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 27 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. Vanesa Balboa; Jacobo de Uña-Álvarez: Estimation of Transition Probabilities for the Illness-Death Model: Package TP.idm (2018)
  4. 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)
  5. 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)
  6. Francesco Bartolucci; Silvia Pandolfi; Fulvia Pennoni: LMest: An R Package for Latent Markov Models for Longitudinal Categorical Data (2017)
  7. Jouni Helske, Satu Helske: Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R (2017) arXiv
  8. Mauricio Sarrias and Ricardo Daziano: Multinomial Logit Models with Continuous and Discrete Individual Heterogeneity in R: The gmnl Package (2017)
  9. Moriña, D.; Navarro, A.: Competing risks simulation with the survsim R package (2017)
  10. Christopher Jackson: flexsurv: A Platform for Parametric Survival Modeling in R (2016)
  11. Mauricio Sarrias: Discrete Choice Models with Random Parameters in R: The Rchoice Package (2016)
  12. Agnieszka Król; Philippe Saint-Pierre: SemiMarkov: An R Package for Parametric Estimation in Multi-State Semi-Markov Models (2015)
  13. Lawless, J. F.; Rad, N. Nazeri: Estimation and assessment of Markov multistate models with intermittent observations on individuals (2015)
  14. Nello Blaser; Luisa Vizcaya; Janne Estill; Cindy Zahnd; Bindu Kalesan; Matthias Egger; Thomas Gsponer; Olivia Keiser: gems: An R Package for Simulating from Disease Progression Models (2015)
  15. Artur Araújo; Luís Meira-Machado; Javier Roca-Pardiñas: TPmsm: Estimation of the Transition Probabilities in 3-State Models (2014)
  16. Farewell, Vernon T.; Tom, Brian D. M.: The versatility of multi-state models for the analysis of longitudinal data with unobservable features (2014)
  17. Visser, Ingmar; Speekenbrink, Maarten: Comments on: “Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates” (2014)
  18. Biagini, Francesca; Groll, Andreas; Widenmann, Jan: Intensity-based premium evaluation for unemployment insurance products (2013)
  19. Chow, Sy-Miin; Zhang, Guangjian: Nonlinear regime-switching state-space (RSSS) models (2013)
  20. 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)

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