HistDAWass 1.0.9

Bug fixes

• c_WH_ADPT_KMEANS_TOTALSSQ() (used by WH_adaptive.kmeans() and the adaptive Kohonen maps): the vector of cluster weights assigned to each row of the weight matrix was allocated with k (number of clusters) elements instead of vars (number of variables). When k < vars the remaining variables read uninitialised memory. This made the examples fail with the forthcoming Rcpp release (Rcpp PR #1513, which checks the length of vectors assigned into matrix rows). Thanks to the Rcpp team for the report and the patch.
• Empty bins (bins with zero weight) are now handled consistently: a weight of 1e-8 is assigned to each empty bin and the cdf is renormalised. This happens when a distributionH (or TdistributionH) object is created, in data2hist() (that used a weight of 0.001 counts), in checkEmptyBins() and in the internal C++ registration of distributions (that used 1e-14). Up to 1.0.8 a distribution with an empty bin produced misaligned quantiles when several distributions were put on a common grid of cdf values, with errors in the computed distances (up to 7% in the China_Seas data).
• The China_Seas, China_Month and Age_Pyramids_2014 datasets contained 683 distributions with empty bins: they have been fixed accordingly.
• compQ() returned wrong quantiles for distributions with repeated values in x (e.g. compQ(distributionH(c(1,2,2,3), c(0,.3,.6,1)), 0.7) returned 2 instead of 2.25).
• compP() returned wrong values for distributions with empty bins.
• The validity check of distributionH objects never checked that p is not decreasing; distributionH(x) with a single value now creates an impulse distribution as documented.
• whichLowerEQThanX() (C++) could read one element beyond the end of the vector; c_DISTA_M2() (C++) accessed the second element of a list of length one when data had a single variable.

Graphics

• All the plot methods (distributionH, TdistributionH, MatH, HTS) and plotPredVsObs() now draw the plot on the current graphic device and return the ggplot object invisibly. Up to 1.0.8 the distributionH and MatH methods returned the object without drawing it, so nothing was shown inside loops, functions or source().
• WH.1d.PCA() no longer opens new graphic devices with dev.new(): the four plots of each factorial plane are drawn on the current device (2 x 2 panels), restoring the graphical parameters on exit. In the plot coloured by the standard deviations the densities were centred on the standard deviation instead of the mean.
• WH.MultiplePCA() gains a graph argument (default TRUE) passed to FactoMineR::MFA().
• WH.plot_multiple_Spanish.funs() and WH.plot_multiple_indivs() no longer print their plots twice; WH.plot_multiple_indivs() failed with stat different from "mean", and its cut of the highest densities was applied to the wrong column.
• The VIOLIN plot of HTS objects showed the data of the first block of timestamps in all the rows of the plot: fixed. HTS plots are now a single ggplot with one facet for each block of maxno.perplot timestamps.
• plotPredVsObs() returns a single ggplot (faceted by object) instead of drawing several plots in a grid of viewports.
• Updated to ggplot2 >= 3.4.0 (no more deprecation warnings with ggplot2 4.x): linewidth instead of size for lines, guides(fill = "none"), aes() with the .data pronoun instead of aes_all() and with().
• The data of the plots are built in a vectorised way (quantiles computed in C++ for all the probabilities at once, no rbind() in loops): summaryHTS() and the HTS violin plot are about twice as fast.
• data2hist(algo = "base") no longer draws a histogram as a side effect.
• Removed leftover browser() calls from the code of the adaptive Kohonen maps and of the adaptive weights.

Documentation

• The documentation has been revised and regenerated with roxygen2 8 (Config/roxygen2/version replaces RoxygenNote): descriptive titles, a single usage for each method (no duplicated generic/method usage, no initialize methods in the usage of the classes), accurate return values, documented slots of the classes, dataset formats and examples, and an overview of the package in ?HistDAWass.
• Related functions are documented together: the arithmetic operators (?"Arith-distributionH"), WH.SSQ/WH.var.covar/WH.correlation and their two-matrix versions, WH.vec.sum/WH.vec.mean, WH.bind*, registerMH/is.registeredMH, get.cell.MatH/set.cell.MatH and the get.MatH.* accessors.
• \dontrun{} examples replaced by \donttest{} (they all run).

New features

• checkEmptyBins() has a method for MatH objects, useful for objects created with previous versions of the package.
• Added unit tests (testthat).

Performance

• The C++ functions computing L2 Wasserstein distances between quantile functions (c_MM_L2_SQ_WASS_D(), c_DISTA_M(), c_Fast_D_Mat(), c_STEP_2_ADA_KMEANS(), c_ComputeFast_L2_SQ_WASS_DMAT()) no longer allocate temporary vectors nor access S4 slots in the inner loops, and c_WH_ADPT_KMEANS_TOTALSSQ() computes cluster-invariant distances once. Results are numerically identical to 1.0.8; on a 300 x 4 matrix of histograms WH_MAT_DIST() and WH_hclust() are about 20 times faster, WH_kmeans() about 6 times, WH_adaptive.kmeans() about 4 times and the Kohonen maps about 3 times faster.