Imports: MASS, wdm, FactoMineR, nleqslv, nnet, ggplot2, ggrepel,
(phi, Cramér V, correlation coefficient, eta-squared...).

Suggests: rmarkdown, knitr, rmdformats, cluster, WeightedCluster, vcd,
measure for contingency tables ("Percentages of Maximum Deviation from Independence", aka PEM, see Cibois (1993) <doi:10.1177/075910639304000103>) and some tools to measure
and plot bivariate associations between variables

Suggests: rmarkdown, knitr, rmdformats, cluster, WeightedCluster

Title: GD device for creating bitmap graphics as jpeg, png or gif files using gd library
Description:  Platform and X11 independent device for creating bitmaps (png, gif and jpeg).
SystemRequirements: libgd (>= 2.0.29 http://www.boutell.com/gd/), recommended: freetype2, fontconfig and msttcorefonts

Title: GD device for creating bitmap graphics as jpeg, png or gif files
Description: Platform and X11 independent device for creating bitmaps
SystemRequirements: libgd (>= 2.0.29 http://www.boutell.com/gd/),
recommended: freetype2, fontconfig and msttcorefonts

SystemRequirements: libgd (freetype2, fontconfig and msttcorefonts recommended)

SystemRequirements: libgd (>= 2.0.28 http://www.boutell.com/gd/), recommended: freetype2, fontconfig and msttcorefonts

Title: Download, slice, and normalize GDELT data
Author: Stephen R. Haptonstahl, Thomas Scherer, Timo Thoms, and John Beieler
Description: The GDELT data set is over 60 GB now and growing 100 MB a month.
The number of source articles has increased over time and unevenly across
countries. This package makes it easy to download a subset of that data,
then normalize that data to facilitate valid timeseries analysis.
Packaged: 2013-12-02 15:27:36 UTC; shaptonstahl

Author: Stephen R. Haptonstahl, Thomas Scherer, Timo Thoms, and Patrick Wheatley
then normalize that data to facilitate valid time series analysis.

Title: Download, Slice, and Normalize GDELT V1 Data
Description: The GDELT V1 Event data set is over 37 GB now and growing 250 MB
a month. The number of source articles has increased over time and unevenly
across countries. This package makes it easy to download a subset of that
data, then normalize that data to facilitate valid time series analysis.