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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.

Version: 1.4.2
Depends: R (≥ 3.5.0), statmod, MASS
Imports: Rcpp (≥ 1.0.7), dplyr, nlme, caret, survival, timeROC
LinkingTo: Rcpp, RcppEigen
Suggests: testthat (≥ 3.0.0), spelling
Published: 2024-03-01
DOI: 10.32614/CRAN.package.FastJM
Author: Shanpeng Li [aut, cre], Ning Li [ctb], Hong Wang [ctb], Jin Zhou [ctb], Hua Zhou [ctb], Gang Li [ctb]
Maintainer: Shanpeng Li <lishanpeng0913 at ucla.edu>
License: GPL (≥ 3)
NeedsCompilation: yes
Language: en-US
Materials: README NEWS
CRAN checks: FastJM results

Documentation:

Reference manual: FastJM.pdf

Downloads:

Package source: FastJM_1.4.2.tar.gz
Windows binaries: r-devel: FastJM_1.4.2.zip, r-release: FastJM_1.4.2.zip, r-oldrel: FastJM_1.4.2.zip
macOS binaries: r-release (arm64): FastJM_1.4.2.tgz, r-oldrel (arm64): FastJM_1.4.2.tgz, r-release (x86_64): FastJM_1.4.2.tgz, r-oldrel (x86_64): FastJM_1.4.2.tgz
Old sources: FastJM archive

Reverse dependencies:

Reverse imports: jmBIG

Linking:

Please use the canonical form https://CRAN.R-project.org/package=FastJM to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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