Package: HDJM 0.1.0

HDJM: Penalized High-Dimensional Joint Model

Joint models have been widely used to study the associations between longitudinal biomarkers and a survival outcome. However, existing joint models only consider one or a few longitudinal biomarkers and cannot deal with high-dimensional longitudinal biomarkers. This package can be used to fit our recently developed penalized joint model that can handle high-dimensional longitudinal biomarkers. Specifically, an adaptive lasso penalty is imposed on the parameters for the effects of the longitudinal biomarkers 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]

HDJM_0.1.0.tar.gz
HDJM_0.1.0.zip(r-4.5)HDJM_0.1.0.zip(r-4.4)HDJM_0.1.0.zip(r-4.3)
HDJM_0.1.0.tgz(r-4.4-x86_64)HDJM_0.1.0.tgz(r-4.4-arm64)HDJM_0.1.0.tgz(r-4.3-x86_64)HDJM_0.1.0.tgz(r-4.3-arm64)
HDJM_0.1.0.tar.gz(r-4.5-noble)HDJM_0.1.0.tar.gz(r-4.4-noble)
HDJM_0.1.0.tgz(r-4.4-emscripten)HDJM_0.1.0.tgz(r-4.3-emscripten)
HDJM.pdf |HDJM.html✨
HDJM/json (API)

# Install 'HDJM' in R:
install.packages('HDJM', repos = c('https://jjr1234.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

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

On CRAN:

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

1.00 score 166 downloads 1 exports 7 dependencies

Last updated 1 years agofrom:e1b19a2607. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 10 2024
R-4.5-win-x86_64OKNov 10 2024
R-4.5-linux-x86_64OKNov 10 2024
R-4.4-win-x86_64OKNov 10 2024
R-4.4-mac-x86_64OKNov 10 2024
R-4.4-mac-aarch64OKNov 10 2024
R-4.3-win-x86_64OKNov 10 2024
R-4.3-mac-x86_64OKNov 10 2024
R-4.3-mac-aarch64OKNov 10 2024

Exports:HDJM_fit

Dependencies:latticeMatrixRcppRcppArmadilloRcppEnsmallenstatmodsurvival