Histogram-Valued Data Analysis (HistDAWass)
In the framework of Symbolic Data Analysis, a relatively new approach to the statistical analysis of multi-valued data, we consider histogram-valued data, i.e., data described by univariate histograms. The methods and the basic statistics for histogram-valued data are mainly based on the L2 Wasserstein metric between distributions, i.e., a Euclidean metric between quantile functions. The package contains unsupervised classification techniques, least square regression and tools for histogram-valued data and for histogram time series.

Provides Access to Git Repositories (git2r)
Interface to the libgit2 library, which is a pure C implementation of the Git core methods. Provides access to Git repositories to extract data and running some basic git commands.

Faisal Conjoint Model: A New Approach to Conjoint Analysis (faisalconjoint)
It is used for systematic analysis of decisions based on attributes and its levels.

Cluster Distances Through Trees (treeClust)
Create a measure of inter-point dissimilarity useful for clustering mixed data, and, optionally, perform the clustering.

Bootstrap Tolerance Levels for Credit Scoring Validation Statistics (boottol)
Used to create bootstrap tolerance levels for the Kolmogorov-Smirnov (KS) statistic, the area under receiver operator characteristic curve (AUROC) statistic, and the Gini coefficient for each score cutoff.

Disparity Filter Algorithm of Weighted Network (disparityfilter)
Disparity filter is a network reduction algorithm to extract the backbone structure of both directed and undirected weighted networks. Disparity filter can reduce the network without destroying the multi-scale nature of the network. The algorithm has been developed by M. Angeles Serrano, Marian Boguna, and Alessandro Vespignani in Extracting the multiscale backbone of complex weighted networks.