Package: VBJM 0.1.0
Jiehuan Sun
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:
VBJM_0.1.0.tar.gz
VBJM_0.1.0.tar.gz(r-4.5-noble)VBJM_0.1.0.tar.gz(r-4.4-noble)
VBJM_0.1.0.tgz(r-4.4-emscripten)VBJM_0.1.0.tgz(r-4.3-emscripten)
VBJM.pdf |VBJM.html✨
VBJM/json (API)
| # Install 'VBJM' in R: |
| install.packages('VBJM', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 1 years agofrom:a39617bbe9. Checks:OK: 2. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Dec 04 2024 |
| R-4.5-linux-x86_64 | OK | Dec 04 2024 |
Exports:VBJM_fit
Dependencies:latticeMatrixpracmaRcppRcppArmadilloRcppEnsmallenstatmodsurvival
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| control_list | control_list |
| Simulated Longtidunal Data | LongData |
| Simulated Survival Data | SurvData |
| The function to fit VBJM. | VBJM_fit |
