Package: glmmEP 1.0-3.1
glmmEP: Generalized Linear Mixed Model Analysis via Expectation Propagation
Approximate frequentist inference for generalized linear mixed model analysis with expectation propagation used to circumvent the need for multivariate integration. In this version, the random effects can be any reasonable dimension. However, only probit mixed models with one level of nesting are supported. The methodology is described in Hall, Johnstone, Ormerod, Wand and Yu (2018) <arxiv:1805.08423v1>.
Authors:
glmmEP_1.0-3.1.tar.gz
glmmEP_1.0-3.1.zip(r-4.5)glmmEP_1.0-3.1.zip(r-4.4)glmmEP_1.0-3.1.zip(r-4.3)
glmmEP_1.0-3.1.tgz(r-4.5-x86_64)glmmEP_1.0-3.1.tgz(r-4.5-arm64)glmmEP_1.0-3.1.tgz(r-4.4-x86_64)glmmEP_1.0-3.1.tgz(r-4.4-arm64)glmmEP_1.0-3.1.tgz(r-4.3-x86_64)glmmEP_1.0-3.1.tgz(r-4.3-arm64)
glmmEP_1.0-3.1.tar.gz(r-4.5-noble)glmmEP_1.0-3.1.tar.gz(r-4.4-noble)
glmmEP_1.0-3.1.tgz(r-4.4-emscripten)glmmEP_1.0-3.1.tgz(r-4.3-emscripten)
glmmEP.pdf |glmmEP.html✨
glmmEP/json (API)
| # Install 'glmmEP' in R: |
| install.packages('glmmEP', repos = c('https://mattwand.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 5 years agofrom:31f200bb27. Checks:4 OK, 7 NOTE. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Feb 01 2025 |
| R-4.5-win-x86_64 | NOTE | Feb 01 2025 |
| R-4.5-mac-x86_64 | NOTE | Feb 01 2025 |
| R-4.5-mac-aarch64 | NOTE | Feb 01 2025 |
| R-4.5-linux-x86_64 | NOTE | Feb 01 2025 |
| R-4.4-win-x86_64 | NOTE | Feb 01 2025 |
| R-4.4-mac-x86_64 | NOTE | Feb 01 2025 |
| R-4.4-mac-aarch64 | NOTE | Feb 01 2025 |
| R-4.3-win-x86_64 | OK | Feb 01 2025 |
| R-4.3-mac-x86_64 | OK | Feb 01 2025 |
| R-4.3-mac-aarch64 | OK | Feb 01 2025 |
Exports:glmmEPglmmEP.controlglmmEPvignetteglmmSimDatasummary.glmmEP
Dependencies:bootlatticelme4MASSMatrixmatrixcalcminqanlmenloptrrbibutilsRcppRcppEigenRdpackreformulas
