Histogram-Valued Data Analysis
A histogram-valued dataset of returns
Method rQQ
Method set.cell.MatH assign a histogram to a cell of a matrix of histo...
Shortes distance from a point o a 2d segment
Method show for distributionH
Method show for MatH
Method skewH: computes the skewness of a distribution
Stations coordinates of China_Month and China_Seas datasets
Method stdH: computes the standard deviation of a distribution
Method subsetHTS: extract a subset of a histogram time series
A function for summarize HTS
Class TdistributionH
Method *
Class TMatH
Method WassSqDistH
Principal components analysis of histogram variable based on Wasserste...
Method WH.bind
Method WH.bind.col
Method WH.bind.row
Method WH.correlation
Method WH.correlation2
Method WH.mat.prod
Method WH.mat.sum
Fuzzy c-means with adaptive distances for histogram-valued data
Fuzzy c-means of a dataset of histogram-valued data
Hierarchical clustering of histogram data
K-means of a dataset of histogram-valued data
L2 Wasserstein distance matrix
Age pyramids of all the countries of the World in 2014
Agronomique data
Blood dataset for Histogram data analysis
Blood dataset from Brito P. for Histogram data analysis
Method Center.cell.MatH Centers all the cells of a matrix of distribut...
Method checkEmptyBins
A monthly climatic dataset of China
A seasonal climatic dataset of China
Method compP
Method compQ
Method crwtransform: returns the centers and the radii of bins of a ...
From real data to distributionH.
Class distributionH.
Method dotpW
Ramer-Douglas-Peucker algorithm for curve fitting with a PolyLine
extract from a MatH Method [
Method get.cell.MatH Returns the histogram in a cell of a matrix of di...
Method get.distr: show the distribution
Method get.histo: show the distribution with bins
Method get.m: the mean of a distribution
Method get.MatH.main.info
Method get.MatH.ncols
Method get.MatH.nrows
Method get.MatH.rownames
Method get.MatH.stats
Method get.MatH.varnames
Method get.s: the standard deviation of a distribution
Histogram-Valued Data Analysis
Class HTS
Smoothing with exponential smoothing of a histogram time series
Smoothing with moving averages of a histogram time series
K-NN predictions of a histogram time series
Method is.registeredMH
Method kurtH: computes the kurthosis of a distribution
Class MatH.
Method meanH: computes the mean of a distribution
Method -
Full Ozone dataset for Histogram data analysis
Complete Ozone dataset for Histogram data analysis
plot for a distributionH object
Method plot for a histogram time series
Method plot for a matrix of histograms
plot for a TdistributionH object
A function for plotting functions of errors
A function for comparing observed vs predicted histograms
Method +
Method register
Method registerMH
Principal components analysis of a set of histogram variable based on ...
Plot histograms of individuals after a Multiple factor analysis of His...
Plotting Spanish fun plots for Multiple factor analysis of Histogram V...
Goodness of Fit indices for Multiple regression of histogram variables...
Multiple regression analysis for histogram variables based on a two co...
Multiple regression analysis for histogram variables based on a two co...
Method WH.SSQ
Method WH.SSQ2
Method WH.var.covar
Method WH.var.covar2
Method WH.vec.mean
Method WH.vec.sum
Batch Kohonen self-organizing 2d maps using adaptive distances for his...
Batch Kohonen self-organizing 2d maps for histogram-valued data
K-means of a dataset of histogram-valued data using adaptive Wasserste...
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>.