msm

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 45 articles , 1 standard article )

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  1. Williams, Jonathan P.; Storlie, Curtis B.; Therneau, Terry M.; Clifford, R. Jack Jr.; Hannig, Jan: A Bayesian approach to multistate hidden Markov models: application to dementia progression (2020)
  2. Bulla, Jan (ed.); Langrock, Roland (ed.); Maruotti, Antonello (ed.): Guest editor’s introduction to the special issue on “Hidden Markov models: theory and applications” (2019)
  3. Farewell, Vernon T.; Su, Li; Jackson, Christopher: Partially hidden multi-state modelling of a prolonged disease state defined by a composite outcome (2019)
  4. Theodor Balan; Hein Putter: frailtyEM: An R Package for Estimating Semiparametric Shared Frailty Models (2019) not zbMATH
  5. van den Hout, Ardo; Muniz-Terrera, Graciela: Hidden three-state survival model for bivariate longitudinal count data (2019)
  6. van den Hout, Ardo; Tan, Wenhui: Flexible parametric multistate modelling of employment history (2019)
  7. Chow, Sy-Miin; Ou, Lu; Ciptadi, Arridhana; Prince, Emily B.; You, Dongjun; Hunter, Michael D.; Rehg, James M.; Rozga, Agata; Messinger, Daniel S.: Representing sudden shifts in intensive dyadic interaction data using differential equation models with regime switching (2018)
  8. 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
  9. Kotas, Jakob; Ghate, Archis: Bayesian learning of dose-response parameters from a cohort under response-guided dosing (2018)
  10. Sharples, Linda D.: The role of statistics in the era of big data: electronic health records for healthcare research (2018)
  11. Vanesa Balboa; Jacobo de Uña-Álvarez: Estimation of Transition Probabilities for the Illness-Death Model: Package TP.idm (2018) not zbMATH
  12. 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)
  13. 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) not zbMATH
  14. Cook, Richard J.; Lawless, Jerald F.: Analysis of chronic disease processes based on cohort and registry data (2017)
  15. Francesco Bartolucci; Silvia Pandolfi; Fulvia Pennoni: LMest: An R Package for Latent Markov Models for Longitudinal Categorical Data (2017) not zbMATH
  16. Jouni Helske, Satu Helske: Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R (2017) arXiv
  17. Mauricio Sarrias and Ricardo Daziano: Multinomial Logit Models with Continuous and Discrete Individual Heterogeneity in R: The gmnl Package (2017) not zbMATH
  18. Moriña, D.; Navarro, A.: Competing risks simulation with the survsim R package (2017)
  19. Christopher Jackson: flexsurv: A Platform for Parametric Survival Modeling in R (2016) not zbMATH
  20. Mauricio Sarrias: Discrete Choice Models with Random Parameters in R: The Rchoice Package (2016) not zbMATH

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