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binequality  

Methods for Analyzing Binned Income Data
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("binequality", "1.0.4")



Attach the package and use:
library("binequality")
Maintained by
Samuel V. Scarpino
[Scholar Profile | Author Map]
First Published: 2014-09-15
Latest Update: 2018-11-05
Description:
Methods for model selection, model averaging, and calculating metrics, such as the Gini, Theil, Mean Log Deviation, etc, on binned income data where the topmost bin is right-censored. We provide both a non-parametric method, termed the bounded midpoint estimator (BME), which assigns cases to their bin midpoints; except for the censored bins, where cases are assigned to an income estimated by fitting a Pareto distribution. Because the usual Pareto estimate can be inaccurate or undefined, especially in small samples, we implement a bounded Pareto estimate that yields much better results. We also provide a parametric approach, which fits distributions from the generalized beta (GB) family. Because some GB distributions can have poor fit or undefined estimates, we fit 10 GB-family distributions and use multimodel inference to obtain definite estimates from the best-fitting distributions. We also provide binned income data from all United States of America school districts, counties, and states.
How to cite:
Samuel V. Scarpino (2014). binequality: Methods for Analyzing Binned Income Data. R package version 1.0.4, https://cran.r-project.org/web/packages/binequality. Accessed 30 Apr. 2025.
Previous versions and publish date:
0.6.1 (2014-09-15 07:27), 1.0.1 (2016-12-17 01:36), 1.0.2 (2017-07-09 21:57), 1.0.3 (2018-02-18 22:05)
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