simData

A simulated dataset generated from a matrix-variate t Hidden Markov Model with 2 states and an EE - EE covariance structure.

datasets

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.

Salvatore D. Tomarchio

MatrixHMM

Parsimonious Families of Hidden Markov Models for Matrix-Variate
Longitudinal Data

simData function

A list containing two elements:<dl>
<dt>1)</dt>
<dd>An array with <code>p = 2</code> variables in the rows, <code>r = 3</code> variables in the columns, <code>num = 50</code> matrices, and <code>t = 3</code> time points.</dd><dt>2)</dt>
<dd>An <code>num</code> x <code>t</code> matrix containing the state memberships.</dd>
</dl>

Format

A Simulated Dataset from a Matrix-Variate t Hidden Markov Model — simData

A list containing two elements:<dl>
<dt>1)</dt>
<dd>An array with <code>p = 2</code> variables in the rows, <code>r = 3</code> variables in the columns, <code>num = 50</code> matrices, and <code>t = 3</code> time points.</dd>

<dt>2)</dt>
<dd>An <code>num</code> x <code>t</code> matrix containing the state memberships.</dd>


</dl>

simData: A Simulated Dataset from a Matrix-Variate t Hidden Markov Model

Description

Usage

Arguments

Format