In Airpino/HistDAWass: Histogram-Valued Data Analysis

knitr::opts_chunk$set(fig.path='figure/', warning=FALSE, message=FALSE, error=FALSE)

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)

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

Airpino/HistDAWass documentation built on Jan. 30, 2024, 7:53 p.m.