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 47 articles , 1 standard article )

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  1. King, Aaron A.; Lin, Qianying; Ionides, Edward L.: Markov genealogy processes (2022)
  2. Le, Thi Minh Thao; Madec, Sten; Gjini, Erida: Disentangling how multiple traits drive 2 strain frequencies in SIS dynamics with coinfection (2022)
  3. Benjamin Christoffersen: dynamichazard: Dynamic Hazard Models Using State Space Models (2021) not zbMATH
  4. 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)
  5. Michaud, N., de Valpine, P., Turek, D., Paciorek, C. J., Nguyen, D.: Sequential Monte Carlo Methods in the nimble and nimbleSMC R Packages (2021) not zbMATH
  6. Narci, Romain; Delattre, Maud; Larédo, Catherine; Vergu, Elisabeta: Inference for partially observed epidemic dynamics guided by Kalman filtering techniques (2021)
  7. Nguyen-Van-Yen, Benjamin; Del Moral, Pierre; Cazelles, Bernard: Stochastic epidemic models inference and diagnosis with Poisson random measure data augmentation (2021)
  8. 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)
  9. Bretó, Carles; Ionides, Edward L.; King, Aaron A.: Panel data analysis via mechanistic models (2020)
  10. Clairon, Quentin; Samson, Adeline: Optimal control for estimation in partially observed elliptic and hypoelliptic linear stochastic differential equations (2020)
  11. Ganyani, Tapiwa; Faes, Christel; Hens, Niel: Inference of the generalized-growth model via maximum likelihood estimation: a reflection on the impact of overdispersion (2020)
  12. 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)
  13. 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)
  14. Park, Joonha; Ionides, Edward L.: Inference on high-dimensional implicit dynamic models using a guided intermediate resampling filter (2020)
  15. Daniel Kaschek; Wolfgang Mader; Mirjam Fehling-Kaschek; Marcus Rosenblatt; Jens Timmer: Dynamic Modeling, Parameter Estimation, and Uncertainty Analysis in R (2019) not zbMATH
  16. García, Oscar: Estimating reducible stochastic differential equations by conversion to a least-squares problem (2019)
  17. Guo, Guangbao; Allison, James; Zhu, Lixing: Bootstrap maximum likelihood for quasi-stationary distributions (2019)
  18. Johan Dahlin, Thomas B. Schön: Getting Started with Particle Metropolis-Hastings for Inference in Nonlinear Dynamical Models (2019) not zbMATH
  19. Bhattacharya, Arnab; Wilson, Simon P.: Sequential Bayesian inference for static parameters in dynamic state space models (2018)
  20. Bjørnstad, Ottar N.: Epidemics. Models and data using R (2018)

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