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 41 articles , 1 standard article )

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  1. Cai, Yongli; Zhao, Shi; Niu, Yun; Peng, Zhihang; Wang, Kai; He, Daihai; Wang, Weiming: Modelling the effects of the contaminated environments on tuberculosis in Jiangsu, China (2021)
  2. Niu, Mu; Wandy, Joe; Daly, Rónán; Rogers, Simon; Husmeier, Dirk: R package for statistical inference in dynamical systems using kernel based gradient matching: KGode (2021)
  3. Bretó, Carles; Ionides, Edward L.; King, Aaron A.: Panel data analysis via mechanistic models (2020)
  4. Clairon, Quentin; Samson, Adeline: Optimal control for estimation in partially observed elliptic and hypoelliptic linear stochastic differential equations (2020)
  5. Ganyani, Tapiwa; Faes, Christel; Hens, Niel: Inference of the generalized-growth model via maximum likelihood estimation: a reflection on the impact of overdispersion (2020)
  6. Lin, Qianying; Musa, Salihu S.; Zhao, Shi; He, Daihai: Modeling the 2014--2015 Ebola virus disease outbreaks in Sierra Leone, Guinea, and Liberia with effect of high- and low-risk susceptible individuals (2020)
  7. Musa, Salihu S.; Zhao, Shi; Gao, Daozhou; Lin, Qianying; Chowell, Gerardo; He, Daihai: Mechanistic modelling of the large-scale Lassa fever epidemics in Nigeria from 2016 to 2019 (2020)
  8. Park, Joonha; Ionides, Edward L.: Inference on high-dimensional implicit dynamic models using a guided intermediate resampling filter (2020)
  9. Daniel Kaschek; Wolfgang Mader; Mirjam Fehling-Kaschek; Marcus Rosenblatt; Jens Timmer: Dynamic Modeling, Parameter Estimation, and Uncertainty Analysis in R (2019) not zbMATH
  10. García, Oscar: Estimating reducible stochastic differential equations by conversion to a least-squares problem (2019)
  11. Guo, Guangbao; Allison, James; Zhu, Lixing: Bootstrap maximum likelihood for quasi-stationary distributions (2019)
  12. Johan Dahlin, Thomas B. Schön: Getting Started with Particle Metropolis-Hastings for Inference in Nonlinear Dynamical Models (2019) not zbMATH
  13. Bhattacharya, Arnab; Wilson, Simon P.: Sequential Bayesian inference for static parameters in dynamic state space models (2018)
  14. Bjørnstad, Ottar N.: Epidemics. Models and data using R (2018)
  15. Bretó, Carles: Modeling and inference for infectious disease dynamics: a likelihood-based approach (2018)
  16. 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)
  17. Ho, Lam Si Tung; Crawford, Forrest W.; Suchard, Marc A.: Direct likelihood-based inference for discretely observed stochastic compartmental models of infectious disease (2018)
  18. Law, Jonathan; Wilkinson, Darren J.: Composable models for online Bayesian analysis of streaming data (2018)
  19. Picchini, Umberto; Samson, Adeline: Coupling stochastic EM and approximate Bayesian computation for parameter inference in state-space models (2018)
  20. Rami Yaari; Itai Dattner: simode: R Package for statistical inference of ordinary differential equations using separable integral-matching (2018) arXiv

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