{ "_id": "6ad7c40d4b18b9008999a131", "Package": "DistributionalDDA", "Type": "Package", "Title": "Distributional Data Analysis in Bayes Spaces", "Version": "0.1.1", "Maintainer": "K. Gerald van den Boogaart ", "Authors@R": "c(\nperson(\"Ivana\", \"Pavlu\", email = \"ivana.pavlu@upol.cz\", role = \"aut\",\ncomment = c(ORCID = \"0000-0001-9322-246X\")),\nperson(\"Raimon\", \"Tolosana-Delgado\", role = \"aut\", comment = c(ORCID = \"0000-0001-9847-0462\")),\nperson(\"Matthias\", \"Templ\", role = \"aut\", comment = c(ORCID = \"0000-0002-8638-5276\")),\nperson(given=\"K. Gerald\", family=\"van den Boogaart\",\nrole = c(\"aut\",\"cre\"),\ncomment = c(ORCID = \"0000-0003-4646-943X\"),\nemail=\"support@boogaart.de\")\n)", "Description": "Statistical analysis of data sets whose observations are\nwhole probability distributions, using Bayes space calculus.\nDistributions (provided as samples, histograms or density\nfunctions) are treated as elements of a Hilbert space, so that\noperations such as mean, variance, distance, principal\ncomponent analysis and clustering carry over to densities.\nReference measures defining the geometry of the space can be\nchosen and combined. The methodology was first published with\npeer review by van den Boogaart, Egozcue and Pawlowsky-Glahn\n(2014) .", "License": "LGPL-3", "Encoding": "UTF-8", "LazyData": "true", "Collate": "DistributionalDDA-package.R topics.R helpers.R integration.R\nListData.R classes.R converters.R Bayes_spaces.R Cartesian.R\noperations.R summaries.R plots.R simulations.R datasets.R\nlinear_models.R PCA.R bulding_blocks.R statistics.R", "VignetteBuilder": "knitr, rmarkdown", "Config/testthat/edition": "3", "Config/roxygen2/version": "8.1.0", "RoxygenNote": "7.3.3", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-20 19:38:00 UTC", "User": "root" }, "Author": "Ivana Pavlu [aut] (ORCID:\n), Raimon\nTolosana-Delgado [aut] (ORCID:\n), Matthias Templ [aut]\n(ORCID: ), K. Gerald van\nden Boogaart [aut, cre] (ORCID:\n)", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-10-20 15:10:02 UTC", "RemoteUrl": "https://github.com/cran/DistributionalDDA", "RemoteRef": "HEAD", "RemoteSha": "981e91890e4a9a20dc0aaca4634ce35c025d3dc2", "_user": "cran", "_type": "src", "_file": "DistributionalDDA_0.1.1.tar.gz", "_fileid": "https://r2.ropensci.org/d0b915adfbedcb8847553273522b628a15f31642ef640793b3e26e11c7160f01", "_filesize": 5803334, "_sha256": "d0b915adfbedcb8847553273522b628a15f31642ef640793b3e26e11c7160f01", "_expires": "2027-01-28T19:42:03.000Z", "_created": "2026-10-20T19:38:00.000Z", "_published": "2026-10-20T19:42:05.663Z", "_buildurl": "https://github.com/r-universe/cran/actions/runs/39484475839", "_status": "success", "_host": "GitHub-Actions", "_jobs": [ { "job": 118843662272, "time": 189, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "12293825744" }, { "job": 118843662162, "time": 184, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "12292767997" }, { "job": 118841399434, "time": 277, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "12293645875" }, { "job": 118843661949, "time": 129, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "12292638806" }, { "job": 118843662003, "time": 128, "config": "windows-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "12293032213" }, { "job": 118843662139, "time": 122, "config": "windows-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "12292799066" } ], "_upstream": "https://github.com/cran/DistributionalDDA", "_commit": { "id": "981e91890e4a9a20dc0aaca4634ce35c025d3dc2", "author": "K. 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"closingConstant.BDataSplines", "closingConstant.function" ] }, { "page": "clr", "title": "compute the clr transform", "topics": [ "clr", "clr.BData", "clr.default" ] }, { "page": "computeL22BayesSpacesBasisMatrix", "title": "Computes the matrix mapping an L2-space basis to a Bayes-Space-Basis.", "topics": [ "computeL22BayesSpacesBasisMatrix" ] }, { "page": "computeL22BayesSpacesBasisMatrix.basisfd", "title": "create the transform matrix A from a B-spline basis (fda::create.bspline.basis())", "topics": [ "computeL22BayesSpacesBasisMatrix.basisfd" ] }, { "page": "computeLimit", "title": "computes the limit of a function", "topics": [ "computeLimit" ] }, { "page": "CovarianceOperator", "title": "Computes a covariance with respect to a measure. The function can be considered internal implementation details of the package, but needs to be exported due to its generic nature.", "topics": [ "CovarianceOperator", "CovarianceOperator.ReferenceMeasure", "CovarianceOperator.ReferenceMeasureDiscrete" ] }, { "page": "DiscretizedFunction", "title": "Creates a function based on a regular discretization", "topics": [ "DiscretizedFunction", "DiscretizedFunction.Discretizer", "DiscretizedFunction.function", "DiscretizedFunction.numeric", "DiscretizedFunction.ReferenceMeasureTransformed", "DiscretizedFunction.ReferenceMeasureUniform" ] }, { "page": "dist.BData", "title": "Distance method for BData objects", "topics": [ "dist.BData", "dist.BDataLogDensities", "dist.BDataSplines" ] }, { "page": "distanceToCenter", "title": "Distance to Center", "concept": [ "caseStatistics" ], "topics": [ "distanceToCenter", "distanceToCenter.default" ] }, { "page": "Div", "title": "Division of functional objects", "concept": [ "Add" ], "topics": [ "Div", "Div.BData", "Div.function", "Div.function.function", "Div.function.list", "Div.function.numeric", "Div.list", "Div.list.function", "Div.list.list", "Div.list.numeric", "Div.numeric", "Div.numeric.function", "Div.numeric.list", "Div.numeric.numeric" ] }, { "page": "DTD", "title": "An example dataset for distributional data analysis", "topics": [ "DTD" ] }, { "page": "ensureType", "title": "Returns the argument, but ensures that it has a certain class.", "topics": [ "ensureType" ] }, { "page": "expectation", "title": "Computes expectations from reference measures", "concept": [ "ReferenceMeasures" ], "topics": [ "expectation", "expectation.ReferenceMeasure", "expectation.ReferenceMeasureDiscrete" ] }, { "page": "expectation.ReferenceMeasureUniform", "title": "integrate a function over the uniform reference measure", "topics": [ "expectation.ReferenceMeasureUniform" ] }, { "page": "findJumps", "title": "Finds jumps of a function of a certain minimum height", "topics": [ "findJumps" ] }, { "page": "fx", "title": "Shortcut for creating functions of x", "topics": [ "fx" ] }, { "page": "getBayesSpace", "title": "Generic function returning the Bayes space associated to the given object.", "concept": [ "getBayesSpace" ], "topics": [ "getBayesSpace", "getBayesSpace.BayesSpace", "getBayesSpace.BayesSpaceBasis", "getBayesSpace.BData", "getBayesSpace.default", "getBayesSpace.L2Space", "getBayesSpace.NULL", "getBayesSpace.ReferenceMeasure", "getL2Space.L2Space" ] }, { "page": "getBayesSpace.ReferenceMeasureCartesianProduct", "title": "Construct the product Bayes Space for a product reference measure", "concept": [ "Cartesian" ], "topics": [ "getBayesSpace.ReferenceMeasureCartesianProduct" ] }, { "page": "getBayesSpaceBasis", "title": "Generic function returning the BayesSpaceBasis associated to the given object.", "topics": [ "getBayesSpaceBasis", "getBayesSpaceBasis.BayesSpace", "getBayesSpaceBasis.BayesSpaceBasis", "getBayesSpaceBasis.BData", "getBayesSpaceBasis.variationKernel", "getBayesSpaceBasis.varKernel" ] }, { "page": "getBayesSpaceBasisDimension", "title": "the dimension of the basis", "topics": [ "getBayesSpaceBasisDimension", "getBayesSpaceBasisDimension.BayesSpaceBasis" ] }, { "page": "getBreaks", "title": "Common utilities for BDataHistograms objects", "concept": [ "BDataHistograms" ], "topics": [ "getBreaks", "getBreaks.BDataHistograms" ] }, { "page": "getCartesianFactors", "title": "Decomposes a cartesian product into a list of its factors.", "concept": [ "Cartesian" ], "topics": [ "getCartesianFactors" ] }, { "page": "getDimension", "title": "Gets number of elements in a Basis", "topics": [ "getDimension", "getDimension.BayesSpaceBasis", "getDimension.BDataSplines", "getDimension.matrix", "getDimension.numeric" ] }, { "page": "getDiscretizer", "title": "Computes Discretization points for nonequidistant discretization", "topics": [ "getDiscretizer", "getDiscretizer.BayesSpaceBasis", "getDiscretizer.ReferenceMeasureDiscrete", "getDiscretizer.ReferenceMeasureTransformed", "getDiscretizer.ReferenceMeasureUniform" ] }, { "page": "getL2Space", "title": "Generic function returning the L2Space associated to the given object.", "concept": [ "getBayesSpace" ], "topics": [ "getL2Space", "getL2Space.BayesSpace", "getL2Space.BayesSpaceBasis", "getL2Space.BData", "getL2Space.default", "getL2Space.NULL", "getL2Space.ReferenceMeasure" ] }, { "page": "getMetricTensor", "title": "Metric tensor of a space or basis", "topics": [ "getMetricTensor" ] }, { "page": "getPDF", "title": "returns the probability density function", "topics": [ "getPDF", "getPDF.BData", "getPDF.ReferenceMeasureTransformed", "getPDF.ReferenceMeasureUniform" ] }, { "page": "getReferenceMeasure", "title": "Getter for the ReferenceMeasure of an object", "topics": [ "getReferenceMeasure", "getReferenceMeasure.BayesSpace", "getReferenceMeasure.default", "getReferenceMeasure.L2Space", "getReferenceMeasure.ReferenceMeasure" ] }, { "page": "getSample", "title": "Get a sample from the reference measure", "concept": [ "ReferenceMeasures" ], "topics": [ "getSample", "getSample.BayesSpace", "getSample.L2Space", "getSample.ReferenceMeasure", "getSample.ReferenceMeasureCartesianProduct", "getSample.ReferenceMeasureDiscrete", "getWeights", "hasWeights", "hasWeights.default" ] }, { "page": "getSplineCoefs", "title": "Coefficients of a single BayesSpace element.", "topics": [ "getSplineCoefs", "getSplineCoefs.BData", "getSplineCoefs.BDataSplines", "getSplineCoefs.default", "getSplineCoefs.function", "getSplineCoefs.hist" ] }, { "page": "hasSameBayesSpace", "title": "Checks whether two objects have the same BayesSpace", "topics": [ "hasSameBayesSpace" ] }, { "page": "hasSameBayesSpaceBasis", "title": "Checks whether the associated BayesSpaceBasis of to objects is the same", "topics": [ "hasSameBayesSpaceBasis" ] }, { "page": "idt.BData", "title": "Isometric default representations for BData objects", "concept": [ "BayesSpaceConverters" ], "topics": [ "idt.BData", "idtInv.BData", "idtInv.BDataSplines" ] }, { "page": "isClosed", "title": "Returns whether its know that the object is closed or centered", "topics": [ "isCentered", "isCentered.BayesSpaceBasis", "isCentered.BData", "isCentered.BDataSplines", "isCentered.default", "isClosed", "isClosed.BData", "isClosed.default" ] }, { "page": "isInDomain", "title": "Checks whether values are in the domain of the ReferenceMeasure", "concept": [ "ReferenceMeasures" ], "topics": [ "isInDomain", "isInDomain.BayesSpace", "isInDomain.BayesSpaceBasis", "isInDomain.L2Space", "isInDomain.ReferenceMeasureCartesianProduct", "isInDomain.ReferenceMeasureDiscrete", "isInDomain.ReferenceMeasureTransformed" ] }, { "page": "isInDomain.ReferenceMeasureUniform", "title": "assess whether a vector of points belong to the domain of the uniform reference measure", "topics": [ "isInDomain.ReferenceMeasureUniform" ] }, { "page": "isValidBayesSpaceBasis", "title": "Checks that an object is a BayesSpaceBasis", "topics": [ "isValidBayesSpaceBasis" ] }, { "page": "isValidBayesSpaceElement", "title": "Checks whether an object is an element (or dataset of elements) of a BayesSpace", "topics": [ "isValidBayesSpaceElement", "isValidBayesSpaceElement.BData", "isValidBayesSpaceElement.default", "isValidBayesSpaceElement.function", "isValidBayesSpaceElement.histogram" ] }, { "page": "kdeDensity", "title": "Computes an evaluable kernel density estimate on a dataset", "topics": [ "kdeDensity", "kdeDensity.data.frame", "kdeDensity.list", "kdeDensity.matrix", "kdeDensity.numeric" ] }, { "page": "L2Space", "title": "Creates the L2-space for a Reference Measure", "concept": [ "BayesSpace" ], "topics": [ "L2Space", "L2Space.BayesSpace", "L2Space.ReferenceMeasure" ] }, { "page": "listDataElementAccess", "title": "Accesses list elements with an expression", "topics": [ "listDataElementAccess" ] }, { "page": "ListData", "title": "Makes a list includable in a data.frame", "topics": [ "as.data.frame.BData", "as.data.frame.ListData", "format.BData", "format.ListData", "ListData", "ListDataEnv", "ListDataRecover", "storingDigester" ] }, { "page": "Exp", "title": "Functional mapping a function to its exp version", "concept": [ "Exp" ], "topics": [ "Exp", "Exp.function", "Exp.list", "Exp.numeric", "Ln", "Ln.function", "Ln.list", "Ln.numeric" ] }, { "page": "makeFunctionOuter", "title": "Makes a vectorized 2-ary function into one that returns a matrix", "topics": [ "makeFunctionOuter" ] }, { "page": "makeLimitStable", "title": "Makes a real function returning limits at undefined points", "topics": [ "makeLimitStable" ] }, { "page": "mapToRealLineWithSupport", "title": "Gets coordinates on an axis for possibly non real data", "topics": [ "mapToRealLineWithSupport" ] }, { "page": "maxFunc", "title": "Compute Maximum and Minimum Functions", "topics": [ "maxFunc", "minFunc" ] }, { "page": "mean.BDataSplines", "title": "Computes the distribution mean for BDataSplines.", "topics": [ "mean.BDataDensities", "mean.BDataLogDensities", "mean.BDataLogPDFs", "mean.BDataPDFs", "mean.BDataSplines" ] }, { "page": "median.BData", "title": "A distributional median", "concept": [ "caseStatistics" ], "topics": [ "median.BData" ] }, { "page": "Mul", "title": "Multiplication of functional objects", "concept": [ "Add" ], "topics": [ "Mul", "Mul.BDataDensities", "Mul.BDataLogDensities", "Mul.BDataLogPDFs", "Mul.BDataPDFs", "Mul.BDataSplines", "Mul.function", "Mul.function.function", "Mul.function.list", "Mul.function.numeric", "Mul.list", "Mul.list.function", "Mul.list.list", "Mul.list.numeric", "Mul.numeric", "Mul.numeric.BData", "Mul.numeric.function", "Mul.numeric.list", "Mul.numeric.numeric" ] }, { "page": "mvar.BData", "title": "Compute the metric variance", "topics": [ "msd.BData", "mvar.BData", "mvar.BDataSplines" ] }, { "page": "nCases", "title": "Accessing cases regardless how a dataset is stored.", "topics": [ "nCases", "nCases.default", "selectCases", "selectCases.default" ] }, { "page": "norm.BData", "title": "Computes the Bayes-Space norm of BData objects", "topics": [ "norm", "norm.BData", "norm.BDataSplines" ] }, { "page": "numberOfCases", "title": "A generic function computing the number of cases.", "topics": [ "numberOfCases", "numberOfCases.data.frame", "numberOfCases.default" ] }, { "page": "pdf2Support", "title": "Guesses the support from a pdf", "concept": [ "Support" ], "topics": [ "pdf2Support" ] }, { "page": "plot.BayesSpaceBasis", "title": "plot the 'BayesSpaceBasis'", "concept": [ "BayesSpaceBasis" ], "topics": [ "plot.BayesSpaceBasis" ] }, { "page": "plot.BDataHistograms", "title": "Plot histograms as segments", "concept": [ "BDataHistograms" ], "topics": [ "plot.BDataHistograms" ] }, { "page": "plot.BDataSamples", "title": "Plotting BDataSample Objects", "topics": [ "plot.BDataSamples" ] }, { "page": "plot.BDataSplines", "title": "Plot a set of spline functions (using visualisation tools from fda)", "concept": [ "BDataSplines" ], "topics": [ "plot.BDataSplines" ] }, { "page": "plot.princomp.BData", "title": "Plot Method for PCA of Distributional Data", "concept": [ "PCA" ], "topics": [ "plot.princomp.BData" ] }, { "page": "plot.princomp.BData.grouped", "title": "Plot Method for Grouped PCA of Distributional Data", "concept": [ "PCA" ], "topics": [ "plot.princomp.BData.grouped" ] }, { "page": "plot.princomp.BData.multivariate", "title": "Plot Method for Multivariate SFPCA of Distributional Data", "concept": [ "PCA" ], "topics": [ "plot.princomp.BData.multivariate" ] }, { "page": "plot.varKernel", "title": "Plotting varKernel objects", "topics": [ "plot.variationKernel", "plot.varKernel" ] }, { "page": "plotCoefs", "title": "Plot multiple coefficients in a parallel coordinate plot", "topics": [ "plotCoefs" ] }, { "page": "plotFuncs", "title": "Helper function to plot multiple functions", "concept": [ "Support" ], "topics": [ "plot.BDataDensities", "plot.BDataLogDensities", "plot.BDataLogPDFs", "plot.BDataPDFs", "plotFuncs" ] }, { "page": "predict.princomp.BData", "title": "Predict Scores for New Distributional Data", "concept": [ "PCA" ], "topics": [ "predict.princomp.BData" ] }, { "page": "princomp.BData", "title": "Principal Component Analysis for Distributional Data", "concept": [ "PCA" ], "topics": [ "princomp.BData", "print.princomp.BData" ] }, { "page": "print.BayesSpace", "title": "Pretty printing of BayesSpaces", "topics": [ "print.BayesSpace" ] }, { "page": "print.BDataLogDensities", "title": "Print a BDataLogDensities object", "topics": [ "print.BDataLogDensities" ] }, { "page": "print.BDataLogPDFs", "title": "Print a BDataLogPDFs object", "topics": [ "print.BDataLogPDFs" ] }, { "page": "print.BDataSplines", "title": "Print a BDataSplines object", "topics": [ "print.BDataSplines" ] }, { "page": "print.L2Space", "title": "prints \"L2Space with reference measure\" and than the reference measure.", "topics": [ "print.L2Space" ] }, { "page": "print.princomp.BData.grouped", "title": "Print a grouped distributional PCA", "topics": [ "print.princomp.BData.grouped" ] }, { "page": "print.princomp.BData.multivariate", "title": "Print a multivariate (2D-)SFPCA on distributional data", "topics": [ "print.princomp.BData.multivariate" ] }, { "page": "print.ReferenceMeasure", "title": "Printing ReferenceMeasures", "topics": [ "print.ReferenceMeasure" ] }, { "page": "ReferenceMeasure", "title": "Reference measures: the geometry of a Bayes space", "topics": [ "ReferenceMeasure" ] }, { "page": "ReferenceMeasureCartesianProduct", "title": "Compute a cartesian Product of measures", "concept": [ "Cartesian" ], "topics": [ "ReferenceMeasureCartesianProduct" ] }, { "page": "ReferenceMeasureDiscrete", "title": "Discrete Reference Measure for BayesSpaces", "concept": [ "ReferenceMeasures" ], "topics": [ "ReferenceMeasureDiscrete" ] }, { "page": "ReferenceMeasureTransformed", "title": "A general continuous reference measure on the real line", "concept": [ "ReferenceMeasures" ], "topics": [ "getSample.ReferenceMeasureTransformed", "ReferenceMeasureTransformed" ] }, { "page": "ReferenceMeasureUniform", "title": "create the ReferenceMeasureUniform class", "concept": [ "BDataBayesSpaceSplines", "ReferenceMeasures" ], "topics": [ "ReferenceMeasureUniform" ] }, { "page": "sample2Support", "title": "Guesses the support from a sample", "concept": [ "Support" ], "topics": [ "sample2Support" ] }, { "page": "scalarProduct", "title": "Computes the scalar product of (datasets of) vectors from various spaces", "topics": [ "scalarProduct", "scalarProduct.BDataDensities", "scalarProduct.BDataLogDensities", "scalarProduct.BDataSplines", "scalarProduct.numeric" ] }, { "page": "scalarProductOfLogDensities", "title": "Computes the scalar product of log densities in a BayesSpace", "topics": [ "scalarProductOfLogDensities" ] }, { "page": "scale.BData", "title": "Scale and/or center a BData set", "topics": [ "scale.BData" ] }, { "page": "screeplot.princomp.BData", "title": "Screeplot for PCA of Distributional Data", "concept": [ "PCA" ], "topics": [ "screeplot.princomp.BData" ] }, { "page": "selectIntegrationMethod", "title": "Identifies the integration method to be choosen for a measure", "topics": [ "selectIntegrationMethod", "selectIntegrationMethod.CartesianProductMeasure", "selectIntegrationMethod.ReferenceMeasure" ] }, { "page": "simulate.B2DistHyper", "title": "Simulates a B2DistHyper distribution", "topics": [ "simulate.B2DistHyper" ] }, { "page": "simulate.BData", "title": "Simulate from the distribution described by the BData objects", "topics": [ "simulate.BData" ] }, { "page": "simulate.ReferenceMeasureCartesianProduct", "title": "Simulates from a ReferenceMeasureCartesianProduct", "topics": [ "simulate.ReferenceMeasureCartesianProduct" ] }, { "page": "simulate.ReferenceMeasureDiscrete", "title": "Generates simulations from the Discrete Reference Measure seen as distribution.", "topics": [ "simulate.ReferenceMeasureDiscrete" ] }, { "page": "simulate.ReferenceMeasureTransformed", "title": "Generates simulations from the Reference Measure seen as distribution.", "topics": [ "simulate.ReferenceMeasureTransformed" ] }, { "page": "simulate.ReferenceMeasureUniform", "title": "simulation method for the uniform referencemeasure", "topics": [ "simulate.ReferenceMeasureUniform" ] }, { "page": "spatialDepth", "title": "Spatial Depth in dataset", "concept": [ "caseStatistics" ], "topics": [ "spatialDepth", "spatialDepth.BData", "spatialDepth.matrix", "spatialDepth.numeric" ] }, { "page": "B2Unif01", "title": "The standard B2(Unif(0,1)) Bayes space.", "concept": [ "ReferenceMeasures" ], "topics": [ "B2Unif01", "B2Unif01Basis20", "B2Unif01Basis5", "B2Unif01Basis7", "StdReferenceMeasureN01", "StdReferenceMeasureUnif01" ] }, { "page": "Sub", "title": "Substract functional objects", "concept": [ "Add" ], "topics": [ "Sub", "Sub.BDataDensities", "Sub.BDataHistograms", "Sub.BDataHistograms.BDataHistograms", "Sub.BDataLogDensities", "Sub.BDataLogPDFs", "Sub.BDataPDFs", "Sub.BDataSplines", "Sub.function", "Sub.function.function", "Sub.function.list", "Sub.function.numeric", "Sub.list", "Sub.list.function", "Sub.list.list", "Sub.list.numeric", "Sub.numeric", "Sub.numeric.function", "Sub.numeric.list", "Sub.numeric.numeric" ] }, { "page": "subBasisIndices", "title": "Computes the indices of a certain marginal basis", "concept": [ "Cartesian" ], "topics": [ "subBasisIndices" ] }, { "page": "summary.princomp.BData", "title": "Summary of PCA for Distributional Data", "concept": [ "PCA" ], "topics": [ "summary.princomp.BData" ] }, { "page": "Support", "title": "The support of the measures", "concept": [ "Support" ], "topics": [ "getSupport", "getSupport.BayesSpace", "getSupport.BayesSpaceBasis", "getSupport.BData", "getSupport.function", "getSupport.list", "getSupport.ListData", "getSupport.numeric", 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"author": "Matthias Templ", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "What PCA for Distributions Reveals", "Questions for the Age Data", "Loading and Exploring the Data", "Visualizing the Raw Data", "Converting to BData Format", "Step 1: Create the Bayes Space", "Step 2: Convert Histogram Data to BData", "Step 3: Visualize the BData", "Performing PCA", "Summary Statistics", "Visualizing PCA Results", "Screeplot: How Many Components?", "Cumulative Variance Plot", "Score Plot: Exploring Structure", "Coloring by Sex", "Using Labels", "Loadings Plot: The Key to Interpretation", "Adjusting Scale", "Alternative Representations", "Covariance Surface", "Reconstruction: Checking Approximation Quality", "Boxplot of Scores", "Biplot", "Grouped PCA: Comparing Subgroups", "Comparing Screeplots", "Comparing Loadings", "Comparing Score Distributions", "Multivariate SFPCA: Joint Analysis Across Groups", "Running Multivariate SFPCA", "Screeplot: Variance Across All Groups", "Score 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