R package dynr. Dynamic Modeling in R. Dynamic modeling of all kinds in R. These include models of processes in discrete time or continuous time. They also include processes that are linear or nonlinear. Latent variables can be continuous (e.g. state space models) or discrete (e.g. regime-switching models). The general approach involves maximum likelihood estimation of single- and multi-subject models of latent time series with the extended Kalman filter and Kim filter. The user provides recipes and data which are combined into a model that is then cooked to obtain free parameter estimates.
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References in zbMATH (referenced in 6 articles )
Showing results 1 to 6 of 6.
- Chow, Sy-Miin; Lee, Jungmin; Hofman, Abe D.; van der Maas, Han L. J.; Pearl, Dennis K.; Molenaar, Peter C. M.: Control theory forecasts of optimal training dosage to facilitate children’s arithmetic learning in a digital educational application (2022)
- Hunter, Michael D.; Fatimah, Haya; Bornovalova, Marina A.: Two filtering methods of forecasting linear and nonlinear dynamics of intensive longitudinal data (2022)
- Li, Yanling; Oravecz, Zita; Zhou, Shuai; Bodovski, Yosef; Barnett, Ian J.; Chi, Guangqing; Zhou, Yuan; Friedman, Naomi P.; Vrieze, Scott I.; Chow, Sy-Miin: Bayesian forecasting with a regime-switching zero-inflated multilevel Poisson regression model: an application to adolescent alcohol use with spatial covariates (2022)
- Ryan, Oisín; Hamaker, Ellen L.: Time to intervene: a continuous-time approach to network analysis and centrality (2022)
- Smith, Daniel M.; Walls, Theodore A.: Pursuing collective synchrony in teams: a regime-switching dynamic factor model of speed similarity in soccer (2021)
- 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)