We extend two general methods of moment estimators to panel vector 
    autoregression models (PVAR) with p lags of endogenous variables, predetermined 
    and strictly exogenous variables. This general PVAR model contains the first 
    difference GMM estimator by Holtz-Eakin et al. (1988) <doi:10.2307/1913103>, 
    Arellano and Bond (1991) <doi:10.2307/2297968> and the system GMM estimator 
    by Blundell and Bond (1998) <doi:10.1016/S0304-4076(98)00009-8>. We also 
    provide specification tests (Hansen overidentification test, lag selection 
    criterion and stability test of the PVAR polynomial) and classical structural 
    analysis for PVAR models such as orthogonal and generalized impulse response 
    functions, bootstrapped confidence intervals for impulse response analysis and 
    forecast error variance decompositions.

Visualizes panel data. It has three main functionalities: (1) it plots the treatment status and missing values in a panel dataset; (2) it visualizes the temporal dynamics of a main variable of interest; (3) it depicts the bivariate relationships between a treatment variable and an outcome variable either by unit or in aggregate. For details, see <doi:10.18637/jss.v107.i07>.

Leading/lagging a panel, creating dummy variables,
             taking panel differences, looking for panel autocorrelations,
             and more. Implemented via a 'data.table' back end.

Tools to interact with the 'Pangaea' Database
    (<https://www.pangaea.de>), including functions for searching for data,
    fetching 'datasets' by 'dataset' 'ID', and working with the 'Pangaea'
    'OAI-PMH' service.

A central decision in a parametric regression is how to specify the relation between an dependent variable and each explanatory variable. This package provides a semi-parametric tool for comparing different transformations of an  explanatory variables in a parametric regression. The functions is relevant in a situation, where you would use a box-cox or Box-Tidwell transformations.  In contrast to the classic power-transformations, the methods in this package allows for  theoretical driven user input and the possibility to compare with a non-parametric transformation.