graphics, class, FactoMineR, ggplot2, ggridges, grid,
histogram, grDevices, stats, utils, Rcpp

c(person(given="Antonio",
family= "Irpino",
role = c("aut", "cre"),
email = "antonio.irpino@unicampania.it",
comment = c(ORCID = "0000-0001-9293-7180")))
Antonio Irpino [aut, cre] (<https://orcid.org/0000-0001-9293-7180>)

In the framework of Symbolic Data Analysis, a relatively new
approach to the statistical analysis of multi-valued data, we consider
histogram-valued data, i.e., data described by univariate histograms. The
methods and the basic statistics for histogram-valued data are mainly based
on the L2 Wasserstein metric between distributions, i.e., the Euclidean metric
between quantile functions. The package contains unsupervised classification
techniques, least square regression and tools for histogram-valued data and for
histogram time series. An introducing paper is Irpino A. Verde R. (2015) \doi{10.1007/s11634-014-0176-4}.

In the framework of Symbolic Data Analysis, a relatively new
approach to the statistical analysis of multi-valued data, we consider
histogram-valued data, i.e., data described by univariate histograms. The
methods and the basic statistics for histogram-valued data are mainly based
on the L2 Wasserstein metric between distributions, i.e., the Euclidean metric
between quantile functions. The package contains unsupervised classification
techniques, least square regression and tools for histogram-valued data and for
histogram time series. An introducing paper is Irpino A. Verde R. (2015) <doi: 10.1007/s11634-014-0176-4>.

graphics, class, FactoMineR, ggplot2, ggridges, grid,
histogram, grDevices, stats, utils, Rcpp, plyr

Summary Statistics for Histogram/Count Data
In some cases you will have data in a histogram format, where
you have a vector of all possible observations, and a vector of how many
times each observation appeared. You could expand this into a single 1D
vector, but this may not be advisable if the counts are extremely large.
'HistDat' allows for the calculation of summary statistics without the need
for expanding your data.