Gini-Based Composite Indicators

An implementation of Gini-based weighting approaches in constructing composite indicators, providing functionalities for normalization, aggregation, and ranking comparison.

giniCI: Gini-based Composite Indicators

giniCI provides an implementation of Gini-based weighting approaches for composite indicator construction. The package includes functions for normalization, aggregation, and ranking comparison to support multidimensional measurement based on distributional dispersion across individual components.

Installation

You can install the latest released version from CRAN:

install.packages("giniCI")

Alternatively, you can install the development version from GitHub:

devtools::install_github("novidu/giniCI", build_vignettes = TRUE)

Usage

Below is a simple example of constructing Gini-based composite indicators. For more details, please take a look at the package vignettes using browseVignettes("giniCI").

library(giniCI)
data(bli)

# Indicator polarity
bli.pol = c("neg", "pos", "pos", "pos", "pos", "neg",
            "pos", "pos", "pos", "neg", "pos")

# Goalpost normalization using time factors and a reference time
bli.norm.2014 <- normalize(inds = bli[, 3:13], method = "goalpost",
                           ind.pol = bli.pol, time = bli$YEAR,
                           ref.time = 2014)

# Composite indices
ci.gini <- giniCI(bli.norm.2014, method = "gini",
                  ci.pol = "pos", time = bli$YEAR, ref.time = 2014,
                  only.ci = TRUE)
ci.reci <- giniCI(bli.norm.2014, method = "reci", agg = "geo",
                  ci.pol = "pos", time = bli$YEAR, ref.time = 2014,
                  only.ci = TRUE)

# Ranking comparison
ci.comp <- rankComp(ci.gini, ci.reci, id = bli$COUNTRY, time = bli$YEAR)
summary(ci.comp)

Authors and Contributions

Authors: Viet Duong Nguyen (maintainer), Chiara Gigliarano, and Mariateresa Ciommi

Suggested improvements, as well as technical issues and bug reports, are highly welcome.

Please direct development questions to [email protected].

References

Ciommi, M., Gigliarano, C., Emili, A., Taralli, S., & Chelli, F. M. (2017). A new class of composite indicators for measuring well-being at the local level: An application to the Equitable and Sustainable Well-being (BES) of the Italian Provinces. Ecological Indicators, 76, 281–296. https://doi.org/10.1016/j.ecolind.2016.12.050