Package: JLPM 1.0.3

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:Viviane Philipps [aut, cre], Tiphaine Saulnier [aut], Cecile Proust-Lima [aut]

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JLPM.pdf |JLPM.html✨
JLPM/json (API)

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

Peer review:

Bug tracker:https://github.com/vivianephilipps/jlpm/issues

Uses libs:
  • fortran– Runtime library for GNU Fortran applications

On CRAN:

4 exports 1.26 score 22 dependencies 298 downloads

Last updated 2 months agofrom:19a0c755d8. Checks:OK: 4 NOTE: 5. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 22 2024
R-4.5-win-x86_64NOTEAug 22 2024
R-4.5-linux-x86_64NOTEAug 22 2024
R-4.4-win-x86_64NOTEAug 22 2024
R-4.4-mac-x86_64NOTEAug 22 2024
R-4.4-mac-aarch64NOTEAug 22 2024
R-4.3-win-x86_64OKAug 22 2024
R-4.3-mac-x86_64OKAug 22 2024
R-4.3-mac-aarch64OKAug 22 2024

Exports:convertjointLPMlogliksojournTime

Dependencies:clicodetoolsdoParallelforeachglueiteratorslatticelcmmlifecyclemagrittrmarqLevAlgMatrixmvtnormnlmenumDerivrandtoolboxrlangrngWELLstringistringrsurvivalvctrs