NEWS.md
In DistributionalDDA: Distributional Data Analysis in Bayes Spaces

DistributionalDDA 0.1.1

Bug fix: as.BDataDensities() and as.BDataLogDensities() on histogram objects applied the bin weights in reversed order, so that all histogram-derived step densities (and everything computed from them) were mirrored in the domain (.DensityFromHist()/.LogDensityFromHist()). Quantities that are invariant under a coherent relabeling of the domain (variances, distances, depths, explained-variance shares) were only mildly affected through the unequal bin widths, but plotted densities, means, and eigenfunctions appeared reflected. Densities supplied as functions (BDataPDFs, BDataDensities) were not affected.
ReferenceMeasureUniform() gains a seed argument: the Monte Carlo sample of the reference measure is drawn under that seed and the global RNG state is restored afterwards (same convention as simulate()). Reproducibility of a reference measure no longer depends on the global seed being set immediately before construction.
plot() on princomp.BData objects with type = "scores" now accepts pch (recycled across observations), so groups can be distinguished by plotting symbol in addition to color.
plot() on multivariate SFPCA objects (type = "eigenfunctions") forwards ... to the panel set-up call, so axis labels such as xlab and ylab can be customized.