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HistDAWass: Histogram-Valued Data Analysis
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:
Linking:
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https://CRAN.R-project.org/package=HistDAWass
to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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