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.
- Version1.0.2
- R version≥ 2.14.0
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?Yes
- Last release10/06/2023
Team
Viviane Philipps
MaintainerShow author detailsCecile Proust-Lima
Show author detailsRolesAuthorTiphaine Saulnier
Show author detailsRolesAuthor
Insights
Last 30 days
This package has been downloaded 180 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 7 times.
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Last 365 days
This package has been downloaded 2,849 times in the last 365 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The day with the most downloads was Sep 11, 2024 with 35 downloads.
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Dependencies
- Depends1 package
- Imports4 packages