simulate_initial_probs

simulate_transition_probs

simulate_emission_probs

These are helper functions for quick construction of initial values for various
model building functions.
Mostly useful for global optimization algorithms which do not depend on initial values.

Designed for estimating variants of hidden (latent) Markov models
(HMMs), mixture HMMs, and non-homogeneous HMMs (NHMMs) for social sequence
data and other categorical time series. Special cases include
feedback-augmented NHMMs, Markov models without latent layer, mixture
Markov models, and latent class models. The package supports models for one
or multiple subjects with one or multiple parallel sequences (channels).
External covariates can be added to explain cluster membership in mixture
models as well as initial, transition and emission probabilities in NHMMs.
The package provides functions for evaluating and comparing models, as well
as functions for visualizing of multichannel sequence data and HMMs. For
NHMMs, methods for computing average causal effects and marginal state and
emission probabilities are available. Models are estimated using maximum
likelihood via the EM algorithm or direct numerical maximization with
analytical gradients. Documentation is available via several vignettes,
and Helske and Helske (2019, <doi:10.18637/jss.v088.i03>). For methodology
behind the NHMMs, see Helske (2025, <doi:10.48550/arXiv.2503.16014>).

Jouni Helske

seqHMM

Mixture Hidden Markov Models for Social Sequence Data and Other
Multivariate, Multichannel Categorical Time Series

Satu Helske

simulate_initial_probs function

<dl><dt>n_states</dt>
<dd>Number of states in each cluster.</dd>
<dt>n_clusters</dt>
<dd>Number of clusters.</dd>
<dt>alpha</dt>
<dd>A scalar, or a vector of length S (number of states) or M
(number of symbols) defining the parameters of the Dirichlet distribution
used to simulate the probabilities.</dd>
<dt>left_right</dt>
<dd>Constrain the transition probabilities to upper triangular.
Default is <code>FALSE</code>.</dd>
<dt>diag_c</dt>
<dd>A constant value to be added to diagonal of transition matrices before scaling.</dd>
<dt>n_symbols</dt>
<dd>Number of distinct symbols in each channel.</dd></dl>

Arguments

Simulate Parameters of Hidden Markov Models — simulate_initial_probs

<dl>

<dt>n_states</dt>
<dd>Number of states in each cluster.</dd>


<dt>n_clusters</dt>
<dd>Number of clusters.</dd>


<dt>alpha</dt>
<dd>A scalar, or a vector of length S (number of states) or M
(number of symbols) defining the parameters of the Dirichlet distribution
used to simulate the probabilities.</dd>


<dt>left_right</dt>
<dd>Constrain the transition probabilities to upper triangular.
Default is <code>FALSE</code>.</dd>


<dt>diag_c</dt>
<dd>A constant value to be added to diagonal of transition matrices before scaling.</dd>


<dt>n_symbols</dt>
<dd>Number of distinct symbols in each channel.</dd>

</dl>

simulate_initial_probs: Simulate Parameters of Hidden Markov Models

Description

Usage

Arguments