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FastJM  

Semi-Parametric Joint Modeling of Longitudinal and Survival Data
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("FastJM", "1.5.3")



Attach the package and use:
library("FastJM")
Maintained by
Shanpeng Li
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-01-06
Latest Update: 2025-07-24
Description:
Maximum likelihood estimation for the semi-parametric joint modeling of competing risks and longitudinal data applying customized linear scan algorithms, proposed by Li and colleagues (2022) . The time-to-event data is modelled using a (cause-specific) Cox proportional hazards regression model with time-fixed covariates. The longitudinal outcome is modelled using a linear mixed effects model. The association is captured by shared random effects. The model is estimated using an Expectation Maximization algorithm.
How to cite:
Shanpeng Li (2022). FastJM: Semi-Parametric Joint Modeling of Longitudinal and Survival Data. R package version 1.5.3, https://cran.r-project.org/web/packages/FastJM. Accessed 18 Sep. 2026.
Previous versions and publish date:
1.0.0 (2022-01-06 11:10), 1.0.1 (2022-01-12 00:02), 1.1.0 (2022-02-08 08:50), 1.1.1 (2022-02-14 09:50), 1.1.2 (2022-02-16 18:00), 1.1.3 (2022-06-01 18:50), 1.2.0 (2022-08-06 08:40), 1.3.1 (2023-03-26 18:00), 1.4.0 (2023-10-10 23:40), 1.4.1 (2024-01-09 10:40), 1.4.2 (2024-03-01 01:12), 1.5.0 (2025-06-22 11:00), 1.5.1 (2025-07-24 02:10), 1.5.2 (2025-10-18 08:10), 1.5.3 (2025-11-08 15:00), 1.6.0 (2026-03-28 17:00), 1.7.0 (2026-07-21 23:00), (2026-08-21 12:20)
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Complete documentation for FastJM
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