Description: A fast, scalable, and versatile framework for simulating large
systems with Gillespie's Stochastic Simulation Algorithm ('SSA').
This package is the spiritual successor to the 'GillespieSSA' package
originally written by Mario Pineda-Krch. Benefits of this package
include major speed improvements (>100x), easier to understand documentation,
and many unit tests that try to ensure the package works as intended.
Suggests: ggplot2, GillespieSSA, knitr, rmarkdown, testthat (>= 2.1.0)
Author: Robrecht Cannoodt [aut, cre] (<https://orcid.org/0000-0003-3641-729X>)

Authors@R: c(person("Zhicheng Du", role = c("aut","cre","cph"), email = "dgdzc@hotmail.com"),
Description: Providing various equations to calculate Gini coefficients.
The methods used in this package can be referenced from Brown MC (1994) <doi: 10.1016/0277-9536(94)90189-9>.

Title: A New Gini Correlation Between Quantitative and Qualitative
Description: An implementation of a new Gini covariance and correlation to measure dependence between a categorical and numerical variables. Dang, X., Nguyen, D., Chen, Y. and Zhang, J., (2018) <arXiv:1809.09793>.
Imports: Rcpp (>= 1.0.0), energy, readxl, randomForest
Collate: GiniDistance-package.R RcppExports.R gmd.R gCov.R gCor.R
dCov.R dCor.R Kgmd.R KgCov.R KgCor.R KdCov.R KdCor.R

Title: Computing the Gini-Based Coefficients for Weighted and Negative
Author: Emanuela Raffinetti, Fabio Aimar
Description: Gini-based coefficients and plot of the ordinary and generalized curve of maximum inequality in the presence of weighted and negative attributes.

Title: Computing the Gini Coefficient for Weighted and Negative
Description: Computation of the Gini coefficient in the presence of weighted and/or negative attributes. Two different approaches are considered in order to fulfill, in the case of negative attributes, the normalization principle, that is a value of the Gini coefficient bounded into the close range [0,1]. The first approach is based on the proposal by Chen, Tsaur and Rhai (1982) and Berebbi and Silber (1985), while the second approach is based on a recent proposal by Raffinetti, Siletti and Vernizzi (2015). The plot of the curve of maximum inequality, defined in the contribution of Raffinetti, Siletti and Vernizzi (2015), is provided.