pomp

R package pomp: Statistical Inference for Partially Observed Markov Processes. Tools for working with partially observed Markov processes (POMPs, AKA stochastic dynamical systems, state-space models). ’pomp’ provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods. It is also a platform for the implementation of new inference methods.


References in zbMATH (referenced in 29 articles , 1 standard article )

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  1. García, Oscar: Estimating reducible stochastic differential equations by conversion to a least-squares problem (2019)
  2. Guo, Guangbao; Allison, James; Zhu, Lixing: Bootstrap maximum likelihood for quasi-stationary distributions (2019)
  3. Bhattacharya, Arnab; Wilson, Simon P.: Sequential Bayesian inference for static parameters in dynamic state space models (2018)
  4. Bretó, Carles: Modeling and inference for infectious disease dynamics: a likelihood-based approach (2018)
  5. Eichner, Martin (ed.); Halloran, M. Elizabeth (ed.); O’Neill, Philip D. (ed.): Design and analysis of infectious disease studies. Abstracts from the workshop held February 18--24, 2018 (2018)
  6. Ho, Lam Si Tung; Crawford, Forrest W.; Suchard, Marc A.: Direct likelihood-based inference for discretely observed stochastic compartmental models of infectious disease (2018)
  7. Picchini, Umberto; Samson, Adeline: Coupling stochastic EM and approximate Bayesian computation for parameter inference in state-space models (2018)
  8. Rami Yaari; Itai Dattner: simode: R Package for statistical inference of ordinary differential equations using separable integral-matching (2018) arXiv
  9. Zhao, Shi; Lou, Yijun; Chiu, Alice P. Y.; He, Daihai: Modelling the skip-and-resurgence of Japanese encephalitis epidemics in Hong Kong (2018)
  10. Charles Driver and Johan Oud and Manuel Voelkle: Continuous Time Structural Equation Modeling with R Package ctsem (2017) not zbMATH
  11. D’Silva, Jeremy P.; Eisenberg, Marisa C.: Modeling spatial invasion of Ebola in West Africa (2017)
  12. Fan, Jianqing; Yao, Qiwei: The elements of financial econometrics (2017)
  13. Jouni Helske: KFAS: Exponential Family State Space Models in R (2017) not zbMATH
  14. Nguyen, Dao; Ionides, Edward L.: A second-order iterated smoothing algorithm (2017)
  15. Nicholas Michaud, Perry de Valpine, Daniel Turek, Christopher J. Paciorek: Sequential Monte Carlo Methods in the nimble R Package (2017) arXiv
  16. Tobias Liboschik; Konstantinos Fokianos; Roland Fried: tscount: An R Package for Analysis of Count Time Series Following Generalized Linear Models (2017) not zbMATH
  17. Aaron King; Dao Nguyen; Edward Ionides: Statistical Inference for Partially Observed Markov Processes via the R Package pomp (2016) not zbMATH
  18. Bhadra, Anindya; Ionides, Edward L.: Adaptive particle allocation in iterated sequential Monte Carlo via approximating meta-models (2016)
  19. Giles Hooker and James Ramsay and Luo Xiao: CollocInfer: Collocation Inference in Differential Equation Models (2016) not zbMATH
  20. Philipp H Boersch-Supan, Leah R Johnson: deBInfer: Bayesian inference for dynamical models of biological systems in R (2016) arXiv

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