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.
Keywords for this software
References in zbMATH (referenced in 9 articles )
Showing results 1 to 9 of 9.
- Fan, Jianqing; Yao, Qiwei: The elements of financial econometrics (2017)
- Nicholas Michaud, Perry de Valpine, Daniel Turek, Christopher J. Paciorek: Sequential Monte Carlo Methods in the nimble R Package (2017) arXiv
- Bhadra, Anindya; Ionides, Edward L.: Adaptive particle allocation in iterated sequential Monte Carlo via approximating meta-models (2016)
- Philipp H Boersch-Supan, Leah R Johnson: deBInfer: Bayesian inference for dynamical models of biological systems in R (2016) arXiv
- Aaron A. King, Dao Nguyen, Edward L. Ionides: Statistical Inference for Partially Observed Markov Processes via the R Package pomp (2015) arXiv
- Bretó, Carles: On idiosyncratic stochasticity of financial leverage effects (2014)
- Sheinson, Daniel M.; Niemi, Jarad; Meiring, Wendy: Comparison of the performance of particle filter algorithms applied to tracking of a disease epidemic (2014)
- Ionides, Edward L.: Discussion of “Feature matching in time series modeling” by Y. Xia and H. Tong (2011)
- Bretó, Carles; He, Daihai; Ionides, Edward L.; King, Aaron A.: Time series analysis via mechanistic models (2009)