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>.
Version: 1.0.8 Depends: R (≥ 3.1), methods Imports: graphics, class, FactoMineR, ggplot2, ggridges, grid, histogram, grDevices, stats, utils, Rcpp LinkingTo: Rcpp, RcppArmadillo Published: 2024-01-24 DOI: 10.32614/CRAN.package.HistDAWass Author: Antonio Irpino [aut, cre] Maintainer: Antonio Irpino <antonio.irpino at unicampania.it> License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] NeedsCompilation: yes Materials: README CRAN checks: HistDAWass results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=HistDAWass to link to this page.
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