FastJM
Semi-Parametric Joint Modeling of Longitudinal and Survival Data
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) <doi:10.1155/2022/1362913>. 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.
- Version1.4.2
- R versionunknown
- LicenseGPL (≥ 3)
- Needs compilation?Yes
- Languageen-US
- Last release03/01/2024
Documentation
Team
Shanpeng Li
MaintainerShow author detailsNing Li
Show author detailsRolesContributorGang Li
Show author detailsRolesContributorHong Wang
Show author detailsRolesContributorJin Zhou
Show author detailsRolesContributorHua Zhou
Show author detailsRolesContributor
Insights
Last 30 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Depends2 packages
- Imports6 packages
- Suggests2 packages
- Linking To2 packages
- Reverse Imports1 package