Package: VBJM 0.1.0

VBJM: Variational Inference for Joint Model

The shared random effects joint model is one of the most widely used approaches to study the associations between longitudinal biomarkers and a survival outcome and make dynamic risk predictions using the longitudinally measured biomarkers. One major limitation of joint models is that they could be computationally expensive for complex models where the number of the shared random effects is large. This package can be used to fit complex multivariate joint models using our newly developed algorithm Jieqi Tu and Jiehuan Sun (2023) <doi:10.1002/sim.9619>, which is based on Gaussian variational approximate inference and is computationally efficient.

Authors:Jiehuan Sun [aut, cre]

VBJM_0.1.0.tar.gz
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VBJM_0.1.0.tgz(r-4.4-x86_64)VBJM_0.1.0.tgz(r-4.4-arm64)VBJM_0.1.0.tgz(r-4.3-x86_64)VBJM_0.1.0.tgz(r-4.3-arm64)
VBJM_0.1.0.tar.gz(r-4.5-noble)VBJM_0.1.0.tar.gz(r-4.4-noble)
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VBJM.pdf |VBJM.html✨
VBJM/json (API)

# Install 'VBJM' in R:
install.packages('VBJM', repos = c('https://jjr1234.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

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

1.00 score 113 downloads 1 exports 8 dependencies

Last updated 1 years agofrom:a39617bbe9. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 31 2024
R-4.5-win-x86_64OKOct 31 2024
R-4.5-linux-x86_64OKOct 31 2024
R-4.4-win-x86_64OKOct 31 2024
R-4.4-mac-x86_64OKOct 31 2024
R-4.4-mac-aarch64OKOct 31 2024
R-4.3-win-x86_64OKOct 31 2024
R-4.3-mac-x86_64OKOct 31 2024
R-4.3-mac-aarch64OKOct 31 2024

Exports:VBJM_fit

Dependencies:latticeMatrixpracmaRcppRcppArmadilloRcppEnsmallenstatmodsurvival