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cff-version: 1.2.0
message: 'To cite package "VBJM" in publications use:'
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license: GPL-2.0-only
title: 'VBJM: Variational Inference for Joint Model'
version: 0.1.0
doi: 10.32614/CRAN.package.VBJM
abstract: 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) <https://doi.org/10.1002/sim.9619>, which
  is based on Gaussian variational approximate inference and is computationally efficient.
authors:
- family-names: Sun
  given-names: Jiehuan
  email: jiehuan.sun@gmail.com
repository: https://CRAN.R-project.org/package=VBJM
date-released: '2023-09-02'
contact:
- family-names: Sun
  given-names: Jiehuan
  email: jiehuan.sun@gmail.com