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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>.
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Package details |
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| Author | Viviane Philipps [aut, cre], Tiphaine Saulnier [aut], Cecile Proust-Lima [aut] |
| Maintainer | Viviane Philipps <Viviane.Philipps@u-bordeaux.fr> |
| License | GPL (>= 2.0) |
| Version | 1.0.2 |
| Package repository | View on CRAN |
| Installation |
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