A tutorial on panel data analysis using partially observed Markov processes via the R package panelPomp

TL;DR

This paper introduces the R package panelPomp, which facilitates analysis of panel data using partially observed Markov process models, incorporating advanced Monte Carlo methods and tools for handling large parameter spaces.

Contribution

It provides a comprehensive tutorial on representing PanelPOMP models in software and demonstrates their application with recent computational advances.

Findings

Implementation of iterated filtering algorithm for PanelPOMP

Use of Monte Carlo adjusted profile methodology

Tools for model and data manipulation in panel structure

Abstract

The R package panelPomp supports analysis of panel data via a general class of partially observed Markov process models (PanelPOMP). This package tutorial describes how the mathematical concept of a PanelPOMP is represented in the software and demonstrates typical use-cases of panelPomp. Monte Carlo methods used for POMP models require adaptation for PanelPOMP models due to the higher dimensionality of panel data. The package takes advantage of recent advances for PanelPOMP, including an iterated filtering algorithm, Monte Carlo adjusted profile methodology and block optimization methodology to assist with the large parameter spaces that can arise with panel models. In addition, tools for manipulation of models and data are provided that take advantage of the panel structure.