{ "_id": "6a103a5eacfb0bcc41c9b380", "Package": "HistDAWass", "Type": "Package", "Title": "Histogram-Valued Data Analysis", "Version": "1.0.8", "Date": "2024-01-24", "Authors@R": "c(person(given=\"Antonio\",\nfamily= \"Irpino\",\nrole = c(\"aut\", \"cre\"),\nemail = \"antonio.irpino@unicampania.it\",\ncomment = c(ORCID = \"0000-0001-9293-7180\")))", "Maintainer": "Antonio Irpino ", "Description": "In the framework of Symbolic Data Analysis, a relatively\nnew approach to the statistical analysis of multi-valued data,\nwe consider histogram-valued data, i.e., data described by\nunivariate histograms. The methods and the basic statistics for\nhistogram-valued data are mainly based on the L2 Wasserstein\nmetric between distributions, i.e., the Euclidean metric\nbetween quantile functions. The package contains unsupervised\nclassification techniques, least square regression and tools\nfor histogram-valued data and for histogram time series. An\nintroducing paper is Irpino A. Verde R. (2015) .", "License": "GPL (>= 2)", "LazyData": "true", "Collate": "'For_Rccp_int.R' 'All_classes.R' 'RcppExports.R' 'Utility.R'\n'Met_HTS.R' 'Met_MatH.R' 'Met_distributionH.R' 'Fuzzy_cmeans.R'\n'H_time_series.R' 'HistDAWass-package.R' 'Kohonen_maps.R'\n'principal_components.R' 'regression.R'\n'unsuperv_classification.R' 'Plotting_with_ggplot.R'", "Encoding": "UTF-8", "RoxygenNote": "7.3.1", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-11 08:27:16 UTC", "User": "root" }, "Author": "Antonio Irpino [aut, cre]\n()", "Config/pak/sysreqs": "cmake make libicu-dev libuv1-dev", "Repository": "https://airpino.r-universe.dev", "Date/Publication": "2024-01-25 10:14:06 UTC", "RemoteUrl": "https://github.com/airpino/histdawass", "RemoteRef": "HEAD", "RemoteSha": "be7447e3ba3c025ae26e80f2402e6536adafa8cf", "MD5sum": "b9f39524144fa041cb1d70005c649e3a", "_user": "airpino", "_type": "src", "_file": "HistDAWass_1.0.8.tar.gz", "_fileid": 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"_upstream": "https://github.com/airpino/histdawass", "_commit": { "id": "be7447e3ba3c025ae26e80f2402e6536adafa8cf", "author": "Antonio Irpino ", "committer": "Antonio Irpino ", "message": "1.0.8 new commit\n", "time": 1706177646 }, "_maintainer": { "name": "Antonio Irpino", "email": "antonio.irpino@unicampania.it", "login": "airpino", "uuid": 6860334, "orcid": "0000-0001-9293-7180" }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 3.1", "role": "Depends" }, { "package": "methods", "role": "Depends" }, { "package": "Rcpp", "role": "LinkingTo" }, { "package": "RcppArmadillo", "role": "LinkingTo" }, { "package": "graphics", "role": "Imports" }, { "package": "class", "role": "Imports" }, { "package": "FactoMineR", "role": "Imports" }, { "package": "ggplot2", "role": "Imports" }, { "package": "ggridges", "role": "Imports" }, { "package": "grid", "role": "Imports" }, { "package": "histogram", "role": "Imports" }, { "package": "grDevices", "role": "Imports" }, { 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"Center.cell.MatH,MatH-method" ] }, { "page": "checkEmptyBins-methods", "title": "Method 'checkEmptyBins'", "topics": [ "checkEmptyBins", "checkEmptyBins,distributionH-method" ] }, { "page": "China_Month", "title": "A monthly climatic dataset of China", "topics": [ "China_Month" ] }, { "page": "China_Seas", "title": "A seasonal climatic dataset of China", "topics": [ "China_Seas" ] }, { "page": "compP-methods", "title": "Method 'compP'", "topics": [ "compP", "compP,distributionH,numeric-method", "compP,distributionH-method" ] }, { "page": "compQ-methods", "title": "Method 'compQ'", "topics": [ "compQ", "compQ,distributionH,numeric-method", "compQ,distributionH-method" ] }, { "page": "crwtransform-methods", "title": "Method 'crwtransform': returns the centers and the radii of bins of a distribution", "topics": [ "crwtransform", "crwtransform,distributionH-method" ] }, { "page": "data2hist", "title": "From real data to distributionH.", "topics": [ "data2hist" ] }, { "page": "distributionH-class", "title": "Class distributionH.", "topics": [ "distributionH", "distributionH-class", "initialize,distributionH-method" ] }, { "page": "dotpW-methods", "title": "Method 'dotpW'", "topics": [ "dotpW", "dotpW,distributionH,distributionH-method", "dotpW,distributionH,numeric-method", "dotpW,distributionH-method", "dotpW,numeric,distributionH-method" ] }, { "page": "DouglasPeucker", "title": "Ramer-Douglas-Peucker algorithm for curve fitting with a PolyLine", "topics": [ "DouglasPeucker" ] }, { "page": "get.cell.MatH-methods", "title": "Method get.cell.MatH Returns the histogram in a cell of a matrix of distributions", "topics": [ "get.cell.MatH", "get.cell.MatH,MatH,numeric,numeric-method", "get.cell.MatH,MatH-method" ] }, { "page": "get.distr-methods", "title": "Method 'get.distr': show the distribution", "topics": [ "get.distr", "get.distr,distributionH-method" ] }, { "page": "get.histo-methods", "title": "Method 'get.histo': show the distribution with bins", "topics": [ "get.histo", "get.histo,distributionH-method" ] }, { "page": "get.m-methods", "title": "Method 'get.m': the mean of a distribution", "topics": [ "get.m", "get.m,distributionH-method" ] }, { "page": "get.MatH.main.info-methods", "title": "Method get.MatH.main.info", "topics": [ "get.MatH.main.info", "get.MatH.main.info,MatH-method" ] }, { "page": "get.MatH.ncols-methods", "title": "Method get.MatH.ncols", "topics": [ "get.MatH.ncols", "get.MatH.ncols,MatH-method" ] }, { "page": "get.MatH.nrows-methods", "title": "Method get.MatH.nrows", "topics": [ "get.MatH.nrows", "get.MatH.nrows,MatH-method" ] }, { "page": "get.MatH.rownames-methods", "title": "Method get.MatH.rownames", "topics": [ "get.MatH.rownames", "get.MatH.rownames,MatH-method" ] }, { "page": "get.MatH.stats-methods", "title": "Method get.MatH.stats", "topics": [ "get.MatH.stats", "get.MatH.stats,MatH-method" ] }, { "page": "get.MatH.varnames-methods", "title": "Method get.MatH.varnames", "topics": [ "get.MatH.varnames", "get.MatH.varnames,MatH-method" ] }, { "page": "get.s-methods", "title": "Method 'get.s': the standard deviation of a distribution", "topics": [ "get.s", "get.s,distributionH-method" ] }, { "page": "HTS-class", "title": "Class HTS", "topics": [ "HTS", "HTS-class", "initialize,HTS-method" ] }, { "page": "HTS.exponential.smoothing", "title": "Smoothing with exponential smoothing of a histogram time series", "topics": [ "HTS.exponential.smoothing" ] }, { "page": "HTS.moving.averages", "title": "Smoothing with moving averages of a histogram time series", "topics": [ "HTS.moving.averages" ] }, { "page": "HTS.predict.knn", "title": "K-NN predictions of a histogram time series", "topics": [ "HTS.predict.knn" ] }, { "page": "is.registeredMH-methods", "title": "Method is.registeredMH", "topics": [ "is.registeredMH", "is.registeredMH,MatH-method" ] }, { "page": "kurtH-methods", "title": "Method 'kurtH': computes the kurthosis of a distribution", "topics": [ "kurtH", "kurtH,distributionH-method" ] }, { "page": "MatH-class", "title": "Class MatH.", "topics": [ "initialize,MatH-method", "MatH", "MatH-class" ] }, { "page": "meanH-methods", "title": "Method 'meanH': computes the mean of a distribution", "topics": [ "meanH", "meanH,distributionH-method" ] }, { "page": "minus", "title": "Method -", "topics": [ "-,distributionH,distributionH-method", "-,distributionH,numeric-method", "-,numeric,distributionH-method", "minus" ] }, { "page": "OzoneFull", "title": "Full Ozone dataset for Histogram data analysis", "topics": [ "OzoneFull" ] }, { "page": "OzoneH", "title": "Complete Ozone dataset for Histogram data analysis", "topics": [ "OzoneH" ] }, { "page": "plot_errors", "title": "A function for plotting functions of errors", "topics": [ "plot_errors" ] }, { "page": "plot-distributionH", "title": "plot for a distributionH object", "topics": [ "plot,distributionH-method", "plot-distributionH" ] }, { "page": "plot-HTS", "title": "Method plot for a histogram time series", "topics": [ "plot,HTS-method", "plot-HTS" ] }, { "page": "plot-MatH", "title": "Method plot for a matrix of histograms", "topics": [ "plot,MatH-method", "plot-MatH" ] }, { "page": "plot-TdistributionH", "title": "plot for a TdistributionH object", "topics": [ "plot,TdistributionH-method", "plot-TdistributionH" ] }, { "page": "plotPredVsObs", "title": "A function for comparing observed vs predicted histograms", "topics": [ "plotPredVsObs" ] }, { "page": "register-methods", "title": "Method 'register'", "topics": [ "register", "register,distributionH,distributionH-method", "register,distributionH-method" ] }, { "page": "registerMH-methods", "title": "Method registerMH", "topics": [ "registerMH", "registerMH,MatH-method" ] }, { "page": "RetHTS", "title": "A histogram-valued dataset of returns", "topics": [ "RetHTS" ] }, { "page": "rQQ-methods", "title": "Method 'rQQ'", "topics": [ "rQQ", "rQQ,distributionH,distributionH-method", "rQQ,distributionH-method" ] }, { "page": "set.cell.MatH-methods", "title": "Method set.cell.MatH assign a histogram to a cell of a matrix of histograms", "topics": [ "set.cell.MatH", "set.cell.MatH,distributionH,MatH,numeric,numeric-method", "set.cell.MatH,MatH-method" ] }, { "page": "ShortestDistance", "title": "Shortes distance from a point o a 2d segment", "topics": [ "ShortestDistance" ] }, { "page": "show-distributionH-methods", "title": "Method show for distributionH", "topics": [ "show", "show,distributionH-method" ] }, { "page": "show-MatH-methods", "title": "Method show for MatH", "topics": [ "show,MatH-method", "show-MatH" ] }, { "page": "skewH-methods", "title": "Method 'skewH': computes the skewness of a distribution", "topics": [ "skewH", "skewH,distributionH-method" ] }, { "page": "stations_coordinates", "title": "Stations coordinates of China_Month and China_Seas datasets", "topics": [ "stations_coordinates" ] }, { "page": "stdH-methods", "title": "Method 'stdH': computes the standard deviation of a distribution", "topics": [ "stdH", "stdH,distributionH-method" ] }, { "page": "subsetHTS-methods", "title": "Method 'subsetHTS': extract a subset of a histogram time series", "topics": [ "subsetHTS", "subsetHTS,HTS,numeric,numeric-method" ] }, { "page": "summaryHTS", "title": "A function for summarize HTS", "topics": [ "summaryHTS" ] }, { "page": "TdistributionH-class", "title": "Class TdistributionH", "topics": [ "initialize,TdistributionH-method", "TdistributionH", "TdistributionH-class" ] }, { "page": "TMatH-class", "title": "Class TMatH", "topics": [ "initialize,TMatH-method", "TMatH", "TMatH-class" ] }, { "page": "WassSqDistH-methods", "title": "Method 'WassSqDistH'", "topics": [ "WassSqDistH", "WassSqDistH,distributionH,distributionH-method", "WassSqDistH,distributionH-method" ] }, { "page": "WH_2d_Adaptive_Kohonen_maps", "title": "Batch Kohonen self-organizing 2d maps using adaptive distances for histogram-valued data", "topics": [ "WH_2d_Adaptive_Kohonen_maps" ] }, { "page": "WH_2d_Kohonen_maps", "title": "Batch Kohonen self-organizing 2d maps for histogram-valued data", "topics": [ "WH_2d_Kohonen_maps" ] }, { "page": "WH_adaptive_fcmeans", "title": "Fuzzy c-means with adaptive distances for histogram-valued data", "topics": [ "WH_adaptive_fcmeans" ] }, { "page": "WH_adaptive.kmeans", "title": "K-means of a dataset of histogram-valued data using adaptive Wasserstein distances", "topics": [ "WH_adaptive.kmeans" ] }, { "page": "WH_fcmeans", "title": "Fuzzy c-means of a dataset of histogram-valued data", "topics": [ "WH_fcmeans" ] }, { "page": "WH_hclust", "title": "Hierarchical clustering of histogram data", "topics": [ "WH_hclust" ] }, { "page": "WH_kmeans", "title": "K-means of a dataset of histogram-valued data", "topics": [ "WH_kmeans" ] }, { "page": "WH_MAT_DIST", "title": "L2 Wasserstein distance matrix", "topics": [ "WH_MAT_DIST" ] }, { "page": "WH.1d.PCA", "title": "Principal components analysis of histogram variable based on Wasserstein distance", "topics": [ "WH.1d.PCA" ] }, { "page": "WH.bind-methods", "title": "Method WH.bind", "topics": [ "WH.bind", "WH.bind,MatH,MatH-method", "WH.bind,MatH-method" ] }, { "page": "WH.bind.col-methods", "title": "Method WH.bind.col", "topics": [ "WH.bind.col", "WH.bind.col,MatH,MatH-method", "WH.bind.col,MatH-method" ] }, { "page": "WH.bind.row-methods", "title": "Method WH.bind.row", "topics": [ "WH.bind.row", "WH.bind.row,MatH,MatH-method", "WH.bind.row,MatH-method" ] }, { "page": "WH.correlation-methods", "title": "Method WH.correlation", "topics": [ "WH.correlation", "WH.correlation,MatH-method" ] }, { "page": "WH.correlation2-methods", "title": "Method WH.correlation2", "topics": [ "WH.correlation2", "WH.correlation2,MatH,MatH-method", "WH.correlation2,MatH-method" ] }, { "page": "WH.mat.prod-methods", "title": "Method WH.mat.prod", "topics": [ "WH.mat.prod", "WH.mat.prod,MatH,MatH-method", "WH.mat.prod,MatH-method" ] }, { "page": "WH.mat.sum-methods", "title": "Method WH.mat.sum", "topics": [ "WH.mat.sum", "WH.mat.sum,MatH,MatH-method", "WH.mat.sum,MatH-method" ] }, { "page": "WH.MultiplePCA", "title": "Principal components analysis of a set of histogram variable based on Wasserstein distance", "topics": [ "WH.MultiplePCA" ] }, { "page": "WH.plot_multiple_indivs", "title": "Plot histograms of individuals after a Multiple factor analysis of Histogram Variables", "topics": [ "WH.plot_multiple_indivs" ] }, { "page": "WH.plot_multiple_Spanish.funs", "title": "Plotting Spanish fun plots for Multiple factor analysis of Histogram Variables", "topics": [ "WH.plot_multiple_Spanish.funs" ] }, { "page": "WH.regression.GOF", "title": "Goodness of Fit indices for Multiple regression of histogram variables based on a two component model and L2 Wasserstein distance", "topics": [ "WH.regression.GOF" ] }, { "page": "WH.regression.two.components", "title": "Multiple regression analysis for histogram variables based on a two component model and L2 Wasserstein distance", "topics": 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