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

Keywords for this software

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References in zbMATH (referenced in 11 articles , 1 standard article )

Showing results 1 to 11 of 11.
Sorted by year (citations)

  1. Mair, Patrick: Modern psychometrics with R (2018)
  2. Francesco Bartolucci; Silvia Pandolfi; Fulvia Pennoni: LMest: An R Package for Latent Markov Models for Longitudinal Categorical Data (2017) not zbMATH
  3. Jouni Helske, Satu Helske: Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R (2017) arXiv
  4. Zuanetti, Daiane Aparecida; Milan, Luis Aparecido: A generalized mixture model applied to diabetes incidence data (2017)
  5. Volodymyr Melnykov: ClickClust: An R Package for Model-Based Clustering of Categorical Sequences (2016) not zbMATH
  6. Zucchini, Walter; MacDonald, Iain L.; Langrock, Roland: Hidden Markov models for time series. An introduction using R (2016)
  7. 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)
  8. Visser, Ingmar; Speekenbrink, Maarten: Comments on: “Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates” (2014)
  9. Lee, Yi-Hsuan; von Davier, Alina A.: Monitoring scale scores over time via quality control charts, model-based approaches, and time series techniques (2013)
  10. Visser, Ingmar: Seven things to remember about hidden Markov models: A tutorial on Markovian models for time series (2011)
  11. Ingmar Visser; Maarten Speekenbrink: depmixS4: An R Package for Hidden Markov Models (2010) not zbMATH