The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
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.
| Version: | 0.1.0 |
| Depends: | R (≥ 3.6.0) |
| Imports: | Rcpp (≥ 1.0.0), survival (≥ 3.2), statmod (≥ 1.4), pracma (≥ 2.2), Matrix (≥ 1.3) |
| LinkingTo: | Rcpp, RcppArmadillo, RcppEnsmallen |
| Published: | 2023-09-02 |
| DOI: | 10.32614/CRAN.package.VBJM |
| Author: | Jiehuan Sun [aut, cre] |
| Maintainer: | Jiehuan Sun <jiehuan.sun at gmail.com> |
| License: | GPL-2 |
| NeedsCompilation: | yes |
| CRAN checks: | VBJM results |
| Reference manual: | VBJM.pdf |
| Package source: | VBJM_0.1.0.tar.gz |
| Windows binaries: | r-devel: VBJM_0.1.0.zip, r-release: VBJM_0.1.0.zip, r-oldrel: VBJM_0.1.0.zip |
| macOS binaries: | r-release (arm64): VBJM_0.1.0.tgz, r-oldrel (arm64): VBJM_0.1.0.tgz, r-release (x86_64): VBJM_0.1.0.tgz, r-oldrel (x86_64): VBJM_0.1.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=VBJM to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.