Package: PJFM 0.1.0
PJFM: Variational Inference for High-Dimensional Joint Frailty Model
Joint frailty models have been widely used to study the associations between recurrent events and a survival outcome. However, existing joint frailty models only consider one or a few recurrent events and cannot deal with high-dimensional recurrent events. This package can be used to fit our recently developed penalized joint frailty model that can handle high-dimensional recurrent events. Specifically, an adaptive lasso penalty is imposed on the parameters for the effects of the recurrent events on the survival outcome, which allows for variable selection. Also, our algorithm is computationally efficient, which is based on the Gaussian variational approximation method.
Authors:
PJFM_0.1.0.tar.gz
PJFM_0.1.0.tar.gz(r-4.5-noble)PJFM_0.1.0.tar.gz(r-4.4-noble)
PJFM_0.1.0.tgz(r-4.4-emscripten)PJFM_0.1.0.tgz(r-4.3-emscripten)
PJFM.pdf |PJFM.html✨
PJFM/json (API)
# Install 'PJFM' in R: |
install.packages('PJFM', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 1 months agofrom:d14a7805e6. Checks:OK: 2. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Dec 07 2024 |
R-4.5-linux-x86_64 | OK | Dec 07 2024 |
Exports:PJFM_fitPJFM_predictionPJFM_summary
Dependencies:latticeMatrixpracmaRcppRcppArmadilloRcppEnsmallenstatmodsurvival
Readme and manuals
Help Manual
Help page | Topics |
---|---|
control_list | control_list |
The function to fit PJFM. | PJFM_fit |
The function to calculate predicted probabilities | PJFM_prediction |
The function to get summary table of PJFM fit. | PJFM_summary |
Simulated Recurrent Events Data | RecurData |
Simulated Survival Data | SurvData |