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>.
| Version: | 0.1.1 |
| Depends: | R (≥ 4.1), compositions, splines |
| Imports: | fda, methods, graphics, stats, utils, rlang, digest, MASS, Matrix, grDevices |
| Suggests: | knitr, igraph, ggraph, rmarkdown, testthat (≥ 3.0.0) |
| Published: | 2026-10-20 |
| DOI: | 10.32614/CRAN.package.DistributionalDDA (may not be active yet) |
| Author: | Ivana Pavlu  [aut],
Raimon Tolosana-Delgado
[aut],
Matthias Templ
[aut],
K. Gerald van den Boogaart
[aut, cre] |
| Maintainer: | K. Gerald van den Boogaart <support at boogaart.de> |
| License: | LGPL-3 |
| NeedsCompilation: | no |
| Materials: | README, NEWS |
| CRAN checks: | DistributionalDDA results |
Documentation:
| Reference manual: | DistributionalDDA.html , DistributionalDDA.pdf |
| Vignettes: |
DistributionalDDA easy howto (source, R code) Principal Component Analysis for Distributional Data (source, R code) |
Downloads:
| Package source: | DistributionalDDA_0.1.1.tar.gz |
| Windows binaries: | r-devel: not available, r-release: not available, r-oldrel: not available |
| macOS binaries: | r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available |
Linking:
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