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.

Evaluates the sensitivity of a given association to unmeasured confounding. The package consists of three functions. The first starts a 'shiny' app which assesses how strong a time-invariant confounder needs to be associated with the exposure and outcome to explain away a proposed causal association. The second and third functions simulate a time-dependent confounder over time either using a fit from the qmle() function from the 'yuima' package or directly using the observed effect estimate.

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.

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.