MatrixHMM
Parsimonious Families of Hidden Markov Models for Matrix-Variate Longitudinal Data
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.
- Version1.0.0
- R version≥ 2.10
- LicenseGPL (≥ 3)
- Needs compilation?No
- Last release08/28/2024
Team
Salvatore D. Tomarchio
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- Imports10 packages