person("Andrie", "de Vries", role = "ctb"))

Description: The document converter 'pandoc' <https://pandoc.org/> is widely used
(for more information see <https://pandoc.org/>) but is also bound to the same

Description: Functions and datasets for fitting models to Panel data.

Packaged: Tue May 25 14:58:08 2004; rgentlem

Packaged: Thu Jun 30 08:41:59 2005; rgentlem

Packaged: Fri Jun  9 08:35:39 2006; robert

Title: Estimation of Linear AR(1) Panel Data Models with
Description: The package estimates linear models on panel data structures in the presence of AR(1)-type autocorrelation as well as panel heteroskedasticity and/or contemporaneous correlation. First, AR(1)-type autocorrelation is addressed via a two-step Prais-Winsten feasible generalized least squares (FGLS) procedure, where the autocorrelation coefficients may be panel-specific. A number of common estimators for the autocorrelation coefficient are supported. In case of panel heteroskedasticty, one can choose to use a sandwich-type robust standard error estimator with OLS or a panel weighted least squares estimator after the two-step Prais-Winsten estimator. Alternatively, if panels are both heteroskedastic and contemporaneously correlated, the package supports panel-corrected standard errors (PCSEs) as well as the Parks-Kmenta FGLS estimator.

Title: Visualizing Panel Data with Dichotomous Treatments
Description: Visualizes panel data with dichotomous treatments. 'panelView' has two main functionalities: (1) it visualizes the treatment and missing-value statuses of each observation in a panel/time-series-cross-sectional (TSCS) dataset; and (2) it plots the outcome variable (either continuous or discrete) in a time-series fashion.
Imports: ggplot2 (>= 2.1.0), gridExtra, grid

Description: Visualizes panel data with dichotomous treatments. It has two main functionalities: (1) it visualizes the treatment and missing-value statuses of each observation in a panel/time-series-cross-sectional (TSCS) dataset; and (2) it plots the outcome variable (either continuous or discrete) in a time-series fashion.