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>.

Version: 1.0.2
Depends: R (≥ 2.14.0), lcmm
Imports: survival (≥ 2.37-2), randtoolbox, stringr, marqLevAlg (≥ 2.0.6)
Published: 2023-10-06
DOI: 10.32614/CRAN.package.JLPM
Author: Viviane Philipps [aut, cre], Tiphaine Saulnier [aut], Cecile Proust-Lima [aut]
Maintainer: Viviane Philipps <Viviane.Philipps at u-bordeaux.fr>
BugReports: https://github.com/VivianePhilipps/JLPM/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2.0)]
NeedsCompilation: yes
CRAN checks: JLPM results


Reference manual: JLPM.pdf


Package source: JLPM_1.0.2.tar.gz
Windows binaries: r-devel: JLPM_1.0.2.zip, r-release: JLPM_1.0.2.zip, r-oldrel: JLPM_1.0.2.zip
macOS binaries: r-release (arm64): JLPM_1.0.2.tgz, r-oldrel (arm64): JLPM_1.0.2.tgz, r-release (x86_64): JLPM_1.0.2.tgz, r-oldrel (x86_64): JLPM_1.0.2.tgz
Old sources: JLPM archive


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