depmixS4

R package depmixS4: Dependent Mixture Models - Hidden Markov Models of GLMs and Other Distributions in S4. Fit latent (hidden) Markov models on mixed categorical and continuous (time series) data, otherwise known as dependent mixture models

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References in zbMATH (referenced in 8 articles )

Showing results 1 to 8 of 8.
Sorted by year (citations)

  1. Jouni Helske, Satu Helske: Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R (2017) arXiv
  2. Zuanetti, Daiane Aparecida; Milan, Luis Aparecido: A generalized mixture model applied to diabetes incidence data (2017)
  3. Volodymyr Melnykov: ClickClust: An R Package for Model-Based Clustering of Categorical Sequences (2016)
  4. Zucchini, Walter; MacDonald, Iain L.; Langrock, Roland: Hidden Markov models for time series. An introduction using R (2016)
  5. 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)
  6. Visser, Ingmar; Speekenbrink, Maarten: Comments on: “Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates” (2014)
  7. Lee, Yi-Hsuan; von Davier, Alina A.: Monitoring scale scores over time via quality control charts, model-based approaches, and time series techniques (2013)
  8. Visser, Ingmar: Seven things to remember about hidden Markov models: A tutorial on Markovian models for time series (2011)