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 5 articles )
Showing results 1 to 5 of 5.
- Bhadra, Anindya; Ionides, Edward L.: Adaptive particle allocation in iterated sequential Monte Carlo via approximating meta-models (2016)
- 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)