A high performance package implementing random effects and/or
    sample selection models for panel count data. The details of the models are discussed in Peng and Van den Bulte (2023) <doi:10.2139/ssrn.2702053>.

Provides a comprehensive set of tools for describing and visualizing panel data structures,
             as well as for summarizing and visualizing variables within a panel data context.

Understanding the dynamics of potentially heterogeneous variables is important in statistical applications.
    This package provides tools for estimating the degree of heterogeneity across cross-sectional units in the panel data analysis.
    The methods are developed by Okui and Yanagi (2019) <doi:10.1016/j.jeconom.2019.04.036> and Okui and Yanagi (2020) <doi:10.1093/ectj/utz019>.

Implements a set of methodological tools
	     that enable researchers to apply matching methods to
	     time-series cross-sectional data. Imai, Kim, and Wang
	     (2023) <http://web.mit.edu/insong/www/pdf/tscs.pdf> 
	     proposes a nonparametric generalization of the
	     difference-in-differences estimator, which does not rely
	     on the linearity assumption as often done in
	     practice. Researchers first select a method of matching
	     each treated observation for a given unit in a
	     particular time period with control observations from
	     other units in the same time period that have a similar
	     treatment and covariate history. These methods include
	     standard matching methods based on propensity score and
	     Mahalanobis distance, as well as weighting methods. Once 
	     matching and refinement is done,  
	     treatment effects can be estimated with 
	     standard errors. The package also offers diagnostics for researchers to assess the quality 
	     of their results.

Data analysis based on panel partially-observed Markov process (PanelPOMP) models. To implement such models, simulate them and fit them to panel data, 'panelPomp' extends some of the facilities provided for time series data by the 'pomp' package. Implemented methods include filtering (panel particle filtering) and maximum likelihood estimation (Panel Iterated Filtering) as proposed in Breto, Ionides and King (2020) "Panel Data Analysis via Mechanistic Models" <doi:10.1080/01621459.2019.1604367>.