Package: glmmEP 1.0-3.1

Matt P. Wand

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:Matt P. Wand [aut, cre], James C.F. Yu [aut]

glmmEP_1.0-3.1.tar.gz
glmmEP_1.0-3.1.tar.gz(r-4.7-arm64)glmmEP_1.0-3.1.tar.gz(r-4.7-x86_64)glmmEP_1.0-3.1.tar.gz(r-4.6-arm64)glmmEP_1.0-3.1.tar.gz(r-4.6-x86_64)
glmmEP_1.0-3.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
glmmEP/json (API)

# Install 'glmmEP' in R:
install.packages('glmmEP', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openblas– Optimized BLAS

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblas

2.08 score 12 scripts 203 downloads 5 exports 15 dependencies

Last updated from:31f200bb27. Checks:4 NOTE, 2 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64NOTE112
linux-devel-x86_64NOTE124
source / vignettesOK161
linux-release-arm64NOTE162
linux-release-x86_64NOTE124
wasm-releaseOK100

Exports:glmmEPglmmEP.controlglmmEPvignetteglmmSimDatasummary.glmmEP

Dependencies:bootlatticelme4MASSMatrixmatrixcalcminqanlmenloptrrbibutilsRcppRcppEigenRdpackreformulasrlang

glmmEP User Manual

Rendered frommanual.Rnwusingutils::Sweaveon Jun 13 2026.

Last update: 2018-05-29
Started: 2018-05-29