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:

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