panelPomp-package | R Documentation |
The panelPomp package provides facilities for inference on panel data using panel partially-observed Markov process (PanelPOMP) models. To do so, it relies on and extends a number of facilities that the pomp package provides for inference on time series data using partially-observed Markov process (POMP) models.
The panelPomp package extends to panel data some of the capabilities of the pomp package to fit nonlinear, non-Gaussian dynamic models. This is done accomodating both fixed and random effects. Currently, the focus is on likelihood-based approaches. In addition to these likelihood-based tools, panelPomp also provides a framework under which alternative statistical methods for PanelPOMP models can be developed (very much like pomp provides a platform upon which statistical inference methods for POMP models can be implemented).
The first step in using panelPomp is to encode one's model(s) and data
in objects of class panelPomp
.
One does this via a call to the panelPomp constructor
function.
panelPomp version NA provides algorithms for
particle filtering of panel data (AKA sequential Monte Carlo or
sequential importance sampling), as proposed in \Breto, Ionides and King
(2020). This reference provides the fundamental theoretical support for the
averaging of Monte Carlo replicates of panel unit likelihoods as implemented
in panelPomp; see pfilter
the panel iterated filtering method of \Breto, Ionides and King
(2020). This reference provides the fundamental theoretical support for the
extensions of the iterated filtering ideas of Ionides et al. (2006, 2011,
2015) to panel data as implemented in panelPomp; see
mif2
The package also provides various tools for handling and extracting information on models and data.
panelPomp extends to panel data the general interface to the components of POMP models provided by pomp. In doing so, it contributes to the goal of the pomp project of facilitating the development of new algorithms in an environment where they can be tested and compared on a growing body of models and datasets.
Contributions are welcome, as are suggestions for improvement, feature requests, and bug reports. Please submit these via the panelPomp issues page. We particularly welcome minimal working examples displaying uninformative, misleading or inacurate error messages. We also welcome suggestions for clarifying obscure passages in the documentation. Help requests are welcome, but please consider before sending requests whether they are regarding the use of panelPomp or that of pomp. For help with pomp, please visit pomp's FAQ.
Examples are provided via the contacts()
, panelGompertz()
and panelRandomWalk()
functions.
panelPomp is provided under the GPL-3 License.
Maintainer: Carles Breto carles.breto@uv.es (ORCID)
Authors:
Edward L. Ionides (ORCID)
Aaron A. King (ORCID)
Jesse Wheeler jeswheel@umich.edu (ORCID)
2020
pomp package, panelPomp
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