| JLPM-package {JLPM} | R Documentation |
Estimation of joint latent process models
Description
Functions for the estimation of joint latent process models (JLPM). Continuous and ordinal outcomes are handled for the longitudinal part, whereas the survival part considers multiple competing events. The likelihood is computed using Monte-Carlo integration. Estimation is achieved by maximizing the log-likelihood using a robust iterative algorithm.
Details
Please report to the JLPM-team any question or suggestion regarding the package via github only (https://github.com/VivianePhilipps/JLPM/issues).
Author(s)
Cecile Proust-Lima, Viviane Philipps, Tiphaine Saulnier
References
Saulnier, Philipps, Meissner, Rascol, Pavy-Le-Traon, Foubert-Samier, Proust-Lima (2022). Joint models for the longitudinal analysis of measurement scales in the presence of informative dropout, Methods 203.
Philipps, Hejblum, Prague, Commenges, Proust-Lima (2021). Robust and efficient optimization using a Marquardt-Levenberg algorithm with R package marqLevAlg, The R Journal 13:2.