lorenz: Tools for Deriving Income Inequality Estimates from Grouped Income Data

Provides two methods of estimating income inequality statistics from binned income data, such as the income data provided in the Census. These methods use different interpolation techniques to infer the distribution of incomes within income bins. One method is an implementation of Jargowsky and Wheeler's mean-constrained integration over brackets (MCIB). The other method is based on a new technique, Lorenz interpolation, which estimates income inequality by constructing an interpolated Lorenz curve based on the binned income data. These methods can be used to estimate three income inequality measures: the Gini (the default measure returned), the Theil, and the Atkinson's index. Jargowsky and Wheeler (2018) <doi:10.1177/0081175018782579>.

Version: 0.1.0
Imports: magrittr, dineq
Suggests: testthat
Published: 2020-09-01
DOI: 10.32614/CRAN.package.lorenz
Author: Andrew Carr [aut, cre, cph]
Maintainer: Andrew Carr <andrew.carr at duke.edu>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README
CRAN checks: lorenz results


Reference manual: lorenz.pdf


Package source: lorenz_0.1.0.tar.gz
Windows binaries: r-devel: lorenz_0.1.0.zip, r-release: lorenz_0.1.0.zip, r-oldrel: lorenz_0.1.0.zip
macOS binaries: r-release (arm64): lorenz_0.1.0.tgz, r-oldrel (arm64): lorenz_0.1.0.tgz, r-release (x86_64): lorenz_0.1.0.tgz, r-oldrel (x86_64): lorenz_0.1.0.tgz


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