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.
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)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: 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 viet-duong.nguyen@outlook.com.
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