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VBJM  

Variational Inference for Joint Model
View on CRAN: Click here


Download and install VBJM package within the R console
Install from CRAN:
install.packages("VBJM")

Install from Github:
library("remotes")
install_github("cran/VBJM")

Install by package version:
library("remotes")
install_version("VBJM", "0.1.0")



Attach the package and use:
library("VBJM")
Maintained by
Jiehuan Sun
[Scholar Profile | Author Map]
First Published: 2023-09-02
Latest Update: 2023-09-02
Description:
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.
How to cite:
Jiehuan Sun (2023). VBJM: Variational Inference for Joint Model. R package version 0.1.0, https://cran.r-project.org/web/packages/VBJM. Accessed 22 Feb. 2025.
Previous versions and publish date:
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Complete documentation for VBJM
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