pomp
Statistical Inference for Partially Observed Markov Processes
Tools for data analysis with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models). The package 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 versatile platform for implementation of inference methods for general POMP models.
pomp
an R package for statistical inference on partially observed Markov processes
For installation instructions and user guides, see the package website.
Related packages:
- PartiallyObservedMarkovProcesses.jl: Experimental port of pomp to Julia
- phylopomp: pomp for phylodynamics
- panelPomp: pomp for panel data
- spatPomp: Spatiotemporal dynamics
- circumstance: Experimental parallelization tools
- subplex: The subplex optimization algorithm