The document converter 'pandoc' <https://pandoc.org/> is widely used
in the R community. One feature of 'pandoc' is that it can produce and consume
JSON-formatted abstract syntax trees (AST). This allows to transform a given
source document into JSON-formatted AST, alter it by so called filters and pass
the altered JSON-formatted AST back to 'pandoc'. This package provides functions
which allow to write such filters in native R code.
Although this package is inspired by the Python package 'pandocfilters'
<https://github.com/jgm/pandocfilters/>, it provides additional convenience functions which make it simple to use the 'pandocfilters' package as a
report generator. Since 'pandocfilters' inherits most of it's functionality
from 'pandoc' it can create documents in many formats
(for more information see <https://pandoc.org/>) but is also bound to the same
limitations as 'pandoc'.

Functions and datasets for fitting models to Panel data.

Estimation of Linear AR(1) Panel Data Models with
Cross-Sectional Heteroskedasticity and/or Correlation
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.

Visualizing Panel Data with Dichotomous Treatments
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.