summary.mnhmm

Summary method for mixture non-homogenous hidden Markov models

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

summary.mnhmm function

<dl><dt>object</dt>
<dd>Non-homogeneous hidden Markov model of class <code>mnhmm</code>.</dd>
<dt>...</dt>
<dd>Ignored</dd></dl>

Arguments

Summary method for mixture non-homogenous hidden Markov models — summary.mnhmm

<dl>

<dt>object</dt>
<dd>Non-homogeneous hidden Markov model of class <code>mnhmm</code>.</dd>


<dt>...</dt>
<dd>Ignored</dd>

</dl>

summary.mnhmm: Summary method for mixture non-homogenous hidden Markov models

Description

Usage

Arguments