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.
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References in zbMATH (referenced in 10 articles )
Showing results 1 to 10 of 10.
- Lawless, J.F.; Rad, N.Nazeri: Estimation and assessment of Markov multistate models with intermittent observations on individuals (2015)
- Farewell, Vernon T.; Tom, Brian D.M.: The versatility of multi-state models for the analysis of longitudinal data with unobservable features (2014)
- Visser, Ingmar; Speekenbrink, Maarten: Comments on: “Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates” (2014)
- Biagini, Francesca; Groll, Andreas; Widenmann, Jan: Intensity-based premium evaluation for unemployment insurance products (2013)
- Chow, Sy-Miin; Zhang, Guangjian: Nonlinear regime-switching state-space (RSSS) models (2013)
- 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)
- Beyersmann, Jan; Allignol, Arthur; Schumacher, Martin: Competing risks and multistate models with R (2012)
- Visser, Ingmar: Seven things to remember about hidden Markov models: A tutorial on Markovian models for time series (2011)
- García-Mora, B.; Santamaría, C.; Navarro, E.; Rubio, G.: Modeling bladder cancer using a Markov process with multiple absorbing states (2010)
- Yang, Manshu; Chow, Sy-Miin: Using state-space model with regime switching to represent the dynamics of facial electromyography (EMG) data (2010)