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lorenz  

Tools for Deriving Income Inequality Estimates from Grouped Income Data
View on CRAN: Click here


Download and install lorenz package within the R console
Install from CRAN:
install.packages("lorenz")

Install from Github:
library("remotes")
install_github("cran/lorenz")

Install by package version:
library("remotes")
install_version("lorenz", "0.1.0")



Attach the package and use:
library("lorenz")
Maintained by
Andrew Carr
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-09-01
Latest Update: 2020-09-01
Description:
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) .
How to cite:
Andrew Carr (2020). lorenz: Tools for Deriving Income Inequality Estimates from Grouped Income Data. R package version 0.1.0, https://cran.r-project.org/web/packages/lorenz. Accessed 22 Dec. 2024.
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Complete documentation for lorenz
Functions, R codes and Examples using the lorenz R package
Some associated functions: lorenz_interp . mcib . 
Some associated R codes: atkinsons.R . lorenz_interp.R . mcib.R . mcib_helpers.R . special_lorenz_funcs.R . utils.R .  Full lorenz package functions and examples
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