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depmixS4: An R Package for Hidden Markov Models

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Cited by:

  1. Gerald C. Kane & Sam Ransbotham, 2016. "Research Note—Content and Collaboration: An Affiliation Network Approach to Information Quality in Online Peer Production Communities," Information Systems Research, INFORMS, vol. 27(2), pages 424-439, June.
  2. Denis S Willett & Justin George & Nora S Willett & Lukasz L Stelinski & Stephen L Lapointe, 2016. "Machine Learning for Characterization of Insect Vector Feeding," PLOS Computational Biology, Public Library of Science, vol. 12(11), pages 1-14, November.
  3. Ramya Raviram & Pedro P Rocha & Christian L Müller & Emily R Miraldi & Sana Badri & Yi Fu & Emily Swanzey & Charlotte Proudhon & Valentina Snetkova & Richard Bonneau & Jane A Skok, 2016. "4C-ker: A Method to Reproducibly Identify Genome-Wide Interactions Captured by 4C-Seq Experiments," PLOS Computational Biology, Public Library of Science, vol. 12(3), pages 1-23, March.
  4. Liu, Wei-han, 2018. "Hidden Markov model analysis of extreme behaviors of foreign exchange rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 1007-1019.
  5. Geoffrey Decrouez & Andrew Robinson, 2013. "Time‐Series Models for Border Inspection Data," Risk Analysis, John Wiley & Sons, vol. 33(12), pages 2142-2153, December.
  6. Marco Sandri & Paola Zuccolotto & Marica Manisera, 2020. "Markov switching modelling of shooting performance variability and teammate interactions in basketball," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1337-1356, November.
  7. Michael Gibilisco, 2023. "Mowing the grass," Journal of Theoretical Politics, , vol. 35(3), pages 204-231, July.
  8. Tessa J P van Schijndel & Kim Huijpen & Ingmar Visser & Maartje E J Raijmakers, 2018. "Investigating the development of causal inference by studying variability in 2- to 5-year-olds' behavior," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-15, April.
  9. Salamalikis, Vasileios & Tzoumanikas, Panayiotis & Argiriou, Athanassios A. & Kazantzidis, Andreas, 2022. "Site adaptation of global horizontal irradiance from the Copernicus Atmospheric Monitoring Service for radiation using supervised machine learning techniques," Renewable Energy, Elsevier, vol. 195(C), pages 92-106.
  10. Ahmet Akca & Ethem Çanakoğlu, 2021. "Adaptive stochastic risk estimation of firm operating profit," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 48(3), pages 463-504, September.
  11. Jan Bulla & Roland Langrock & Antonello Maruotti, 2019. "Guest editor’s introduction to the special issue on “Hidden Markov Models: Theory and Applications”," METRON, Springer;Sapienza Università di Roma, vol. 77(2), pages 63-66, August.
  12. Saadi, Ismaïl & Mustafa, Ahmed & Teller, Jacques & Farooq, Bilal & Cools, Mario, 2016. "Hidden Markov Model-based population synthesis," Transportation Research Part B: Methodological, Elsevier, vol. 90(C), pages 1-21.
  13. Si Chen & Waseem Muhammad & Joo-Heon Lee & Tae-Woong Kim, 2018. "Assessment of Probabilistic Multi-Index Drought Using a Dynamic Naive Bayesian Classifier," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(13), pages 4359-4374, October.
  14. Davey, Calum & Dirawo, Jeffrey & Mushati, Phillis & Magutshwa, Sitholubuhle & Hargreaves, James R. & Cowan, Frances M., 2019. "Mobility and sex work: why, where, when? A typology of female-sex-worker mobility in Zimbabwe," Social Science & Medicine, Elsevier, vol. 220(C), pages 322-330.
  15. Lolea Iulian Cornel & Stamule Simona, 2021. "Trading using Hidden Markov Models during COVID-19 turbulences," Management & Marketing, Sciendo, vol. 16(4), pages 334-351, December.
  16. Melnykov, Volodymyr, 2016. "ClickClust: An R Package for Model-Based Clustering of Categorical Sequences," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i09).
  17. Yi-Hsuan Lee & Alina Davier, 2013. "Monitoring Scale Scores over Time via Quality Control Charts, Model-Based Approaches, and Time Series Techniques," Psychometrika, Springer;The Psychometric Society, vol. 78(3), pages 557-575, July.
  18. Judith H. I. Haarhuis & Robin H. Weide & Vincent A. Blomen & Koen D. Flach & Hans Teunissen & Laureen Willems & Thijn R. Brummelkamp & Benjamin D. Rowland & Elzo Wit, 2022. "A Mediator-cohesin axis controls heterochromatin domain formation," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
  19. Andrew C. Meldrum & Oleg Sokolinskiy, 2023. "The Effects of Volatility on Liquidity in the Treasury Market," Finance and Economics Discussion Series 2023-028, Board of Governors of the Federal Reserve System (U.S.).
  20. Ingmar Visser & Maarten Speekenbrink, 2014. "Comments on: Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 478-483, September.
  21. Hosun Ryou & Han Hee Bae & Hee Soo Lee & Kyong Joo Oh, 2020. "Momentum Investment Strategy Using a Hidden Markov Model," Sustainability, MDPI, vol. 12(17), pages 1-16, August.
  22. Reetam Majumder & Qing Ji & Nagaraj K. Neerchal, 2023. "Optimal Stock Portfolio Selection with a Multivariate Hidden Markov Model," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 177-198, May.
  23. Rob Hayward & Jens Hölscher, 2017. "The Forward-Discount Puzzle in Central and Eastern Europe," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 59(4), pages 472-497, December.
  24. Dionne, Georges & Saissi-Hassani, Samir, 2016. "Hidden Markov Regimes in Operational Loss Data: Application to the Recent Financial Crisis," Working Papers 15-3, HEC Montreal, Canada Research Chair in Risk Management.
  25. Morteza Amini & Afarin Bayat & Reza Salehian, 2023. "hhsmm: an R package for hidden hybrid Markov/semi-Markov models," Computational Statistics, Springer, vol. 38(3), pages 1283-1335, September.
  26. Pozdnukhov, Alexey, 2016. "Demand Forecasting and Activity-based Mobility Modeling from Cell Phone Data," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt4hc9r218, Institute of Transportation Studies, UC Berkeley.
  27. Santos-Fernandez Edgar & Wu Paul & Mengersen Kerrie L., 2019. "Bayesian statistics meets sports: a comprehensive review," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(4), pages 289-312, December.
  28. Georges Dionne & Amir Saissi Hassani, 2015. "Endogenous Hidden Markov Regimes in Operational Loss Data: Application to the Recent Financial Crisis," Cahiers de recherche 1516, CIRPEE.
  29. Juan E. Ruiz-Castro & Christian Acal & Ana M. Aguilera & Juan B. Roldán, 2021. "A Complex Model via Phase-Type Distributions to Study Random Telegraph Noise in Resistive Memories," Mathematics, MDPI, vol. 9(4), pages 1-16, February.
  30. Shima Ghassempour & Federico Girosi & Anthony Maeder, 2014. "Clustering Multivariate Time Series Using Hidden Markov Models," IJERPH, MDPI, vol. 11(3), pages 1-23, March.
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