Package: HistDAWass 1.0.8

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>.

Authors:Antonio Irpino [aut, cre]

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# Install 'HistDAWass' in R:
install.packages('HistDAWass', repos = c('https://airpino.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/airpino/histdawass/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • Age_Pyramids_2014 - Age pyramids of all the countries of the World in 2014
  • Agronomique - Agronomique data
  • BLOOD - Blood dataset for Histogram data analysis
  • BloodBRITO - Blood dataset from Brito P. for Histogram data analysis
  • China_Month - A monthly climatic dataset of China
  • China_Seas - A seasonal climatic dataset of China
  • OzoneFull - Full Ozone dataset for Histogram data analysis
  • OzoneH - Complete Ozone dataset for Histogram data analysis
  • RetHTS - A histogram-valued dataset of returns
  • stations_coordinates - Stations coordinates of China_Month and China_Seas datasets

On CRAN:

4.74 score 5 stars 74 scripts 396 downloads 3 mentions 68 exports 103 dependencies

Last updated 10 months agofrom:be7447e3ba. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 20 2024
R-4.5-win-x86_64OKNov 20 2024
R-4.5-linux-x86_64OKNov 20 2024
R-4.4-win-x86_64OKNov 20 2024
R-4.4-mac-x86_64OKNov 20 2024
R-4.4-mac-aarch64OKNov 20 2024
R-4.3-win-x86_64OKNov 20 2024
R-4.3-mac-x86_64OKNov 20 2024
R-4.3-mac-aarch64OKNov 20 2024

Exports:Center.cell.MatHcheckEmptyBinscompPcompQcrwtransformdata2histdistributionHdotpWDouglasPeuckerget.cell.MatHget.distrget.histoget.mget.MatH.main.infoget.MatH.ncolsget.MatH.nrowsget.MatH.rownamesget.MatH.statsget.MatH.varnamesget.sHTS.exponential.smoothingHTS.moving.averagesHTS.predict.knnis.registeredMHkurtHMatHmeanHplotplot_errorsplotPredVsObsregisterregisterMHrQQset.cell.MatHShortestDistanceskewHstdHsubsetHTSsummaryHTSWassSqDistHWH_2d_Adaptive_Kohonen_mapsWH_2d_Kohonen_mapsWH_adaptive_fcmeansWH_adaptive.kmeansWH_fcmeansWH_hclustWH_kmeansWH_MAT_DISTWH.1d.PCAWH.bindWH.bind.colWH.bind.rowWH.correlationWH.correlation2WH.mat.prodWH.mat.sumWH.MultiplePCAWH.plot_multiple_indivsWH.plot_multiple_Spanish.funsWH.regression.GOFWH.regression.two.componentsWH.regression.two.components.predictWH.SSQWH.SSQ2WH.var.covarWH.var.covar2WH.vec.meanWH.vec.sum

Dependencies:abindbackportsbase64encbootbroombslibcachemcarcarDataclasscliclustercolorspacecowplotcpp11crosstalkDerivdigestdoBydplyrDTellipseemmeansestimabilityevaluateFactoMineRfansifarverfastmapflashClustfontawesomeFormulafsgenericsggplot2ggrepelggridgesgluegtablehighrhistogramhtmltoolshtmlwidgetshttpuvisobandjquerylibjsonliteknitrlabelinglaterlatticelazyevalleapslifecyclelme4magrittrMASSMatrixMatrixModelsmemoisemgcvmicrobenchmarkmimeminqamodelrmultcompViewmunsellmvtnormnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpromisespurrrquantregR6rappdirsRColorBrewerRcppRcppArmadilloRcppEigenrlangrmarkdownsassscalesscatterplot3dSparseMstringistringrsurvivaltibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunyaml

Readme and manuals

Help Manual

Help pageTopics
Histogram-Valued Data AnalysisHistDAWass-package HistDAWass _PACKAGE
extract from a MatH Method [[ [,MatH,ANY,ANY,ANY-method [,MatH-method
Method **,distributionH,distributionH-method *,distributionH,numeric-method *,numeric,distributionH-method *-methods
Method ++ +,distributionH,distributionH-method +,distributionH,numeric-method +,numeric,distributionH-method
Age pyramids of all the countries of the World in 2014Age_Pyramids_2014
Agronomique dataAgronomique
Blood dataset for Histogram data analysisBLOOD
Blood dataset from Brito P. for Histogram data analysisBloodBRITO
Method Center.cell.MatH Centers all the cells of a matrix of distributionsCenter.cell.MatH Center.cell.MatH,MatH-method
Method 'checkEmptyBins'checkEmptyBins checkEmptyBins,distributionH-method
A monthly climatic dataset of ChinaChina_Month
A seasonal climatic dataset of ChinaChina_Seas
Method 'compP'compP compP,distributionH,numeric-method compP,distributionH-method
Method 'compQ'compQ compQ,distributionH,numeric-method compQ,distributionH-method
Method 'crwtransform': returns the centers and the radii of bins of a distributioncrwtransform crwtransform,distributionH-method
From real data to distributionH.data2hist
Class distributionH.distributionH distributionH-class initialize,distributionH-method
Method 'dotpW'dotpW dotpW,distributionH,distributionH-method dotpW,distributionH,numeric-method dotpW,distributionH-method dotpW,numeric,distributionH-method
Ramer-Douglas-Peucker algorithm for curve fitting with a PolyLineDouglasPeucker
Method get.cell.MatH Returns the histogram in a cell of a matrix of distributionsget.cell.MatH get.cell.MatH,MatH,numeric,numeric-method get.cell.MatH,MatH-method
Method 'get.distr': show the distributionget.distr get.distr,distributionH-method
Method 'get.histo': show the distribution with binsget.histo get.histo,distributionH-method
Method 'get.m': the mean of a distributionget.m get.m,distributionH-method
Method get.MatH.main.infoget.MatH.main.info get.MatH.main.info,MatH-method
Method get.MatH.ncolsget.MatH.ncols get.MatH.ncols,MatH-method
Method get.MatH.nrowsget.MatH.nrows get.MatH.nrows,MatH-method
Method get.MatH.rownamesget.MatH.rownames get.MatH.rownames,MatH-method
Method get.MatH.statsget.MatH.stats get.MatH.stats,MatH-method
Method get.MatH.varnamesget.MatH.varnames get.MatH.varnames,MatH-method
Method 'get.s': the standard deviation of a distributionget.s get.s,distributionH-method
Class HTSHTS HTS-class initialize,HTS-method
Smoothing with exponential smoothing of a histogram time seriesHTS.exponential.smoothing
Smoothing with moving averages of a histogram time seriesHTS.moving.averages
K-NN predictions of a histogram time seriesHTS.predict.knn
Method is.registeredMHis.registeredMH is.registeredMH,MatH-method
Method 'kurtH': computes the kurthosis of a distributionkurtH kurtH,distributionH-method
Class MatH.initialize,MatH-method MatH MatH-class
Method 'meanH': computes the mean of a distributionmeanH meanH,distributionH-method
Method --,distributionH,distributionH-method -,distributionH,numeric-method -,numeric,distributionH-method minus
Full Ozone dataset for Histogram data analysisOzoneFull
Complete Ozone dataset for Histogram data analysisOzoneH
A function for plotting functions of errorsplot_errors
plot for a distributionH objectplot,distributionH-method plot-distributionH
Method plot for a histogram time seriesplot,HTS-method plot-HTS
Method plot for a matrix of histogramsplot,MatH-method plot-MatH
plot for a TdistributionH objectplot,TdistributionH-method plot-TdistributionH
A function for comparing observed vs predicted histogramsplotPredVsObs
Method 'register'register register,distributionH,distributionH-method register,distributionH-method
Method registerMHregisterMH registerMH,MatH-method
A histogram-valued dataset of returnsRetHTS
Method 'rQQ'rQQ rQQ,distributionH,distributionH-method rQQ,distributionH-method
Method set.cell.MatH assign a histogram to a cell of a matrix of histogramsset.cell.MatH set.cell.MatH,distributionH,MatH,numeric,numeric-method set.cell.MatH,MatH-method
Shortes distance from a point o a 2d segmentShortestDistance
Method show for distributionHshow show,distributionH-method
Method show for MatHshow,MatH-method show-MatH
Method 'skewH': computes the skewness of a distributionskewH skewH,distributionH-method
Stations coordinates of China_Month and China_Seas datasetsstations_coordinates
Method 'stdH': computes the standard deviation of a distributionstdH stdH,distributionH-method
Method 'subsetHTS': extract a subset of a histogram time seriessubsetHTS subsetHTS,HTS,numeric,numeric-method
A function for summarize HTSsummaryHTS
Class TdistributionHinitialize,TdistributionH-method TdistributionH TdistributionH-class
Class TMatHinitialize,TMatH-method TMatH TMatH-class
Method 'WassSqDistH'WassSqDistH WassSqDistH,distributionH,distributionH-method WassSqDistH,distributionH-method
Batch Kohonen self-organizing 2d maps using adaptive distances for histogram-valued dataWH_2d_Adaptive_Kohonen_maps
Batch Kohonen self-organizing 2d maps for histogram-valued dataWH_2d_Kohonen_maps
Fuzzy c-means with adaptive distances for histogram-valued dataWH_adaptive_fcmeans
K-means of a dataset of histogram-valued data using adaptive Wasserstein distancesWH_adaptive.kmeans
Fuzzy c-means of a dataset of histogram-valued dataWH_fcmeans
Hierarchical clustering of histogram dataWH_hclust
K-means of a dataset of histogram-valued dataWH_kmeans
L2 Wasserstein distance matrixWH_MAT_DIST
Principal components analysis of histogram variable based on Wasserstein distanceWH.1d.PCA
Method WH.bindWH.bind WH.bind,MatH,MatH-method WH.bind,MatH-method
Method WH.bind.colWH.bind.col WH.bind.col,MatH,MatH-method WH.bind.col,MatH-method
Method WH.bind.rowWH.bind.row WH.bind.row,MatH,MatH-method WH.bind.row,MatH-method
Method WH.correlationWH.correlation WH.correlation,MatH-method
Method WH.correlation2WH.correlation2 WH.correlation2,MatH,MatH-method WH.correlation2,MatH-method
Method WH.mat.prodWH.mat.prod WH.mat.prod,MatH,MatH-method WH.mat.prod,MatH-method
Method WH.mat.sumWH.mat.sum WH.mat.sum,MatH,MatH-method WH.mat.sum,MatH-method
Principal components analysis of a set of histogram variable based on Wasserstein distanceWH.MultiplePCA
Plot histograms of individuals after a Multiple factor analysis of Histogram VariablesWH.plot_multiple_indivs
Plotting Spanish fun plots for Multiple factor analysis of Histogram VariablesWH.plot_multiple_Spanish.funs
Goodness of Fit indices for Multiple regression of histogram variables based on a two component model and L2 Wasserstein distanceWH.regression.GOF
Multiple regression analysis for histogram variables based on a two component model and L2 Wasserstein distanceWH.regression.two.components
Multiple regression analysis for histogram variables based on a two component model and L2 Wasserstein distanceWH.regression.two.components.predict
Method WH.SSQWH.SSQ WH.SSQ,MatH-method
Method WH.SSQ2WH.SSQ2 WH.SSQ2,MatH,MatH-method WH.SSQ2,MatH-method
Method WH.var.covarWH.var.covar WH.var.covar,MatH-method
Method WH.var.covar2WH.var.covar2 WH.var.covar2,MatH,MatH-method WH.var.covar2,MatH-method
Method WH.vec.meanWH.vec.mean WH.vec.mean,MatH-method
Method WH.vec.sumWH.vec.sum WH.vec.sum,MatH-method