Package: gmvjoint 0.4.5

gmvjoint: Joint Models of Survival and Multivariate Longitudinal Data

Fit joint models of survival and multivariate longitudinal data. The longitudinal data is specified by generalised linear mixed models. The joint models are fit via maximum likelihood using an approximate expectation maximisation algorithm. Bernhardt (2015) <doi:10.1016/j.csda.2014.11.011>.

Authors:James Murray [aut, cre]

gmvjoint_0.4.5.tar.gz
gmvjoint_0.4.5.zip(r-4.5)gmvjoint_0.4.5.zip(r-4.4)gmvjoint_0.4.5.zip(r-4.3)
gmvjoint_0.4.5.tgz(r-4.4-x86_64)gmvjoint_0.4.5.tgz(r-4.4-arm64)gmvjoint_0.4.5.tgz(r-4.3-x86_64)gmvjoint_0.4.5.tgz(r-4.3-arm64)
gmvjoint_0.4.5.tar.gz(r-4.5-noble)gmvjoint_0.4.5.tar.gz(r-4.4-noble)
gmvjoint_0.4.5.tgz(r-4.4-emscripten)gmvjoint_0.4.5.tgz(r-4.3-emscripten)
gmvjoint.pdf |gmvjoint.html✨
gmvjoint/json (API)
NEWS

# Install 'gmvjoint' in R:
install.packages('gmvjoint', repos = c('https://jamesmurray7.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/jamesmurray7/gmvjoint/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • PBC - Primary biliary cirrhosis data

On CRAN:

glmmjoint-modelslongitudinalmixed-modelsmodelpredictionsurvivalsurvival-analysis

4.26 score 3 stars 20 scripts 347 downloads 9 exports 23 dependencies

Last updated 2 months agofrom:541fc56b48. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 05 2024
R-4.5-win-x86_64OKNov 05 2024
R-4.5-linux-x86_64OKNov 05 2024
R-4.4-win-x86_64OKNov 05 2024
R-4.4-mac-x86_64OKNov 05 2024
R-4.4-mac-aarch64OKNov 05 2024
R-4.3-win-x86_64OKNov 05 2024
R-4.3-mac-x86_64OKNov 05 2024
R-4.3-mac-aarch64OKNov 05 2024

Exports:boot.jointcond.ranefsdynPredjointparseCoxphrgenpoisROCsimDataxtable.joint

Dependencies:bootglmmTMBlatticelme4MASSMatrixmgcvminqamvtnormnlmenloptrnumDerivpracmarbibutilsRcppRcppArmadilloRcppEigenRdpackreformulasstatmodsurvivalTMBxtable