Package: JLPM 1.0.4
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:
JLPM_1.0.4.tar.gz
JLPM_1.0.4.zip(r-4.7)JLPM_1.0.4.zip(r-4.6)JLPM_1.0.4.zip(r-4.5)
JLPM_1.0.4.tgz(r-4.6-x86_64)JLPM_1.0.4.tgz(r-4.6-arm64)JLPM_1.0.4.tgz(r-4.5-x86_64)JLPM_1.0.4.tgz(r-4.5-arm64)
JLPM_1.0.4.tar.gz(r-4.7-arm64)JLPM_1.0.4.tar.gz(r-4.7-x86_64)JLPM_1.0.4.tar.gz(r-4.6-arm64)JLPM_1.0.4.tar.gz(r-4.6-x86_64)
JLPM_1.0.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
JLPM/json (API)
| # Install 'JLPM' in R: |
| install.packages('JLPM', repos = c('https://vivianephilipps.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/vivianephilipps/jlpm/issues
Pkgdown/docs site:https://vivianephilipps.github.io
Last updated from:d7e6c8ca28. Checks:13 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 137 | ||
| linux-devel-x86_64 | OK | 143 | ||
| source / vignettes | OK | 201 | ||
| linux-release-arm64 | OK | 134 | ||
| linux-release-x86_64 | OK | 142 | ||
| macos-release-arm64 | OK | 122 | ||
| macos-release-x86_64 | OK | 204 | ||
| macos-oldrel-arm64 | OK | 114 | ||
| macos-oldrel-x86_64 | OK | 184 | ||
| windows-devel | OK | 119 | ||
| windows-release | OK | 125 | ||
| windows-oldrel | OK | 124 | ||
| wasm-release | OK | 127 |
Exports:convertcreateFargscreateX0diffSojournTimeIRT4SselectingIRT4SstagingjointLPMloglikposteriorLogLikelihoodpredictREREconddensityremoveNAsojournTimeWaldMult
Dependencies:clicodetoolsdoParallelforeachglueiteratorslatticelcmmlifecyclemagrittrmarqLevAlgMatrixmvtnormnlmenumDerivRcpprlangspacefillrstringistringrsurvivalvctrs
