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BJM  

Backward Joint Model for the Dynamic Prediction of Both Time-to-Event and Longitudinal Outcomes
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("BJM", "0.2.0")



Attach the package and use:
library("BJM")
Maintained by
Wenhao Li
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-07-04
Latest Update: 2026-07-04
Description:
Provides tools to fit joint models of multivariate longitudinal data and time-to-event data for dynamic prediction. It allows the joint prediction of both future time-to-event outcomes and future longitudinal outcomes conditional on survival. The models accommodate irregularly measured longitudinal data and competing risks outcomes. The use of the backward joint model enables fast and efficient computation, especially for applications with large sample sizes and many longitudinal variables.
How to cite:
Wenhao Li (2026). BJM: Backward Joint Model for the Dynamic Prediction of Both Time-to-Event and Longitudinal Outcomes. R package version 0.2.0, https://cran.r-project.org/web/packages/BJM. Accessed 24 Oct. 2026.
Previous versions and publish date:
0.1.0 (2026-07-04 09:50), (2026-09-25 04:30)
Other packages that cited BJM R package
View BJM citation profile
Other R packages that BJM depends, imports, suggests or enhances
Complete documentation for BJM
Functions, R codes and Examples using the BJM R package
Full BJM package functions and examples
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