Aaron A. King's POMP package provides tools for analyzing partially observed Markov processes, also known as stochastic dynamical systems and hidden Markov models. It implements facilities for simulating these models and fitting them to time series data using frequentist and Bayesian methods. The package serves as a versatile platform for inference methods applicable to general POMP models.
Use Cases
- Simulating stochastic dynamical systems based on POMP model implementations
- Fitting hidden Markov models to time series data using frequentist methods
- Applying Bayesian inference techniques to partially observed Markov processes
- Implementing custom inference methods for nonlinear, non-Gaussian state-space models
Strengths
- Package authored by Aaron A. King, a recognized contributor in the field
- Supports both frequentist and Bayesian inference methods for model fitting
- Designed as a versatile platform for implementing general POMP inference methods
Limitations
- Description metadata is limited; actual data quality requires manual inspection after download
- Column-level documentation is absent; field semantics must be inferred after download
Provenance
- Source
- Aaron A. King