Package: cAIC4 1.0

cAIC4: Conditional Akaike Information Criterion for 'lme4' and 'nlme'

Provides functions for the estimation of the conditional Akaike information in generalized mixed-effect models fitted with (g)lmer() from 'lme4', lme() from 'nlme' and gamm() from 'mgcv'. For a manual on how to use 'cAIC4', see Saefken et al. (2021) <doi:10.18637/jss.v099.i08>.

Authors:Benjamin Saefken, David Ruegamer, Philipp Baumann and Rene-Marcel Kruse, with contributions from Sonja Greven and Thomas Kneib

cAIC4_1.0.tar.gz
cAIC4_1.0.zip(r-4.5)cAIC4_1.0.zip(r-4.4)cAIC4_1.0.zip(r-4.3)
cAIC4_1.0.tgz(r-4.5-any)cAIC4_1.0.tgz(r-4.4-any)cAIC4_1.0.tgz(r-4.3-any)
cAIC4_1.0.tar.gz(r-4.5-noble)cAIC4_1.0.tar.gz(r-4.4-noble)
cAIC4_1.0.tgz(r-4.4-emscripten)cAIC4_1.0.tgz(r-4.3-emscripten)
cAIC4.pdf |cAIC4.html
cAIC4/json (API)

# Install 'cAIC4' in R:
install.packages('cAIC4', repos = c('https://druegamer.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • Zambia - Subset of the Zambia data set on childhood malnutrition
  • guWahbaData - Data from Gu and Wahba

On CRAN:

Conda:

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

3.44 score 2 stars 1 packages 75 scripts 1.5k downloads 4 mentions 9 exports 16 dependencies

Last updated 4 years agofrom:ec9cd4b045. Checks:1 OK, 8 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 20 2025
R-4.5-winNOTEMar 20 2025
R-4.5-macNOTEMar 20 2025
R-4.5-linuxNOTEMar 20 2025
R-4.4-winNOTEMar 20 2025
R-4.4-macNOTEMar 20 2025
R-4.4-linuxNOTEMar 20 2025
R-4.3-winNOTEMar 20 2025
R-4.3-macNOTEMar 20 2025

Exports:anocAICcAICdeleteZeroComponentsgetcondLLgetWeightsmodelAvgpredictMAstepcAICsummaryMA

Dependencies:bootlatticelme4MASSMatrixmgcvminqamvtnormnlmenloptrrbibutilsRcppRcppEigenRdpackreformulasRLRsim