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 18 articles , 1 standard article )

Showing results 1 to 18 of 18.
Sorted by year (citations)

  1. Picchini, Umberto; Samson, Adeline: Coupling stochastic EM and approximate Bayesian computation for parameter inference in state-space models (2018)
  2. Charles Driver and Johan Oud and Manuel Voelkle: Continuous Time Structural Equation Modeling with R Package ctsem (2017)
  3. D’Silva, Jeremy P.; Eisenberg, Marisa C.: Modeling spatial invasion of Ebola in West Africa (2017)
  4. Fan, Jianqing; Yao, Qiwei: The elements of financial econometrics (2017)
  5. Jouni Helske: KFAS: Exponential Family State Space Models in R (2017)
  6. Nguyen, Dao; Ionides, Edward L.: A second-order iterated smoothing algorithm (2017)
  7. Nicholas Michaud, Perry de Valpine, Daniel Turek, Christopher J. Paciorek: Sequential Monte Carlo Methods in the nimble R Package (2017) arXiv
  8. Aaron King; Dao Nguyen; Edward Ionides: Statistical Inference for Partially Observed Markov Processes via the R Package pomp (2016)
  9. Bhadra, Anindya; Ionides, Edward L.: Adaptive particle allocation in iterated sequential Monte Carlo via approximating meta-models (2016)
  10. Giles Hooker and James Ramsay and Luo Xiao: CollocInfer: Collocation Inference in Differential Equation Models (2016)
  11. Philipp H Boersch-Supan, Leah R Johnson: deBInfer: Bayesian inference for dynamical models of biological systems in R (2016) arXiv
  12. Pokharel, Gyanendra; Deardon, Rob: Gaussian process emulators for spatial individual-level models of infectious disease (2016)
  13. Aaron A. King, Dao Nguyen, Edward L. Ionides: Statistical Inference for Partially Observed Markov Processes via the R Package pomp (2015) arXiv
  14. Ionides, Edward L.; Nguyen, Dao; Atchadé, Yves; Stoev, Stilian; King, Aaron A.: Inference for dynamic and latent variable models via iterated perturbed Bayes maps (2015)
  15. Bretó, Carles: On idiosyncratic stochasticity of financial leverage effects (2014)
  16. Sheinson, Daniel M.; Niemi, Jarad; Meiring, Wendy: Comparison of the performance of particle filter algorithms applied to tracking of a disease epidemic (2014)
  17. Ionides, Edward L.: Discussion of “Feature matching in time series modeling” by Y. Xia and H. Tong (2011)
  18. Bretó, Carles; He, Daihai; Ionides, Edward L.; King, Aaron A.: Time series analysis via mechanistic models (2009)