Title: Deterministic Blockmodeling of Signed, One-Mode and Two-Mode
Description: It contains functions to apply blockmodeling of signed (positive and negative weights are assigned to the links), one-mode  and valued one-mode and two-mode (two sets of nodes are considered, e.g. employees and organizations) networks (Brusco et al. (2019) <doi:10.1111/bmsp.12192>).

Title: Dimension Reduction with Dynamic CUR
Author: Cesar Gamboa-Sanabria [aut, mdc, cph, cre] (<https://orcid.org/0000-0001-6733-4759>),
Maria Purificacion Galindo-Villardon [aqt] (<https://orcid.org/0000-0001-6977-7545>)
Description: Dynamic CUR (dCUR) boosts the CUR decomposition (Mahoney MW., Drineas P. (2009) <doi:10.1073/pnas.0803205106>) varying the k, the number of columns and rows used, and its final purposes to help find the stage, which minimizes the relative error to reduce matrix dimension.
The goal of CUR Decomposition is to give a better interpretation of the matrix decomposition employing proper variable selection in the data matrix, in a way that yields a simplified structure. Its origins come from analysis in genetics.
The goal of this package is to show an alternative to variable selection (columns) or individuals (rows). The idea proposed consists of adjusting the probability distributions to the leverage scores and selecting the best columns and rows that minimize the reconstruction error of the matrix approximation ||A-CUR||. It also includes a method that recalibrates the relative importance of the leverage scores according to an external variable of the user's interest.
Imports: parallel, magrittr, stackoverflow, mclust, MASS, ppcor,

Description: Functions for reading DCP and CDF.bin files generated by the dChip software.

Title: Distance Covariance and Correlation for Time Series Analysis
person("Maria", "Pitsillou", role=c("aut", "cre"), email="pitsillou.maria@ucy.ac.cy"),
Description: Computing and plotting the distance covariance and correlation
function of a univariate or a multivariate time series. Test statistics for
testing pairwise independence are also implemented. Some data sets are also included.