HMMHSMM: Inference and Estimation of Hidden Markov Models and Hidden
Semi-Markov Models
Provides flexible maximum likelihood estimation and inference for Hidden
Markov Models (HMMs) and Hidden Semi-Markov Models (HSMMs), as well as the
underlying systems in which they operate. The package supports a wide range
of observation and dwell-time distributions, offering a flexible modelling
framework suitable for diverse practical data. Efficient implementations of
the forward-backward and Viterbi algorithms are provided via 'Rcpp' for enhanced
computational performance. Additional functionality includes model simulation,
residual analysis, non-initialised estimation, local and global decoding,
calculation of diverse information criteria, computation of confidence intervals
using parametric bootstrap methods, numerical covariance matrix estimation, and
comprehensive visualisation functions for interpreting the data-generating
processes inferred from the models. Methods follow standard approaches described
by Guédon (2003) <doi:10.1198/1061860032030>,
Zucchini and MacDonald (2009, ISBN:9781584885733), and
O'Connell and Højsgaard (2011) <doi:10.18637/jss.v039.i04>.
| Version: |
0.1.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
Rcpp (≥ 1.0.0), evd, extRemes, stats, MASS, mnormt, grDevices, graphics, utils |
| LinkingTo: |
Rcpp |
| Published: |
2025-12-18 |
| DOI: |
10.32614/CRAN.package.HMMHSMM (may not be active yet) |
| Author: |
Aimee Cody [aut],
Ting Wang [cre, ctb] (Research Supervisor) |
| Maintainer: |
Ting Wang <ting.wang at otago.ac.nz> |
| License: |
GPL-3 |
| NeedsCompilation: |
yes |
| CRAN checks: |
HMMHSMM results |
Documentation:
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