Inherited favorable theoretical properties of particle Markov Chain Monte Carlo methods, the second-order Particle Iterated Smoothing can improve the parameter estimations in term of i) shortening the burn-in period, (ii) accelerating the mixing of the Markov chain at the stationary period, and (iii) simplifying tuning.
It is inherited from R package pomp and is2, so it can virtually be applied to every model therein.
At the moment, support is provided for - particle iterated filtering - particle iterated smoothing - 2nd order particle iterated smoothing - average particle iterated smoothing
Simple worked examples are provided in the test directory of the installed package.
Future support for a variety of other algorithms is envisioned.
Please let the developers know if you find pis2 useful, if you publish results obtained using it, if you come up with improvements, find bugs, or have suggestions or feature requests!
The package is provided under the GPL. Contributions are welcome, as are comments, suggestions, feature requests, examples, and bug reports. Please send these to nguyenxd at umich dot edu.
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