Imports: quanteda, extrafont, ggplot2, ggrepel, grid, sna, igraph,
Matrix, methods, network, RColorBrewer, Rcpp (>= 0.12.12),

Title: Textual Statistics for the Quantitative Analysis of Textual Data
Description: Textual statistics functions formerly in the 'quanteda' package.
Textual statistics for characterizing and comparing textual data. Includes
functions for measuring term and document frequency, the co-occurrence of
words, similarity and distance between features and documents, feature entropy,
keyword occurrence, readability, and lexical diversity.  These functions
extend the 'quanteda' package and are specially designed for sparse textual data.
Imports: quanteda, data.table (>= 1.9.6), digest, Matrix (>= 1.2),
methods, nsyllable, proxyC (>= 0.1.4), Rcpp (>= 0.12.12),
Suggests: entropy, ExPosition, proxy, rmarkdown, spelling, svs,

Imports: quanteda, Matrix, methods, nsyllable, proxyC (>= 0.1.4), Rcpp

Title: Quantification of Qualitative Survey Data
Description: This package provides different functions for quantifying qualitative survey data. It supports the Carlson-Parkin method, the regression approach and the balance approach.
Packaged: 2015-01-26 22:12:49 UTC; Zuckarelli

Description: Provides different functions for quantifying qualitative survey data. It supports the Carlson-Parkin method, the regression approach and the balance approach.

Description: Provides different functions for quantifying qualitative survey data. It supports the Carlson-Parkin method, the regression approach, the balance approach and the conditional expectations method.

Description: Code for centroid, median and quantile classifiers.

Packaged: 2016-02-02 11:55:22 UTC; cinzia.viroli2

Description: Estimation and inference methods for the
cross-quantilogram.  The cross-quantilogram is a measure of nonlinear
dependence between two variables, based on either unconditional or
conditional quantile functions.  The cross-quantilogram can be
considered as an extension of the correlogram, which is a correlation
function over multiple lag periods and mainly focuses on linear
dependency.  One can use the cross-quantilogram to detect the presence
of directional predictability from one time series to another.  This
package provides a statistical inference method based on the
stationary bootstrap.  See Linton and Whang (2007)
<doi:10.1016/j.jeconom.2007.01.004> for univariate time series
analysis and Han, Linton, Oka and Whang (2016)
<doi:10.1016/j.jeconom.2016.03.001> for multivariate time series