz(r-4.6-x86_64)
glmmrOptim_0.5.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
glmmrOptim/json (API)

# Install 'glmmrOptim' in R:
install.packages('glmmrOptim', repos = c('https://samuel-watson.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/samuel-watson/glmmroptim/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

cppopenmp

3.30 score 1 stars 50 downloads 3 exports 12 dependencies

Last updated from:43ceb87374. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK202
linux-devel-x86_64OK196
source / vignettesOK268
linux-release-arm64OK180
linux-release-x86_64OK203
macos-release-arm64OK148
macos-release-x86_64OK261
macos-oldrel-arm64OK115
macos-oldrel-x86_64OK339
windows-develOK199
windows-releaseOK202
windows-oldrelOK196
wasm-releaseOK146

Exports:apportionDesignSpacesetParallelOptim

Dependencies:BHdigestglmmrBaselatticeMatrixR6RcppRcppEigenRcppParallelRcppProgressrminqaSparseChol