Package: PJFM 0.1.0

Jiehuan Sun

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:Jiehuan Sun [aut, cre]

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'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascpp

1.00 score 485 downloads 3 exports 8 dependencies

Last updated 1 months agofrom:d14a7805e6. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 07 2024
R-4.5-linux-x86_64OKDec 07 2024

Exports:PJFM_fitPJFM_predictionPJFM_summary

Dependencies:latticeMatrixpracmaRcppRcppArmadilloRcppEnsmallenstatmodsurvival