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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>.

Versions across snapshots

VersionRepositoryFileSize
1.0.2 rolling linux/jammy R-4.5 JLPM_1.0.2.tar.gz 187.0 KiB
1.0.2 rolling linux/noble R-4.5 JLPM_1.0.2.tar.gz 188.3 KiB
1.0.2 rolling source/ R- JLPM_1.0.2.tar.gz 58.9 KiB
1.0.2 latest linux/jammy R-4.5 JLPM_1.0.2.tar.gz 187.0 KiB
1.0.2 latest linux/noble R-4.5 JLPM_1.0.2.tar.gz 188.3 KiB
1.0.2 latest source/ R- JLPM_1.0.2.tar.gz 58.9 KiB
1.0.2 2026-04-26 source/ R- JLPM_1.0.2.tar.gz 58.9 KiB
1.0.2 2026-04-23 source/ R- JLPM_1.0.2.tar.gz 58.9 KiB
1.0.2 2026-04-09 windows/windows R-4.5 JLPM_1.0.2.zip 362.4 KiB
1.0.2 2025-04-20 source/ R- JLPM_1.0.2.tar.gz 58.9 KiB

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