quanteda, Matrix, methods, nsyllable, proxyC (>= 0.1.4), Rcpp
(>= 0.12.12), RcppParallel, stringi

Quantification of Qualitative Survey Data
This package provides different functions for quantifying qualitative survey data. It supports the Carlson-Parkin method, the regression approach and the balance approach.

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

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.

Code for centroid, median and quantile classifiers.

c(person(given = "Tatsushi",
family = "Oka",
role = c("aut", "cre"),
email = "oka.econ@gmail.com"),
person(given = "Heejon",
family = "Han",
role = "ctb"),
person(given = "Oliver",
family = "Linton",
role = "ctb"),
person(given = "Yoon-Jae",
family = "Whang",
role = "ctb"))
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
analysis.
Tatsushi Oka [aut, cre],
Heejon Han [ctb],
Oliver Linton [ctb],
Yoon-Jae Whang [ctb]

c(
person("Tatsushi", "Oka", , "oka.econ@gmail.com", role = c("aut", "cre")),
person("Heejon", "Han", role = "ctb"),
person("Oliver", "Linton", role = "ctb"),
person("Yoon-Jae", "Whang", role = "ctb")
)
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
analysis.