Contains general data structures and
  functions for longitudinal data with multiple variables, 
  repeated measurements, and irregularly spaced time points.
  Also implements a shrinkage estimator of dynamical correlation
  and dynamical covariance.

Regression analysis of mixed sparse synchronous and asynchronous longitudinal covariates. Please cite the manuscripts corresponding to this package: Sun, Z. et al. (2023) <arXiv:2305.17715>  and  Liu, C. et al. (2023) <arXiv:2305.17662>.

Creates a series of sets of graphics and statistics related to the longitudinal cascade, all included in a single object. The longitudinal cascade inputs longitudinal data to identify gaps in the HIV and related cascades by observing differences using time to event and survival methods. The stage definitions are set by the user, with default standard options. Outputs include graphics, datasets, and formal statistical tests.

Tools for longitudinal data and joint longitudinal data (used by packages kml and kml3d).

Random forests are a statistical learning method widely used in many areas of scientific research essentially for its ability to learn complex relationships between input and output variables and also its capacity to handle high-dimensional data. However, current random forests approaches are not flexible enough to handle longitudinal data.  In this package, we propose a general approach of random forests for high-dimensional longitudinal data. It includes a flexible stochastic model which allows the covariance structure to vary over time. Furthermore, we introduce a new method which takes intra-individual covariance into consideration to build random forests. The method is fully detailled in Capitaine et.al. (2020) <doi:10.1177/0962280220946080> Random forests for high-dimensional longitudinal data.

The proposed method aims at predicting the longitudinal mean response trajectory by a 
              kernel-based estimator. The kernel estimator is constructed by imposing weights based on 
              subject-wise similarity on L2 metric space between predictor trajectories as well as time proximity. 
              Users could also perform variable selections to derive functional predictors with predictive significance
              by the proposed multiplicative model with multivariate Gaussian kernels.