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.

Datasets and Functionality from
  'Jan Beran' (1994). Statistics for Long-Memory Processes; Chapman & Hall.
  Estimation of Hurst (and more) parameters for fractional Gaussian noise,
  'fARIMA' and 'FEXP' models.

Long Memory Time Series is a collection of functions for estimation, simulation and testing of long memory processes, spurious long memory processes and fractionally cointegrated systems.

An adaption of the consensus clustering approach from
    'ConsensusClusterPlus' for longitudinal data. The longitudinal data is
    clustered with flexible mixture models from 'flexmix', while the consensus
    matrices are hierarchically clustered as in 'ConsensusClusterPlus'. By using
    the flexibility from 'flexmix' and 'FactoMineR', one can use mixed data
    types for the clustering.