Introduction | Loading and Managing Distributional data | Example Data | BayesSpace and BayesSpaceBasis | Parallel analysis of data in lists | Selecting a Bayes-Space and a BayesSpaceBasis | Data Containing Classes | Basic analysis and change of representation | Descriptive statistics | Change of representation | Statistics | Computations with distributions
Package: DistributionalDDA 0.1.1
K. Gerald van den Boogaart
DistributionalDDA: Distributional Data Analysis in Bayes Spaces
Statistical analysis of data sets whose observations are whole probability distributions, using Bayes space calculus. Distributions (provided as samples, histograms or density functions) are treated as elements of a Hilbert space, so that operations such as mean, variance, distance, principal component analysis and clustering carry over to densities. Reference measures defining the geometry of the space can be chosen and combined. The methodology was first published with peer review by van den Boogaart, Egozcue and Pawlowsky-Glahn (2014) <doi:10.1111/anzs.12074>.
Authors:
DistributionalDDA_0.1.1.tar.gz
DistributionalDDA_0.1.1.zip(r-4.7-any)DistributionalDDA_0.1.1.zip(r-4.6-any)
DistributionalDDA_0.1.1.tar.gz(r-4.7-any)DistributionalDDA_0.1.1.tar.gz(r-4.6-any)
DistributionalDDA_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
DistributionalDDA/json (API)
| # Install 'DistributionalDDA' in R: |
| install.packages('DistributionalDDA', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Datasets:
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:981e91890e. Checks:6 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 189 | ||
| source / vignettes | OK | 277 | ||
| linux-release | OK | 184 | ||
| windows-devel | OK | 128 | ||
| windows-release | OK | 122 | ||
| wasm-release | OK | 129 |
Exports:.DensityFromHist.LogDensityFromHist%..%%.%AddaddFrequencyas.BayesSpaceBasisas.BDataDensitiesas.BDataDensityHistogramsas.BDataFrequencyHistogramsas.BDataHistogramsas.BDataLogDensitiesas.BDataLogPDFsas.BDataPDFsas.BDataSamplesas.BDataSplinesB2DistHyperB2GaussianDistributionB2N01B2N01Basis20B2N01Basis5B2N01Basis7B2Unif01B2Unif01Basis20B2Unif01Basis5B2Unif01Basis7basisEvalBayesSpaceBayesSpaceBasisBayesSpaceCartesianProductBDataDensitiesBDataDensityHistogramsBDataFrequencyHistogramsBDataGeneralBDataLogDensitiesBDataLogPDFsBDataPDFsBDataSamplesBDataSplinescartesianProductcaseEcaseLogRatioscaseMeanscaseQuantilescaseSdscaseVarscdf2densitycdf2SupportcencenteringConstantcentralityQuantilecentralityRankcloclosingConstantclrcomputeL22BayesSpacesBasisMatrixcomputeLimitCovarianceOperatorDiscretizedFunctiondistanceToCenterDivensureTypeExpexpectationfindJumpsfxgetBayesSpacegetBayesSpaceBasisgetBayesSpaceBasisDimensiongetBreaksgetCartesianFactorsgetDimensiongetDiscretizergetL2SpacegetMetricTensorgetPDFgetReferenceMeasuregetSamplegetSplineCoefsgetSupportgetSupport1DPlottingLimitsgetSupportAtomsgetWeightshasSameBayesSpacehasSameBayesSpaceBasishasWeightsisCenteredisClosedisInDomainisValidBayesSpaceBasisisValidBayesSpaceElementkdeDensityL2SpaceL2SpaceCartesianProductListDatalistDataElementAccessListDataEnvListDataRecoverLnmakeFunctionOutermapToRealLineWithSupportmaxFuncminFuncMulnCasesnormnumberOfCasespdf2SupportplotCoefsplotFuncsReferenceMeasureCartesianProductReferenceMeasureDiscreteReferenceMeasureTransformedReferenceMeasureUniformsample2SupportscalarProductscalarProductOfLogDensitiesselectCasesselectIntegrationMethodspatialDepthStdReferenceMeasureN01StdReferenceMeasureUnif01storingDigesterSubsubBasisIndicesSupportSupportCartesianProductsupportDiscretizationtoHistogramvariationKernelvarKernelweightedStats
Dependencies:ashbayesmbitopscliclustercolorspacecompositionscpp11DEoptimRdeSolvedigestfarverfdafdsFNNggplot2gluegtablehdrcdeisobandkernlabKernSmoothkslabelinglatticelifecyclelocfitMASSMatrixmclustmgcvmulticoolmvtnormnlmepcaPPpracmaR6rainbowRColorBrewerRcppRcppArmadilloRCurlrlangrobustbaseS7scalestensorAvctrsviridisLitewithr
DistributionalDDA easy howto
Last update: 2026-10-20
Started: 2026-10-20
Principal Component Analysis for Distributional Data
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 Plot: Communes in Joint PC Space | Eigenfunctions: How Groups Vary Together | When to Use Multivariate vs Separate Grouped PCA | Predicting Scores for New Data | Practical Workflow Summary | Tips and Best Practices | Session Info
Last update: 2026-10-20
Started: 2026-10-20
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Compute the density relativ to a measure from histogram weights. | .DensityFromHist |
| Compute the log density relativ to a measure from histogram weights. | .LogDensityFromHist |
| Subsetting BDataHistograms | [.BDataHistograms |
| Operator to act on a list | %.% %..% |
| Operations for BData objects | *.BData +.BData -.BData /.BData |
| Adds functional objects | Add Add.BDataDensities Add.BDataDensities.BDataDensities Add.BDataDensities.default Add.BDataHistograms Add.BDataHistograms.BDataHistograms Add.BDataLogDensities Add.BDataLogDensities.BDataLogDensities Add.BDataLogDensities.default Add.BDataLogPDFs Add.BDataPDFs Add.BDataSplines Add.BDataSplines.BDataSplines Add.BDataSplines.default Add.function Add.function.function Add.function.list Add.function.numeric Add.list Add.list.function Add.list.list Add.list.numeric Add.numeric Add.numeric.function Add.numeric.list Add.numeric.numeric |
| Pool absolute frequencies | addFrequency addFrequency.BDataFrequencyHistograms.BDataFrequencyHistograms addFrequency.BDataFrequencyHistograms.BDataUnivariateSamples addFrequency.BDataUnivariateSamples.BDataFrequencyHistograms addFrequency.BDataUnivariateSamples.BDataUnivariateSamples |
| Age Distribution Data for Upper Austrian Communes | age |
| Finds a BayesSpaceBasis representation of the object | as.BayesSpaceBasis as.BayesSpaceBasis.BayesSpace as.BayesSpaceBasis.BayesSpaceBasis |
| Converts to a BDataDensities object | as.BDataDensities as.BDataDensities.BDataDensities as.BDataDensities.BDataHistograms as.BDataDensities.BDataLogDensities as.BDataDensities.BDataLogPDFs as.BDataDensities.BDataPDFs as.BDataDensities.BDataSamples as.BDataDensities.BDataSplines |
| Convert to a BDataDensityHistograms dataset | as.BDataDensityHistograms as.BDataDensityHistograms.BDataUnivariateSamples |
| Build a distributional dataset as density histograms from a list | as.BDataDensityHistograms.list |
| Converts to a BDataFrequencyHistograms | as.BDataFrequencyHistograms as.BDataFrequencyHistograms.BDataUnivariateSamples |
| Build a distributional dataset as histograms from a list | as.BDataFrequencyHistograms.list |
| Represent data as a BDataHistogram (BDataDensityHistogram or BDataFrequencyHistogram | as.BDataHistograms as.BDataHistograms.BDataDensityHistograms as.BDataHistograms.BDataFrequencyHistograms as.BDataHistograms.BDataHistograms as.BDataHistograms.histogram as.BDataHistograms.list |
| Converts to a BDataLogDensities object | as.BDataLogDensities as.BDataLogDensities.BDataDensities as.BDataLogDensities.BDataHistograms as.BDataLogDensities.BDataLogDensities as.BDataLogDensities.BDataLogPDFs as.BDataLogDensities.BDataPDFs as.BDataLogDensities.BDataSamples as.BDataLogDensities.BDataSplines as.BDataLogDensities.list |
| Converts to representations as Log Probability Density functions | as.BDataLogPDFs as.BDataLogPDFs.BDataDensities as.BDataLogPDFs.BDataDensityHistograms as.BDataLogPDFs.BDataFrequencyHistograms as.BDataLogPDFs.BDataLogDensities as.BDataLogPDFs.BDataLogPDFs as.BDataLogPDFs.BDataPDFs as.BDataLogPDFs.BDataSamples as.BDataLogPDFs.BDataSplines |
| Converts to representations as Probability Density functions | as.BDataPDFs as.BDataPDFs.BDataDensities as.BDataPDFs.BDataDensityHistograms as.BDataPDFs.BDataFrequencyHistograms as.BDataPDFs.BDataLogDensities as.BDataPDFs.BDataLogPDFs as.BDataPDFs.BDataPDFs as.BDataPDFs.BDataSamples as.BDataPDFs.BDataSplines |
| Converts to representations as BDataSamples | as.BDataSamples as.BDataSamples.BData as.BDataSamples.BDataSamples as.BDataSamples.BDataUnivariateSamples |
| Convert distributional data to a spline representation | as.BDataSplines as.BDataSplines.BayesSpaceBasis as.BDataSplines.BDataDensities as.BDataSplines.BDataGeneral as.BDataSplines.BDataLogDensities as.BDataSplines.BDataLogPDFs as.BDataSplines.BDataPDFs as.BDataSplines.BDataSamples as.BDataSplines.BDataSplines as.BDataSplines.function as.BDataSplines.histogram |
| A distribution of distributions based a distribution family with random hyperparameters. | B2DistHyper |
| A Gaussian Process distribution for probabilities | B2GaussianDistribution simulate.B2GaussianDistribution |
| The standard B2(N(0,1)) Bayes space. | B2N01 B2N01Basis20 B2N01Basis5 B2N01Basis7 |
| Evaluates a BayesSpaceBasis | as.function.BayesSpaceBasis basisEval basisEval.basisfd basisEval.BayesSpaceBasis |
| Creates the Bayes space for a reference measure | BayesSpace |
| Create a basis for a Bayes space | BayesSpaceBasis BayesSpaceBasis.basisfd BayesSpaceBasis.BayesSpace BayesSpaceBasis.fd |
| Create a BayesSpaceBasis for a Cartesian Product of Bayes Spaces | BayesSpaceBasis.BayesSpaceCartesianProduct BayesSpaceBasis.ReferenceMeasureCartesianProduct |
| Creates a BayesSpaceBasis from a function | BayesSpaceBasis.function |
| Coded Basis for ReferenceMeasureDiscrete | BayesSpaceBasis.ReferenceMeasureDiscrete |
| Generates a BayesSpaceBasis for transformed Bayes Spaces | BayesSpaceBasis.ReferenceMeasureTransformed |
| create the `BayesSpaceBasis` object for `ReferenceMeasureUniform` | BayesSpaceBasis.ReferenceMeasureUniform |
| Computes the product basis of BayesSpaceBasis for the product BayesSpace | BayesSpaceBasisCartesianProduct |
| The Cartesian Product of Bayes Spaces | BayesSpaceCartesianProduct L2SpaceCartesianProduct |
| Distributional data containers (BData) | BData |
| Constructor of BDataDensities objects representing a Dataset of Densities in a BayesSpace | BDataDensities BDataDensities.function BDataDensities.list BDataLogDensities.list BDataLogPDFs.list BDataPDFs BDataPDFs.function BDataPDFs.list |
| Data container for distributions as histograms | BDataDensityHistograms |
| Data container for distributions as histograms | BDataFrequencyHistograms |
| BayesSpace data in different formats | BDataGeneral |
| Constructor of BDataLogDensities objects representing a Dataset of log Densities in a BayesSpace | BDataDensities.BDataLogDensities BDataLogDensities BDataLogDensities.BDataDensities BDataLogPDFs BDataLogPDFs.BDataPDFs BDataPDFs.BDataLogPDFs |
| BData container with distributions as samples | BDataSamples summary.BDataSamples summary.BDataUnivariateSamples [.BDataSamples [.BDataUnivariateSamples |
| create a `BDataSplines` object: a set of distributions represented as splines; possible representation of individual distribution through a vector of coefficients. | BDataSplines [.BDataSplines |
| Biplot for PCA of Distributional Data | biplot.princomp.BData |
| Functional Boxplot for BData objects | boxplot.BData boxplot.BDataSamples boxplot.BDataSplines |
| Compute the cartesian product | cartesianProduct cartesianProduct.BayesSpace cartesianProduct.BayesSpaceBasis cartesianProduct.ReferenceMeasure cartesianProduct.Support |
| Expected values of functions of distributions | caseE caseE.BData caseE.BDataSamples |
| Log-density ratios for each distribution (case) | caseLogRatios |
| Expected values (means) of real-valued distributions | caseMeans |
| Quantiles of distributions in a data set of distributions | caseQuantiles caseQuantiles.BData caseQuantiles.BDataSamples |
| Standard deviations of real-valued distributions | caseSds |
| Variances of real-valued distributions | caseVars caseVars.BData caseVars.BDataSamples |
| numerically compute a density from a cdf | cdf2density |
| Try to guess the support from cdf | cdf2Support |
| Centers the representation | cen cen.BayesSpaceBasis cen.BDataDensities cen.BDataLogDensities cen.BDataLogPDFs cen.BDataPDFs cen.function |
| Finds the centering constant(s) | centeringConstant centeringConstant.BayesSpaceBasis centeringConstant.BDataDensities centeringConstant.BDataLogDensities centeringConstant.BDataLogPDFs centeringConstant.BDataPDFs centeringConstant.BDataSplines centeringConstant.function |
| centralityQuantile | centralityQuantile centralityQuantile.default centralityRank centralityRank.default |
| Closes to a given total measure | clo clo.BayesSpaceBasis clo.BDataDensities clo.BDataLogDensities clo.BDataLogPDFs clo.BDataPDFs clo.default clo.function |
| Closure methods for BData objects | clo.BDataHistograms |
| Finds the closing constant(s) | closingConstant closingConstant.BayesSpaceBasis closingConstant.BDataDensities closingConstant.BDataLogDensities closingConstant.BDataLogPDFs closingConstant.BDataPDFs closingConstant.BDataSplines closingConstant.function |
| compute the clr transform | clr clr.BData clr.default |
| Computes the matrix mapping an L2-space basis to a Bayes-Space-Basis. | computeL22BayesSpacesBasisMatrix |
| create the transform matrix A from a B-spline basis (fda::create.bspline.basis()) | computeL22BayesSpacesBasisMatrix.basisfd |
| computes the limit of a function | computeLimit |
| 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. | CovarianceOperator CovarianceOperator.ReferenceMeasure CovarianceOperator.ReferenceMeasureDiscrete |
| Creates a function based on a regular discretization | DiscretizedFunction DiscretizedFunction.Discretizer DiscretizedFunction.function DiscretizedFunction.numeric DiscretizedFunction.ReferenceMeasureTransformed DiscretizedFunction.ReferenceMeasureUniform |
| Distance method for BData objects | dist.BData dist.BDataLogDensities dist.BDataSplines |
| Distance to Center | distanceToCenter distanceToCenter.default |
| Division of functional objects | 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 |
| An example dataset for distributional data analysis | DTD |
| Returns the argument, but ensures that it has a certain class. | ensureType |
| Computes expectations from reference measures | expectation expectation.ReferenceMeasure expectation.ReferenceMeasureDiscrete |
| integrate a function over the uniform reference measure | expectation.ReferenceMeasureUniform |
| Finds jumps of a function of a certain minimum height | findJumps |
| Shortcut for creating functions of x | fx |
| Generic function returning the Bayes space associated to the given object. | getBayesSpace getBayesSpace.BayesSpace getBayesSpace.BayesSpaceBasis getBayesSpace.BData getBayesSpace.default getBayesSpace.L2Space getBayesSpace.NULL getBayesSpace.ReferenceMeasure getL2Space.L2Space |
| Construct the product Bayes Space for a product reference measure | getBayesSpace.ReferenceMeasureCartesianProduct |
| Generic function returning the BayesSpaceBasis associated to the given object. | getBayesSpaceBasis getBayesSpaceBasis.BayesSpace getBayesSpaceBasis.BayesSpaceBasis getBayesSpaceBasis.BData getBayesSpaceBasis.variationKernel getBayesSpaceBasis.varKernel |
| the dimension of the basis | getBayesSpaceBasisDimension getBayesSpaceBasisDimension.BayesSpaceBasis |
| Common utilities for BDataHistograms objects | getBreaks getBreaks.BDataHistograms |
| Decomposes a cartesian product into a list of its factors. | getCartesianFactors |
| Gets number of elements in a Basis | getDimension getDimension.BayesSpaceBasis getDimension.BDataSplines getDimension.matrix getDimension.numeric |
| Computes Discretization points for nonequidistant discretization | getDiscretizer getDiscretizer.BayesSpaceBasis getDiscretizer.ReferenceMeasureDiscrete getDiscretizer.ReferenceMeasureTransformed getDiscretizer.ReferenceMeasureUniform |
| Generic function returning the L2Space associated to the given object. | getL2Space getL2Space.BayesSpace getL2Space.BayesSpaceBasis getL2Space.BData getL2Space.default getL2Space.NULL getL2Space.ReferenceMeasure |
| Metric tensor of a space or basis | getMetricTensor |
| returns the probability density function | getPDF getPDF.BData getPDF.ReferenceMeasureTransformed getPDF.ReferenceMeasureUniform |
| Getter for the ReferenceMeasure of an object | getReferenceMeasure getReferenceMeasure.BayesSpace getReferenceMeasure.default getReferenceMeasure.L2Space getReferenceMeasure.ReferenceMeasure |
| Get a sample from the reference measure | getSample getSample.BayesSpace getSample.L2Space getSample.ReferenceMeasure getSample.ReferenceMeasureCartesianProduct getSample.ReferenceMeasureDiscrete getWeights hasWeights hasWeights.default |
| Coefficients of a single BayesSpace element. | getSplineCoefs getSplineCoefs.BData getSplineCoefs.BDataSplines getSplineCoefs.default getSplineCoefs.function getSplineCoefs.hist |
| Checks whether two objects have the same BayesSpace | hasSameBayesSpace |
| Checks whether the associated BayesSpaceBasis of to objects is the same | hasSameBayesSpaceBasis |
| Isometric default representations for BData objects | idt.BData idtInv.BData idtInv.BDataSplines |
| Returns whether its know that the object is closed or centered | isCentered isCentered.BayesSpaceBasis isCentered.BData isCentered.BDataSplines isCentered.default isClosed isClosed.BData isClosed.default |
| Checks whether values are in the domain of the ReferenceMeasure | isInDomain isInDomain.BayesSpace isInDomain.BayesSpaceBasis isInDomain.L2Space isInDomain.ReferenceMeasureCartesianProduct isInDomain.ReferenceMeasureDiscrete isInDomain.ReferenceMeasureTransformed |
| assess whether a vector of points belong to the domain of the uniform reference measure | isInDomain.ReferenceMeasureUniform |
| Checks that an object is a BayesSpaceBasis | isValidBayesSpaceBasis |
| Checks whether an object is an element (or dataset of elements) of a BayesSpace | isValidBayesSpaceElement isValidBayesSpaceElement.BData isValidBayesSpaceElement.default isValidBayesSpaceElement.function isValidBayesSpaceElement.histogram |
| Computes an evaluable kernel density estimate on a dataset | kdeDensity kdeDensity.data.frame kdeDensity.list kdeDensity.matrix kdeDensity.numeric |
| Creates the L2-space for a Reference Measure | L2Space L2Space.BayesSpace L2Space.ReferenceMeasure |
| Accesses list elements with an expression | listDataElementAccess |
| Makes a list includable in a data.frame | as.data.frame.BData as.data.frame.ListData format.BData format.ListData ListData ListDataEnv ListDataRecover storingDigester |
| Functional mapping a function to its exp version | Exp Exp.function Exp.list Exp.numeric Ln Ln.function Ln.list Ln.numeric |
| Makes a vectorized 2-ary function into one that returns a matrix | makeFunctionOuter |
| Makes a real function returning limits at undefined points | makeLimitStable |
| Gets coordinates on an axis for possibly non real data | mapToRealLineWithSupport |
| Compute Maximum and Minimum Functions | maxFunc minFunc |
| Computes the distribution mean for BDataSplines. | mean.BDataDensities mean.BDataLogDensities mean.BDataLogPDFs mean.BDataPDFs mean.BDataSplines |
| A distributional median | median.BData |
| Multiplication of functional objects | 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 |
| Compute the metric variance | msd.BData mvar.BData mvar.BDataSplines |
| Accessing cases regardless how a dataset is stored. | nCases nCases.default selectCases selectCases.default |
| Computes the Bayes-Space norm of BData objects | norm norm.BData norm.BDataSplines |
| A generic function computing the number of cases. | numberOfCases numberOfCases.data.frame numberOfCases.default |
| Guesses the support from a pdf | pdf2Support |
| plot the 'BayesSpaceBasis' | plot.BayesSpaceBasis |
| Plot histograms as segments | plot.BDataHistograms |
| Plotting BDataSample Objects | plot.BDataSamples |
| Plot a set of spline functions (using visualisation tools from fda) | plot.BDataSplines |
| Plot Method for PCA of Distributional Data | plot.princomp.BData |
| Plot Method for Grouped PCA of Distributional Data | plot.princomp.BData.grouped |
| Plot Method for Multivariate SFPCA of Distributional Data | plot.princomp.BData.multivariate |
| Plotting varKernel objects | plot.variationKernel plot.varKernel |
| Plot multiple coefficients in a parallel coordinate plot | plotCoefs |
| Helper function to plot multiple functions | plot.BDataDensities plot.BDataLogDensities plot.BDataLogPDFs plot.BDataPDFs plotFuncs |
| Predict Scores for New Distributional Data | predict.princomp.BData |
| Principal Component Analysis for Distributional Data | princomp.BData print.princomp.BData |
| Pretty printing of BayesSpaces | print.BayesSpace |
| Print a BDataLogDensities object | print.BDataLogDensities |
| Print a BDataLogPDFs object | print.BDataLogPDFs |
| Print a BDataSplines object | print.BDataSplines |
| prints "L2Space with reference measure" and than the reference measure. | print.L2Space |
| Print a grouped distributional PCA | print.princomp.BData.grouped |
| Print a multivariate (2D-)SFPCA on distributional data | print.princomp.BData.multivariate |
| Printing ReferenceMeasures | print.ReferenceMeasure |
| Reference measures: the geometry of a Bayes space | ReferenceMeasure |
| Compute a cartesian Product of measures | ReferenceMeasureCartesianProduct |
| Discrete Reference Measure for BayesSpaces | ReferenceMeasureDiscrete |
| A general continuous reference measure on the real line | getSample.ReferenceMeasureTransformed ReferenceMeasureTransformed |
| create the ReferenceMeasureUniform class | ReferenceMeasureUniform |
| Guesses the support from a sample | sample2Support |
| Computes the scalar product of (datasets of) vectors from various spaces | scalarProduct scalarProduct.BDataDensities scalarProduct.BDataLogDensities scalarProduct.BDataSplines scalarProduct.numeric |
| Computes the scalar product of log densities in a BayesSpace | scalarProductOfLogDensities |
| Scale and/or center a BData set | scale.BData |
| Screeplot for PCA of Distributional Data | screeplot.princomp.BData |
| Identifies the integration method to be choosen for a measure | selectIntegrationMethod selectIntegrationMethod.CartesianProductMeasure selectIntegrationMethod.ReferenceMeasure |
| Simulates a B2DistHyper distribution | simulate.B2DistHyper |
| Simulate from the distribution described by the BData objects | simulate.BData |
| Simulates from a ReferenceMeasureCartesianProduct | simulate.ReferenceMeasureCartesianProduct |
| Generates simulations from the Discrete Reference Measure seen as distribution. | simulate.ReferenceMeasureDiscrete |
| Generates simulations from the Reference Measure seen as distribution. | simulate.ReferenceMeasureTransformed |
| simulation method for the uniform referencemeasure | simulate.ReferenceMeasureUniform |
| Spatial Depth in dataset | spatialDepth spatialDepth.BData spatialDepth.matrix spatialDepth.numeric |
| The standard B2(Unif(0,1)) Bayes space. | B2Unif01 B2Unif01Basis20 B2Unif01Basis5 B2Unif01Basis7 StdReferenceMeasureN01 StdReferenceMeasureUnif01 |
| Substract functional objects | 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 |
| Computes the indices of a certain marginal basis | subBasisIndices |
| Summary of PCA for Distributional Data | summary.princomp.BData |
| The support of the measures | getSupport getSupport.BayesSpace getSupport.BayesSpaceBasis getSupport.BData getSupport.function getSupport.list getSupport.ListData getSupport.numeric getSupport.ReferenceMeasureCartesianProduct getSupport.ReferenceMeasureDiscrete getSupport.ReferenceMeasureTransformed getSupport.ReferenceMeasureUniform getSupport.Support getSupport.variationKernel getSupport.varKernel getSupport1DPlottingLimits getSupportAtoms Support |
| Creates a cartesian product of supports | SupportCartesianProduct |
| Computes the discretization of a mixed support | supportDiscretization supportDiscretization.DiscreteSupport supportDiscretization.RealSupport |
| Convert something to a histogram | toHistogram toHistogram.BData toHistogram.BDataHistograms toHistogram.BDataUnivariateSamples toHistogram.histogram toHistogram.numeric |
| Compute the variation kernel | variationKernel variationKernel.BData variationKernel.BDataSplines variationKernel.function variationKernel.variationKernel |
| Compute the kernel of the Variance Kovariance operator | varKernel varKernel.BData varKernel.BDataSplines varKernel.function varKernel.varKernel |
| Computes mean and variance for weighted reference measures | weightedStats weightedStats.ReferenceMeasure weightedStats.ReferenceMeasureDiscrete |