lcmm
Estimation of latent class mixed models by maximum likelihood approach. It includes linear mixed models, mixed models for continuous curvilinear outcomes or ordinal outcomes, multivariate mixed models, joint latent class mixed models.
https://CRAN.R-project.org/package=lcmm
https://github.com/CecileProust-Lima/lcmm
JLPM
Estimation of joint shared random effects models by maximum likelihood approach. Longitudinal data can be continuous or ordinal and are modelled in a mixed model framework. The survival proportional model treats competing events. The shared structure is either the vector of the random effects or the current level of the longitudinal process. The likelihood is computed using Monte Carlo integration.
https://CRAN.R-project.org/package=JLPM
https://github.com/VivianePhilipps/JLPM
marqLevAlg
A parallelized Marquardt-Levenberg algorithm. It provides a numerical solution to the problem of unconstrained local optimization.
https://CRAN.R-project.org/package=marqLevAlg
https://github.com/VivianePhilipps/marqLevAlgParallel
multLPM
Estimation of multivariate latent process models. Multiple longitudinal data can be modelled using several mixed models correlated through the random effects. Repeated diagnosis indicators are modelled by a degradation process. A frequentist maximum likelihood approach is proposed.
https://github.com/VivianePhilipps/multLPM
weightQuant
Weights for incomplete longitudinal data and quantile regression.
https://CRAN.R-project.org/package=weightQuant
NormPsy
Normalization of the MMSE scores.
https://CRAN.R-project.org/package=NormPsy