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-4.5-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://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS

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

2.08 score 12 scripts 203 downloads 5 exports 11 dependencies

Last updated 5 years agofrom:31f200bb27. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKOct 15 2024
R-4.5-linux-x86_64NOTEOct 15 2024

Exports:glmmEPglmmEP.controlglmmEPvignetteglmmSimDatasummary.glmmEP

Dependencies:bootlatticelme4MASSMatrixmatrixcalcminqanlmenloptrRcppRcppEigen

glmmEP User Manual

Rendered frommanual.Rnwusingutils::Sweaveon Oct 15 2024.

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