Package: JLPM 1.0.2
Viviane Philipps
JLPM: Joint Latent Process Models
Estimation of extended joint models with shared random effects. Longitudinal data are handled in latent process models for continuous (Gaussian or curvilinear) and ordinal outcomes while proportional hazard models are used for the survival part. We propose a frequentist approach using maximum likelihood estimation. See Saulnier et al, 2022 <doi:10.1016/j.ymeth.2022.03.003>.
Authors:
JLPM_1.0.2.tar.gz
JLPM_1.0.2.tar.gz(r-4.5-noble)JLPM_1.0.2.tar.gz(r-4.4-noble)
JLPM_1.0.2.tgz(r-4.4-emscripten)JLPM_1.0.2.tgz(r-4.3-emscripten)
JLPM.pdf |JLPM.html✨
JLPM/json (API)
| # Install 'JLPM' in R: |
| install.packages('JLPM', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/vivianephilipps/jlpm/issues
Last updated 1 years agofrom:7743608648. Checks:1 OK, 1 NOTE. Indexed: no.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Jan 28 2025 |
| R-4.5-linux-x86_64 | NOTE | Jan 28 2025 |
Exports:jointLPM
Dependencies:clicodetoolsdoParallelforeachglueiteratorslatticelcmmlifecyclemagrittrmarqLevAlgMatrixmvtnormnlmenumDerivrandtoolboxrlangrngWELLstringistringrsurvivalvctrs
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Estimation of joint latent process models | JLPM-package |
| Conversion | convert |
| Estimation of latent process joint models for multivariate longitudinal outcomes and time-to-event data. | jointLPM |
| Brief summary of a joint latent process model | print.jointLPM |
| Standard methods for estimated models | coef.jointLPM StandardMethods vcov.jointLPM |
| Summary of a joint latent process model | summary.jointLPM |
