Multi-state Markov and Hidden Markov Models in Continuous Time
Continuous-time Markov and hidden Markov multi-state
models for longitudinal data.  Designed for processes observed at
arbitrary times in continuous time (panel data) but some other
observation schemes are supported. Both Markov transition rates and
the hidden Markov output process can be modelled in terms of
covariates, which may be constant or piecewise-constant in time.

Multi-State Markov and Hidden Markov Models in Continuous Time
Functions for fitting continuous-time Markov and hidden
Markov multi-state models to longitudinal data.  Designed for
processes observed at arbitrary times in continuous time (panel data)
but some other observation schemes are supported. Both Markov
transition rates and the hidden Markov output process can be modelled
in terms of covariates, which may be constant or piecewise-constant
in time.

There are several functions to implement the method for analysis in a multiblock multivariable data. If the input is only a matrix, then the principal components analysis (PCA) is implemented. If the input is a list of matrices, then the multiblock PCA is implemented. If the input is two matrices for exploratory and objective variables, then the partial least squares (PLS) analysis is implemented. If the input is two list of matrices for exploratory and objective variables, then the multiblock PLS analysis is implemented. Moreover, if the extra outcome variable is specified, then the supervised version for the methods above is implemented. For each methods, the sparse modeling is also incorporated. Functions to select the number of components and the regularized parameters are also provided.

Several functions can be used to analyze multiblock multivariable data. If the input is a single matrix, then principal components analysis (PCA) is implemented. If the input is a list of matrices, then multiblock PCA is implemented. If the input is two matrices, for exploratory and objective variables, then partial least squares (PLS) analysis is implemented. If the input is two lists of matrices, for exploratory and objective variables, then multiblock PLS analysis is implemented. Additionally, if an extra outcome variable is specified, then a supervised version of the methods above is implemented. For each method, sparse modeling is also incorporated. Functions for selecting the number of components and regularized parameters are also provided.