Distributional Data Analysis in Bayes Spaces R package DistributionalDDA version 0.1.1 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>. Software 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) K. Gerald van den Boogaart <support@boogaart.de> Comprehensive R Archive Network (CRAN) Ivana Pavlu [aut] (ORCID: <https://orcid.org/0000-0001-9322-246X>), Raimon Tolosana-Delgado [aut] (ORCID: <https://orcid.org/0000-0001-9847-0462>), Matthias Templ [aut] (ORCID: <https://orcid.org/0000-0002-8638-5276>), K. Gerald van den Boogaart [aut, cre] (ORCID: <https://orcid.org/0000-0003-4646-943X>) LGPL-3 2026-10-20 application/tgz https://CRAN.R-project.org/package=DistributionalDDA doi:10.32614/CRAN.package.DistributionalDDA