R package msm: Multi-state Markov and hidden Markov models in continuous time. Functions for fitting general continuous-time Markov and hidden Markov multi-state models to longitudinal data. A variety of observation schemes are supported, including processes observed at arbitrary times (panel data), continuously-observed processes, and censored states. Both Markov transition rates and the hidden Markov output process can be modelled in terms of covariates, which may be constant or piecewise-constant in time.

References in zbMATH (referenced in 20 articles )

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  1. Danilo Alvares, Sebastien Haneuse, Catherine Lee, Kyu Ha Lee: SemiCompRisks: An R Package for Independent and Cluster-Correlated Analyses of Semi-Competing Risks Data (2018) arXiv
  2. Kotas, Jakob; Ghate, Archis: Bayesian learning of dose-response parameters from a cohort under response-guided dosing (2018)
  3. Cassarly, Christy; Martin, Renee’ H.; Chimowitz, Marc; Peña, Edsel A.; Ramakrishnan, Viswanathan; Palesch, Yuko Y.: Assessing type I error and power of multistate Markov models for panel data. A simulation study (2017)
  4. Célia Touraine and Thomas Gerds and Pierre Joly: SmoothHazard: An R Package for Fitting Regression Models to Interval-Censored Observations of Illness-Death Models (2017)
  5. Jouni Helske, Satu Helske: Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R (2017) arXiv
  6. Mauricio Sarrias and Ricardo Daziano: Multinomial Logit Models with Continuous and Discrete Individual Heterogeneity in R: The gmnl Package (2017)
  7. Moriña, D.; Navarro, A.: Competing risks simulation with the survsim R package (2017)
  8. Christopher Jackson: flexsurv: A Platform for Parametric Survival Modeling in R (2016)
  9. Mauricio Sarrias: Discrete Choice Models with Random Parameters in R: The Rchoice Package (2016)
  10. Lawless, J.F.; Rad, N.Nazeri: Estimation and assessment of Markov multistate models with intermittent observations on individuals (2015)
  11. Farewell, Vernon T.; Tom, Brian D.M.: The versatility of multi-state models for the analysis of longitudinal data with unobservable features (2014)
  12. Visser, Ingmar; Speekenbrink, Maarten: Comments on: “Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates” (2014)
  13. Biagini, Francesca; Groll, Andreas; Widenmann, Jan: Intensity-based premium evaluation for unemployment insurance products (2013)
  14. Chow, Sy-Miin; Zhang, Guangjian: Nonlinear regime-switching state-space (RSSS) models (2013)
  15. Doss, Charles R.; Suchard, Marc A.; Holmes, Ian; Kato-Maeda, Midori; Minin, Vladimir N.: Fitting birth-death processes to panel data with applications to bacterial DNA fingerprinting (2013)
  16. Bello, Nora M.; Steibel, Juan P.; Tempelman, Robert J.: Hierarchical Bayesian modeling of heterogeneous cluster- and subject-level associations between continuous and binary outcomes in dairy production (2012)
  17. Beyersmann, Jan; Allignol, Arthur; Schumacher, Martin: Competing risks and multistate models with R (2012)
  18. Visser, Ingmar: Seven things to remember about hidden Markov models: A tutorial on Markovian models for time series (2011)
  19. García-Mora, B.; Santamaría, C.; Navarro, E.; Rubio, G.: Modeling bladder cancer using a Markov process with multiple absorbing states (2010)
  20. Yang, Manshu; Chow, Sy-Miin: Using state-space model with regime switching to represent the dynamics of facial electromyography (EMG) data (2010)