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 49 articles , 1 standard article )

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  1. Machado, Robson J. M.; van den Hout, Ardo; Marra, Giampiero: Penalised maximum likelihood estimation in multi-state models for interval-censored data (2021)
  2. Möstel, Linda; Pfeuffer, Marius; Fischer, Matthias: Statistical inference for Markov chains with applications to credit risk (2020)
  3. Ruiz-Castro, Juan Eloy; Zenga, Mariangela: A general piecewise multi-state survival model: application to breast cancer (2020)
  4. 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)
  5. 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)
  6. Farewell, Vernon T.; Su, Li; Jackson, Christopher: Partially hidden multi-state modelling of a prolonged disease state defined by a composite outcome (2019)
  7. Patterson, Toby: Book review of: W. Zucchini et al., Hidden Markov models for time series: an introduction using R. 2nd ed. (2019)
  8. Theodor Balan; Hein Putter: frailtyEM: An R Package for Estimating Semiparametric Shared Frailty Models (2019) not zbMATH
  9. van den Hout, Ardo; Muniz-Terrera, Graciela: Hidden three-state survival model for bivariate longitudinal count data (2019)
  10. van den Hout, Ardo; Tan, Wenhui: Flexible parametric multistate modelling of employment history (2019)
  11. 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)
  12. 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
  13. Kotas, Jakob; Ghate, Archis: Bayesian learning of dose-response parameters from a cohort under response-guided dosing (2018)
  14. Sharples, Linda D.: The role of statistics in the era of big data: electronic health records for healthcare research (2018)
  15. Vanesa Balboa; Jacobo de Uña-Álvarez: Estimation of Transition Probabilities for the Illness-Death Model: Package TP.idm (2018) not zbMATH
  16. 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)
  17. 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
  18. Cook, Richard J.; Lawless, Jerald F.: Analysis of chronic disease processes based on cohort and registry data (2017)
  19. Francesco Bartolucci; Silvia Pandolfi; Fulvia Pennoni: LMest: An R Package for Latent Markov Models for Longitudinal Categorical Data (2017) not zbMATH
  20. Jouni Helske, Satu Helske: Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R (2017) arXiv

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