rmarkdown, knitr, rmdformats, cluster, WeightedCluster

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

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

libgd (freetype2, fontconfig and msttcorefonts recommended)

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

Download, slice, and normalize GDELT data
Stephen R. Haptonstahl, Thomas Scherer, Timo Thoms, and John Beieler
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.
'DownloadGdelt.R' 'FileInfo.R' 'FileListFromDates.R'
'FilterGdeltDataframe.R' 'GdeltZipToDataframe.R' 'GetGdelt.R'
'LocalVersusRemote.R' 'NormEventCounts.R'

Stephen R. Haptonstahl, Thomas Scherer, Timo Thoms, and Patrick Wheatley
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 time series analysis.

Download, Slice, and Normalize GDELT V1 Data
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.

c(person(given = "Wenchao",family = "Ma", role = c("aut", "cre"),email = "wenchao.ma@rutgers.edu"),person(given = "Jimmy", family = "de la Torre", role = "aut"))
A set of psychometric tools for cognitive diagnostic analyses for both dichotomous and polytomous responses. Various cognitive diagnosis models (CDMs) can be estimated, include the generalized deterministic inputs, noisy and gate (G-DINA) model by de la Torre (2011) <DOI:10.1007/s11336-011-9207-7>, the sequential G-DINA model by Ma and de la Torre (2016)<DOI:10.1111/bmsp.12070>, and many other models they subsume. Joint attribute distribution can be saturated, higher-order or structured. Q-matrix validation, item and model fit statistics, model comparison at test and item level and differential item functioning can also be conducted. A graphical user interface is also provided.
stats, graphics, utils, Rcpp (>= 0.12.1), nloptr, MASS,
numDeriv, alabama, Rsolnp, ggplot2, shiny, shinydashboard
'CR.R' 'GDI.R' 'GDINA-package.R' 'GDINA.R' 'HO.R'
'HOsupplement.R' 'M.R' 'M.mono.R' 'Mstep.R' 'Msupplement.R'
'RcppExports.R' 'alpha.R' 'anova.GDINA.R' 'att.struc.R'
'autoGDINA.R' 'designmatrix.R' 'dif.R' 'ecpe.R' 'eta.loc.R'
'exportedUtils.R' 'extract.GDINA.R' 'extract.Qval.R'
'itemfit.R' 'extract.itemfit.R' 'modelcomp.R'
'extract.modelcomp.R' 'simGDINA.R' 'extract.simGDINA.R'
'frac20.R' 'heatplot.itemfit.R' 'hoparm.R' 'initials.R'
'internalExtract.R' 'itemparm.GDINA.R' 'monocheck.R'
'personparm.GDINA.R' 'plotIRF.GDINA.R' 'plotPVAF.Qval.R'
'print.GDINA.R' 's3GDINA.R' 'sim10GDINA.R' 'sim20seqGDINA.R'
'sim21seqDINA.R' 'sim30DINA.R' 'sim30GDINA.R' 'sim30pGDINA.R'
'startGDINA.R' 'summary.GDINA.R' 'utils.R' 'zzz.R'
Wenchao Ma [aut, cre],
Jimmy de la Torre [aut]