References in zbMATH (referenced in 13 articles , 1 standard article )

Showing results 1 to 13 of 13.
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  1. Bulla, Jan (ed.); Langrock, Roland (ed.); Maruotti, Antonello (ed.): Guest editor’s introduction to the special issue on “Hidden Markov models: theory and applications” (2019)
  2. Patterson, Toby: Book review of: W. Zucchini et al., Hidden Markov models for time series: an introduction using R. 2nd ed. (2019)
  3. Mair, Patrick: Modern psychometrics with R (2018)
  4. Francesco Bartolucci; Silvia Pandolfi; Fulvia Pennoni: LMest: An R Package for Latent Markov Models for Longitudinal Categorical Data (2017) not zbMATH
  5. Jouni Helske, Satu Helske: Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R (2017) arXiv
  6. Zuanetti, Daiane Aparecida; Milan, Luis Aparecido: A generalized mixture model applied to diabetes incidence data (2017)
  7. Volodymyr Melnykov: ClickClust: An R Package for Model-Based Clustering of Categorical Sequences (2016) not zbMATH
  8. Zucchini, Walter; MacDonald, Iain L.; Langrock, Roland: Hidden Markov models for time series. An introduction using R (2016)
  9. Kazor, Karen; Hering, Amanda S.: Assessing the performance of model-based clustering methods in multivariate time series with application to identifying regional wind regimes (2015)
  10. Visser, Ingmar; Speekenbrink, Maarten: Comments on: “Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates” (2014)
  11. Lee, Yi-Hsuan; von Davier, Alina A.: Monitoring scale scores over time via quality control charts, model-based approaches, and time series techniques (2013)
  12. Visser, Ingmar: Seven things to remember about hidden Markov models: A tutorial on Markovian models for time series (2011)
  13. Ingmar Visser; Maarten Speekenbrink: depmixS4: An R Package for Hidden Markov Models (2010) not zbMATH