NEWS.md

In HistDAWass: Histogram-Valued Data Analysis

HistDAWass 1.0.10

Performance

Large speed-ups when the histograms have different bins (the default simplify = FALSE), with results identical to 1.0.9 (up to differences of the order of 1e-13 in covariances and fuzzy c-means, due to the order of the sums). Times on a matrix of 300 histograms x 3 variables with different bins:

| Function | 1.0.9 | 1.0.10 | |---|---:|---:| | WH_adaptive_fcmeans() | 117 s | 4.1 s | | WH_2d_Kohonen_maps() | 106 s | 4.6 s | | WH_2d_Adaptive_Kohonen_maps() | 101 s | 4.4 s | | WH.var.covar(), WH.correlation(), WH.SSQ() | 4.8 s | 0.1 s | | WH.vec.mean() | 2.3 s | 0.08 s | | WH_fcmeans() | 7.8 s | 1.6 s | | WH_kmeans() | 6.8 s | 2.5 s | | WH_adaptive.kmeans() | 7.5 s | 3.1 s | | WH_MAT_DIST(), WH_hclust() | 3.0 s | 1.2 s | | registerMH() | 5.9 s | 2.1 s |

• SSQ_RCPP() (C++, used by WH.SSQ(), WH.var.covar(), WH.correlation()) no longer builds a dense diagonal matrix of the weights (of size G x G, G being the number of points of the common grid of cdf values) for each individual; the sum of the weights is now stored in a double instead of a float.
• The Kohonen maps and the adaptive fuzzy c-means computed the total sum of squares with WH.SSQ() on the already prepared data: they now use the fast c_ComputeFASTSSQ().
• Fuzzy c-means: the prototypes are computed with a matrix-vector product instead of building a G x n matrix of weights at each step.
• WH_kmeans() and WH_adaptive.kmeans(): the prototypes are computed without copying the columns of the data of each cluster (new c_ComputeFASTSSQ_sel()).
• PREPARE_A_VEC_MAT() (C++, puts a set of histograms on a common grid of cdf values) computes the quantiles with a single ordered scan of the grid; COMP_Q_VECT() scans the cdf only once for sorted levels; c_Prepare() no longer copies and transposes the data.
• registerMH(), the arithmetic of histograms with numbers and the regression with simplify = TRUE avoid repeated validity checks and quantile computations.

Bug fixes

• WH.1d.PCA(): INERTIA (total inertia) was the sum of the eigenvalues returned by FactoMineR::PCA(), which since FactoMineR 2.15 are only the first ones; it is now computed from the percentage of inertia of the first axis.
• HTS.moving.averages(): each average was assigned the timestamp of the element following the central one of its window; weights of length different from k are now an error.
• HTS.predict.knn(): with ties in the distances, which() returned several positions (with a warning) and could select wrong neighbours; k larger than the available history is now an error.
• WH_2d_Adaptive_Kohonen_maps() with init.weights = "PROP": the condition schema == 2 || 4 was always true, so the initial weights of schemas 1, 3 and 5 were overwritten; for these schemas the vector of initial weights also had twice the needed length.
• d1 - d2 failed (object 'tmp' not found) when the two distributions share the same cdf, e.g. after registerMH().

• a * d and d * a with a < 0, and a - d, returned (or failed to create) objects with a decreasing x; they now return the distribution of a * X and a - X.

• plot() of a MatH (type "HISTO") cut the densities at the fixed value 2: variables with a narrow range were drawn as flat, truncated histograms. The heights are now normalised within each variable.

• WH.plot_multiple_indivs() drew the histograms of the individuals as flat lines (the normalised domain and densities were computed but not used). The size of the histograms is now set automatically: strx (default 0.1) is the average width of the histograms and stry (default 1/8) the height of the highest density, as fractions of the ranges of the coordinates of the individuals on the two axes. Each histogram is centred on its mean, and histograms with very narrow bins (e.g. quantiles with ties) are drawn on 30 equal-width bins to avoid spikes.
• The printout of a histogram with many bins used two different formats for the bins.
• [ on a MatH object: columns or rows selected by name got NA names; negative and logical indices are now allowed (e.g. BLOOD[-1, ], BLOOD[, "Cholesterol"]).

Vignettes

Five vignettes have been added:

• vignette("HistDAWass-01-data"): creating histograms (distributionH, data2hist()) and matrices of histograms (MatH), also from a data frame of raw data;
• vignette("HistDAWass-02-basic-statistics"): statistics of histograms and of histogram variables (means, quantiles, distances, covariances, correlations);
• vignette("HistDAWass-03-clustering"): k-means and fuzzy c-means, also with adaptive distances, and hierarchical clustering;
• vignette("HistDAWass-04-pca"): principal component analysis of one or more histogram variables;
• vignette("HistDAWass-05-regression"): the two-component linear regression of histogram variables.

Tests

• New tests for the fixes above and for the fast computations.

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.

HistDAWass 1.0.7 What’s new

Minor bug fixes fo compatibility isseues with ggridges.

HistDAWass 1.0.6 What’s new

Added the WH_WASS_MAT_DIST for computing a distance matrix

HistDAWass 1.0.5 What’s new

Fixed some minor bugs. Added the names of columns and the rows in weights matrices of adaptive distances based methods.

HistDAWass 1.0.4 What’s new

Fixed some minor bugs.

HistDAWass 1.0.3 What’s new

Histogram-valued Data analysis using Wasserstein metric

Fixed some visualizations accordingly to the new ggplot2 release.

HistDAWass 1.0.2 What’s new ##(Histogram-valued Data analysis using

Wasserstein metric)

Soelf Organizing maps have been improved for speed and aptions.

HistDAWass 1.0.1 What’s new ##(Histogram-valued Data analysis using

Wasserstein metric)

Some bugs have been resolved when processing data with a single variable.

HistDAWass 1.0.0 What’s new ##(Histogram-valued Data analysis using

Wasserstein metric)

Now, a set of methods for MatH (WH.SSQ, WH.vec.mean, and all the methods using them) are much faster due to RCPP implementation.

HistDAWass 0.1.7 What’s new ##(Histogram-valued Data analysis using

Wasserstein metric)

Some general improvements have been added for reducing elaboration times.

unsupervised learning

WH_kmeans now returns more informative outputs

principal components analysis

Multiple factor analysis has new functions for plotting results

HistDAWass 0.1.6 What’s new ##(Histogram-valued Data analysis using

Wasserstein metric)

unsupervised learning

fuzzy c-means and kohonen maps are 10 times faster

HistDAWass 0.1.5 What’s new ##(Histogram-valued Data analysis using

Wasserstein metric)

some minor bug fixed

HistDAWass 0.1.4 What’s new ##(Histogram-valued Data analysis using

Wasserstein metric)

some minor bug fixed

HistDAWass 0.1.3 What’s new Some fixes to the worksapce

HistDAWass 0.1.2 What’s new ##(Histogram-valued Data analysis using

Wasserstein metric) > From raw data to histograms

A new function was added for generating a histogram from a set of raw data

Visualization

Now for plotting, ggplot2 package is used generally.

A new plot for visualizing Histogram Time Series was added.

A new plot for comparing obversed vs predicted histogram was added.

A new plot for showing the differences between an observed and a predicted histogram was added.

Regression and predictive techniques

A new function for computing Goodness of Fit of a model was added

HistDAWass documentation built on Oct. 29, 2026, 5:07 p.m.