Parsimonious Families of Hidden Markov Models for Matrix-Variate Longitudinal Data
Atypical Detection Points Using Matrix-Variate Contaminated Normal Hid...
Atypical Detection Points Using Matrix-Variate t Hidden Markov Models
Fitting Parsimonious Hidden Markov Models for Matrix-Variate Longitudi...
Initialization for ECM Algorithms in Matrix-Variate Hidden Markov Mode...
Selection of the best fitting model(s)
Random Number Generation for Matrix-Variate Hidden Markov Models
Implements three families of parsimonious hidden Markov models (HMMs) for matrix-variate longitudinal data using the Expectation-Conditional Maximization (ECM) algorithm. The package supports matrix-variate normal, t, and contaminated normal distributions as emission distributions. For each hidden state, parsimony is achieved through the eigen-decomposition of the covariance matrices associated with the emission distribution. This approach results in a comprehensive set of 98 parsimonious HMMs for each type of emission distribution. Atypical matrix detection is also supported, utilizing the fitted (heavy-tailed) models.