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HistDAWass  

Histogram-Valued Data Analysis
View on CRAN: Click here


Download and install HistDAWass package within the R console
Install from CRAN:
install.packages("HistDAWass")

Install from Github:
library("remotes")
install_github("cran/HistDAWass")

Install by package version:
library("remotes")
install_version("HistDAWass", "1.0.8")



Attach the package and use:
library("HistDAWass")
Maintained by
Antonio Irpino
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2015-02-18
Latest Update: 2024-01-24
Description:
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) .
How to cite:
Antonio Irpino (2015). HistDAWass: Histogram-Valued Data Analysis. R package version 1.0.8, https://cran.r-project.org/web/packages/HistDAWass. Accessed 26 Oct. 2026.
Previous versions and publish date:
0.1.1 (2015-02-24 15:29), 0.1.2 (2015-03-17 20:44), 0.1.3 (2015-07-16 14:24), 0.1.4 (2016-01-12 22:21), 0.1.6 (2017-02-13 11:27), 0.1.7 (2017-09-21 22:46), 0.1.8 (2017-10-06 17:55), 0.1 (2015-02-18 17:08), 1.0.0 (2017-12-07 17:02), 1.0.1 (2018-03-20 17:23), 1.0.3 (2019-06-05 15:40), 1.0.4 (2020-02-19 10:00), 1.0.5 (2021-05-24 18:20), 1.0.6 (2021-06-22 19:20), 1.0.7 (2022-09-26 11:40), 1.0.8 (2024-01-24 18:42), (2026-10-18 21:20)
Other packages that cited HistDAWass R package
View HistDAWass citation profile
Other R packages that HistDAWass depends, imports, suggests or enhances
Complete documentation for HistDAWass
Functions, R codes and Examples using the HistDAWass R package
Some associated functions: Age_Pyramids_2014 . Agronomique . BLOOD . BloodBRITO . Center.cell.MatH-methods . China_Month . China_Seas . DouglasPeucker . HTS-class . HTS.exponential.smoothing . HTS.moving.averages . HTS.predict.knn . HistDAWass-package . MatH-class . OzoneFull . OzoneH . RetHTS . ShortestDistance . TMatH-class . TdistributionH-class . WH.1d.PCA . WH.MultiplePCA . WH.SSQ-methods . WH.SSQ2-methods . WH.bind-methods . WH.bind.col-methods . WH.bind.row-methods . WH.correlation-methods . WH.correlation2-methods . WH.mat.prod-methods . WH.mat.sum-methods . WH.plot_multiple_Spanish.funs . WH.plot_multiple_indivs . WH.regression.GOF . WH.regression.two.components . WH.regression.two.components.predict . WH.var.covar-methods . WH.var.covar2-methods . WH.vec.mean-methods . WH.vec.sum-methods . WH_2d_Adaptive_Kohonen_maps . WH_2d_Kohonen_maps . WH_MAT_DIST . WH_adaptive.kmeans . WH_adaptive_fcmeans . WH_fcmeans . WH_hclust . WH_kmeans . WassSqDistH-methods . checkEmptyBins-methods . compP-methods . compQ-methods . crwtransform-methods . data2hist . distributionH-class . dotpW-methods . extract-methods . get.MatH.main.info-methods . get.MatH.ncols-methods . get.MatH.nrows-methods . get.MatH.rownames-methods . get.MatH.stats-methods . get.MatH.varnames-methods . get.cell.MatH-methods . get.distr-methods . get.histo-methods . get.m-methods . get.s-methods . is.registeredMH-methods . kurtH-methods . meanH-methods . minus . plot-HTS . plot-MatH . plot-TdistributionH . plot-distributionH . plotPredVsObs . plot_errors . plus-methods . rQQ-methods . register-methods . registerMH-methods . set.cell.MatH-methods . show-MatH-methods . show-distributionH-methods . skewH-methods . stations_coordinates . stdH-methods . subsetHTS-methods . times-methods . 
Some associated R codes: All_classes.R . For_Rccp_int.R . Fuzzy_cmeans.R . H_time_series.R . HistDAWass-package.R . Kohonen_maps.R . Met_HTS.R . Met_MatH.R . Met_distributionH.R . Plotting_with_ggplot.R . RcppExports.R . Utility.R . principal_components.R . regression.R . unsuperv_classification.R .  Full HistDAWass package functions and examples
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