R package hsmm: Hidden Semi Markov Models: This package allows for the simulation and maximum likelihood estimation of hidden semi-Markov models. The implemented Expectation Maximization algorithm assumes that the time spent in the last visited state is subject to right-censoring. It is therefore not subject to the common limitation that the last visited state terminates at the last observation. Additionally, hsmm permits the user to make inferences about the underlying state sequence via the Viterbi algorithm and smoothing probabilities. (Source: http://cran.r-project.org/web/packages)

References in zbMATH (referenced in 18 articles , 1 standard article )

Showing results 1 to 18 of 18.
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  1. Jennifer Pohle, Timo Adam, Larissa T. Beumer: Flexible estimation of the state dwell-time distribution in hidden semi-Markov models (2021) arXiv
  2. Maruotti, Antonello; Petrella, Lea; Sposito, Luca: Hidden semi-Markov-switching quantile regression for time series (2021)
  3. Morteza Amini, Afarin Bayat: hhsmm: An R package for hidden hybrid Markov/semi-Markov models (2021) arXiv
  4. Barbu, Vlad Stefan; Karagrigoriou, Alex; Makrides, Andreas: Statistical inference for a general class of distributions with time-varying parameters (2020)
  5. Francesco Bartolucci; Silvia Pandolfi; Fulvia Pennoni: LMest: An R Package for Latent Markov Models for Longitudinal Categorical Data (2017) not zbMATH
  6. Pertsinidou, C. E.; Tsaklidis, G.; Papadimitriou, E.; Limnios, N.: Application of hidden semi-Markov models for the seismic hazard assessment of the North and South Aegean Sea, Greece (2017)
  7. Zucchini, Walter; MacDonald, Iain L.; Langrock, Roland: Hidden Markov models for time series. An introduction using R (2016)
  8. Nystrup, Peter; Madsen, Henrik; Lindström, Erik: Stylised facts of financial time series and hidden Markov models in continuous time (2015)
  9. Pertsinidou, Christina-Elisavet; Limnios, Nikolaos: Viterbi algorithms for hidden semi-Markov models with application to DNA analysis (2015)
  10. Economou, Theodoros; Bailey, Trevor C.; Kapelan, Zoran: MCMC implementation for Bayesian hidden semi-Markov models with illustrative applications (2014)
  11. Bulla, J.; Lagona, F.; Maruotti, A.; Picone, M.: A multivariate hidden Markov model for the identification of sea regimes from incomplete skewed and circular time series (2012)
  12. Christopher Jackson: Multi-State Models for Panel Data: The msm Package for R (2011) not zbMATH
  13. Hammer, Hugo; Tjelmeland, Håkon: Approximate forward-backward algorithm for a switching linear Gaussian model (2011)
  14. Jared O’Connell; Søren Højsgaard: Hidden Semi Markov Models for Multiple Observation Sequences: The mhsmm Package for R (2011) not zbMATH
  15. Langrock, Roland; Zucchini, Walter: Hidden Markov models with arbitrary state dwell-time distributions (2011)
  16. Bulla, Jan; Bulla, Ingo; Nenadić, Oleg: hsmm -- an R package for analyzing hidden semi-Markov models (2010)
  17. Gatu, Cristian (ed.); McCullough, B. D. (ed.): Editorial: Second special issue on statistical algorithms and software (2010)
  18. Yu, Shun-Zheng: Hidden semi-Markov models (2010)