hidden Markov multi-state models to longitudinal data.  A
variety of observation schemes are supported, including
processes observed at arbitrary times (panel data),
continuously-observed processes, and censored states. Both

hidden Markov multi-state models to longitudinal data.  A variety of
observation schemes are supported, including processes observed at
arbitrary times (panel data), continuously-observed processes, and
censored states. Both Markov transition rates and the hidden Markov
output process can be modelled in terms of covariates, which may be

Title: Multi-state Markov and Hidden Markov Models in Continuous Time
Description: 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.

Title: Multi-State Markov and Hidden Markov Models in Continuous Time
Description: 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
in terms of covariates, which may be constant or piecewise-constant

Title: Multiblock Sparse Multivariable Analysis
Description: 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.